Sawal Acharya

About: I am an incoming PhD student in Computer Science at Johns Hopkins University. I will be joining Professor Michael Oberst's group.

I completed my masters in computational and mathematical engineering from Stanford. Before that, I studied applied mathematics at Columbia and conducted research in economics.

Research Interests: I am particularly interested in developing principled frameworks for assessing the reliability of data-driven models in healthcare. This includes quantifying uncertainty, establishing statistical guarantees, and/or enhancing model interpretability, properties that are essential for reliable clinical decision-making. In the process, I draw on ideas from machine learning, causal inference, and statistics.

The above interest is a culmination of my previous experiences in probabilistic modeling (Bayesian ML and stochastic simulation), optimization, and empirical evaluations of LLM-powered causal inference.

Other Interests: I enjoy reading books (fiction and non-fiction), running, biking, and hiking in the mountains. Be on the lookout for my new blog, where I will describe my adventures hiking in the mountains.

Publications

Peer-reviewed Journal Publications

• [1] T. E. Gibson, S. Acharya, A. Parashar, J. E. Gaudio, and A. Annaswamy, "On the stability of gradient descent with second order dynamics for time-varying cost functions," Transactions on Machine Learning Research, 2025, Journal-to-Conference Certification: Presented at ICLR 2026.
• [2] T. E. Gibson, Y. Kim, S. Acharya, D. E. Kaplan, N. DiBenedetto, R. Lavin, B. Berger, J. R. Allegretti, L. Bry, and G. K. Gerber, "Learning ecosystem-scale dynamics from microbiome data with mdsine2," Nature Microbiology, vol. 10, no. 10, pp. 2550–2564, 2025.
• [3] Y. Kim, C. J. Worby, S. Acharya, L. R. van Dijk, D. Alfonsetti, Z. Gromko, P. N. Azimzadeh, K. W. Dodson, G. K. Gerber, S. J. Hultgren, A. M. Earl, B. Berger, and T. E. Gibson, "Longitudinal profiling of low-abundance strains in microbiomes with chronostrain," Nature Microbiology, vol. 10, no. 6, pp. 1551–1551, 2025.

Peer-reviewed Workshops

• [4] S. Acharya, T. J. Zhang, A. Kim, A. Haghighat, S. Xianlin, R. B. Shrestha, M. Mordig, F. Danisman, C. Jose, Y. Qi, P. Cobben, B. Schölkopf, M. Sachan, and Z. Jin, "Causcibench: Assessing LLM causal reasoning for scientific research," in NeurIPS 2025 Workshop on CauScien: Uncovering Causality in Science, 2025.
• [5] V. Verma, S. Acharya, S. Simko, D. Bhardwaj, A. Haghighat, D. Janzing, M. Sachan, Z. Jin, and Y. Yang, "Causal AI scientist: Facilitating causal data science with large language models," in NeurIPS 2025 AI for Science Workshop, 2025.
• [6] Y. Kim, S. Acharya, D. Alfonsetti, G. Gerber, B. Berger, and T. E. Gibson, "Chronostrain: Sequence quality and time-aware strain tracking with shotgun metagenomic data," in ICML Workshop on Computational Biology, 2020.

Preprints

• [7] T. E. Gibson and S. Acharya, Regret Analysis: A Control Perspective, 2025. arXiv: 2501.04572.

Working Papers

• C. Bhandari and S. Acharya. Do Civil Wars Influence Voter Participation? A Case Study from Nepal.

Teaching Experience

• Teaching Assistant, Math 21: Integral Calculus and Series, Winter and Spring 2025. Stanford University.
• Course Assistant, CME 106: Introduction to Probability and Statistics for Engineers, Summer 2024. Stanford University.
• Teaching Assistant, MATH 51: Linear Algebra and Differential Calculus of Several Variables, Autumn 2023 and Winter 2024. Stanford University.
• Teaching Assistant, APMA E2000: Multivariable Calculus for Engineers, Fall 2017, Spring 2018, Fall 2018, and Winter 2019. Columbia University.