Advanced Database and Intelligence Lab
People
Amarnath Gupta
Lab Director
Amarnath Gupta received his PhD in Computer Science from Jadavpur University in India. He is currently a full Research Scientist at the San Diego Supercomputer Center of UC San Diego, and directs the Advanced Database and Intelligence Lab. His primary areas of research include heterogeneous information integration, large-scale graph databases, ontologies and knowledge graphs, query languages, and processing techniques as well as conversational interfaces to information systems. Before joining UC San Diego, he was the Chief Scientist at Virage, Inc., a startup company in multimedia information systems. Dr. Gupta has authored over 100 papers and a book on Event Modeling, holds 13 patents, and is a recipient of the 2011 ACM Distinguished Scientist award.
Celeste Bobryk-Ozaki
Fund Manager
Celeste Bobryk-Ozaki has a Master’s in Business Administration from California State University, San Marcos and has been working in the accounting field for almost 10 years. Prior to joining UCSD, Celeste worked in various accounting roles in both corporate and non-profit organizations. She joined the SDSC Fiscal Team in April 2024 as Research Administrator and is currently the Fund Manager for Dr. Amarnath Gupta. Celeste works with members of the NOURISH team as a financial administrator to ensure expenditures are processed in accordance with policy and procedures as well as monitor the project’s funding sources.
Elaine Chi
Research Scientist
Elaine Chi earned her Master’s degree in Computer Science from the University of California, San Diego. In April 2024, she joined the San Diego Supercomputer Center as a Research Scientist, where she focuses on heterogeneous data management, natural language querying, and knowledge graph construction. Her current work focuses on developing an intent analyzer and enhancing query processing with the support of LLMs. Previously, she worked as a Software Engineer at ByteDance, where she contributed to large-scale recommendation systems. She gained experience with content ranking algorithms and personalization strategies, which further deepened her expertise in data-driven technologies.
Subhasis Dasgupta
Research Scientist
Subhasis Dasgupta, PhD is a researcher at the San Diego Supercomputer Center, known for his innovative work in data management and security. He developed the AWESOME polystore system, which has greatly improved the way complex and varied data are integrated and analyzed. His expertise has been key in important projects like the National Data Platform (NDP) and the NOURISH project, showing his dedication to solving big societal challenges through data science. With a strong background in access control and a range of important publications, patents, and books, Dr. Dasgupta has made significant contributions to secure data processing systems. His work connects scientific research with technological advancements, which are crucial for public health and national research efforts. Additionally, Dr. Dasgupta was one of the founders of the cloud management company Kaavo Inc. Dr. Dasgupta has also worked on projects like DER Security and Quantum Data Hub and was a key member of the China Data Lab, where he set up a facility for integrating information across China to understand Chinese policy, which was well appreciated by social scientists and legal scholars. He is currently involved in cutting-edge projects like COVID-19 monitoring, interdialytic hypertension, green energy, etc. He also advises various labs in medical and engineering schools in the USA, UK, and India.
Shengqi Li
Research Scientist
Shengqi Li received his M.S. in Computer Science from UC San Diego and B.S. in Computer Science and Mathematics from Northeastern University. In April 2025, he joined the San Diego Supercomputer Center as a Research Scientist, where he focuses on ontology development, knowledge graph construction, and advanced query processing. His current work centers on enhancing data indexing techniques and developing robust methods for large-scale entity matching, leveraging innovative machine learning approaches to optimize performance. He has worked on projects involving large-scale entity matching and heterogeneous data indexing and has published his work on ICCS about ontology augmentation.
Safikureshi Mondal
Postdoctoral Researcher
Safikureshi Mondal is a postdoctoral researcher at the San Diego Supercomputer Center (SDSC) of the University of California, San Diego (UCSD). Before joining SDSC, he was an Associate Professor in the Department of Computer Science and Engineering, Meghnath Saha Institute of Technology, Kolkata, India. He earned his PhD in Engineering from Jadavpur University, Kolkata, India. He has published his research in reputable journals, international conferences, and book chapters, in addition to being a reviewer of the Journal of Biomedical Informatics, Neural Processing Letters, Neurocomputing, etc. Currently working on intent analysis in conversational recommendation systems, his research areas are recommendation systems, NLP, LLM, graph analytics, and AI/ML.
Bhaavya Naharas
Master’s Student
Bhaavya Naharas is a data science professional specializing in applied AI and machine learning, with a focus on developing practical solutions across various domains. Her work encompasses areas such as Natural Language Processing, Generative AI, and Sustainability, where she has contributed to tools that enhance operational efficiency, promote environmental responsibility, and improve public safety. Notable projects include a Brand Compliance Checker powered by Generative AI, a Carbon Footprint Calculator encouraging sustainable practices, and a facial recognition system utilizing Generative Adversarial Networks to detect masked individuals in surveillance footage. She is passionate about leveraging data-driven methodologies to address real-world challenges and drive meaningful impact across diverse sectors.
David Padilla
Web Developer and UI Engineer
David Padilla is an experienced web developer specializing in UI/UX design and front-end development. Proficient in diverse back end technologies, he also has a strong background in full-stack solutions. He is adept at crafting modern, responsive, and user-friendly interfaces, with expertise in design principles, component-driven development, and performance optimization. He is focused on building seamless user experiences for ADIL through clean, maintainable code. His accomplishments include building CURE, the proprietary learning management system used at UCSD’s School of Medicine.
Shruti Sawant
Master’s Student
Shruti Sawant is a data science graduate student with a focus on automation, scalable data processing, large language models and natural language processing. She is currently working on building end-to-end pipelines for extracting, transforming, and indexing information from large-scale text corpora. Her ongoing projects involve named entity recognition, Locality Sensitive Hashing (LSH)-based clustering, and document processing workflows. Prior to this, she has worked extensively on processing large-scale datasets and building cloud-based automated pipelines to develop efficient and reliable data systems. She is passionate about applying data-driven solutions to solve complex real-world problems.
Jon Stephens
Research Scientist
Jon Stephens received his Masters in Mathematics from University of California at San Diego (UCSD). He is a software engineer and research scientist at the San Diego Supercomputer Center (SDSC) at UCSD. Jon’s research focuses on systematizing knowledge graph construction using large language models (LLMs) and extending ontology generation through AI. At SDSC, Jon is at the forefront of integrating AI-driven methodologies into scalable systems that address real-world challenges across diverse domains. Additionally, Jon works with the Halıcıoğlu Data Science Institute (HDSI) analyzing and modeling high-dimensional, physiological time series data, producing a novel framework for health based physiological clustering.
Xiuwen Zheng
PhD. Student
Xiuwen Zheng is a PhD student co-advised by Amarnath Gupta and Arun Kumar. Her research focuses on building scalable polystore systems, which includes designing user-friendly polystore languages and designing optimizers for cross-model and cross-DBMS query processing. She is broadly interested in cross-model data management and the application of AI to database systems. Her previous work leverages machine learning for query optimization and using large language models (LLMs) for data augmentation to generate benchmark query workloads.