Alex Fabbri is a research scientist at Salesforce AI Research working on text summarization and subproblems such as conversation summarization, evaluation, and factual consistency. He defended his PhD at Yale University, advised by Professor Dragomir Radev.

Yu Su is a Distinguished Assistant Professor of Engineering at the Ohio State University and a Researcher at Microsoft Semantic Machines. He obtained his Ph.D. from University of California, Santa Barbara and his bachelor's degree from Tsinghua University. The overarching goal of his research is to lower the barrier of access to data, knowledge, and intelligence through foundational and applied AI innovations, with a recent focus on conversational AI, foundation models, and grounded language understanding. His work at Microsoft has led to a new conversational interface for Microsoft Outlook. He serves in leadership roles in multiple national AI institutes, including as Co-Lead of Foundational AI in the ICICLE AI Institute and as Lead of Machine Learning Foundations in the Imageomics Institute. His research has been recognized with awards such as Outstanding Paper Award at COLING 2022, Outstanding Dissertation Award from UC Santa Barbara, and the third-place honor of the inaugural Amazon Alexa Prize TaskBot Challenge.

Michihiro Yasunaga is a PhD student in Computer Science at Stanford University, advised by Percy Liang and Jure Leskovec. His research interest is in natural language processing and machine learning, with a focus on developing language models with knowledge and reasoning abilities. https://cs.stanford.edu/~myasu/

Lauren is a 4th year Computer Science PhD student at Stanford University also affiliated with the Carnegie Institution for Science. She is a machine learning researcher interested in increasing our understanding of global change ecology using state-of-the-art machine learning methods. Her research touches on topics spanning machine learning, global ecology, genomics, population genetics, and environmental data science. 

Dr. Luyao Shi joined IBM Research Almaden as a Research Staff Member in 2020. Prior to joining IBM, he received his Ph.D. degree in Biomedical Engineering with an emphasis on medical imaging and machine learning from Yale University. His current research interests include computer vision and information retrieval.

Tianxiao Li is a graduate student in Computational Biology & Bioinformatics at Yale University, under the supervision of Prof Mark Gerstein. His research focuses on applying interpretable machine learning to the understanding of human genome, and using generative modeling for protein and drug designs.

Haoran Liu is a Ph.D. student in the Department of Computer Science & Engineering at Texas A&M University, advised by Dr.James Caverlee. Previously, she worked with Dr.Shuiwang Ji on geometric representation learning for biomedical data. Her current research focuses on deep learning on graphs.

Lang is currently a Ph.D. candidate at the Department of Information & Communication Engineering, The University of Tokyo, working with Prof. Toshihiko Yamasaki. Prior to that, he received a Master’s degree from the Peking University in 2021. His research interests lie at the intersection of deep learning and computer vision, especially solving vision problems with self-supervised representation learning and deep neural networks, e.g., Vision Transformers.

Yujie is currently a master’s student studying Biostatistics at Yale University. Her research interests center on taking an integrative approach by collating the expertise of diverse researchers from different domains and coupling it with statistical modeling to advance understanding in biomedicine. In particular, she is interested in investigating the intersection of natural language processing (NLP), machine learning (ML), and molecular medicine to enhance access to clinically relevant information to ultimately improve patients’ health.

Vanessa Yan is a Staff Product Manager at OctoML, building a SaaS platform that helps engineers deploy Machine Learning models to production with fastest time to market, lowest cost per inference, and lowest application latency. Previously, she built Machine Learning software as an engineer for Meta's AI and Messenger teams; launched products which leveraged Machine Learning to deliver 8-figure annual recurring revenue for Apple's Commerce teams; and conducted Machine Learning research which has been published in NeurIPS and ACL— top conferences in Machine Learning and Natural Language Processing. She graduated from Yale with a 4-year joint B.S./M.A. in Statistics and Data Science plus a B.S. in Computer Science.