Presenters:

• Dr. Vijeta Sharma is a High Performance Computing and Artificial Intelligence (HPC-AI) scientist with over 15+ years of R&D experience in both industry and academia. She holds a master’s degree in Data Science and a PhD in the convergence of Artificial Intelligence and High-Performance Computing (HPC). She is actively engaged in Research, Teaching, and the mentorship. She is deeply involved in international collaborations for research, workshops, conferences, and knowledge dissemination. Her core research interests span Artificial Intelligence, Computer Vision (image & video processing), Human Action Recognition, Deep Learning, Large Vision Models (LVM), High-Performance Computing (HPC), Embodied AI, and Robotics. Specifically, in AI on HPC, her passion lies in AI model parallelisation across accelerators such as CPU, GPU, and TPU – a central theme of her research work. She has delivered 40+ invited talks on international platforms, including as a keynote speaker. She is an active member of Women in HPC (WHPC). Beyond technology, she is also recognised as a woman leader and recipient of the Global Women Leadership 2022 award, honouring her inspiring contributions to women in science and leadership.

• Dr. Bruce Rushing is a Research Scientist within the Data Analytics Center (DAC). The DAC provides machine learning (ML) and artificial intelligence (AI) analytics to UVA researchers as a core part of its mission. Dr. Rushing has expertise in ML and AI, with an emphasis on generative probabilistic modeling, deep learning with neural networks, and large language models (LLMs). His previous work at the DAC includes probabilistic models for glaucoma patient trajectories, next-image segmentation and generation with time-series MRI data, and LLM fine-tuning for modeling semantic shifts in scientific descriptions of American wildlife. Previously, he conducted foundational research on ML, statistics, LLMs, and causal discovery as a Postdoc at Purdue University’s Virtual Reality and Artificial Intelligence lab. He is a trained computational philosopher and statistician, and he has a PhD in Philosophy and MA in Mathematical Behavioral Sciences from the University of California, Irvine, where his dissertation focused on the decision-theoretic foundations for Bayesian and causal inference. His research interests focus on the foundations of statistics and causal modeling and scientific theorizing that emphasize building algorithms for reliable induction.
  • Dr. Jie Ren is an Assistant Professor of Computer Science at William & Mary. She received her Ph.D. from the University of California, Merced, from PASA Lab and received her B.S. degree from the Beijing Institute of Technology. Her research interests span operating systems, computer architecture, and their intersection with machine learning and high-performance computing. Her research aims to improve the performance and resource efficiency of heterogeneous computing systems while making it easier for users to deploy and manage their applications. Her publications appear in top conferences such as SC, PPoPP, HPCA, FAST, and NeurIPS.