I’m a PhD student working with Prof. Sriraam Natarajan and the Statistical Artificial Intelligence and Relational Learning Group (StARLinG Lab) at the University of Texas at Dallas.
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I work on probabilistic AI and AI for healthcare. Specifically, I am interested in developing methods to reconcile information from multi-source data with different forms of domain knowledge into human-understandable forms. Since this scenario is frequently encountered in knowledge-rich but data-poor domains such as healthcare, I am also interested in adapting these methods for complex clinical problems such as modeling the risk of adverse pregnancy outcomes (e.g., preterm birth and gestational diabetes) and understanding the causes of neurological injury in children on life-support (ECMO).
To this effect, my work has considered clinical domain knowledge about relationships between two or more variables including causal influence, monotonicities, and independence. We have developed methods to combine these diverse forms of domain knowledge with noisy, sparse, and uncertain data to construct explainable and interpretable probabilistic graphical models including Bayesian and credal networks, tractable probabilistic models such as sum-product networks and cutset networks, and neurosymbolic models.
Before joining UTD, I was a Master’s student at Indiana University. I worked with Prof. David Crandall on bayesian uncertainty quantification algorithms for deep neural networks. I completed my M.S. in May 2020.
As an undergraduate student at Vellore Institute of Technology, I worked with Prof. Daphne Lopez on deep models for image caption generation and chatbots. I have interned at Microsoft, Bangalore during my undergrad and Synopsys, Mountain View during my masters.
The 39th Annual AAAI Conference on Artificial Intelligence (AAAI) 2025
The 39th Annual AAAI Conference on Artificial Intelligence (AAAI) 2025
The 28th International Conference on Artificial Intelligence and Statistics (AISTATS) 2025.
The 22nd International Conference in Artificial Intelligence in Medicine (AIME 2024)
The 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)
The 27th International Conference on Information Fusion (FUSION 2024)
The 2nd Workshop of Deployable AI (DAI) at AAAI 2024
The 2nd Workshop of Deployable AI (DAI) at AAAI 2024
ASAIO Journal 2023
The Sixth Workshop On Tractable Probabilistic Modeling (TPM) at UAI 2023
The Sixth Workshop On Tractable Probabilistic Modeling (TPM) at UAI 2023
The 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023)
Pacific Symposium on Biocomputing (PSB 2023)
The 11th International Conference on Probabilistic Graphical Models (PGM 2022)
The Fifth Workshop On Tractable Probabilistic Modeling (TPM) at UAI 2022
Constructing causal graphs to understand the risk of neurological injury for patients on life support by refining an LLM's output using real clinical data and domain knowledge.
Deep learning based end-to-end speech recognition system
Deep learning and PGM based image segmentation
Deep learning based image caption generator to improve accessibility on the web.
A web-server for mobile applications that streamline access to academic information for students at VIT.
Detecting clickbait headlines in the wild with 90% accuracy.
A comparitive study of collaborative filtering algorithms.
Web-portals for VIT University’s annual technical festival.