FFSE-2024 Keynotes

The 4th Forum on Frontiers of Science and Engineering (FFSE)

Towards Interdisciplinary Sustainability Research (ISR)

Lingnan University, Hong Kong, July 21 - 22, 2024

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Keynote Presentations

Structural Pharmacology of Nav and Cav channels

Nieng Yan

Shenzhen Medical Academy of Research and Translation (SMART)

Shenzhen Bay Laboratory

School of Life Sciences, Tsinghua University

Voltage-gated sodium (Nav) and calcium (Cav) channels are responsible for the initiation of electrical signaling. Being associated with a variety of disorders, Nav and Cav channels are targeted by multiple pharmaceutical drugs and natural toxins. Taking advantage of the resolution revolution of single particle cryo-EM, we have determined the structures of different Nav and Cav subtypes from human and other eukaryotes. These structures, alone or in complex with distinct auxiliary subunits, toxins, and drugs, not only afford unprecedented insights into the working and disease mechanism of these channels, but also reveal novel pharmacological sites. In light of these structural advances, we proposed a structure-based nomenclature for ligand binding sites on Nav and Cav channels, which may facilitate rational drug design and optimization.

Challenges and Opportunities of Interpretable AI

Wenjun (Kevin) Zeng, Ph.D.

Chair Professor, Eastern Institute of Technology, Ningbo, China

Artificial Intelligence (AI) has experienced tremendous growth and brought waves of excitement in the past decade, thanks to the advancement of deep learning technologies and more recently large generative AI models such as ChatGPT/Sora. However, the “black-box” nature of current AI technologies poses significant challenges on the trustworthiness of AI Models, which potentially will impede the adoption of AI, especially for mission critical applications, and be a road blocker for achieving Artificial General Intelligence. In this talk, we first present the concept of interpretable AI and highlight its importance as well as challenges. We then discuss the opportunities to address this issue, highlighting the concept and approaches of disentangled representation learning, and the interpretable and controllable process of AI content generation. We will also discuss some future trends.

Wenjun (Kevin) Zeng has been a Chair Professor and Vice President for Research of the Eastern Institute of Technology (EIT), Ningbo, China since Oct. 2021. He is also the founding Executive President of the Ningbo Institute of Digital Twin (IDT). Prior to that, he was a Sr. Principal Research Manager and a member of the Senior Leadership Team at Microsoft Research Asia where he was leading the video analytics research powering the Microsoft Cognitive Services, Azure Media Analytics Services, Microsoft Office, Dynamics, and Windows Machine Learning. He was a professor with the Computer Science Dept. of Univ. of Missouri from 2003 to 2016. Prior to that, he worked for PacketVideo Corp, San Diego, CA, Sharp Labs of America, Camas, WA, Bell Labs, Murray Hill, NJ, and Panasonic Technology, Princeton, NJ. He has contributed significantly to the development of international standards (ISO MPEG, JPEG2000, and Open Mobile Alliance). He received his B.E., M.S., and Ph.D. degrees from Tsinghua Univ., the Univ. of Notre Dame, and Princeton Univ., respectively. He is on the Editorial Board of International Journal of Computer Vision, and was an Associate Editor-in-Chief, Associate Editor, or Steering Committee Member for a number of IEEE journals. He has served as the General Chair or TPC Chair for several IEEE flagship conferences (e.g., ICME’2018, ICIP’2017). He is a Fellow of the IEEE and a Fellow of Canadian Academy of Engineering.

Build an end-to-end scalable data science ecosystem by integrating statistics, ML, and Domain Science

Xihong Lin

Department of Biostatistics and Department of Statistics

Harvard University

The data science ecosystem encompasses data fairness, statistical, ML methods and tools, interpretable data analysis, and trustworthy decision-making. Rapid advancements in ML have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable data science ecosystem that integrates statistics, ML, and domain science. I will illustrate key points using the analysis of whole genome sequencing data and electronic health records. This talk aims to ignite proactive and thought-provoking discussions, foster collaboration, and cultivate open-minded approaches to advance scientific discovery.

Xihong Lin is Professor and former Chair of Biostatistics, and Coordinating Director of the Program in Quantitative Genomics at Harvard School of Public Health, and Professor of Statistics at Harvard University. Dr. Lin works on the development and application of statistical and machine learning methods for analysis of big and complex genomic and health data. Dr. Lin was elected to the US National Academy of Medicine in 2018 and the US National Academy of Sciences in 2023. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS). She also received the 2017 COPSS FN David Award, the 2022 National Institute of Statistical Sciences Sacks Award for Outstanding Cross-Disciplinary Research, and the 2022 Zelen Leadership in Statistical Science Award. She is an elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute. Dr. Lin’s research has been supported by the MERIT Award (2007-2015) and the Outstanding Investigator Award (2015-2029) from the National Cancer Institute. Dr. Lin is the former Chair of COPSS and is the former Editor of several biostatistical journals

Addressing Climate Change: Negative Emission based on AI-Driven Evolution of Advanced Materials

Xi Chen

Chair Professor and Dean, School of Interdisciplinary Studies

Director, Shenzhen Research Institute

Lingnan University

To address the global challenge of climate change and sustainability, revolutionary solutions are needed to develop highly efficient pathway for mitigating carbon dioxide and greenhouse gas emissions. The “Holy Grail” is engineered removal of CO2 directly from the atmosphere, known as negative emission. Conventional carbon capture method developed for power and chemical plants does not work well for air capture, due to the very different CO2 concentration in flue gas and air. We present a disruptive approach of DAC (direct air capture of CO2), which is enabled by unconventional reverse chemical reaction driven by water quantities in nanopores. The humidity-swing system absorbs CO2 from the air when the surrounding is dry, whereas releases CO2 when wet. AI-driven material and system design is employed to significantly promote DAC capacity and efficiency, leading to perhaps the world’s cheapest solution of negative emission. The carbon loop is further closed by distributed and need-based capture of CO2 and various pathways of CO2 conversion, forming the technological roadmap of distributed carbon capture, utilization, and sequestration (distributed CCUS). The future prospects of engineering the carbon loop and grand cycles of sustainability are also discussed.

Xi Chen is Chair Professor and Dean of the School of Interdisciplinary Studies at Lingnan University, and Director of Shenzhen Research Institute of Lingnan University. He received his M.S. from Tsinghua University, and Ph.D. in Solid Mechanics from Harvard University, and spent 20 years as a professor in the Department of Earth and Environmental Engineering at Columbia University, before joining Lingnan in 2023. He received numerous awards including the NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), ASME Sia Nemat-Nasser Early Career Award, ASME Thomas J. R. Hughes Young Investigator Award, and SES Young Investigator Medal. He is a Fellow of ASME. He has published over 400 journal papers with a h-index over 70. He uses multiscale theoretical, experimental, and numerical approaches to investigate various research frontiers in engineering science addressing real-world challenges in energy, environment, nanotechnology and biology. He pioneered the scientific and technological framework of distributed carbon capture, utilization, and sequestration (distributed CCUS), and established Asia’s first direct air capture factory for carbon dioxide, and China’s first carbon negative industrial park zone.