Speakers

Heng Ji

Presenter:

Heng Ji, Professor, UIUC

Title: 3D spatial reconstruction

Bio:

Heng Ji is a Professor of Computer Science at the University of Illinois Urbana-Champaign with affiliated appointments in Electrical and Computer Engineering and the Coordinated Science Laboratory, and she serves as an Amazon Scholar and Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE). Her research spans natural language processing—with emphasis on multimedia multilingual information extraction, knowledge-enhanced language and vision models, and trustworthy AI—and large-scale projects in information fusion, knowledge base population, and multimodal understanding supported by DARPA, NSF, and other agencies. She is an Association for Computational Linguistics (ACL) Fellow, has received numerous best paper and scientific honors, and has held leadership roles in major NLP conferences and evaluation tasks such as TAC-KBP.

Jiajun Wu

Presenter:

Jiajun Wu, Assistant Professor, Stanford

Title: TBD

Bio:

He received his B.Eng. from Tsinghua University and his Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT). He was a Visiting Faculty Researcher at Google before joining Stanford University, where he is an Assistant Professor of Computer Science (by courtesy, Psychology). His research focuses on machine learning, computer vision, and cognitive science. He has received multiple honors, including the NSF CAREER Award, ACM Doctoral Dissertation Honorable Mention, MIT’s George M. Sprowls PhD Thesis Award, and faculty research awards from Google, Amazon, Meta, Samsung, and J.P. Morgan.

Mengdi Wang

Presenter:

Mengdi Wang, Professor, Princeton University

Title: TBD

Bio:

Mengdi Wang is a professor at the Department of Electrical and Computer Engineering and Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Computer Science, Princeton’s ML Theory Group. She was a visiting research scientist at DeepMind, IAS and Simons Institute on Theoretical Computer Science. Her research focuses on machine learning, reinforcement learning, generative AI, AI for science and intelligence system applications . Mengdi received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013, where she was affiliated with the Laboratory for Information and Decision Systems and advised by Dimitri P. Bertsekas.

Beidi Chen

Presenter:

Beidi Chen, Assistant Professor, CMU

Title: TBD

Bio:

Beidi Chen is an Assistant Professor at Carnegie Mellon University. Previously, she was a visiting Research Scientist at FAIR. Before that, she was a postdoctoral scholar at Stanford University. She received her Ph.D. from Rice University. Her research focuses on efficient AI; specifically, she designs and optimizes algorithms on current hardware to accelerate large machine learning systems. Her work has won best paper runner-up at ICML 2022 and she was selected as a Rising Star in EECS by MIT and UIUC.

Parisa Kordjamshidi

Presenter:

Parisa Kordjamshidi, Associate Professor, MSU

Title: TBD

Bio:

Parisa Kordjamshidi is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, where she leads the Heterogeneous Learning and Reasoning (HLR) lab. She received her Ph.D. in Computer Science from KU Leuven and has conducted postdoctoral research at the University of Illinois at Urbana-Champaign; her work spans artificial intelligence, machine learning, natural language processing, neuro-symbolic reasoning, and multimodal large-language models with a focus on extracting structured semantic representations from language and vision. Her research has been supported by awards and grants including the NSF CAREER Award, Office of Naval Research projects, and an Amazon Faculty Research Award.

Soumalya Sarkar

Presenter:

Soumalya Sarkar, Senior Principal Scientist, RTRC

Title: TBD

Bio:

He is Senior Principal Scientist in the AI discipline at Raytheon Technologies Research Center (RTRC). He earned his Ph.D. in Mechanical Engineering (with a focus on machine learning in electromechanical systems) from Penn State University in 2015. He leads projects in physics-informed AI, multi-fidelity modeling, simulation acceleration, knowledge graphs, and black-box optimization in aerospace/engineering domains. His honors include the 2021 Technical Excellence Award at RTRC, multiple Outstanding Achievement Awards, and selection to the National Academy of Engineering’s US Frontiers of Engineering symposium.

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