Prof. Pavel Pevzner

Pavel Pevzner is the Ronald R. Taylor Chair and Distinguished Professor of Computer Science and Engineering at the University of California, San Diego. He earned his Ph.D. from Moscow Institute of Physics and Technology, Russia. Dr. Pevzner was named a Howard Hughes Medical Institute Professor in 2006 and has received various accolades, including election as a Fellow of the Association for Computing Machinery (2010), the International Society for Computational Biology (2012), the Academia Europaea (2016), and the American Association for the Advancement of Science (2018). He has been awarded honorary doctorates from Simon Fraser University (2011) and Tel Aviv University (2023), the Senior Scientist Award from the International Society for Computational Biology (2017), and the Kanellakis Theory and Practice Award from the Association for Computing Machinery (2019). Dr. Pevzner authored textbooks "Computational Molecular Biology: An Algorithmic Approach", "Introduction to Bioinformatics Algorithms" (with Neal Jones), “Bioinformatics Algorithms: an Active Learning Approach” (with Phillip Compeau), and “Learning Algorithms through Programming and Puzzle Solving” (with Alexander Kulikov). He co-developed the Coursera online specializations in Bioinformatics and Algorithms with over 600,000 enrolled students.

The replacement of some human professors by AI professors is not a matter of possibility, but inevitability—it’s only a question of when. Meanwhile, universities continue to pack hundreds of students into large lecture halls, despite the fact that this “hoarding” approach offers little pedagogical value. This issue is particularly acute in Science, Technology, Engineering, and Mathematics (STEM) courses, where limited opportunities for student questions can lead to learning breakdowns—points at which students cannot progress without individualized guidance. I introduce Massive Adaptive Interactive Texts (MAITs), which diagnose and address these individual learning breakdowns and outperform traditional classroom instruction by adjusting to the unique learning needs of each student. AI advancements will make adaptive MAITs ubiquitous, digitizing individual learning paths and transforming educational psychology into a data-driven science.

The rise of AI-powered adaptive learning tools like MAITs means universities must rethink their teaching methods. Unlike AI, professors cannot offer office hours 24/7, and they cost significantly more than a MAIT. Forward-thinking universities will likely experiment with AI faculty, as it promises both cost reduction and improved educational quality. Moreover, while AI won’t replace professors in the near future, professors who use AI may replace those who don’t. I explore how professors can work alongside AI, even as it evolves into a superior instructor in virtual classrooms.

Prof. Kyoung Mu Lee

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 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).

One of the important problems in computer vision is reconstructing and understanding three-dimensional (3D) objects and scenes from images. The environment we live in is composed of 3D structures, and the ability to reconstruct and comprehend 3D information is essential for survival. Moreover, it is a crucial element in creating AI systems, such as autonomous vehicles and robots, that resemble humans. Recently, advancements in deep learning and generative Large Multimodal Models (LMMs) have enabled not only 3D reconstruction and understanding but also the generation of 3D scenes or objects. Furthermore, it has become possible to automatically manufacture these reconstructed and generated 3D objects. In this talk, we will review the past, present, and future of 3D problems and technologies in computer vision from the perspectives of 3D reconstruction, generation, and manufacturing.

Prof. Philip S. Yu

Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from ICDM in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,600 referred conference and journal papers cited more than 209,000 times with an H-index of 201. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM TKDD (2011-2017) and IEEE TKDE (2001-2004).

Graph is a ubiquitous non-Euclidean structure, describing the intercorrelated objects in the complex system, ranging from social networks, transportation systems, financial transaction networks to biochemical and molecule structures. Nowadays, graph neural networks are becoming the de facto solution for learning on graphs, generating node or graph embeddings in representation space, such as the traditional Euclidean space. However, a natural and fundamental question that has been rarely explored is: which representation space is more suitable for complex graphs? In fact, the "flat" Euclidean space is suitable for grid structures but is not geometrically aligned with generic graphs with complex structures. Thus, it is interesting to explore deep graph learning in different geometric spaces. This talk will delve into the fascinating properties of mixing various geometric spaces (e.g., hyperbolic and hyperspherical spaces), grounded in the elegant framework of Riemannian geometry, and will discuss recent advancements in tasks such as classification, clustering, contrastive learning, graph structure learning, and continual graph learning. With graph foundation models drawing increasing attention, the talk will also cover preliminary work on building a foundation model for graph structures by exploring mixed geometric spaces. These endeavors pave the way for the next generation of deep graph learning.

Prof. Yiran Chen

Yiran Chen is the John Cocke Distinguished Professor of Electrical and Computer Engineering at Duke University. He serves as the principal investigator and director of the NSF AI Institute for Edge Computing - Athena, the NSF Industry-University Cooperative Research Center for Alternative Sustainable and Intelligent Computing (ASIC), and the co-director of the Duke Center for Computational Evolutionary Intelligence (DCEI). Dr. Chen is the author of over 600 publications and holds 96 US patents. His works have been recognized with numerous awards and honors, including the IEEE Circuits and Systems Society's Charles A. Desoer Technical Achievement Award and the IEEE Computer Society's Edward J. McCluskey Technical Achievement Award. He is a Fellow of AAAS, ACM, IEEE, and NAI, and the inaugural Editor-in-Chief of IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI).

As artificial intelligence (AI) transforms industries, state-of-the-art models have exploded in size and capability. However, deploying these models on resource-constrained edge devices remains a significant challenge. Smartphones, wearables, and IoT sensors face stringent limitations on compute, memory, power, and communication, creating a gap between demanding AI models and edge hardware capabilities that hinders the deployment of intelligence. In this talk, we will re-examine techniques to bridge this gap and embed big AI on small devices. We will begin by discussing how the properties of various hardware platforms impact the design strategies of efficient deep neural network (DNN) models, such as quantization and pruning. Next, we will discuss techniques aimed at reducing the inference and training costs of distributed collaborative edge AI systems. Finally, we will delve into the underlying design philosophies and their evolution toward efficient, scalable, robust, and secure edge computing systems.

Prof. Limsoon Wong

Wong Limsoon is Kwan-Im-Thong-Hood-Cho-Temple Professor in Computing at the National University of Singapore (NUS). He currently works mostly on knowledge discovery technologies and their application to biomedicine. He has also done significant research in database query language theory and finite model theory, as well as significant development work in broad-scale data integration systems. Recognized for his contributions to database theory and computational biology, he was inducted as a Fellow of the ACM in 2013 and as a Fellow of the Singapore National Academy of Science in 2024.

Many students in computer science, engineering, and data science are familiar with principal component analysis (PCA) as a tool for dimension reduction or data visualization. But what if that’s only scratching the surface? PCA's true power lies in its ability to untangle complex variations in data and reveal meaningful patterns that are often overlooked. In this talk, we’ll dive into some unexpected and powerful applications of PCA, uncovering its potential and reshaping the way you think about this fundamental technique.

Prof. C. Mohan

Professor C. Mohan is currently a Distinguished Professor of Science at Hong Kong Baptist University, a Distinguished Visiting Professor at Tsinghua University in China, and a member of the inaugural Board of Governors of Digital University Kerala. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database, blockchain, AI and related areas, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the well-known ARIES family of database locking and recovery algorithms, and the Presumed Abort distributed commit protocol. This IBM (1997-2020), ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the United States and Indian National Academies of Engineering (2009), and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore. In 2019, he became an Honorary Advisor to the Tamil Nadu e-Governance Agency. In 2020, he joined the Advisory Board of the Kerala Blockchain Academy. Since 2016, Mohan has been a Distinguished Visiting Professor of China’s prestigious Tsinghua University. In 2023, he was named Distinguished Professor of Science of Hong Kong Baptist University. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala. Mohan has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. During most of 2022, he was a consultant at Google with the title of Visiting Researcher. He has also been a Consultant to the Microsoft Data Team in 2020. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge network of followers. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK

Ever since the conduct of the historically famous 1956 Dartmouth Summer Workshop, which has come to be regarded as the birthplace of the field of Artificial Intelligence (AI), significant amount of progress has been made in AI with intermittent periods of intense activity. Topics of focus for research and development have varied over the last 6 decades. Unfortunately, during that time, many promises and disappointments have also been associated with AI. Developments in related areas of computer science (e.g., hardware and big data) have also been significantly responsible for the recent renewed momentum behind AI. As progress is made, while numerous benefits of AI are anticipated, many apprehensions have also arisen with respect to the societal impact of AI. Of late, there is also the widespread tendency to equate AI to just machine learning and that is incorrect! Even more recently, ChatGPT and similar systems based on Large Language Models (LLMs) have become topics of intense speculation.

In this talk, first I will survey some of the historical AI developments and then focus on the current areas of investigations across academia and industry. Then, I will briefly touch on the remaining areas of exploration for more positive outcomes to emerge from the adoption of AI technologies in major segments of society at large. Some of the ongoing work in my former workplace, IBM, will also be discussed. Both software and hardware developments will be covered. I will also briefly touch upon on-going AI work in Asia (e.g., China, India), Europe and USA. Even people outside of computer science will be able to appreciate this talk.