About Me

Welcome! I am an assistant computational statistician at Argonne National Laboratory, where I work on developing and applying statistical and machine learning methods to solve complex scientific problems.

My research focuses on uncertainty quantification, calibration of computer models, and developing scalable computational methods for large-scale scientific applications. I received my Ph.D. in Statistics from Virginia Tech and have been at Argonne since 2019.

Recent Manuscripts

DDUADS HIV decision support paper

Uncertainty Aware Decision Support with Computationally Expensive Simulation Models: A Case Study of HIV Intervention Scenarios

Introduces a data-driven, uncertainty-aware decision support (DDUADS) workflow that combines sensitivity screening, Bayesian calibration, and multi-surrogate integration for stochastic simulators under tight compute budgets. Demonstrated on an HIV agent-based model in Chicago to evaluate ART and PrEP disruption scenarios.

Fadikar, A., Hotton, A., Nascimento de Lima, P., Vardavas, R., Collier, N., Jia, K., Rimer, S., Khanna, A., Schneider, J., Ozik, J.

medRxiv preprint, 2026

MetaRVM WSC 2025 paper

Developing and Deploying a Use-Inspired Metapopulation Modeling Framework for Detailed Tracking of Stratified Health Outcomes

Presents MetaRVM, an open-source R package for stratified metapopulation modeling co-developed with the Chicago Department of Public Health. Uses structured mixing and SEIR-style dynamics to give richer subpopulation detail than compartmental models while remaining cheap enough for routine operational use, demonstrated on Chicago influenza.

Fadikar, A., Stevens, A., Rimer, S., Martinez-Moyano, I., Collier, N., et al.

Proceedings of the 2025 Winter Simulation Conference (WSC), 2025

Adaptive Grid-Based Thompson Sampling paper

Adaptive Grid-Based Thompson Sampling for Efficient Trajectory Discovery

Proposes an adaptive grid-based Thompson sampling strategy for trajectory-oriented Bayesian optimization of stochastic simulators, balancing global exploration with local refinement. Discovers higher-quality trajectories with fewer expensive evaluations than fixed-grid alternatives, supporting faster uncertainty-aware calibration.

Fadikar, A., Stevens, A., Binois, M., Collier, N., Ozik, J.

arXiv preprint, 2025

SACOMA bioRxiv preprint

A spatially-aware unsupervised pipeline to identify co-methylation regions in DNA methylation data

Introduces SACOMA, an unsupervised pipeline that identifies co-methylated regions in DNA methylation arrays by combining genomic proximity and methylation similarity through spatially constrained hierarchical clustering. A tunable mixing parameter avoids rigid threshold choices while maintaining strong sensitivity and false-positive control.

Meshram, S., Fadikar, A., Arunkumar, G., Chatterjee, S.

bioRxiv preprint, 2025

Recent and Upcoming Talks

Beyond Research

Coffee

Trying my hands on espresso and latte art.

Running

My daily driver, I call it Groot.

Snowboarding

It's the time of the year when I strap my feet to a piece of board.

Hiking in the Blue Ridge Mountains

Hiking in the Blue Ridge Mountains whenever I can get to the trails.