Sameena Shah

Managing Director, J.P. Morgan AI Research
Sameena is a managing director at J.P. Morgan AI Research. Previously, Sameena was Managing Director, Head of Data Science at S&P Global Ratings where she led the firm’s strategy and development for Augmented Intelligence. Prior to that, Sameena worked at Thomson Reuters for seven years. Sameena has a PhD in Distributed Machine Learning and a Masters in Computer Science from IIT Delhi. She is the winner of the top PhD in the country award, Cloudera top AI/ML application award, and several best paper awards and recognitions. She has contributed more than 60 publications and 30 patents. She was the invited speakers at IJCAI 2021 and KDD 2022. She organized several workshops in AAAI, KDD, ICML, etc.

Xiaodan Zhu

Associate Professor, Queen’s University, Canada
Xiaodan is Mitchell Professor and Associate Professor at the ECE Department of Queen’s University Canada. He is a Faculty Affiliate at the Vector Institute for Artificial Intelligence. His recent research interests include NLP, neural symbolic models for NLP, and NLP applications on financial and legal text analytics. Xiaodan was a co-organizer for the SemEval 2019 and 2020 workshops. He has also co-organized the KDD WISDOM workshops in 2018 and 2019. His other service roles include chair for the 33rd Canadian Conference on AI and the best paper selection committee member for ACL 2019 and COLING 2020. Xiaodan has actively served on organizing committees for different NLP conferences.

Wenhu Chen

Assistant Professor, University of Waterloo
Dr. Wenhu Chen is currently an assistant professor Cheriton R. Computer Science School at University of Waterloo and Vector Institute. He has previously worked in Google Research as a research scientist. His research interest covers natural language processing, deep learning, knowledge representation and reasoning. Specifically, he aims at developing models that can ground and reason over external world knowledge to understand human language and communicate with humans. He is also interested in multi-modal problems like visual question answering and captioning. He publishes and serve as program committee in ACL, NAACL, EMNLP, ICLR, NeurIPS, etc. He received the WACV best-student paper honorable mention in 2021. He also received outstanding dissertation award from UCSB in June 2021. He organized SUKI workshop at NAACL 2022.

Manling Li

PhD Candidate, UIUC
Manling Li is a final-year Ph.D. student at the Computer Science Department of UIUC. Her work on multimedia knowledge extraction won the ACL’20 Best Demo Paper Award and NAACL’21 Best Demo Paper Award. She was selected as a DARPA Riser and a EE CS Rising Star in 2022. She was a recipient of Microsoft Research PhD Fellowship in 2021. She was awarded C.L. Dave and Jane W.S. Liu Award, and has been selected as Mavis Future Faculty Fellow. She has more than 30 publications on multimedia knowledge extraction and reasoning, and gave tutorials about multimedia event understanding at AAAI’21, ACL’21, NAACL’22, and AAAI’23. She serves as an area chair at ACL’23, a senior PC member at IJCAI’21 and PC members for multiple conferences and workshops.

Armineh Nourbakhsh

Research Director, J.P. Morgan AI Research
Armineh Nourbakhsh is a Director at J.P. Morgan AI Research, where she leads a team on multimodal document AI. Her career spans a decade of research in NLP in areas such as targeted sentiment analysis, event detection and verification, information extraction, and social data mining. Prior to J.P. Morgan, Armineh was a Director of Data Science at S&P Global, where she led efforts to transform operational workflows related to the ingestion and processing of financial disclosures. In addition to numerous publications and patents, Armineh’s research has been deployed in award-winning AI-driven technologies such as Reuters Tracer, Westlaw Quick Check, and the SocialZ Verve index. She has previously organized workshops at AAAI and ICAIF, and served on the Program Committee of several conferences.

Xiaomo Liu

Research Director, J.P. Morgan AI Research
Dr. Liu is an AI Research Lead at JP Morgan AI Research focusing on machine learning and natural language processing to improve productivity in financial services. Prior to JP Morgan, Xiaomo was a Director of Data Science at S&P Global and a senior research scientist at Thomson Reuters. His work has been reported by numerous news media and won industry awards. Xiaomo holds a PhD in computer science from Virginia Tech and published more than 30 peer reviewed papers and 3 US patents. Xiaomo has serviced as PCs in various conferences like KDD, IJCAI, CIKM, IWSC etc. He also organized three AAAI workshops.

Zhiqiang Ma

Research Lead, J.P. Morgan AI Research
Zhiqiang is a research lead at JP Morgan AI Research. His work concentrates on natural language processing such as information extraction from financial documents, news text clustering and classification, event detection, and topic modeling. Previously, he was a Senior Data Scientist at S&P Global Ratings. He received his Ph.D. in computer science from UNC at Charlotte. He had co-organized three AAAI workshops and served as PC members for multiple conferences and workshops.

Charese Smiley

Research Lead, J.P. Morgan AI Research
Charese Smiley is an AI Research Lead on the AI Research team at J.P. Morgan. Her research interests center around relation extraction and entity extraction, knowledge graphs, and question-answering. Before joining J.P. Morgan, Charese worked at Capital One and Thomson Reuters. She holds a Ph.D. in Computational Linguistics from Indiana University. Charese has served on the organizing committee for AI in Africa for Sustainable Economic Development at ICAIF from 2020-2022, the ICML 2021 Workshop on Representation Learning for Finance and e-Commerce Applications, and as co-lead of the 2021 and 2022 Women in AI and Finance Workshops.

Yulong Pei

AI Research Scientist, J.P. Morgan AI Research
Yulong is an AI Research Scientist at JP Morgan AI Research focusing on machine learning and natural language processing in financial domains. He received Ph.D. (cum laude) in computer science from Eindhoven University of Technology. His research interests cover natural language processing and graph machine learning. He has served as program committee in ICLR, NeurIPS, ICML, AAAI, etc. and regular reviewer for journals like TPAMI, TNNLS, TKDE, etc. He gave tutorials at ICDM 2021 and ECML-PKDD 2021.

Akshat Gupta

AI Research Scientist, J.P. Morgan AI Research
Akshat Gupta is an AI Research Scientist at JP Morgan AI Research where he works on Natural Language Processing with focus on areas like information extraction, semi-supervised learning and domain adaption. He received his Masters degree from Carnegie Mellon University. He also has a Masters degree in Applied and Engineering Physics from Technical University of Munich, Germany. He has published in ICASSP, Interspeech, etc. He has served as PC member for multiple conferences and workshops.