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Education
Education
2019 - 2025
PhD
Computer Science and Engineering
University of Washington
Thesis: Mind the Metric: Methods in Metric-Informed Machine Learning
2017-2019
PhD Student
Computational Science and Engineering
Georgia Institute of Technology
(Transferred to UW)
2011-2015
Bachelors of Arts
Physics
Reed College
Honors: Phi Beta Kappa,
Academic Commendation '12, '14
Research
Research
2022-2025
Geometric Representations for Reinforcement Learning
Developed reinforcement learning algorithms that exploit geometric structures in decision processes. Most recently, improved performance of behavioral-distance based reinforcement learning algorithms through locality-preserving Laplacian eigenmaps.
Papers
Adhikary, S., Li, A., and Boots, B., (2024). BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images . International Conference on Machine Learning (ICML)
Adhikary, S. and Boots, B., (2022). Modular Policy Composition with Policy Centroids. Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM)Received RLDM Travel Award! (Declined due to COVID-19)
2021-2022
Geometry-Aware Sampling using Kernel Herding
Extended the kernel herding algorithm to the task of drawing samples from probability distributions over data-spaces corresponding to various structured Riemannian manifolds routinely encountered in robotics.
Papers
Adhikary, S. and Boots, B., (2022). Sampling over Riemannian Manifolds with Kernel Herding. IEEE International Conference on Robotics and Automation (ICRA)
Awarded Best Paper at R:SS Workshop on Geometry and Topology in Robotics!
2019-2021
Quantum-Inspired Probabilistic Modeling
Established formal equivalencies between Hidden Quantum Markov Models (HQMMs), a quantum-inspired probabilistic model for sequential data, and well-known models in classical stochastic processes, weighted automata, and uniform quantum tensor networks. Additionally, developed a new approach to learning HQMMs that exploits its parameterization on the Stiefel manifold.
Papers
Adhikary S.∗, Srinivasan S.∗, Miller J., Rabusseau G., & Boots B. (2021) Quantum Tensor Networks, Stochastic Processes, & Weighted Automata. International Conference on Artificial Intelligence and Statistics (AISTATS).
Srinivasan S., Adhikary S., Miller, J., Pokharel, B., Rabusseau, G. and Boots, B., (2021) Towards a Trace-Preserving Tensor Network Representation of Quantum Channels. Second Workshop on Quantum Tensor Networks in Machine Learning, NeurIPS
Adhikary, S.∗, Srinivasan, S.∗, Gordon, G. & Boots, B. (2020) Expressiveness and Learning of Hidden Quantum Markov Models. International Conference on Artificial Intelligence and Statistics (AISTATS).
2017-2019
Predicting Post-Transplant Outcomes in Renal Transplants
Collaborated with clinical experts to develop machine learning models predicting transplant failures, readmissions, and mortality in renal transplant patients.
Papers
Hogan, J., Arenson, M. D., Adhikary, S., Li, K., Zhang, X., Zhang, R., Valdez, J. N., Lynch, R. J., Sun, J., Adams, A. B., & Patzer, R. E. (2019). Assessing Predictors of Early and Late Hospital Readmission After Kidney Transplantation. Transplantation Direct 5(8)
Valdez J.N., Fu T., Hogan J., Adhikary, S., Arenson M., Zhang R., Zhang X., Li K., Adams A.B., Patzer R.E., Sun J. (2019), Interpretable Post-Transplant Graft Failure Prediction Using Decision Tree Neural Networks. American Journal of Transplantation 19
Hogan J., Arenson M., Adhikary S., Li K., Zhang N., Zhang R., Valdez J., Sun J., Adams A.B., Patzer R.E. Timing matters: Improving Prediction of Hospital Readmission Post Kidney Transplantation, American Journal of Transplantation 19.
Work
Work
June 2025 - Present
Research Scientist
ChipStack, Seattle
Building automated LLM-centered agents for chip-design verification.
Sept 2024 - March 2025
Research Intern
ChipStack, Seattle
Fine-tuning and optimizing large language models for chip design verification.
2019-Present
Robot Learning Lab @ UW
Research Assistant
Robot Learning Lab, University of Washington
Advisor: Professor Byron Boots
Designing machine learning algorithms for prediction and control with a focus on using geometric structure to improve algorithmic efficiency. Published multiple publications in top conferences in machine learning and robotics (e.g. ICML, ICRA, AISTATS).
Oct-Dec 2020
Teaching Assistant
University of Washington
CSE 599: Reinforcement Learning
2017-2019
Research Assistant
Sun Lab, Georgia Institute of Technology
Advisor: Professor Jimeng Sun
Developed machine learning models to predict patient outcomes from electronic health records, including quantitative and natural language data. Provided analysis and recommendations to clinical experts in predicting patient readmissions following kidney transplants.
2017-2019
Teaching Assistant
Georgia Institue of Technology
CSE 4002: Robots and Society (Dec 2018 - May 2019)
CSE 4001: Computing, Society, and Ethics (Aug - Dec 2017)
2015-2017
Marketing Specialist
Adpearance
Implemented search engine optimization strategies for client websites based on user engagement metrics and A/B tests. Developed multiple internal programmatic tools including machine learning models for spam-link prediction.
Aug - Oct 2015
Customer Experience Analyst
Interaction Metrics
Developed recommendations for the improvement of clients' customer experience infrastructure through analysis of customer interaction data.
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