I am a Senior Applied Scientist in AGI Foundations at Amazon, California. My research focuses on advancing Artificial Intelligence through the development of large language models, agentic models, and reasoning models that are helpful, capable, and safe. My specific interests include benchmark curation, the design of robust evaluation metrics, and the evaluation of models to assess their alignment with responsible AI policies. I am also engaged in uncovering model vulnerabilities through novel jailbreak attacks and red-teaming methodologies. I am interested in developing agentic systems and LLMs for applications such as healthcare and others.
Prior to joining Amazon, I completed my Ph.D. in Computing and Information Sciences at the Rochester Institute of Technology (RIT), where I worked under the supervision of Dr. Linwei Wang in the Computational Biomedicine Lab. My doctoral research centered on personalization and uncertainty quantification in multi-scale 3D simulation models of cardiac electrophysiology. This work allowed me to operate at the intersection of machine learning—specifically Bayesian modeling, optimization, generative modeling, and graph convolutional networks—and computational healthcare, with a focus on personalized cardiac modeling.
I am always open to research collaborations in areas related to AI safety, model evaluation, and trustworthy machine learning. Feel free to reach out at jwala [dot] dhamala [at] gmail [dot] com if you are interested in collaborating.
Agentic AI Systems. I am interested in building capable, safe, and reliable agentic and reasoning models—large language models and multi-agent systems that can operate autonomously while remaining aligned with responsible AI policies. [Amazon Nova] [Agentic Benchmarks] [Tree-of-Traversals]
Evaluation & Benchmarking. I design rigorous benchmarks and robust evaluation metrics for assessing model capabilities, safety alignment, and responsible AI compliance across language generation and agentic tasks. [BOLD] [TANGO] [Intrinsic vs Extrinsic Fairness]
Discovering Capabilities & Limitations. I probe for emergent model behaviors through red-teaming, jailbreak attacks, and adversarial methods, uncovering vulnerabilities such as deception in long-horizon interactions and biases in open-ended generation. [LH-Deception] [Intrinsic vs Extrinsic Fairness] [Resolving Ambiguities]
AI for Healthcare. I apply AI to computational healthcare problems, from personalized cardiac modeling and uncertainty quantification to exploring agentic AI applications in clinical decision support. [MedIA 2020] [MICCAI 2019] [MICCAI 2018]
For a comprehensive list of my publications, please visit my Google Scholar profile.
| Role | Venue | Year |
|---|---|---|
| Co-organizer | TrustNLP Workshop — ACL & NAACL | 2021–2026 |
| Area Chair | ACL Rolling Review (ARR) | 2025 |
| Reviewer | ACL Rolling Review (ARR) | 2024–2026 |
| Co-organizer | Responsible AI Workshop — KDD | 2021 |
| Student Co-organizer | Hackathon on PVC, Consortium of ECG Imaging | 2015–2017 |
| Student Co-organizer | Pre-orientation Program, Women in Computing, RIT | 2018 |