ESC

Optimizing AI Solutions and Infrastructure for Science (OASIS)

Department of Computer Science and Engineering • School of Engineering and Applied Sciences (SEAS) • University at Buffalo

Artificial Intelligence Cyber-infrastructure Anomaly Detection Healthcare & Biomedical Research Quantum Spectroscopy Digital Twins

The Optimizing Artificial Intelligence Solutions and Infrastructure for Science (OASIS) research group is located in the Computer Science and Engineering department within the School of Engineering and Applied Sciences (SEAS) at the University at Buffalo.

We develop scalable AI solutions that integrate scalable data science and cyber-infrastructure research to enable scientific discovery in areas including healthcare and biomedical sciences, national security, engineering, and sustainability.

Research Focus

Our group tackles high-impact challenges at the intersection of scalable machine learning and advanced cyberinfrastructure. Highlights of our current research include:

National-Scale Cyberinfrastructure for Personal Data

NSF AWD 2609498

Designing an innovative national data platform and cyberinfrastructure ecosystem that enables secure, robust access to sensitive personal data to fuel next-generation AI models.

AI for Quantum Ramanomics & Live Cell Mapping

Active (2025–2027)

Developing physics-informed machine learning and quantum science models to interpret complex Raman spectral signatures for non-invasive, label-free biochemical mapping and molecular diagnosis.

Medical Digital Twins & Uncertainty Quantification

NSF AWD 2533946

Establishing mathematical foundations and scalable AI algorithms for real-time uncertainty quantification in deep learning-driven digital twins for autonomous medical systems (such as Type 1 Diabetes).

Explore all ongoing and previous initiatives on our Research page →

Prospective Students & Collaborators

We are always looking for motivated graduate and undergraduate researchers passionate about data science, machine learning, and interdisciplinary applications. Check our Team page and reach out!

Varun Chandola

Varun Chandola

Lab Director

Associate Professor

University at Buffalo

  • Ph.D. Computer Science, Univ. of Minnesota
  • Assoc. Professor, CSE, Univ. at Buffalo
  • Co–Director of Graduate Studies (DGS), CSE, Univ. at Buffalo
  • Former Program Director, National Science Foundation (OAC)

News

1 September, 2026
New NSF funding! We have been awarded a planning grant to design the next-generation data infrastructure for personal data.
15 August, 2026
Our work on developing a label-free method that identifies organelles using AI + Quantum Ramanomics is featured in UBNow.
1 January, 2026
OASIS lab member Tanvi Ranga co-authored a high-impact paper on Advancing Deepfake Detection Research at UB.

See all news →