Keynote Sessions

Jian Huang, Chair Professor of Data Science and Analytics, The Hong Kong Polytechnic University

Title: Continuous Normalizing Flows for Generative Modeling and Applications

Abstract: Continuous Normalizing Flows (CNFs) have emerged as a powerful class of generative models, distinguished by their capacity for both high-fidelity sample generation and highly expressive density modeling. By leveraging neural ordinary differential equations (ODEs), CNFs construct a continuous-time, invertible mapping that smoothly transforms a tractable base distribution, such as standard Gaussian, into a complex, high-dimensional target data distribution. In this talk, we will explore the theoretical foundations and computational mechanisms underpinning CNFs. Building on these principles, we will examine the versatility of this framework across a broad spectrum of modern statistical and machine learning tasks. Specifically, we will highlight how the exact invertibility and tractable density evaluation inherent to CNFs can be uniquely leveraged to characterize conditional independence, advance counterfactual estimation in causal inference, provide rigorous uncertainty quantification in conformal prediction, and enable dynamic trajectory modeling in motion generation.

Dr. Jian Huang is a Chair Professor of Data Science and Analytics in the Department of Applied Mathematics at The Hong Kong Polytechnic University. He obtained his Ph.D. degree in Statistics from the University of Washington in Seattle. His current research interests include deep generative models and inference, statistical inference in deep learning, deep neural network approximation theory, representation learning, and statistical analysis leveraging pretrained large models. He has published widely in the fields of Statistics, Biostatistics, Machine Learning, Bioinformatics and Econometrics. He was designated a highly cited researcher in the field of Mathematics from 2015 to 2019 by the Web of Science group at Clarivate and included in the list of top 2% of the world's most cited scientists by Elsevier BV and Stanford University (2019-2024). He serves on the editorial boards of the Journal of the American Statistical Association and Journal of the Royal Statistical Society (Series B). Professor Huang is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.

Hansheng Wang, Professor of Business Statistics and Econometrics, Guanghua School of Management, Peking University

Title: Speech Emotion and AI-Driven Intelligent Marketing

Abstract: This report focuses on speech emotion recognition technology and its applications in AI-driven intelligent marketing, examining three practical scenarios in the automotive industry: live streaming and short-video marketing, telemarketing, and AI tele-robot marketing, to explore its technical framework, implementation, and business value. For live streaming and short-video marketing, a CNN-based speech emotion recognition model is built using a dataset of 9,303 audio clips with MFCC features to quantify hosts’ positive emotions, a key factor in conversion. For telemarketing customer conversion prediction, an emotion-enhanced dual-attention model fusing speech Mel spectrograms and textual dialogue data is proposed, achieving an AUC of 0.921 and significantly improving efficiency while reducing costs. The report also establishes a state-space model-based AI tele-robot framework integrating ASR, TTS, and large language models, with continuously enhanced conversion performance. Finally, a theoretical framework for intelligent speech marketing centered on cost, trust, and benefit is proposed, highlighting the technology’s extensibility to education, healthcare, and public sectors and providing a reference for the integration of speech technology and digital marketing.

Dr. Hannsheng Wang is Professor and PhD Supervisor, Department of Business Statistics and Econometrics, Guanghua School of Management, Peking University. He is a recipient of the National Science Fund for Distinguished Young Scholars, a Changjiang Distinguished Professor appointed by the Ministry of Education, and the Founding President of the Young Statisticians Association of the Chinese Industrial Statistics Teaching and Research Association. He is an IMS Fellow, ASA Fellow, and Elected Member of the ISI. He has served as Associate Editor or Editor for 10 international academic journals. He has published over 200 papers in professional journals worldwide, co-authored one English monograph and five Chinese textbooks. He has been selected as an Elsevier Highly Cited Chinese Researcher in Mathematics (2014–2019), Applied Economics (2020), and Statistics (2021–2025).

Tian Zheng, Professor of Statistics, Columbia University

Title: TBA

Abstract: TBA

Dr. Tian Zheng Professor of Statistics at Columbia University. She obtained her Ph.D. from Columbia in 2002. In her research, she develops novel methods for exploring and understanding patterns in complex data from different application domains such as biology, psychology, climatology, and etc. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling, and social network analysis, collaborating with ecologists and earth scientists. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA, and a Google research award. She became a Fellow of the American Statistical Association in 2014. Professor Zheng is passionate about education and mentoring. From 2015-2016, she was one of the series creators for Columbia’s edX Massive Online Open Course (MOOC) series on data science. From 2017-2020, she was associate director for education of Columbia Data Science Institute. She led a number of education programs, including the MS in Data Science program at Columbia, data science capstone projects with data ethics components, DSI Scholars program that connects students with academic research projects in data science, the Collaboratory program for interdisciplinary data science curriculum development, a number of popular Data Science boot camps. She created DSI’s working group on Data Science Education and has been coordinating data science education efforts across Columbia. Professor Zheng is the receipt of the 2017 Columbia’s Presidential Award for Outstanding Teaching. In 2021, she was recognized by a Lenfest Distinguished Columbia Faculty Award that recognizes the excellence of faculty as teachers and mentors of both undergraduate and graduate students.