Title: The Wild Robot: A Journey Toward Long-Horizon Agentic Intelligence

Time: 11:00am 

Venue: HW312, Haking Wong Building

Speaker(s): Prof. Ivor W Tsang

Remark(s): 

Abstract

Long-horizon planning in robotic manipulation demands translating abstract goals into precise, executable actions while maintaining spatial, temporal, and physical consistency. However,
 language model-based planners often fail to handle extended task decomposition, constraint satisfaction, and adaptive recovery from errors. We present The Wild Robot, a framework for autonomous, feedback-driven reasoning that constructs and refines symbolic instruction graphs to guide code generation in robotic tasks. The system dynamically decomposes complex goals into coherent subtasks and generates executable control programs accordingly. When execution failures occur, it analyzes environmental feedback to induce and propagate new constraints, enabling targeted refinement without restarting the planning process. This structured, interpretable approach fosters resilience, adaptability, and transparency, significantly enhancing performance in long-horizon and constraint-sensitive robotic benchmarks. The Wild Robot represents a step toward truly agentic intelligence capable of robust, self-correcting decision-making in complex, real-world manipulation scenarios.

About the speaker

Professor Ivor W. Tsang is the Director of the A*STAR Centre for Frontier AI Research (CFAR) and an Adjunct Professor at the College of Computing and Data Science, NTU, Singapore. Since January 2022, he has led Singapore’s national initiative on Trustworthy Foundation Models under the National Multimodal LLM Programme. He also drives research on Agentic World Models and oversees major national initiatives such as the AI Singapore Materials Design Grand Challenge and the Maritime AI Programme. Under his leadership, CFAR has secured over S$23 million in strategic research funding, strengthening Singapore’s frontier AI ecosystem.

His research spans transfer learning, deep generative models, and big data analytics involving ultra–high-dimensional data. His influential work has earned international recognition, including the ARC Future Fellowship (2013), the ICCM Best Paper Award (2019), and recognition as the AI 2000 AAAI/IJCAI Most Influential Scholar in Australia (2020). An IEEE Fellow, he has made distinguished contributions to large-scale and transfer learning. He also serves on editorial boards of leading AI journals and top AI conference committees.

Title: Deep Model Fusion

Time: 10:00am 

Venue: HW312, Haking Wong Building

Speaker(s): Prof. Dacheng Tao

Remark(s): 

Abstract

In recent years, we have witnessed a profound transformation in the learning paradigm of deep neural networks, especially in the applications of large language models and other foundation models. While conventional deep learning methodologies maintain their significance, they are now augmented by emergent model-centric approaches such as transferring knowledge, editing models, fusing models, or leveraging unlabelled data to tune models. Among these advances, deep model fusion techniques have demonstrated particular efficacy in boosting model performance, accelerating training, and mitigating the dependency on annotated datasets. Nevertheless, substantial challenges persist in the research and application of effective fusion methodologies and their scalability to large-scale foundation models. In this talk, we systematically present the recent advances in deep model fusion techniques. We provide a comprehensive taxonomical framework for categorizing existing model fusion approaches, and introduce our recent developments, including (1) weight learning-based model fusion and data-adaptive MoE upscaling, (2) subspace learning approaches to model fusion, and (3) enhanced multi-task model fusion incorporating pre- and post-finetuning to minimize representation bias between the merged model and task-specific models.

About the speaker

Prof. Dacheng Tao is the Distinguished University Professor and the Inaugural Director of the Generative AI Lab in the College of Computing and Data Science at Nanyang Technological University. He was an Australian Laureate Fellow and the founding director of the Sydney AI Centre at the University of Sydney, the inaugural director of JD Explore Academy and senior vice president at JD.com, and the chief AI scientist at UBTECH Robotics. He mainly applies statistics and mathematics to artificial intelligence, and his research is detailed in one monograph and over 300 publications. His publications have been cited over 160K times and he has an h-index 180+ in Google Scholar. He received the 2015 and 2020 Australian Eureka Prize, the 2018 IEEE ICDM Research Contributions Award, 2020 research super star by The Australian, the 2019 Diploma of The Polish Neural Network Society, and the 2021 IEEE Computer Society McCluskey Technical Achievement Award. He is a Fellow of the Australian Academy of Science, ACM and IEEE.

Title: Task-Driven Image Restoration: Why, How, and What?

Time: 03:00pm 

Venue: HW312, Haking Wong Building

Speaker(s): Prof. Kyoung Mu Lee

Remark(s): 

Abstract

Image degradation is common in real-world scenarios due to factors such as transmission loss, limited camera capability, or poor shooting conditions. These issues often remove key high-frequency details, causing major performance drops in high-level vision tasks like classification, segmentation, and detection. Image restoration (IR) seeks to recover lost details in low-quality images using learned natural image priors, offering a potential solution to this problem. However, studies show that simply applying IR as a preprocessing step rarely restores the information most relevant to high-level tasks. This insight has led to Task-driven Image Restoration (TDIR), which focuses on enhancing visual quality in ways that directly benefit downstream vision tasks. In this talk, we will discuss the key challenges in TDIR and highlight several recent, efficient approaches to address them.

About the speaker

Kyoung Mu Lee (Fellow, IEEE) is currently the Editor-in-Chief (EiC) of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI); He is a distinguished professor at Seoul National University (SNU). He was the founding director of the Interdisciplinary Graduate Program in SNU. He is an Advisory Board Member of the Computer Vision Foundation (CVF). He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA), from 2012 to 2013. He has received several awards, in particular, the Medal of Merit and the Scientist of Engineers of the Month Award from the Korean Government, in 2018 and 2020, respectively; the Most Influential Paper Over the Decade Award by the IAPR Machine Vision Application, in 2009; the ACCV Honorable Mention Award, in 2007; the Okawa Foundation Research Grant Award, in 2006, and the SNU Excellence in Research Award in 2020. He has also served as a General Chair for ICCV2019, ACMMM2018, and ACCV2018; and an Area Chair for CVPR, ICCV, and ECCV many times. He is the founding member and served as the President of the Korean Computer Vision Society (KCVS). Prof. Lee is a Fellow of IEEE, a member of the Korean Academy of Science and Technology (KAST) and the National Academy of Engineering of Korea (NAEK).

Title: Radio Sensing for Human Sensing

Time: 10:30am 

Venue: CB 308

Speaker(s): Prof. Stephan Sigg

Remark(s): 

Abstract

Radio sensing has seen significant advances over the recent decade. I will provide an overview of recent advances, open challenges and new research opportunities. Particularly, as the technology is increasingly integrated with communication platforms, a ubiquitous, connected sensing system is possible. But will such a system indeed be anticipated, given its privacy, ethical and still also technical challenges? On the other hand, new advances towards Quantum RF sensing have been proposed as well as the integration into medical devices and the perception of sentiment, while holographic sensing techniques promise unprecedented imaging capabilities.

About the speaker

Stephan Sigg is a Professor at Aalto University in the Department of Information and Communications Engineering. With a background in the design, analysis and optimisation of algorithms for distributed and ubiquitous systems, he focuses on sensing systems for environmental perception and Usable (perception-based) Security. Especially, his work covers proactive computing, distributed adaptive beamforming, context-based secure key generation and device-free passive activity recognition. Stephan is an editor for the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). He has served on the organizing and technical committees numerous prestigious conferences including IEEE PerCom, ACM Ubicomp, IEEE ICDCS.