Title: Data Science and AI for Remote Sensing

Time: 03:00pm 

Venue: CB 308

Speaker(s): Departmental Seminar by Prof. Peng Gong, The University of Hong Kong

Remark(s): 

Abstract

Satellite remote sensing is a field expanding exponentially, with data at the PB level. In the past 40 years, it has evolved from partially covering the Earth's surface with 100–30 meter resolution to covering every corner of the Earth at 30 cm resolution. The repeat frequency is improving, and there is a potential point expected to be reached when we collect Earth surface data at submeter resolution constantly. The speed of data acquisition moves ahead, with data processing and information extraction several blocks behind. How should we fill the gap? Environmental scholars learning from the computer science community is clearly not enough. We need better pattern recognition and machine learning technologies to make better use of the explosion of Earth observation data. Will computer scientists, particularly data scientists and AI researchers, join forces to tackle these problems? I propose the concept of iEarth, calling for the participation of data scientists and AI researchers to join hands with environmental scientists to tackle today's grand environmental challenges of the human society, food insecurity, disaster early warning and prevention, water and energy shortages, global health, climate change, etc.

About the speaker

"Professor Peng Gong is the Vice-President and Pro-Vice-Chancellor (Academic Development) at The University of Hong Kong, where he also serves as Chair Professor of Global Sustainability in the Departments of Geography and Earth & Planetary Sciences (since 2021). He holds a BS and MS from Nanjing University and a PhD from the University of Waterloo. His academic career spans York University, the University of Calgary, and UC Berkeley, where he became a full professor in 2001. He later founded Tsinghua University’s Department of Earth System Science (2016) and served as Dean of Science (2017).

In addition to being a Foreign Member of the Academy of Europe, he was the Founding Editor-in-Chief of Geographic Information Sciences (now Annals of GIS). He advises Future Earth as well as Earth Commission; and co-chairs the Lancet Climate Change and Health Commission and Countdown 2030. An interdisciplinary leader, he co-founded the Center for Assessment and Monitoring of Forest and Environmental Resources at UC Berkeley and established key Chinese institutions, including the first Earth System Science Institute in China at Nanjing University. 

His research spans urbanization and health, environmental change monitoring, and infectious disease modelling. He received research awards from the American Society for Photogrammetry and Remote Sensing, the Association of American Geographers and the Joint Board Council of Science China and Science Bulletin. Over 30 of his former PhD students now hold faculty positions at top universities worldwide.

Title: AI Planning for Data Exploration

Time: 04:00pm 

Venue: HW312, Haking Wong Building, HKU

Speaker(s): Prof. Sihem Amer-Yahia

Remark(s): 

Abstract

Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. With the advent of Large Language Models and their ability to reason sequentially, it has become legitimate to ask the question: would LLMs and,more generally AI planning, outperform a customized RL policy in data exploration? More specifically, would LLMs help circumvent retraining for new tasks and strike a balance between specificity and generality? This talk will attempt to answer this question by reviewing RL training and policy reusability for data exploration.

About the speaker

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 algorithmic upskilling. Prior to that she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served as PC chair for SIGMOD 2023 and as the coordinator of the Diversity, Equity and Inclusion initiative for the database community. In 2024, she received the 2024 IEEE TCDE Impact Award, the SIGMOD Contributions Award, and the VLDB Women in Database Award.

Title: Towards Consistent and Physically Plausible Visual Generation

Time: 11:00am 

Venue: CB 308

Speaker(s): Departmental Seminar by Prof. Jianfei Cai, Monash University

Remark(s): 

Abstract

Recent advances in large language models (LLMs) and multimodal large language models (MLLMs) have significantly enhanced the understanding and encoding of textual information. Leveraging these capabilities, a growing number of diffusion-based generative models have emerged for text-conditioned visual generation — spanning text-to-image, text-to-video, and text-to-3D tasks. While these models offer remarkable flexibility and produce increasingly realistic content, they still face fundamental challenges: aligning precisely with user intent, maintaining spatial, view, and temporal consistency, and adhering to the laws of physics. In this talk, I will present several recent research projects from my group that attacks these challenges. PanFusion enforces global consistency in text-to-panorama image generation; MVSplat360 uses image conditions and explicit 3D representation to enhance view consistency of 3D generation. VLIPP integrates physics-informed priors to ensure physically plausible text-to-video generation. I will conclude by pointing out the limitations and discussing future directions such as developing world models.

About the speaker

Jianfei Cai is a Professor at Faculty of IT, Monash University, where he had served as the inaugural Head for the Data Science & AI Department. Before that, he was Head of Visual and Interactive Computing Division and Head of Computer Communications Division in Nanyang Technological University (NTU). His major research interests include computer vision, deep learning and multimedia. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP, and a winner of Monash FIT’s Dean's Researcher of the Year Award and Monash FIT Dean's Award for Excellence in Graduate Research Supervision. He serves or has served as an Associate Editor for TPAMI, IJCV, IEEE T-IP, T-MM, and T-CSVT as well as serving as Senior/Area Chair for CVPR, ICCV, ECCV, ACM Multimedia, ICLR and IJCAI. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He had served as the leading TPC Chair for IEEE ICME 2012, the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019, and the leading General Chair for ACM Multimedia 2024. He is a Fellow of IEEE.