Kevin Tsai

Kevin currently leads a team of Solution Architects with focus on Generative AI and large-scale, accelerated infrastructure. Kevin has also led teams in Data Analytics, Data Management, and ML at Google, and has more than 20 years of experience in the technology industry

Peter Clark

Peter Clark, PhD is the Head of Computational Science & Engineering within the Therapeutics Discovery (TD) organization of Janssen R&D, where he leads a dynamic, interdisciplinary team of scientists focused on accelerating the research and development of protein based therapeutics through the design and implementation of disruptive computational approaches and platforms. Prior to joining Johnson & Johnson, Peter served as the Director of Bioinformatics at the University of Pennsylvania, Perelman School of Medicine, where he worked closely with academic and commercial collaborators on the design, optimization, and evaluation of various gene therapy platform technologies from early research and development through commercially partnered IND enabling clinical studies. Peter is also a clinically trained molecular pathologist (The Children’s Hospital of Philadelphia) with extensive experience in the design, validation, and implementation of diagnostic and prognostic next generation sequencing (NGS) assays within a clinical, CAP/CLIA certified laboratory setting. During his clinical molecular pathology fellowship at CHOP, Peter co-developed and commercialized the first to market, high resolution, next generation sequencing based HLA genotyping assay and was subsequently awarded the Scholar of the Year award by the American Society for Histocompatibility and Immunogenetics (ASHI) in 2015 for his contributions to the field of solid organ and bone marrow transplantation. Peter earned his B.Sc. and Ph.D. from Drexel University, School of Biomedical Engineering prior to completing a postdoctoral fellowship at The Center for Computational Medicine at Thomas Jefferson University.  Peter’s diverse expertise in computational science, engineering, clinical molecular genetics, computational biology, and translational research has led to the publication of over 30 peer-reviewed scientific publications as well as two book chapters, several issued patents and three biotechnology spin-off companies.

Shreesha Jagadeesh

Shreesha Jagadeesh is an Associate Director of Machine Learning at Best Buy. He leads a multi-national team of ML Scientists and Engineers building models that power the online customer journey through personalized recommendations and ads. He leverages his expertise in Multi-Stage Recommender Systems, LLMs, Embeddings, Multi-Arm Bandits, Offline Policy Evaluation and A/B testing to help digital teams to personalize the experiences deepening customer relationship & driving ecommerce revenue for Best Buy.

Prior to Best Buy, he has worked in a variety of corporate and consulting roles including at Amazon, EY and Cisco building Data Science models in a diverse set of domains spanning HR, Tax, Legal and Supply Chain. Outside of his day job, he advises early-stage startup, reviews pre-publication books/courses and has also published 2 online Data Science courses. He lives in Boston with his wife and enjoys travelling to exotic locations with an Antarctica expedition coming up in December 2024.

Hemant Jain

Hemant works on efficiently fine-tuning and serving LLMs as a part of the Platform team at Cohere. Prior to this, he spent 3 years at NVIDIA developing Triton Inference Server, an open-source solution used to deploy machine learning models into production. He has a Masters in Data Science from the University of Washington.