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Past Seminars and Events
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| March 28, 2023 |
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Title: Flexible Learning of Quantum States with Generative Query Neural Networks
Time: 04:30pm
Venue: Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, HKU (Registration is required)
Speaker(s): Mr Yan Zhu, a PhD Candidate from the Department of Computer Science of HKU, and his peer Mr Liu Qiushi as the moderator
Remark(s): Mixed mode (both face-to-face and online)
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| March 27, 2023 |
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| March 03, 2023 |
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| February 24, 2023 |
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| February 13, 2023 |
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| February 10, 2023 |
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| February 04, 2023 |
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| January 31, 2023 |
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Title: IDS Distinguished Speaker Series #2 - Towards AI-Powered Data-Informed Education
Time: 03:30pm
Venue: Hybrid (Registration is required)
Speaker(s): Prof Sihem Amer-Yahia, CNRS Research Director, Lab of Informatics of Grenoble / Moderator: Prof Reynold Cheng, IDS & HKU
Remark(s): Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.
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| January 12, 2023 |
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Title: Commodifying Data Exploration
Time: 10:30am
Venue: CB308, Chow Yei Ching Building, HKU
Speaker(s): Professor Sihem Amer-Yahia
Remark(s): Speaker: Sihem Amer-Yahia (CNRS, Univ. Grenoble Alpes, France)
Date: Janruary 12, 2023 (Thurs)
Time: 10:30 - 11:30am (HK Time)
Venue: CB308, Chow Yei Ching Building, HKU
Mode: Face-to-face only
Abstract:
Exploratory Data Analysis (EDA) is an iterative and tedious process. Several strategies have been proposed to ease the burden on users in EDA ranging from stepwise to full-guidance approaches. Stepwise approaches rely on computing utility functions that determine the best action to take at each step. Full-guidance approaches rely on learning end-to-end exploration policies. Today’s big question is how to commodify EDA and make it easily deployable for all but for that we need to know what users are looking for: are they looking for a needle in a haystack, taking a tour of the data, or are they feeling lucky? This talk will investigate those questions and discuss the challenges of storing learned pathways through data or regenerating them when needed.
Biography:
Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem is PC chair for SIGMOD 2023 and vice president of the VLDB Endowment. She currently leads the Diversity, Equity and Inclusion initiative for the database community.
All are welcome!
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| December 20, 2022 |
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| November 25, 2022 |
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| November 21, 2022 |
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| November 17, 2022 |
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| November 14, 2022 |
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| November 10, 2022 |
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| November 09, 2022 |
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| November 02, 2022 |
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| November 01, 2022 |
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| October 26, 2022 |
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| October 20, 2022 |
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| September 23, 2022 |
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| September 14, 2022 |
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| September 05, 2022 |
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| August 08, 2022 |
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| July 25, 2022 |
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| July 11, 2022 |
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| June 24, 2022 |
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| June 17, 2022 |
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| May 27, 2022 |
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| April 21, 2022 |
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| April 20, 2022 |
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| April 13, 2022 |
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Title: ASTRI Day @ HKU
Video
Time: 04:00pm
Venue: Online via Zoom (Registration is required)
Speaker(s): Leaders & People from ASTRI
Remark(s):
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| April 12, 2022 |
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| April 06, 2022 |
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Title: Advanced Topics in Graph Representation Learning
Time: 02:00pm
Venue: Online via Zoom (Registration is required - by invitation)
Speaker(s): Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Dept. of CS, HKU
Remark(s): 
The Department of Computer Science is pleased to announce the following interesting mini lecture series to be given by Dr. Chao Huang in February - April 2022. All CS RPg students and senior undergraduate students are welcome to join.
Title: Advanced Topics in Graph Representation Learning
Speaker: Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU
Period: February 23 - April 6, 2022 (Wednesday) (total 5 lectures, see detailed schedule below)
Time: 2:00 pm - 3:00 pm
Venue: Online via Zoom (Registration is required - by invitation)
Description:
Graph representation learning research has grown at an incredible pace in data mining and machine learning communities. This lecture series will cover recent core techniques and advances in graph representation research for modeling a variety of real-world applications and problems, including graph representation, heterogeneous graph mining, graph neural networks, recommendation with graphs, graph-based spatial-temporal learning, and others.
About the Speaker:
Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU. His research focuses on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, Recommendation, Graph Mining, and Deep Representation Learning. Prior to that, he received his Ph.D. in Computer Science from the University of Notre Dame in USA.
Schedule and topics:
Lecture 1:
February 23, 2022 (Wed)
Introduction of graph mining;
Core concepts of graph representation learning/network embedding;
Heterogeneous graph analysis
Lecture 2:
March 2, 2022 (Wed)
Graph neural networks (GNN)/GNN-based applications/self-supervised graph learning
Lecture 3:
March 23, 2022 (Wed)
Recommendation with graphs I;
Social and knowledge-aware recommender systems/user personalization
Lecture 4:
March 30, 2022 (Wed)
Recommendation with graphs II;
Recommendation with behavior heterogeneity and diversity
Lecture 5:
April 6, 2022 (Wed)
Graph-based spatial-temporal learning for smart cities
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| March 30, 2022 |
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Title: Elevating the human experience: XR technologies and the Metaverse
Time: 03:30pm
Venue: Online via Zoom (Registration is required)
Speaker(s): Dr. Loretta Choi, Lecturer, Department of Computer Science, HKU
Remark(s):
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Title: Advanced Topics in Graph Representation Learning
Time: 02:00pm
Venue: Online via Zoom (Registration is required - by invitation)
Speaker(s): Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Dept. of CS, HKU
Remark(s):
The Department of Computer Science is pleased to announce the following interesting mini lecture series to be given by Dr. Chao Huang in February - April 2022. All CS RPg students and senior undergraduate students are welcome to join.
Title: Advanced Topics in Graph Representation Learning
Speaker: Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU
Period: February 23 - April 6, 2022 (Wednesday) (total 5 lectures, see detailed schedule below)
Time: 2:00 pm - 3:00 pm
Venue: Online via Zoom (Registration is required - by invitation)
Description:
Graph representation learning research has grown at an incredible pace in data mining and machine learning communities. This lecture series will cover recent core techniques and advances in graph representation research for modeling a variety of real-world applications and problems, including graph representation, heterogeneous graph mining, graph neural networks, recommendation with graphs, graph-based spatial-temporal learning, and others.
About the Speaker:
Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU. His research focuses on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, Recommendation, Graph Mining, and Deep Representation Learning. Prior to that, he received his Ph.D. in Computer Science from the University of Notre Dame in USA.
Schedule and topics:
Lecture 1:
February 23, 2022 (Wed)
Introduction of graph mining;
Core concepts of graph representation learning/network embedding;
Heterogeneous graph analysis
Lecture 2:
March 2, 2022 (Wed)
Graph neural networks (GNN)/GNN-based applications/self-supervised graph learning
Lecture 3:
March 23, 2022 (Wed)
Recommendation with graphs I;
Social and knowledge-aware recommender systems/user personalization
Lecture 4:
March 30, 2022 (Wed)
Recommendation with graphs II;
Recommendation with behavior heterogeneity and diversity
Lecture 5:
April 6, 2022 (Wed)
Graph-based spatial-temporal learning for smart cities
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| March 25, 2022 |
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| March 23, 2022 |
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Title: Mysteries of NFTs and how they shape the Metaverse
Time: 03:30pm
Venue: Online via Zoom (Registration is required)
Speaker(s): Dr. John Yuen, Assistant Professor, Department of Computer Science, HKU
Remark(s):
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Title: Advanced Topics in Graph Representation Learning
Time: 02:00pm
Venue: Online via Zoom (Registration is required - by invitation)
Speaker(s): Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Dept. of CS, HKU
Remark(s):
The Department of Computer Science is pleased to announce the following interesting mini lecture series to be given by Dr. Chao Huang in February - April 2022. All CS RPg students and senior undergraduate students are welcome to join.
Title: Advanced Topics in Graph Representation Learning
Speaker: Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU
Period: February 23 - April 6, 2022 (Wednesday) (total 5 lectures, see detailed schedule below)
Time: 2:00 pm - 3:00 pm
Venue: Online via Zoom (Registration is required - by invitation)
Description:
Graph representation learning research has grown at an incredible pace in data mining and machine learning communities. This lecture series will cover recent core techniques and advances in graph representation research for modeling a variety of real-world applications and problems, including graph representation, heterogeneous graph mining, graph neural networks, recommendation with graphs, graph-based spatial-temporal learning, and others.
About the Speaker:
Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU. His research focuses on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, Recommendation, Graph Mining, and Deep Representation Learning. Prior to that, he received his Ph.D. in Computer Science from the University of Notre Dame in USA.
Schedule and topics:
Lecture 1:
February 23, 2022 (Wed)
Introduction of graph mining;
Core concepts of graph representation learning/network embedding;
Heterogeneous graph analysis
Lecture 2:
March 2, 2022 (Wed)
Graph neural networks (GNN)/GNN-based applications/self-supervised graph learning
Lecture 3:
March 23, 2022 (Wed)
Recommendation with graphs I;
Social and knowledge-aware recommender systems/user personalization
Lecture 4:
March 30, 2022 (Wed)
Recommendation with graphs II;
Recommendation with behavior heterogeneity and diversity
Lecture 5:
April 6, 2022 (Wed)
Graph-based spatial-temporal learning for smart cities
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| March 02, 2022 |
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Title: Advanced Topics in Graph Representation Learning
Time: 02:00pm
Venue: Online via Zoom (Registration is required - by invitation)
Speaker(s): Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Dept. of CS, HKU
Remark(s):
The Department of Computer Science is pleased to announce the following interesting mini lecture series to be given by Dr. Chao Huang in February - April 2022. All CS RPg students and senior undergraduate students are welcome to join.
Title: Advanced Topics in Graph Representation Learning
Speaker: Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU
Period: February 23 - April 6, 2022 (Wednesday) (total 5 lectures, see detailed schedule below)
Time: 2:00 pm - 3:00 pm
Venue: Online via Zoom (Registration is required - by invitation)
Description:
Graph representation learning research has grown at an incredible pace in data mining and machine learning communities. This lecture series will cover recent core techniques and advances in graph representation research for modeling a variety of real-world applications and problems, including graph representation, heterogeneous graph mining, graph neural networks, recommendation with graphs, graph-based spatial-temporal learning, and others.
About the Speaker:
Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU. His research focuses on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, Recommendation, Graph Mining, and Deep Representation Learning. Prior to that, he received his Ph.D. in Computer Science from the University of Notre Dame in USA.
Schedule and topics:
Lecture 1:
February 23, 2022 (Wed)
Introduction of graph mining;
Core concepts of graph representation learning/network embedding;
Heterogeneous graph analysis
Lecture 2:
March 2, 2022 (Wed)
Graph neural networks (GNN)/GNN-based applications/self-supervised graph learning
Lecture 3:
March 23, 2022 (Wed)
Recommendation with graphs I;
Social and knowledge-aware recommender systems/user personalization
Lecture 4:
March 30, 2022 (Wed)
Recommendation with graphs II;
Recommendation with behavior heterogeneity and diversity
Lecture 5:
April 6, 2022 (Wed)
Graph-based spatial-temporal learning for smart cities
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| February 24, 2022 |
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| February 23, 2022 |
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Title: Advanced Topics in Graph Representation Learning
Time: 02:00pm
Venue: Online via Zoom (Registration is required - by invitation)
Speaker(s): Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Dept. of CS, HKU
Remark(s):
The Department of Computer Science is pleased to announce the following interesting mini lecture series to be given by Dr. Chao Huang in February - April 2022. All CS RPg students and senior undergraduate students are welcome to join.
Title: Advanced Topics in Graph Representation Learning
Speaker: Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU
Period: February 23 - April 6, 2022 (Wednesday) (total 5 lectures, see detailed schedule below)
Time: 2:00 pm - 3:00 pm
Venue: Online via Zoom (Registration is required - by invitation)
Description:
Graph representation learning research has grown at an incredible pace in data mining and machine learning communities. This lecture series will cover recent core techniques and advances in graph representation research for modeling a variety of real-world applications and problems, including graph representation, heterogeneous graph mining, graph neural networks, recommendation with graphs, graph-based spatial-temporal learning, and others.
About the Speaker:
Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU. His research focuses on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, Recommendation, Graph Mining, and Deep Representation Learning. Prior to that, he received his Ph.D. in Computer Science from the University of Notre Dame in USA.
Schedule and topics:
Lecture 1:
February 23, 2022 (Wed)
Introduction of graph mining;
Core concepts of graph representation learning/network embedding;
Heterogeneous graph analysis
Lecture 2:
March 2, 2022 (Wed)
Graph neural networks (GNN)/GNN-based applications/self-supervised graph learning
Lecture 3:
March 23, 2022 (Wed)
Recommendation with graphs I;
Social and knowledge-aware recommender systems/user personalization
Lecture 4:
March 30, 2022 (Wed)
Recommendation with graphs II;
Recommendation with behavior heterogeneity and diversity
Lecture 5:
April 6, 2022 (Wed)
Graph-based spatial-temporal learning for smart cities
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| January 19, 2022 |
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Title: Workshop on DAOs & Web3.0
Time: 05:30pm
Venue: Via Zoom (registration is required)
Speaker(s): Kin Ko - Founder of Likecoin, Civic Liker Foundation and #decentralizehk
Remark(s):
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| January 17, 2022 |
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| November 25, 2021 |
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| November 05, 2021 |
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| November 04, 2021 |
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Title: Introduction to Blockchain and Cryptoassets
Time: 05:45pm
Venue: Meng Wah Complex Theatre 2 (MWT2), HKU (Registration is required)
Speaker(s): Desmond, Co-founder of 每日幣研 Crypto Wesearch, Ex Binance analyst, HKU Alumni
Remark(s):
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| November 02, 2021 |
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| November 01, 2021 |
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| October 30, 2021 |
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| October 13, 2021 |
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| October 08, 2021 |
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| October 07, 2021 |
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| October 02, 2021 |
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| September 28, 2021 |
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| August 31, 2021 |
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| August 24, 2021 |
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| July 13, 2021 |
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| May 21, 2021 |
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| May 18, 2021 |
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| April 28, 2021 |
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| April 22, 2021 |
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Title: Short Talks by HKSTP
Time: 03:00pm
Venue: Online via Zoom (Registration is required)
Speaker(s): Dr. Zhiyong CHEN, Mr. Arthur CHUNG, Mr. Wai Pong MOK & Mr. Monsess LEUNG
Remark(s):
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| April 15, 2021 |
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| April 14, 2021 |
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| April 13, 2021 |
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| January 19, 2021 |
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| January 15, 2021 |
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| January 08, 2021 |
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| January 07, 2021 |
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| November 11, 2020 |
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| November 03, 2020 |
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Title: HKU FinTech Day 2020
Time: 04:30pm
Venue: Zoom
Speaker(s): Various
Remark(s): HKU FinTech Day 2020
3 and 4 November 2020 (Tuesday and Wednesday)
4:30 - 6:00 PM Hong Kong Time via Zoom
The fourth annual HKU FinTech Day will be held this year in a virtual format in conjunction with Hong Kong FinTech Week 2020. The event will bring together renowned academics and key industry players to discuss cutting-edge research at the intersection of science, technology, and entrepreneurship.
Speakers and Panelists:
Musheer Ahmed, FinStep Asia
Douglas Arner, The University of Hong Kong
Henri Arslanian, PwC Hong Kong
Janos Barberis, HKU, CFTE and SuperCharger
Stephanie Biedermann, The University of Hong Kong
Terrance Cheung, Next Insurtech Limited
Betty Guo, Brain Investing Limited
Bowie Lau, MaGESpire
David S. Lee, The University of Hong Kong
Chen Lin, The University of Hong Kong
Alesandro Di Lullo, HKU, CFTE and SuperCharger
Aditya Mehta, Bamboo Network
Kevin Ng, Fundergo
Laurence Tang, The University of Hong Kong
Huy Nguyen Trieu, Centre for Finance, Technology and Entrepreneurship (CFTE)
Julia Walker, Refinitiv
SM Yiu, The University of Hong Kong
Please see the attached programme for details.
If you have not yet registered to attend, please save your place from HERE. Zoom information will be provided to registrants prior to the event.
For enquiries: Flora Leung at
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| November 02, 2020 |
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| October 29, 2020 |
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| October 22, 2020 |
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| October 08, 2020 |
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| October 01, 2020 |
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| September 14, 2020 |
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| September 11, 2020 |
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Title: QICI online seminar: Noncommuting conserved quantities in quantum many-body thermalization
Time: 10:00pm
Venue: Online
Speaker(s): Dr. Nicole Yunger Halpern, Harvard University
Remark(s): Date: Sept 11, 2020 (Friday)
Time: 10:00 pm (HK Time) (GMT+8)
Join Zoom Meeting
https://hku.zoom.us/j/95512510759?pwd=cUZ1Z3BjY0ZBV0lDYzdoaGpHVlpkQT09
Meeting ID: 955 1251 0759
Password: 373431
Speaker: Dr. Nicole Yunger Halpern, Harvard University
Abstract:
In statistical mechanics, a small system exchanges conserved quantities— heat, particles, electric charge, etc.—with a bath. The small system may thermalize to the canonical ensemble, the grand canonical ensemble, etc. The conserved quantities are represented by operators usually assumed to commute with each other. But noncommutation distinguishes quantum physics from classical. What if the operators fail to commute? I will argue, using quantum-information-theoretic thermodynamics, that the small system thermalizes to near a “non-Abelian thermal state.” I will present a protocol for realizing this state experimentally, supported with numerical simulations of a spin chain. The protocol is suited to ultracold atoms, trapped ions, quantum dots, and more. This work introduces a nonclassical phenomenon—noncommutation of conserved quantities—into a decades-old thermodynamics problem.
About the Speaker
Dr. Nicole Yunger Halpern currently is an ITAMP Postdoctoral Fellow at Harvard. She completed her Ph.D. in 2018, under John Preskill's supervision at Caltech. Her dissertation won the Ilya Prigogine Prize for a thermodynamics Ph.D. thesis. She earned her Master's degree from the Perimeter Scholars International (PSI) program of the Perimeter Institute for Theoretical Physics, working with Rob Spekkens and Markus P. Müller. Before that, she was at Dartmouth College from where she earned her Bachelor's degree and graduated as a co-valedictorian of her class.
All are welcome!
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| September 03, 2020 |
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| August 27, 2020 |
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| August 26, 2020 |
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Title: [CANCELLED] Informative Planning of Autonomous Robots for Spatiotemporal Environmental Monitoring
Time: 10:00am
Venue: Online
Speaker(s): Professor Lantao Liu, Indiana University
Remark(s):
Zoom meeting link:
https://hku.zoom.us/j/99484141050
Meeting ID: 994 8414 1050
Title: Informative Planning of Autonomous Robots for Spatiotemporal Environmental Monitoring
Speaker: Professor Lantao Liu, Indiana University
Abstract:
Date: August 26, 2020 (Wednesday)
Time: 10:00 am (HK Time) (GMT+8)
Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. I will first discuss a Monte Carlo tree search method which enables the robot to not only well balance the environment exploration and exploitation in space, but also catch up to the environmental dynamics that are related to time. This is achieved by incorporating multi-objective optimization and a look-ahead model-predictive rewarding mechanism. The method produces optimized decision solutions for the robot based on its knowledge (estimation) of the environment model, leading to better adaptation to environmental dynamics. Then I will discuss robot decision-making in uncertain and unstructured environments, such as in the scenario when strong winds and water flows cause robot stochastic behaviors. We explore the time-varying stochasticity of robot motion and investigate robot states' reachability, based on which we develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity.
About the speaker:
Lantao Liu is an Assistant Professor in the Luddy School of Informatics, Computing, and Engineering at Indiana University-Bloomington. He has been working on planning, learning, and coordination techniques for autonomous systems involving single or multiple robots with potential applications in environmental monitoring, surveillance and security, search and rescue, as well as smart transportation. Before joining Indiana University, he was a Research Associate in the Department of Computer Science at the University of Southern California during 2015 - 2017. He also worked as a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University during 2013 - 2015. He received a Ph.D. from the Department of Computer Science and Engineering at Texas A&M University in 2013, and a Bachelor degree from the Department of Automatic Control at Beijing Institute of Technology in 2007.
All are welcome!
Tel: 2859 2180
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| August 20, 2020 |
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| July 16, 2020 |
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| July 14, 2020 |
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| July 10, 2020 |
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Title: Robust Decision Making in a Partially Observable World
Time: 02:00pm
Venue: Online
Speaker(s): Hanna Kurniawati, Australian National University
Remark(s):
Zoom meeting link:
https://hku.zoom.us/j/94650715947
Meeting ID: 946 5071 5947
Title: Robust Decision Making in a Partially Observable World
Speaker: Hanna Kurniawati, Australian National University
Date: July 10, 2020 (Friday)
Time: 2:00 pm (HK Time) (GMT+8)
Abstract:
Robust robot operation must answer: What to do now, to receive good long-term returns, despite notRobust robot operation must answer: What to do now, to receive good long-term returns, despite notknowing the exact effect of its actions, despite various errors in sensors and sensing, and despitelimited information about the environment and itself. This problem is not new. Mathematically principledconcepts --called Partially Observable Markov Decision Processes (POMDPs)-- have been developedmore than five decades ago to address the problem mentioned above. However, such concepts arenotorious for their computational complexity, that they have often been considered impractical. I willpresent some of our effort in addressing the computational complexity issues of solving POMDPs, anddemonstrate that this decision making concept has now become practical (to some extent) for solvingvarious problems in robotics. I will end with a discussion on what this technology could mean inbridging the gap between sensing and acting in robotics, and between planning and learning ingeneral.
About the speaker:
Hanna Kurniawati is a Senior Lecturer with ANU and CS Futures Fellowship at the Research School ofHanna Kurniawati is a Senior Lecturer with ANU and CS Futures Fellowship at the Research School ofComputer Science, Australian National University (ANU). Prior to ANU, she was an academic at theUniversity of Queensland and a Research Scientist at the Singapore-MIT Alliance for Research andTechnology. She earned a PhD in Computer Science from National University of Singapore for work onrobot motion planning. Her current research focuses on the design and development of algorithms thatenable mathematically principled concepts for robust decision making to become practical tools inrobotics. Along with colleagues and students, she won a best paper award at ICAPS’15 and was afinalist of the best paper award at ICRA’15. She was also a keynote speaker at IROS’18.
All are welcome!
Tel: 2859 2180
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| July 02, 2020 |
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| June 30, 2020 |
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| June 19, 2020 |
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Title: Robots Learning (Through) Interactions
Time: 04:00pm
Venue: Online
Speaker(s): Professor Jens Kober, Cognitive Robotics Department (CoR), Delft University of Technology
Remark(s): Date: June 19, 2020 (Friday)
Time: 4:00 pm (HK Time) (GMT+8)
Zoom link: https://hku.zoom.us/j/93201362975
Meeting ID: 932 0136 2975
Title: Robots Learning (Through) Interactions
Speaker: Professor Jens Kober, Cognitive Robotics Department (CoR), Delft University of Technology
Abstract:
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Reinforcement learning and imitation learning are two different but complimentary machine learning approaches commonly used for learning motor skills.
I will discuss various learning techniques we developed that can handle complex interactions with the environment. Complexity arises from non-linear dynamics in general and contacts in particular, taking multiple reference frames into account, dealing with high-dimensional input data, interacting with humans, etc. A human teacher is always involved in the learning process, either directly (providing demonstrations) or indirectly (designing the optimization criterion), which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective?
All these concepts will be illustrated with benchmark tasks and real robot experiments ranging from fun (ball-in-a-cup) to more applied (unscrewing light bulbs).
About the speaker:
Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.
All are welcome!
Tel: 2859 2180
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| June 11, 2020 |
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| May 15, 2020 |
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| May 13, 2020 |
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| May 07, 2020 |
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Title:
Time: 04:00pm
Venue: Online. Registration is required
Speaker(s): Dr. John T.H. Yuen
Remark(s):
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| April 29, 2020 |
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| April 28, 2020 |
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| April 23, 2020 |
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Title: Entanglement and the Architecture of Spacetime
Time: 11:00pm
Venue: Online seminar
Speaker(s): Eugenio Bianchi, Penn State University
Remark(s):
QISS online seminar (quantum information structure of spacetime)
Date: Apr 23, 2020 (Thursday)
Time: 11:00pm HK time (GMT+8)
Zoom:
Meeting ID: 728065144
Title: Entanglement and the Architecture of Spacetime
Speaker: Eugenio Bianchi, Penn State University
Abstract:
The quantum field vacuum is highly entangled, even in causally disconnected regions. In contrast, the state of a quantum geometry of space can be unentangled, resulting in an uncorrelated network of elementary quanta of space. In this talk I discuss how the architecture of spacetime emerges from entanglement between these elementary quanta. I will focus on loop quantum gravity, causal structures and the primordial universe.
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Title: MSc(CS) Dissertation Public Seminars
Time: 02:30pm
Venue: Online
Speaker(s): Chen Yunkun, Yu Gao & Yan Zhehao
Remark(s):
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| April 22, 2020 |
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| April 18, 2020 |
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| April 08, 2020 |
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| April 02, 2020 |
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| March 05, 2020 |
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| February 20, 2020 |
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| February 06, 2020 |
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| January 21, 2020 |
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| January 18, 2020 |
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| January 15, 2020 |
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Title: Google AI for Social Good and the
Power of TensorFlow
Time: 06:00pm
Venue: Hui Pun Hing Lecture Hall (Library Extension, LE1 Lecture Theatre), The University of Hong Kong
Speaker(s): Laurence Moroney, Developer Advocate, Google Brain
Remark(s):
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Title: HKU-Oxford Memorandum of Understanding Signing Ceremony cum HKU-Oxford Joint Lab Inauguration Ceremony
Time: 02:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): -----
Remark(s): Rundown:
| 2:00-2:05pm |
Speech by Professor Bob Coecke, Head of Quantum Group, Department of Computer Science, University of Oxford |
| 2:05-2:10pm |
Speech by Professor Christopher Chao, Dean of Engineering, Faculty of Engineering, The University of Hong Kong |
| 2:10-2:15pm |
Speech by Prof. Alfonso Ngan, Acting-Designate Pro-Vice-Chancellor (Research), The University of Hong Kong |
| 2:15-2:20pm |
Photo taking |
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| January 14, 2020 |
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Title: QICI Distinguished Lecture: What is time?
Time: 04:45pm
Venue: Lecture Theatre A, Chow Yei Ching Building, University of Hong Kong Hong Kong
Speaker(s): Professor Carlo Rovelli, Professor of Exceptional Class, University of Aix-Marseille
Remark(s):
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| January 09, 2020 |
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Title: Blockchain Training Series
Time: 06:30pm
Venue: CPD-3.04, Centennial Campus, The University of Hong Kong
Speaker(s): Various
Remark(s):
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| December 19, 2019 |
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| December 16, 2019 |
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| November 30, 2019 |
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| November 22, 2019 |
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Title: Quantum Information Seminar
Time: 02:00pm
Venue: Rm 313, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr. Jaehak Lee, Korea Institute of Advanced Study (KIAS)
Remark(s):
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| November 11, 2019 |
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Title: Computer Vision of Refractive Media
Time: 11:00am
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Professor Herb Yang, Department of Computing Science, University of Alberta
Remark(s): Abstract:
Computer vision of opaque objects has been extensively studied. However, there is signicantly less attention on refractive media. In this talk, I will give an overview of my recent research in the area of computer vision of refractive media, which includes solids and uids. My interest in this topic began several years ago when my group was asked to develop an undersea 3D vision system for Neptune Canada, which had been merged with Venus Canada to form Ocean Networks Canada. During our research, we discovered that simply applying land-based computer vision techniques to undersea appears trival but is, unfortunately, incorrect. Surprisingly, most photogrammetry methods at the time incorrectly adapted land-based methods to undersea applications with minor tweaking of parameters. By accommodating refraction in our methods, we have developed several physics based algorithms that outperform the accuracy of existing algorithms. Rather than a hindrance, we also discovered that refraction can be leveraged in underwater imaging. For example, we take advantage of dispersion to calibrate an underwater camera with improved accuracy. As well, dispersion can also be used to reconstruct an object in 3D with only one single view, i.e. one single camera, which is not possible for a typical land-based camera. More recently, motivated by our underwater results, we further explore developing new physics based methods to reconstruct shapes of transparent objects, which include transparent solids and dynamic water surfaces and underwater scenes.
About the Speaker:
Herb Yang (SM IEEE) received his B.Sc. (first honours) from the University of Hong Kong, his M.Sc. from Simon Fraser University, and his M.S.E.E. and Ph.D. from the University of Pittsburgh. He was a faculty member in the Department of Computer Science at the University of Saskatchewan from 1983 to 2001 and served as Graduate Chair from 1999 to 2001. Since July, 2001, he has been a Professor in the Department of Computing Science at the University of Alberta. He served as Associate Chair (Graduate Studies) in the same department from 2003 to 2005 and as Science Internship Director from 2016 to 2019. His research interests cover a wide range of topics from computer graphics to computer vision, which include physically based animation of Newtonian and non-Newtonian fluids, texture analysis and synthesis, human body motion analysis and synthesis, computational photography, stereo and multiple view computer vision, underwater imaging and medical imaging. He has published over 150 papers in international journals and conference proceedings in the areas of computer vision, computer graphics and medical imaging. He is a Senior Member of the IEEE and serves on the Editorial Board of the journal Pattern Recognition and IET-Computer Vision. In addition, he has served as reviewer and program committee member to many international conferences and reviewer to many international journals, and funding agencies. In 2007, he was invited to serve on the expert review panel to evaluate computer science research in Finland.
All are welcome!
For enquiries, please call 2859 2180 or
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| November 08, 2019 |
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Title: A Mutual Information Maximization Perspective of Language Representation Learning
Time: 03:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr Lingpeng Kong, Senior Research Scientist, Google DeepMind
Remark(s): Abstract:
In this talk, we show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing).
About the Speaker:
Lingpeng Kong is Senior Research Scientist at Google DeepMind. His research focuses on the computational modeling of structures in natural language processing (NLP) with applications related to sequence labeling, syntactic parsing, and machine translation. He received his Ph.D. from Carnegie Mellon University where he was co-advised by Professor Noah Smith and Professor Chris Dyer.
All are Welcome!
Tel: 2859 2180 for enquiries
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| November 06, 2019 |
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| November 01, 2019 |
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| October 28, 2019 |
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| October 24, 2019 |
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| September 27, 2019 |
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Title: What is Virtual Bank? (Co-organized with HKUGA)
Time: 05:30pm
Venue: Room MBG07, Main Building, The University of Hong Kong
Speaker(s): Mr. Lawrence Li & Dr. S.M. Yiu
Remark(s):
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Title: Using and reusing coherence to realize quantum processes
Time: 02:00pm
Venue: Rm 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr. Matteo Rosati, Universitat Autonoma de Barcelona
Remark(s): Abstract:
Using and reusing coherence to realize quantum processes Coherent superposition is a key feature of quantum mechanics that underlies the advantage of quantum technologies over their classical counterparts. Recently, coherence has been recast as a resource theory in an attempt to identify and quantify it in an operationally well-defined manner.Here we study how the coherence present in a state can be used to implement a quantum channel via incoherent operations and, in turn, to assess its degree of coherence. We introduce the robustness of coherence of a quantum channel-which reduces to the homonymous measure for states when computed on constant-output channels-and prove that: i) it
quantifies the minimal rank of a maximally coherent state required to implement the channel; ii) its logarithm quantifies the amortized cost of implementing the channel provided some coherence is recovered at the output; iii) its logarithm also quantifies the zero-error asymptotic cost of implementation of many independent copies of a channel. We also consider the generalized problem of imperfect implementation with arbitrary resource states. Using the robustness of coherence, we find that in general a quantum channel can be implemented without employing a maximally coherent resource state. In fact, we prove that every pure coherent state in dimension larger than 2, however weakly so, turns out to be a valuable resource to implement some coherent unitary channel. We illustrate our findings for the case of single-qubit unitary channels.
About the Speaker:
Matteo Rosati did his BSc and MSc studies in Physics (2009-2014) at Università La Sapienza, Rome, studying the modelling of disordered and complex systems under the supervision of Prof. Giorgio Parisi. He took his PhD in Theoretical Physics (2017) at Scuola Normale Superiore,
Pisa with Prof. Vittorio Giovannetti, with a thesis aimed at devising efficient and implementable decoders for classical communication on quantum guassian channels. Since then, he has been a postdoctoral fellow at the Universitat Autonoma de Barcelona, working with Profs. Andreas
Winter and John Calsamiglia on resource theories and quantum learning.
In 2019 he has been awarded a Marie Skłodowska-Curie Fellowship from the EU, starting in January 2020.
All are welcome!
For enquiries, please call 2859 2180 or email
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| September 06, 2019 |
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Title: The Power of Data Analytics and AI Techniques in the Digital Sector
Time: 05:30pm
Venue: Lecture Theatre A, Ground Floor, Chow Yei Ching Building, Main Campus, HKU
Speaker(s): Mr Alan Chan
Remark(s): Speaker:
Mr Alan Chan
Executive Vice President Lazada (Alibaba's SE Asia Commerce Business)
Date: September 6, 2019 (Friday)
Time: 5:30 - 6:45pm (Refreshments will be served from 5:00pm)
Venue: Lecture Theatre A, Ground Floor, Chow Yei Ching Building, Main Campus, HKU
About the talk:
In this talk, Mr Alan Chan will introduce how data analytics and AI techniques are used in the digital sector, and the changes that the industry is facing now and in the future. He will also share some tips on starting a career in data and analytics.
About the speaker:
With a background in strategy and analytics, and having led several organisations through their digital transformations, Alan is the Executive Vice President in Lazada (Alibaba’s South East Asia Commerce Business) and also part of the Alibaba Management Council. Alan joined Alibaba Group in 2016 and took on management roles in marketplace policy setting, data analytics and platform governance.
Before joining Alibaba, he spent 13 years in consulting with Accenture and left in 2016 as the Managing Director and Partner of Accenture Digital team in China. Alan is passionate about leadership, digital marketplaces and data science.
Outside of work, Alan engages actively in university collaborations and serves on the ex-officio board of a few start-ups in Asia. He received his Honors Degree in Economics and Statistics from the National University of Singapore, and is currently residing in Singapore.
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| August 28, 2019 |
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Title: Learning Neural Character Controllers from Motion Capture Data
Time: 03:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof. Taku Komura
Remark(s): Prof. Taku Komura
Institute of Perception, Action and Behaviour
School of Informatics
University of Edinburgh
Date: August 28, 2019 Wednesday
Time: 3:00pm
Venue: Room 328 Chow Yei Ching Building The University of Hong Kong
Abstract:
I will cover our recent development of neural network-based character controllers. Using neural networks for character controllers significantly increases the scalability of the system - the controller can be trained with a large amount of motion capture data while the run-time memory can be kept low. As a result, such controllers are suitable for real-time applications such as computer games and virtual reality systems. The main challenge is in designing an architecture that can produce movements in production-quality and also manage a wide variation of motion classes. Our development covers lowlevel locomotion controllers for bipeds and quadrupeds, which allow the characters to walk, run, sidestep and climb over uneven terrain, as well as a high level character controller for humanoid characters to interact with objects and the environment, which allows the character to sit on chairs, open doors and carry objects. In the end of the talk, I will discuss about the open problems and future directions of character animation.
About the speaker:
Taku Komura is a Professor at the Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh. As the leader of the Computer Graphics and Visualization Unit his research has focused on data-driven character animation, physically-based character animation, crowd simulation, cloth animation, anatomy-based modelling, and robotics. Recently, his main research interests have been the application of machine learning techniques for animation synthesis. He received the Royal Society Industry Fellowship (2014) and the Google AR/VR Research Award (2017).
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| August 21, 2019 |
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Title: Deep Composer: Music Generation Using Deep Neural Hashing
Time: 02:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof. Kien A. Hua
Remark(s): Prof. Kien A. Hua
Pegasus Professor and Director of the Data Systems Lab
University of Central Florida
Date: August 21, 2019 Wednesday
Time: 2:00pm
Venue: Room 328 Chow Yei Ching Building The University of Hong Kong
Abstract:
Recurrent neural networks have successfully generated pleasing melodies; however, they have struggled to create a full piece that has structure, theme, and originality. To overcome this limitation, we discuss a music retrieval approach for music generation. Composability, instead of the usual similarity, is used as the metric for retrieval. The musical segments (tiny building blocks each with only 16 music notes) in the database are encoded using a deep hashing method to facilitate the composability retrieval. Music composition is performed by using the current segment as a query to retrieve the next composable segment from the database until the song is complete. This encoding scheme incorporates both theme and structure so that musical segments can be joined to generate a piece that is both unique and pleasing to listen to. Each music segment is assigned four hash codes learned by a multi-LSTM system, each defining the given segment's compatibility with other segments for a distinct structural location (beginning, middle, or ending section) in the piece. Additionally, a two-phase music segmentation technique captures structural information while minimizing the segment length. We compare this scheme to multiple recent music generation methods using both objective and subjective evaluation metrics to demonstrate that the pieces generated by our Deep Composer system are not only unique and musically pleasing but also contain more structure and theme features like that of a professionally composed piece. A secondary goal of this research is to bring back the great composers (e.g., Mozart, Chopin, Beethoven, …) to compose their new original music for us today by using their music segments as the building blocks. In fact, the best composers of different times would be able to collaborate today through the Deep Composer. Deep Composer can also generate world fusion music beyond the capacity of any human composers.
About the speaker:
Dr. Kien A. Hua is a Pegasus Professor and Director of the Data Systems Lab at the University of Central Florida. He was the Associate Dean for Research of the College of Engineering and Computer Science at UCF. Prior to joining the university, he was a Lead Architect at IBM Mid-Hudson Laboratory, where he led a team of senior engineers to develop a highly parallel computer system, the precursor to the highly successful commercial parallel computer known as SP2. More recently, Prof. Hua was serving as a domain expert on spaceport technology at NASA, and a data analytics expert to advise the U.S. Air Force on the Air Force Strategy 2030 Initiative. Prof. Hua received his B.S. in Computer Science, and M.S. and Ph.D. in Electrical Engineering, all from the University of Illinois at Urbana-Champaign, USA. His current research interest includes music generation,deep learning, multimedia database and analytics, network and wireless communications, and the Internet of Things. He has published widely, with 15 papers recognized as best/top papers at a conference and one as the best paper of the year for a journal. Dr. Hua introduced peer-to-peer communications and data sharing in 1997, that has inspired many impactful applications including the Blockchain technology today. He introduced graph-based data mining at the 1999 International Conference on Data Warehousing and Knowledge Discovery; and the paper was recognized as a best paper at this conference. He is also a pioneer in the Internet of Things. with the WISE (Web-based Intelligent Sensor Explorer) prototype introduced in 2004, probably the first IoT platform implemented. It enables publishing, searching, and sharing of connected sensing devices. More recently, he developed a novelrouter in 2015, that transform network congestion into advantage. His other research works such as Skyscraper Broadcasting, Patching, and Zigzag all have been heavily cited and have inspired many commercial systems in use today. Prof. Hua has served as a Conference Chair, an Associate Chair, and a Technical Program Committee Member of numerous international conferences, and on the editorial boards of several professional journals. More recently, he served as a General Co-Chair for the 2014 ACM Multimedia conference; and he is currently organizing the 2018 IEEE International Conference on Cloud Engineering (IC2E) and serving as a General Co-Chair. Prof. Hua is a Fellow of IEEE.
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| August 09, 2019 |
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Title: The curious capacities of quantum channels
Time: 02:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof. Debbie Leung
Remark(s): Prof. Debbie Leung
Institute for Quantum Computing & Department of Combinatorics and Optimization
University of Waterloo, Canada
Date: August 9, 2019 Friday
Time: 2:00 - 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
The best asymptotic rate of a communication channel to process information (such as transmitting data or creating correlation) is called the capacity of the channel for the task involved. This talk focuses on the capacities of a quantum channel to communicate quantum or private classical data. We first summarize well known surprising results, followed by sharing a few recent developments in the subject.
About the speaker:
Debbie Leung joined the Institute for Quantum Computing (IQC) and the Department of Combinatorics and Optimization at the University of Waterloo in 2005. She has been an associate member of the Perimeter Institute since 2019. She was a Tolman postdoctoral fellowship at the Institute for Quantum Information, Caltech, after spending four months at the Workshop on Quantum Computation, September-December 2002, at the Mathematical Sciences Research Institute, Berkeley, and a twoyear stay at the Physics of Information group at the IBM TJ Watson Research Center, 2000-2002. After a BSc in Phys/Math from Caltech in 1995, she did a PhD in Physics at Stanford under the supervision of Professor Yoshihisa Yamamoto and Professor Isaac Chuang. Event website: https://qift.weebly.com/events.html
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| August 08, 2019 |
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Title: Building Systems for Machine Learning
Time: 02:00pm
Venue: Room 313, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr Hong Xu
Remark(s): Dr Hong Xu
Department of Computer Science
City University of Hong Kong
Date: August 9, 2019 Thursday
Time: 2:00pm
Venue: Room 313, Chow Yei Ching Building, The University of Hong Kong
Abstract:
Systems research is critical for machine learning because the recent success of AI and big data is in large part enabled by datacenter-scale computing infrastructures, which employ an army of machines to harness massive datasets in a continuous fashion. In this talk, I will present my research that focuses on systems for machine learning. First, we build a new distributed training system called Stanza that improves the training throughput of parameter server systems by 1.25x to 10.12x. Second, we build a serving system called Saec for recommendation models that reduces the memory footprint of embedding based recommendation models by 27x without performance loss.
About the speaker:
Hong Xu is an associate professor in Department of Computer Science, City University of Hong Kong. His research area is computer networking and systems, particularly data center networks and big data systems. He received the B.Eng. degree from The Chinese University of Hong Kong in 2007, and the M.A.Sc. and Ph.D. degrees from University of Toronto in 2009 and 2013, respectively. He was the recipient of an Early Career Scheme Grant from the Hong Kong Research Grants Council in 2014. He received several best paper awards, including the IEEE ICNP 2015 best paper award. He is a senior member of IEEE and member of ACM.
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| August 06, 2019 |
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Title: Volumetric Representations: the Geometric Modeling of the Next Generation
Time: 03:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Professor Gershon Elber
Remark(s): Professor Gershon Elber
Department of Computer Science
Technion
Date: August 6, 2019 Tuesday
Time: 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
The needs of modern (additive) manufacturing (AM) technologies can be satisfied no longer by boundary representations (B-reps), as AM requires the representation and manipulation of interior fields and materials as well. Further, while the need for a tight coupling between design and analysis has been recognized as crucial almost since geometric modeling (GM) has been conceived, contemporary GM systems only offer a loose link between the two, if at all. For about half a century, (trimmed) Non Uniform Rational B-spline (NURBs) surfaces has been the B-rep of choice for virtually all the GM industry. Fundamentally, B-rep GM has evolved little during this period. In this talk, we seek to examine an extended (trimmed) NURBs volumetric representation (V-rep) that successfully confronts the existing and anticipated design, analysis, and manufacturing foreseen challenges. We extend all fundamental B-rep GM operations, such as primitive and surface constructors and Boolean operations, to trimmed trivariate V-reps. This enables the much needed tight link to (Isogeometric) analysis on one hand and the full support of (heterogeneous and anisotropic) additive manufacturing on the other. Special capabilities toward the support of modern AM and the support of Isogeometric analysis will also be presented, that enable robust queries over the V-reps, including volumetric covering by curves, precise contact analysis, maximal penetration depth, and accurate integration over trimmed domains. Examples and other applications of V-rep GM, including AM and lattice- and micro- structure synthesis (with heterogeneous materials) will also be demonstrated. In collaboration with many others, including Ben Ezair, Fady Massarwi, Boris van Sosin, Jinesh Machchhar, Annalisa Buffa, Giancarlo Sangalli, Pablo Antolin, Massimiliano Martinelli, Stefanie Elgeti, and Robert Haimes.
About the speaker:
Gershon Elber is a professor in the Computer Science Department, Technion, Israel. His research interests span computer aided geometric designs and computer graphics. Prof. Elber received a BSc in computer engineering and an MSc in computer science from the Technion, Israel in 1986 and 1987, respectively, and a PhD in computer science from the University of Utah, USA, in 1992. He is a member of SIAM and the ACM. Prof. Elber has served on the editorial board of the Computer Aided Design, Computer Graphics Forum, The Visual Computer, Graphical Models, and the International Journal of Computational Geometry & Applications and has served in many conference program committees including Solid Modeling, Shape Modeling, Geometric Modeling and Processing, Pacific Graphics, Computer Graphics International, and Siggraph. Prof. Elber was one of the paper chairs of Solid Modeling 2003 and Solid Modeling 2004, one of the conference chairs of Solid and Physical Modeling 2010, the chair of GDM 2014, the conference co-chair of SIAM GD/SPM 2015, and the conference co-chair of SPM 2018. He has published over 200 papers in international conferences and journals and is one of the authors of a book titled "Geometric Modeling with Splines - An Introduction". Prof. Elber received the John Gregory Memorial Award, 2011, in "Appreciation for Outstanding Contributions in Geometric Modeling", the Solid Modeling Association pioneers award in 2016, and the Bezier award in 2019. Elber can be reached at the Technion, Israel Institute of Technology, Department of Computer Science, Haifa 32000, ISRAEL. Email: , Fax: 972-4-829-5538.
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| July 19, 2019 |
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Title: Quantum communication with limited resources
Time: 02:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr. Borivoje Dakić
Remark(s): Dr. Borivoje Dakić
Faculty of Physics
University of Vienna
Date: July 17, 2019 Wednesday
Time: 2:00 - 3:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Abstract:
Generally speaking, communication is the process of transmitting a message (information) from a sender to a receiver. When the distant parties use a single classical particle to communicate, they are restricted to “one-way signaling”, as the particle can carry information in one direction only. In this talk, I will analyze the corresponding quantum scenario, where the parties communicate via a single quantum particle prepared in superposition of different spatial locations. Surprisingly, I will show that such a scenario results in “multi-way signaling”, which is impossible in classical physics. Our framework [1, 2] does not assume (a priori) the use of quantum entanglement, in contrast to majority of known quantum information tasks and protocols. These findings bring novel insights into quantum information processing, ranging from foundational to practical.
[1] F. del Santo and B. Dakić, Two-way communication with a single quantum particle, Phys. Rev. Lett. 120, 060503 (2018),
[2] F. Massa, A. Moqanaki, F. Del Santo, B. Dakić, and P. Walther, Experimental two-way communication with one photon, arXiv:1802.05102 (2018).
About the speaker:
Borivoje Dakić is an assistant professor at the Faculty of Physics at the University of Vienna. He obtained his PhD degree in Physics at the University of Vienna. After being a postdoc at the Centre for Quantum Technologies in Singapore and Oxford University, UK, he returned back to Vienna to run an independent research. Since 2016 he is a member of the Foundational Question Institute (FQXi). His expertise lies in the quantum information theory, entanglement characterization and quantum foundations.
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| July 11, 2019 |
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Title: The strong converse exponent of classical-quantum channel coding with constant compositions
Time: 02:00pm
Venue: Room 328, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Dr. Borivoje Dakić
Remark(s): Dr. Milán Mosonyi
Date: July 11, 2019 Thursday
Time: 2:00 - 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
There are different natural-looking ways to quantify the usefulness of a classical-quantum channel for information transmission. Given a quantum divergence (e.g., a Rényi divergence) and an input distribution P, one can define the corresponding mutual information, which is the divergence "distance" of the joint input-output state from the set of product states with fixed first marginal P. An alternative approach is to measure how spread out the channel states are in the state space, giving rise to the concept of the P-weighted divergence radius. We show that it is this latter notion that admits an operational interpretation in the context of constant composition channel coding, with the divergence being the sandwiched Rényi divergence.
About the speaker:
Milán Mosonyi obtained his PhD in theoretical physics in 2005 at the Catholic University of Leuven, under the supervision of Mark Fannes and Dénes Petz. He has been an assistant professor (since 2005) and later an associate professor (since 2012) at the Institute of Mathematics, Budapest University of Technology and Economics. Between 2006 and 2016 he was in research positions at Tohoku University, National University of Singapore, University of Bristol, Autonomous University of Barcelona, and the Technical University of Münich. His main research interests are quantum Shannon theory and mathematical physics.
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| June 30, 2019 |
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| June 11, 2019 |
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Title: Internet of Video Things (IoVT): Next Generation IoT with Visual Sensors
Time: 03:30pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Professor Chang Wen Chen
Remark(s): Professor Chang Wen Chen
The Chinese University of Hong Kong, Shenzhen China &
State University of New York at Buffalo, USA
Date: June 11, 2019 Tuesday
Time: 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
The worldwide flourishing of the Internet of Things (IoT) in the past decade has enabled numerous new applications through the internetworking of a wide variety of devices and sensors. More recently, visual sensors has seen their considerable booming because they usually capable of providing richer and more versatile information. Internetworking of large scale visual sensors has been named Internet of Video Things (IoVT). IoVT has its own unique characteristics in sensing, transmission, storage, and analysis, which are essentially different from conventional IoT. These new characteristics of IoVT are expected to impose significant challenges to existing technical infrastructures. In this talk, an overview of recent advances in various fronts of IoVT will be introduced and a broad range of technological and system challenges will be presented.
About the speaker:
Chang Wen Chen is currently Dean of School of Science and Engineering at the Chinese University of Hong Kong, Shenzhen. He is also an Empire Innovation Professor of Computer Science and Engineering at the University at Buffalo, State University of New York since 2008. He was Allen Henry Endow Chair Professor at the Florida Institute of Technology from July 2003 to December 2007. He was on the faculty of Electrical and Computer Engineering at the University of Rochester from 1992 to 1996 and on the faculty of Electrical and Computer Engineering at the University of Missouri-Columbia from 1996 to 2003. He has been the Editor-inChief for IEEE Trans. Multimedia from January 2014 to December 2016. He has also served as the Editor-inChief for IEEE Trans. Circuits and Systems for Video Technology from January 2006 to December 2009. He has been an Editor for several other major IEEE Transactions and Journals, including the Proceedings of IEEE, IEEE Journal of Selected Areas in Communications, and IEEE Journal of Emerging and Selected Topics in Circuits and Systems. He has served as Conference Chair for several major IEEE, ACM and SPIE conferences related to multimedia video communications and signal processing. His research is supported by NSF, DARPA, Air Force, NASA, Whitaker Foundation, Microsoft, Intel, Kodak, Huawei, and Technicolor. He received his BS from University of Science and Technology of China in 1983, MSEE from University of Southern California in 1986, and Ph.D. from University of Illinois at Urbana-Champaign in 1992. He and his students have received nine (9) Best Paper Awards or Best Student Paper Awards over the past two decades. He has also received several research and professional achievement awards, including the Sigma Xi Excellence in Graduate Research Mentoring Award in 2003, Alexander von Humboldt Research Award in 2009, the University at Buffalo Exceptional Scholar – Sustained Achievement Award in 2012, and the State University of New York System Chancellor’s Award for Excellence in Scholarshipand Creative Activities in 2016. He is an IEEE Fellow since 2004 and an SPIE Fellow since 2007.
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| June 03, 2019 |
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Title: Some perspectives on relativistic causality
Time: 02:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Professor Pawel Horodecki
Remark(s): Professor Pawel Horodecki
Gdańsk University of Technology
Poland
Date: June 3, 2019 Monday
Time: 2:00 - 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
We shall discuss two perspectives on relativistic causality. The first one is based on discrete systems statistics and shows that minimal assumption on correlation boxes from the perspective of relativistic causality leads to a correlation picture that goes fat beyond standard no-signaling paradigm. The second analysis involves an analysis of dynamics of potential continuous statistics of single system in a quantum-like, linear or not, theory. The main result provides the condition under which such dynamics may be causal.
About the speaker:
Pawel Horodecki graduated from Gdańsk University. He is currently Professor and lecturer at Gdańsk University of Technology, Professor and group leader in International Centre for Theory of Quantum Technologies. His research includes contributions to theory of quantum entanglement and quantum communication, including co-discovery of bound entanglement phenomenon and quantum entanglement witnesses. His research interests are quantum information and foundations of quantum physics.
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| May 31, 2019 |
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Title: Memory effects in quantum metrology
Time: 02:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Yuxiang Yang
Remark(s): Yuxiang Yang
Institute for Theoretical Physics ETH
Zurich
Date: May 31, 2019 Friday
Time: 2:00 - 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
Quantum metrology concerns estimating a parameter from multiple identical uses of a quantum channel. We extend quantum metrology beyond this standard setting and consider estimation of a physical process with quantum memory, here referred to as a parametrized quantum comb. We present a theoretic framework of metrology of quantum combs, and derive a general upper bound of the comb quantum Fisher information. The bound can be operationally interpreted as the quantum Fisher information of a memoryless channel times a dimensional factor. We then show an example where the bound can be attained up to a factor of two. With the example and the bound, we show that memory in quantum sensors plays an even more crucial role in the estimation of combs than in the standard setting of quantum metrology.
About the speaker:
Yuxiang Yang is a postdoctoral fellow at the Institute for Theoretical Physics, ETH Zurich. He holds a PhD in Computer Science from The University of Hong Kong and a BS in Physics from Tsinghua University. In 2017 he was awarded a Microsoft Research Asia Fellowship for his work in quantum information theory. His research aims to identify quantum advantages in communication and computation, and to design optimal protocols for the next generation of quantum computing devices
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| May 29, 2019 |
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Title: The Final of Final Year Project competition
Time: 02:00pm
Venue: CBA, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Various
Remark(s):
Date: May 29, 2019 Wednesday
Time: 9:30pm
Venue: CBA, Chow Yei Ching Building, The University of Hong Kong
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| May 24, 2019 |
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Title: Quantum Shannon theory with superpositions of trajectories
Time: 02:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Hlér Kristjánsson
Remark(s): Hlér Kristjánsson
Department of Computer Science
University of Oxford
Date: May 24, 2019 Friday
Time: 2:00 - 3:00pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Abstract:
Shannon’s theory of information was built on the assumption that the information carriers were classical systems. Its quantum counterpart, quantum Shannon theory, explores the new possibilities arising when the information carriers are quantum systems. Traditionally, quantum Shannon theory has focused on scenarios where the internal state of the information carriers is quantum, while their trajectory is classical. Here we propose a second level of quantisation where both the information and its propagation in spacetime is treated quantum mechanically. The framework is illustrated with a number of examples, showcasing some of the counterintuitive phenomena taking place when information travels simultaneously through multiple transmission lines.
About the speaker:
Hlér Kristjánsson is a PhD student at Department of Computer Science, University of Oxford.
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| May 16, 2019 |
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Title: Computation of emotions
Time: 11:00am
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof Peter Robinson
Remark(s): Date:
May 16, 2019
Thursday
11:00 am
Venue:
Room 308 Chow Yei Ching Building
The University of Hong Kong
Peter Robinson
Professor of Computer Technology
University of Cambridge
Abstract:
The importance of emotional expression as part of human communication has been understood since the seventeenth century, and has been explored scientifically since Charles Darwin and others in the nineteenth century. Recent advances in Psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory.
At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication,decision-making and other applications.
This talk will survey recent advances in theories of emotion and affect, their embodiment in computational systems, the implications for general communications, and broader applications.The combination of new results in psychology with new techniques of computation on new technologies will enable new applications in commerce, education, entertainment, security, therapy and everydaylife. However, there are important issues of privacy and personal expression that must also be considered.
About the Speaker:
Peter Robinson is Professor of Computer Technology at the University of Cambridge, where he works on problems at the boundary between people and computers. This involves investigating new technologies to enhance communication between computers and their users, and new applications to exploit these technologies.
His recent work has included desk-size projected displays, emotionally intelligent interfaces and applications in semi-autonomous vehicles. This has led to broader explorations of what it means to be human in an age of increasingly human-like machines.
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| April 29, 2019 |
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| April 24, 2019 |
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Title: Research Postgraduate Studies in Computer Science at HKU
Time: 03:00pm
Venue: Lecture Theatre A, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): CS Department
Remark(s): This seminar should be of interest for all HKU students interested in Research Postgraduate studies in Computer Science. The faculty members of HKU CS include many internationally renowned scholars in diverse fields of Computer Science, such as Algorithms, Bioinformatics, Computer Forensics, Information Security, Computer Vision and Graphics, Human-Computer Interaction, Database and Data Mining, Network, Programming Languages, Systems, and Quantum Computing.The Department of Computer Science at HKU offers two research postgraduate degrees: Doctor of Philosophy (PhD) and Master of Philosophy (MPhil). A PhD is a 4-year research degree, whereas an MPhil is a 2 years research Master. In both programs students work with a faculty member from the department in research projects and publish results in top Computer Science venues. There are several fully funded positions for students wishing to pursue a PhD or MPhil at HKU.In this seminar we will give an overview of postgraduate studies at HKU, how to apply for graduate studies at HKU and funding possibilities. Regarding funding, we will introduce several scholarships that are available for Hong Kong and HKU students. In particular we will present HKU’s University Postgraduate Fellowships (UPF), and the Hong Kong PhD Fellowship Scheme (HKPF). We will also introduce the tuition waiver for Local Research Postgraduate (RPg) Students.
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| April 10, 2019 |
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| April 08, 2019 |
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Title: From Content-Centric Internet to Tactile Internet: Some Thoughts on Future IoT Architecture Design
Time: 11:00am
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Prof Zhi-Li Zhang
Remark(s): Professor Zhi-Li Zhang
University of Minnesota
Date:
April 8, 2019
Monday
11:00 am
Venue:
CPD-LG.18
Central Podium
Centennial Campus
The University of Hong Kong
Abstract:
Recent years have witnessed the proliferation of various mobile and sensor devices, from mobile phones to smart home devices. These mobile and sensor technologies – together with a whole gamut of emerging applications they enable --usher a new era of Internet of Things (IoT) services that will revolutionize the way how we live E and interactwith each other and the physical world. For example, various kinds of (physical or virtual) sensors in the physical and/or cyber worlds have not only allowed us to collect a whole gamut of (spatial-temporal) data, but also afforded us the opportunity to apply advanced data analytics, machine learning algorithms to extract actionable knowledge (or AI),make intelligent decisions in response to events, take actions and effect changes in the physical world, e.g., via remotely controlling and issuing commands to smart (mobile or embedded) devices (i.e., actuators) -- namely, the so-called "Tactile" Internet.Emerging IoT applications and services are putting a strain on today's network architecture, which has primarily served as a giant information repository and distribution platform. The challenges posed by emerging IoT applications and services call for re-thinking and re-architecting of existing networking, compute and storage infrastructures. In this talk, we will put forth some initial thoughts on the challenges and architectural design issues for future IoT networks. Leveraging and building upon the CONIA – content (provider)-oriented, namespace-independent network architecture --that we have advocated and have been developing for multimedia content delivery, we advance a new IoTarchitecture -- referred to as IoTa -- that represents a paradigm shift from content delivery to remotely accessing, controlling or steering real or virtual objects (such as sensors or actuators) in perceived real-time by human operators or machines. The proposed IoTa architecture is application-driven and software-defined}. While it borrowsideas from software-defined networking (SDN) and network function virtualization (NFV), it constitutes a refactoring of the conventional "control-data plane separation" by incorporating and integrating information (including content, control and command) delivery, compute and storage functions in a unifying (network architecture) framework.
About the Speaker:
Professor Zhang is McKnight Distinguished University Professor and Qwest Chair Professor at Department of Computer Science & Engineering, University of Minnesota. He received his B.S. degree in Computer Science from Nanjing University, China, and his M.S. and Ph.D.‘ degrees in Computer Science from the University of Massachusetts, Amherst. Dr. Zhang’s research interests lie broadly in computer communication and networks, Internet technology,multimedia and emerging applications. His past research was centered on the analysis, design and development of scalable Internet QoS solutions to support performance-demanding multimedia applications. His current research focuses on building highly scalable, resilient and secure Internet and cyber-physical systems & infrastructures and developing mechanisms to enhance Internet service availability, reliability and security, and on developing next generation, service-oriented, manageable Internet architectures to provide better support for creation, deployment, operations and management of value-added smart services and underlying networks, including mobile, cloud and content delivery services and networks. Dr. Zhang has published more than 150 journal, conference andworkshop papers. Dr. Zhang has received several honors for his research, and he is co-recipient of a number of Best Paper Awards. He is a Fellow of IEEE.
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| April 06, 2019 |
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Title: HKU Big Data Workshop
Time: 08:00am
Venue: CPD-LG09, Central Podium Centennial Campus, The University of Hong Kong
Speaker(s): Various
Remark(s): The Big Data and Artificial Intelligence Workshop (BDAI) will be held in the University of Hong Kong on 6th April, 2019. BDAI hosts international speakers from the areas of Big Data and AI, which are important areas in knowledge and world economy. The main goal of BDAI is to provide a platform to exchange research insights and implementation areas. We hope to offer researchers, professors, and students to share their experiences and participate in open discussions.
More Information at BDAI
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| April 04, 2019 |
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Title: Content Recommendation for Viral Social Influence
Time: 01:30pm
Venue: Room 308, Chow Yei Ching Building, The University of Hong Kong
Speaker(s): Panagiotis Karras
Remark(s): Panagiotis Karras
Department of Computer Science
Aarhus University
Date:
April 4, 2019
Thursday
1:30 pm
Venue:
Rm 308
Chow Yei Ching Building
The University of Hong Kong
Abstract:
How do we select content that will become viral in a whole network after we share it with friends or followers? Significant research activity has been dedicated to the problem of strategically selecting a seed set of initial adopters so as to maximize a meme’s spread in a network. Yet this line of work assumes that the success of such a campaign depends solely on the choice of a tunable set of initiators, regardless of how users perceive the propagated meme, which is fixed. Yet inmany real-world settings, the opposite holds: a meme’s propagation depends on users’ perceptions of its tunable characteristics, while the set of initiators is fixed.We address the natural problem that arises in such circumstances: Suggest content, expressed as a limited set of attributes, for a creative promotion campaign that starts out from a given seed set of initiators, so as to maximize its expected spread over a social network. To our knowledge, no previous work addresses this problem. We find that the problem is NP-hard and inapproximable. As a tight approximation guarantee is not admissible, we design an efficient heuristic, Explore-Update, as well as a conventional Greedy solution. Our experimental evaluation demonstrates that Explore-Update selects near-optimal attribute sets with real data, achieves 30% higher spread than baselines, and runs an order of magnitude faster than Greedy.
About the Speaker:
Panagiotis Karras (Panos) is an Associate Professor in Computer Science at Aarhus University. In his research he designs robust and versatile methods for data access, mining, and representation. He earned an MEng in Electrical and Computer Engineering from the National Technical University of Athens and a PhD in Computer Science from the University of Hong Kong. Has has been awarded with a Hong Kong Young Scientist Award, a Lee Kuan Yew Postdoctoral Fellowship at the National University of Singapore, a Teaching Excellence Fellowship at Rutgers Business School, and a Best Faculty Performance Award at the Skolkovo Institute of Science and Technology. Panos' work has been published in venues such as VLDB, KDD, SIGMOD, ICDE, SIGIR, and WWW, and cited over 2000 times. He regularly serves as a PC member and referee for major conferences and journals in those areas.
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