About Me
(in about 744 words)
I am currently an Assistant Computational Statistician jointly in the Decision and Infrastructure Sciences and the Mathematics and Computer Science divisions at Argonne National Laboratory. I am also a scientist at-Large fellow at University of Chicago Consortium for Advanced Science and Engineering (UChicago CASE) Before that, I spent three years as a postdoc with Stefan Wild at Argonne National Laboratory. I obtained my PhD in Statistics from Virginia Tech in July 2019, where I worked on Stochastic Computer Model Calibration and Uncertainty Quantification with Prof. David Higdon.
Research Interest
My broad research interests include, but not limited to, scalable algorithm for calibrating large scale computer models, inverse problem, uncertainty quantification, and bayesian optimization. I am particularly interested in finding ways to represent model inadequacy that is meaningful and facilitates extrapolation. Check project page for more details into my research.
Manuscripts
[2022]
Ramachandra, N., Chaves-Montero, J., Alarcon, A., Fadikar, A., Habib, S., Heitmann, K., 2021. Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z MNRAS, 515(2), pp.1927-1941.
Akinlana, D., Fadikar, A., Wild, S., Zuniga-Garcia, N., Auld, J., 2022. Prediction of Traffic via Data Fusion and Gaussian Process Regression: A Case Study on O’Hare Airport Area. (submitted)
[2021]
Zuniga-Garcia, N., Fadikar, A., Akinlana, D., Auld, J., 2021. O’Hare Airport Short-Term Multimodal Demand Forecast Using Gaussian Processes. (submitted)
Fadikar, A., Wild, S., Chaves-Montero, J., 2021. Scalable Statistical Inference of Photometric Redshift via Data Subsampling. ICCS 2021 - Springer Lecture Notes in Computer Science.
Machi, D., Bhattacharya, P., Hoops, S., Chen, J., Mortveit, H., Venkatramanan, S., Lewis, B., Wilson, M., Fadikar, A., Maiden, T. and Barrett, C.L., 2021. Scalable Epidemiological Workflows to Support Covid-19 Planning and Response. IPDPS 2021.
Venkatramanan, S., Sadilek, A., Fadikar, A., Barrett, C.L., Biggerstaff, M., Chen, J., Dotiwalla, X., Eastham, P., Gipson, B., Higdon, D. and Kucuktunc, O., 2021. Forecasting Influenza Activity Using Machine-learned Mobility Map. Nature communications, 12(1), pp.1-12.
[2020]
- Baker, E., Barbillon, P., Fadikar, A., Gramacy, R.B., Herbei, R., Higdon, D., Huang, J., Johnson, L.R., Ma, P., Mondal, A. and Pires, B., 2020. Analyzing Stochastic Computer Models: A Review with Opportunities. Statistical Science, 37(1), pp.64-89
[2019]
- Venkatramanan, S., Chen, J., Fadikar, A., Gupta, S., Higdon, D., Lewis, B., Marathe, M., Mortveit, H. and Vullikanti, A., 2019. Optimizing Spatial Allocation of Seasonal Influenza Vaccine Under Temporal Constraints. PLoS computational biology, 15(9), p.e1007111.
[2018]
- Fadikar, A., Higdon, D., Chen, J., Lewis, B., Venkatramanan, S. and Marathe, M., 2018. Calibrating a Stochastic, Agent-based Model using Quantile-based Emulation. SIAM/ASA Journal on Uncertainty Quantification, 6(4), pp.1685-1706.
[Thesis] Stochastic Computer Model Calibration and Uncertainty Quantification - Virginia Tech.
Invited and contributed talks
Double Sequential Calibration Strategy For Stochastic Simulation Models. ASA Joint Statistical Meeting, Washington DC, August, 2022.
Combining Calibrated Simulator With Observations to Infer Photometric Redshift. SIAM Conference on Uncertainty Quantification (UQ22), Atlanta GA, April, 2022.
Scalable Statistical Inference of Photometric Redshift via Data Subsampling. International Conference on Computational Science (ICCS 2021), Virtual, June, 2021.
Scalable Gaussian Process Regression Based on Data Subsampling.
SIAM Conference on Computational Science and Engineering (CSE21), Virtual, March, 2021.Characterization of Traffic Flow Using Large Scale User Data.
Joint Statistical Meeting (JSM2020), Virtual, August, 2020.Clustering based Gaussian Process Emulation and Calibration of a Stochastic Agent based Model.
SIAM Conference on Mathematics of Data Science (MDS20), Virtual, June, 2020.Emulation, Calibration and UQ for Stochastic Agent Based Model.
Los Alamos National Laboratory, NM, March, 2020.Dirichlet Process Gaussian Process Model for Photometric Redshift.
Joint Statistical Meeting (JSM2019), Denver CO, July-August, 2019.Stochastic Agent Based Model Calibration.
Spring Research Conference, VT Blacksburg VA, May, 2019.Clustering based Gaussian Process Emulation and Calibration of a Stochastic Agent based Model.
Statistical Perspective on Uncertainty Quantification, UNC Chappel Hill, May, 2019.
In news
- Covid19 support to Virginia Department of Health.
- Exploring optimal vaccine allocation using a national model of influenza - UNC Going Viral Symposium, Chapel Hill, Apr 2018 (Best Poster Award).
- Researchers at Virginia Tech’s Biocomplexity Institute work to forecast flu-like weather. Collegiate Times, Feb 19 2018.
- Virginia Tech flu forecasting technology to be used by AccuWeather. WSLS, Dec 6 2017.
- Forecasting the flu. Argonne National Lab, Mar 14 2021.
- Argonne hosts workshop on cosmology, statistics and machine learning. Argonne National Lab, Dec 4 2019.
- Good things come to those who hustle. Sep 20 2019.
Other stuff I enjoy doing
- Running, biking, cross-country skiing - check my Strava profile.
- Photography - my old album at flickr.
