## 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.