Bhaswar B. Bhattacharya

• Ricardo Castillo Neyra, Sherrie Xie, Brinkley Raynor Bellotti, Elvis W. Diaz, Aris Saxena, Amparo M. Toledo Vizcarra, Gian Franco Condori-Luna, Maria Rieders, Bhaswar B. Bhattacharya, Michael Z. Levy (2024), Optimizing the location of vaccination sites to stop a zoonotic epidemic, Scientific Reports , 14 ().
• Bhaswar B. Bhattacharya, Sandip Das, Sk Samim Islam, Saumya Sen (2024), Growth rate of the number of empty triangles in the plane, Conference on Algorithms and Discrete Applied Mathematics , 14508 (p.p. 77-87).
• Kwonsang Lee, Bhaswar B. Bhattacharya, Jing Qin, Dylan Small (2023), A Nonparametric Likelihood Approach for Inference in Instrumental Variable Models, Journal of the Korean Statistical Society , 52 (p.p. 1055-1077).
• Sagnik Nandy and Bhaswar B. Bhattacharya (Under Review), Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph β-Model.
• Anirban Chatterjee and Bhaswar B. Bhattacharya (Under Review), Boosting the power of kernel two-sample tests.
• Bhaswar B. Bhattacharya, Anirban Chatterjee, Svante Janson (2023), Fluctuations of subgraph counts in graphon based random graphs, Combinatorics, Probability, and Computing, 32 (3), pp. 428-464. https://doi.org/10.1017/S0963548322000335
• Sayak Chatterjee, Dibyendu Saha, Soham Dan, Bhaswar B. Bhattacharya (2023), Two-sample tests for inhomogeneous random graphs in $L_r$ norm: Optimality and asymptotics, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (PMLR), 206 (), pp. 6903-6911.
• Bhaswar B. Bhattacharya and Rajarshi Mukherjee (Under Review), Sparse uniformity testing.
• Somabha Mukherjee, Divyansh Agarwal, Nancy Zhang, Bhaswar B. Bhattacharya (2022), Distribution-free multisample test based on optimal matching with applications to single cell genomics, Journal of the American Statistical Association, 117 (538), pp. 627-638.
• Nabarun Deb, Bhaswar B. Bhattacharya, Bodhisattva Sen (Under Review), Efficiency lower bounds for distribution-free Hotelling-type two-sample tests based on optimal transport.
• All Research from Bhaswar B. Bhattacharya »

Past Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9950 - Dissertation

  Allows for a PhD student to be enrolled full-time to work exclusively on research, writing and preparing his/her doctoral thesis and defense. All required coursework (20 CUs) must be completed, and the student must have passed his/her thesis proposal/oral candidacy examination prior to being enrolled.

• AMCS9990 - Masters Thesis

  For students writing a Master's Thesis to fulfill the program's requirements. All required coursework (8 CUs) must be completed prior to being enrolled.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• MATH4990 - Supervised Study

  Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.

• MATH5999 - Independent Study

  Study under the direction of a faculty member. Hours to be arranged.

• MATH8100 - Reading Seminar

  Reading of mathematical literature under the direction of a faculty member in a group of students. Hours and syllabus to be arranged with the supervising faculty member

• MATH9900 - Master's Thesis

  Study and work on a masters thesis under the supervision of a faculty member. Hours to be arranged.

• MATH9950 - Dissertation

  Research and work on a doctoral dissertation under the supervision of a faculty member. Hours to be arranged.

• STAT3990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT4310 - Statistical Inference

  Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 1010 and 1020. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT4320 - Mathematical Statistics

  An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT5110 - Statistical Inference

  Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course.

• STAT9700 - Mathematical Statistics

  Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

• STAT9950 - Dissertation

  Dissertation

• NSF Career Award, 2021
• Alfred P. Sloan Research Fellowship, 2021
• Probability Dissertation Award, Department of Statistics, Stanford University, 2016
• Sabyasachi Roy Memorial Gold Medal for the best master's thesis, Statistics Program of the Indian Statistical Institute, 2011

Knowledge @ Wharton

• Greenhushing: When Firms Do More Than They Say, Knowledge @ Wharton - 09/08/2026
• AI Is Producing More Software. Why Isn’t It Being Used?, Knowledge @ Wharton - 09/08/2026
• The Hidden Financial Risks of the AI Boom, Knowledge @ Wharton - 09/04/2026
• When Teammates Become Competitors, Knowledge @ Wharton - 09/02/2026
• What Analytics Reveal About College Football’s Top Teams, Knowledge @ Wharton - 09/02/2026
• The Hidden Rules of Workplace Opportunity, Knowledge @ Wharton - 09/01/2026
• The Four Essential Elements of Successful Situations, Knowledge @ Wharton - 09/01/2026
• Can AI Productivity Grow Fast Enough to Justify Big Tech’s Spending?, Knowledge @ Wharton - 09/01/2026
• Cookstove Subsidies Fight Climate Change at a Lower Cost, Knowledge @ Wharton - 09/01/2026
• Why Bond Yields Are Warning Washington, Knowledge @ Wharton - 08/28/2026