Data Science Fall 2024 Capstone Project Showcase

Judges

Dr. Sujata Goswami
Lead Software Engineer, User Services
Advanced Light Source
Lawrence Berkeley National Laboratory

Dr. Sujata Goswami is a lead software engineer at the user office of Advanced Light Source at Berkeley Lab. With a strong background in scientific software development, data management, and processing, she has made significant contributions to the field. Her previous roles at NASA Jet Propulsion Laboratory, California Institute of Technology, and DOE’s ORNL further solidified her expertise in this domain. As a dedicated volunteer at MLCommons, an AI engineering consortium, Dr. Goswami is also working to enhance the accuracy, safety, speed, and efficiency of AI technologies. Dr. Goswami earned her Ph.D. from Leibniz Universitaet Hannover, Germany, specializing in Earth and space sciences. During her doctoral studies, she focused on developing novel data processing, analysis, and visualization methods for Earth science space missions. Her research interests continue to encompass data management, FAIR data management, machine learning, and software development.

Dr. Chandan Gope
Vice President of Data Science
Element Energy

Dr. Chandan Gope is the VP of data science at Element Energy where his team of engineers and scientists are developing AI/ML algorithms in the battery energy storage domain. The team leverages huge amounts of data that are continuously being generated in the field, in order to build AI models that can predict the safety and health of battery packs at individual cell level.

Prior to Element Energy, Dr. Gope was the director of AI & ML at Nice (North America), where he built AI-enabled computer vision products for automotive and home security markets. Dr. Gope received his B.Tech. in instrumentation engineering from Indian Institute of Technology, Kharagpur; master’s in data science from University of California, Berkeley; and Ph.D. in electrical engineering from University of Texas at Dallas. He has published over a dozen peer-reviewed journal articles and conference papers, authored 6 patents, and has been an IEEE senior member since 2010. He also serves on the editorial board for the Journal of Real Time Image Processing.

Gabriel Ohaike
AI/ML Engineer
Solutions Architect

Gabriel Ohaike is a skilled AI and machine learning engineer with extensive experience across healthcare, finance, consumer products, and professional services. Gabriel has contributed his expertise in diverse settings, from agile startups like DineIntel to enterprise-level projects. Through a third-party engagement, he has also provided consulting services in generative AI for PwC, delivering data-driven insights to high-impact initiatives.

Recently, Gabriel built an advanced GenAI model, designing a robust monitoring framework and implementing scalable pipelines on AWS to ensure optimal performance and reliability. His work includes developing retrieval-augmented generation (RAG) models and custom AI agents to enhance information retrieval and automate customer interactions. Additionally, he has created recommendation engines and NLP-based customer insight systems that drive user engagement and support strategic growth. Known for his technical proficiency and practical approach, Gabriel delivers solutions that are scalable, reliable, and seamlessly integrate with varied business environments.

Gabriel holds a master’s degree in information and data science from the University of California, Berkeley, and a bachelor’s degree in petroleum engineering from Rivers State University, Nigeria. As an AWS-certified machine learning engineer and solutions architect professional, he combines deep technical expertise with a strategic understanding of business applications. His diverse project experience allows him to address complex challenges effectively across industries. Gabriel also serves as a mentor for students in the Master of Information and Data Science program at UC Berkeley.