Sikha Pentyala
I am an Assistant Professor (tenure-track) at the School of Engineering & Technology, University of Washington (UW) Tacoma. I am an affiliated member of the eScience Institute at UW Seattle and Responsible Health AI Lab (RHAIL) at UW Tacoma. I am also a member of Privacy Preserving Machine Learning (PPML) Lab at UW Tacoma.
My research focus is on responsible and trustworthy AI, spanning privacy, auditing, and safety. Most of my past and current work is on privacy-preserving synthetic data generation and on auditing AI systems, with applications in healthcare and genomics.
I am currently appointed as Staff Research Scientist at the UW School of Nursing. From July 2025 to July 2026, I was a Postdoctoral Researcher at the UW eScience Institute and UW Tacoma. I completed my Ph.D. at UW Tacoma in 2025, advised by Martine De Cock and Anderson Nascimento. I had the oppurtunity to intern at Mila – Québec AI Institute as Research Intern (Fall 2021 - Winter 2022 and Summer 2022) under Dr. Golnoosh Farnadi and at JPMorgan Chase as an AI Research Associate (Summer 2023 and Summer 2024). I was awarded the 2023 JP Morgan Chase PhD Fellowship for my research on synthetic data generation. Before graduate school I spent seven years as a Scientific Officer at the Nuclear Power Corporation of India.
News
Research Directions
My research builds toward Responsible and Trustworthy AI. My work spans four directions:
- Synthetic Data & Digital Twins — generating privacy-preserving (tabular) synthetic datasets, longitudinal synthetic datasets, multimodal synthetic datasets, and constructing digital twins for simulation and analysis
- Auditing & Safety — auditing AI systems across the pillars of trustworthiness (fairness, privacy, robustness, transparency, and safety) and developing methods to achieve them
- Privacy & Context — grounding privacy and fairness guarantees in social and situational context; bridging formal methods with human norms, with growing emphasis on agentic AI systems where contextual boundaries are dynamic
- AI for Good — applying AI to high-impact domains including healthcare (clinical NLP, synthetic data, digital twins) and education (LLM-assisted learning and personalization)
Selected Publications
Full list on the publications page or Google Scholar.