CV

Education

• Ph.D in Statistics and Data Science, Carnegie Mellon University, August 2020 - May 2025 (expected)
  • Advised by Dr. Arun Kuchibhotla
• Master of Statistics: Theoretical Statistics Specialization, Indian Statistical Institute, Kolkata, July 2018 - May 2020
• Bachelor of Statistics, Indian Statistical Institute, Kolkata, July 2015 - May 2018

Skills

• Languages
  • R
  • Python
  • Excel
  • MATLAB
  • PostgreSQL
  • TensorFlow

Projects

• Assumption Lean Inference (August 2023 - Present)
  • Developing methods to provide confidence sets with minimal assumptions for any general class of functionals.
• Monotonic Regression (April 2023 - Present)
  • Presented new results for monotone regression.
  • Provided asymptotically valid confidence intervals that are uniformly valid over a large class of distributions.
  • Paper
• Conformal Inference (October 2022 - Present)
  • Developed new conformal methods allowing practitioners to update the miscoverage level of a prediction set at any stage.
  • Paper
• Random Forests and Decision Trees (July 2022 - Present)
  • Proved consistency results for density estimation using random forests/trees under general regularity conditions.
  • Explored providing uncertainty quantification for density estimates obtained from random forests/trees.
• Spatio-temporal Methods (December 2020 - January 2022)
  • Proposed a state-space modeling approach to infer the missing trajectory of an under-ice float.
  • Used an efficient Kalman smoother algorithm that leverages temperature and salinity information.
• Network Theory (August 2019 - December 2020)
  • Developed methodology for community detection with missing data.
  • Proved consistency of methods developed under different regimens of missing data.
  • Provided insights into graphon estimation and learned parameters from missing data.
• Causal Inference with Network Interference (May 2019 - December 2020)
  • Designed a randomized design scheme derived from a binary LP to estimate peer influence parameters.
  • Explored simultaneous identifiability issues and arbitrary neighborhood interference function estimation.

Papers Presented

• Joint Statistical Meetings, Toronto (August 2023)
  • Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision.
  • Contributed papers in “Nonparametric inference and decision making”.
• IISA Conference, Mumbai (December 2019)
  • Design of experiments for the identification and estimation of peer effects.
  • Student poster presentation.

Achievements

• D. Basu Memorial Gold Medal Award (2019)
  • For outstanding seminar and best performance in B.Stat. (Hons.) Programme.
  • Seminar Title: False discovery rates: A powerful multiple testing tool
• KVPY Scholarship (2015)
  • Funded by the Department of Science and Technology, Govt. of India.
  • One of the 1065 students selected.