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After my PhD at Cornell University (2011), I joined the Courant Institute of Mathematical Sciences, New York University, as a Courant Instructor, where I stayed till 2014. In the Fall semester of 2014, I arrived at CUNY..

I did my undergraduate (B.Stat degree, 2004) and masters (M.Stat degree, 2006) with Honors in Statistics at the Indian Statistical Institute in Kolkata, India. Then, I received an MS degree (2008) and PhD degree (2011) from Cornell University, Ithaca, NY USA, with major in Operations Research & Information Engineering and minor in Mathematics. 

Theoretical and empirical analysis of statistical inference procedures, machine learning algorithms, and data science approaches to address inference questions involving complex data-types (including network data, high-dimensional data, and complex time series data); Developing relevant inference frameworks and methods to address inferential questions arising in various practical areas (including biosciences, social and behavioral sciences, computer science, and physical sciences); Mathematical and probabilistic modeling of different biological, social, and physical phenomena and complex structures along with their theoretical analysis, stochastic spatial models, percolation and related models, random graph models, processes and dynamics on random graphs.

Relevant Links:

• My Research website: shirshendu.ccny.cuny.edu/Research.ht,l
• My 
• My

Based on the primary focus, aim, and approach, my research outcomes can be broadly categorized (with overlaps) into 3 Groups: Applied and Theoretical Probability (Group 1); Statistics and Data Science (Group 2); and Interdisciplinary Research (Group 3).

Group 3 Interdisciplinary Research (in collaboration with researchers in epidemiology, ecology, biology, agriculture, economics, public health, computer science, cyber security, political science)

51.(Public Health, Statistics) Exploring Social Network Factors of Body Image Perception in Mexican Immigrants. E. Gutierrez, K. R. Florez, N. Hwang, S. Chatterjee. Under Review. Preprint is available upon request.

50.(Agricultural Economy, Data Science, Agent-Based Model) Inoculum Dose, Diversity, Dispersal, and Damage: Simulating Optimal Economic Control of an Aerially-Dispersed Plant Pathogen at the Regional Scale J. Pedro, S. Bhattacharyya, S. Chatterjee, T. L. Marsh, J. Y. Hwang, and D. H. Gent. Under Review. Preprint is available upon request.

49.(Time series clustering, Anomaly Detection, Applications to Cyber Security) Finding Anomalies in Border Gateway Protocol for routing data through internet. L. Metcalf, W. Casey, H. Janwa, S. Chatterjee, E. Battifarano, and T. Snoke. To appear on Springer Lecture Notes in Networks and Systems–Proceedings of ISICN (International Symposium on Intelligent Computing and Networking) 2025. Recipient of the “Best Paper Award” for the ISICN 2025 conference.

48. (Epidemiology, Data Science, Disease Control and Pest Management) How do growers respond to host resistance? A conditional Gaussian Bayesian network for causal inference of fungicide cost savings . J. Y. Hwang, S. Bhattacharyya, S. Chatterjee, T. L. Marsh, J. Pedro, and D. H. Gent. Phytopathology (2025). PMID: 41033659. DOI: 10.1094/PHYTO-06-25-0199-R.

47. (Epidemiology, Data Science, Disease Control and Pest Management) Fungicide Selection, Disease Risk, and Grower Switching Behavior. J. Y. Hwang, S. Bhattacharyya, S. Chatterjee, T. L. Marsh, J. Pedro, and D. H. Gent. Plant Disease (2025). PMID: 40176489. DOI: 10.1094/PDIS-01-25- 0044-RE.

46.(Epidemiology, Data Science, Disease Control and Pest Management) The Diversity and De- terminants of Fungicide Programs for Hop Powdery Mildew. J. Y. Hwang, S. Bhattacharyya, S. Chatterjee, T. L. Marsh, J. Pedro, and D. H. Gent. Plant Disease (2025). PMID: 40176490. DOI: 10.1094/PDIS-01-25-0043-RE.

45.(Epidemiology, Data Science, Disease Control, Pest Management) What Explains Hop Growers’ Fungicide Use Intensity and Costs for Powdery Mildew Management? J. Y. Hwang, S. Bhat- tacharyya, S. Chatterjee, T. L. Marsh, J. Pedro, and D. H. Gent. Phytopathology (2024), vol. 114, no. 10, pages 2287–2299. DOI: 10.1094/PHYTO-04-24-0127-R

44. (Political Science, Statistics) Pandemics and Elections: Estimating the Net Effect of Anxiety and Partisanship. N. Hwang, H. Chang, S. Chatterjee, Y. Di, S. Bhattacharyya. Political Research Quarterly (2025). DOI: 10.1177/10659129251346188.

43.(Public Health, Statistics) Are Remitters at Risk for Lower Food Security and Dietary Quality? An Exploratory Study of Mexican Immigrants in NYC. D. Cruz-Salazar, N. S. Hwang, S. Chatterjee, K. P. Derose, K. R. Fl ́orez. Community Health Equity Research and Policy (2025). DOI: 10.1177/2752535X251355455. I: 10.1101/2022.12.20.22283288.

42.(Plant Epidemiology, Statistics)Identifying Highly Connected Sites for Risk-Based Surveillance and Control of Cucurbit Downy Mildew in the Eastern United States. A. M. E. Ojwang, A. Lloyd, S. Bhattacharyya, S. Chatterjee, D. H. Gent, and P. S. Ojiambo. PeerJ (2024), 12:e17649. DOI: 10.7717/peerj.17649.

41.(Agricultural Economy, Plant Epidemiology, Optimization, Control Theory) Coupling an epidemio- logical and economic model to optimize management of hop powdery mildew at the landscape level. D. H. Gent., J. F. Pedro, T. L. Marsh, S. Chatterjee, and S. Bhattacharyya. Proceedings of IHGC STC 2023 (Meeting of the Scientific-Technical Commission of the International Hop Growers’ Convention).

40.(Plant Epidemiology, Applied Math, Statistics) A general framework for spatio-temporal modeling of epidemics with multiple epicenters with an application to aerially dispersed plant disease. A. M. E. Ojwang, T. Ruiz, S. Bhattacharyya, S. Chatterjee, P. S. Ojiambo, and D. Gent. Frontiers in Applied Mathematics and Statistics, section Dynamical Systems (2021), vol. 7. DOI: 10.3389/fams.2021.721352. Selected for “Outstanding Article Award” for the journal Frontiers in Applied Mathematics and Statistics. Featured in the dedicated collection “2021 Editor’s Pick: Applied Mathematics and Statistics”.

39.(Biology, Statistics) Bacterial Swarmers Enriched during Intestinal Stress Ameliorate Damage. W. Chen, A. De, H. Li, D. J. Lukin, W. Szymczak, K. Sun, L. Kelly, J. R. Wright, R. Lamendella, S. Ghosh, D. B. Kearns, Z. He, C. Jobin, X. Luo, A. Byju, S. Chatterjee, B. San Yeoh, M. Vijay- Kumar, J. X. Tang, S. Mani. Gastroenterology (2021), vol. 161, no. 1, pages 211–224.

38.(Social Network Analysis, Statistics) Using Attendance Data for Social Network Analysis of a Community-Engaged Research Partnership. K. S. Vasquez, S. Chatterjee, C. Khalida, D. Moftah, B. DO ́razio, A. Leinberger-Jabari, J. N. Tobin, and R. G. Kost. Journal of Clinical and Translational Sciences (2021), vol. 5, no. 1, E75, pages 1–13. DOI:10.1017/cts.2020.571.

37.(Math Model for Epidemic Management, Game Theory) Mathematics of YACHT (Yet Another Covid Health Testing) Protocol for Epidemic Management. I. Enaganti, S. Chatterjee, and B. Mishra. Preprint is available on preprints 202204.0031.

36. (Math Model for Epidemic Management, Simulation Study)Towards Optimal Tracing Strategy in Pandemics with applications to Covid-19. I. Enaganti, S. Chatterjee, K. Meel, B. Mishra. Preprint is available at shirshendu.ccny.cuny.edu/PDF Files/ContactTracing.pdf.

35.(Biology, Statistics) Targeting the Pregnane X Receptor Using Microbial Metabolite Mimicry Z. Dvoˇr ́ak, F. Kopp, C. M. Costello, J. S. Kemp, H. Li, A. Vrzalov ́a, M. Sˇtˇep ́ankov ́a, I. Bartonˇkov ́a, E. Jiskrov ́a, K. Poulikov ́a, B. Vyhlidalov ́a, L. U. Nordstroem, C. V. Karunaratne, H. S. Ranhotra, K. S. Mun, A. P. Naren, I. A. Murray, G. H. Perdew, J. Brtko, L. Toporova, A. Schon, W. G. Wallace, W. G. Walton, M. R. Redinbo, K. Sun, A. Beck, S. Kortagere, M.C. Neary, A. Chandran, S. Vishveshwara, M. M. Cavalluzzi, G. Lentini, J. Y. Cui, H. Gu, J. C. March, S. Chatterjee, A. Matson, D. Wright, K. L. Flannigan, S. A. Hirota, R. B. Sartor, S. Mani. EMBO Molecular Medicine (Cover page) (2020), vol. 12, no. 4, pages e11621. DOI: 10.15252/emmm.201911621.

Group 2 Statistics and Data Science: Inference Method and Framework Development, Theoretical Analysis, Applications

34.(Graph Neural Network, Change-point Identification, Applications to Atmospheric Science) Online Precision Matrix Change-point Detection with Localizations to Groups of Dimensions. T. Dinkins, W. Wong, S. Bhattacharyya, S. Chatterjee, and G. Raffa. Under Review.

33.(Change-point Detection, Applications to Cyber Security, Epidemic Prognosis) Modeling and inference of mixed dynamics and detection of causal emergent features. W. Casey, L. Metcalf, S. Chatterjee, H. Janwa, E. Battifarano, and A. Edwards. Under Review.

32.(Network-based Inference Method, Change-point Estimation, Theoretical Analysis) Change-point estimation in sparse dynamic stochastic block models under near-optimal signal strength. T. Sad- hukhan, S. Chatterjee, and S. S. Mukherjee. Proceedings of AISTATS, The 28th International Conference on Artificial Intelligence and Statistics (2025), PMLR series, vol 258, pages 1936–1944.

31. (Statistical Inference Framework for Network Evolution) A dynamic mean-field statistical model of academic collaboration. S. Chatterjee, S. S. Mukherjee, and T. Sadhukhan. Under Review. Preprint: arXiv 2309.10864.

30.(Bayesian Inference) Bayesian Change Point Detection: An Application to Detect Trend Changes in COVID-19. R. Siddani, S. Chatterjee, and C. Fuentes. Under Review.

29.(Network-based Inference Method, Theoretical Analysis) Concentration inequalities for correlated network-valued processes with applications to community estimation and change-point analysis. S. Chatterjee, A. Nath, S. S. Mukherjee, S. Bhattacharyya, and S. Chatterjee. Under Review. Preprint is available on arXiv 2208.01365.

28.(Network-based Inference Methods, Change-point Estimation, Minimax)Minimax-optimal and consistent detection and optimal localization of all detectable change-points in piecewise stationary arbitrarily sparse network-sequences. S. Bhattacharyya, S. Chatterjee, and S. S. Mukherjee. Under Review. Preprint is available on arXiv 2009.02112.

27.(Network-based Inference Methods, Community Detection) Consistent Community Detection for Complex Multilayer Networks under Near-Optimal Sparsity Conditions. S. Bhattacharyya and S. Chatterjee. Under Review. Preprint is available on arXiv 2004.03480.

26.(Sampling, Point processes) Monte Carlo EM Algorithm for Handling Missing Data in Hawkes Process. J. Yu, S. Bhattacharyya, S. Emerson, R. Trangucci, and S. Chatterjee. Under Review.

25. (Survey Data Analysis) Dissecting Political Polarization with Multidimensional Visualization. J. Yu, H. Chang, N. Hwang, S. Chatterjee, Y. Di, S. Bhattacharyya, and C. Stout. Under Review. Preprint available upon request.

24. (Time Series Clustering, Applications to Epidemic Prognosis) A novel ML method for temporal evolution of geographic clusters of disease spread patterns. W. Casey, L. Metcalf, H. Janwa, S. Chatterjee, and E. Battifarano. Proceedings of ISICN 2024, the International Symposium on Intelligent Computing and Networking 2024, pages 149–164. Part of the book series Lecture Notes in Networks and Systems (vol 1094). DOI: 10.1007/978-3-031-67447-1 11.

23.(Semantic Analysis, Prediction, Applications) Getting Local and Personal: Toward Building a Predictive Model for COVID in Three United States Cities. A. Edwards, L. Metcalf, W. A. Casey, S. Chatterjee, H. Janwa, and E. Battifarano. ITNG 2023 20th International Conference on Information Technology-New Generations, vol. 1445 of Advances in Intelligent Systems and Computing, ISBN 978-3-031-28332-1, pages 11–18, DOI: 10.1007/978-3-031-28332-1 2.

22. (Semantic Analysis, Data Science) Detection of Temporal Shifts in Semantics of “China virus” Using Local Graph Clustering. N. Hwang, S. Chatterjee, Y. Di, and S. Bhattacharyya. Machine Learn- ing & Knowledge Extraction (2023), vol. 5, no. 1, pages 128-143. DOI: 10.3390/make5010008. Part of the Special Issue Deep Learning Methods for Natural Language Processing.

21.(Anomaly Detection) Towards Explainable Precision Change-point Detection Through Linear Decomposition. T. Dinkins, S. Bhattacharyya, S. Chatterjee, S. Reis, and W. Wong. ANDEA 2022 (The 2nd Workshop on Anomaly and Novelty Detection, Explanation and Accommodation).

20. (Network-based Inference Methods, Community Detection) On the estimation of the number of communities for sparse networks. N. Hwang, J. Xu, S. Chatterjee, and S. Bhattacharyya. Journal of the American Statistical Association (2023), vol. 119, no. 547, pages 1895–1910. DOI: 10.1080/01621459.2023.2223793.

19.(Applied Statistics) Changes over Time in Association Patterns between Estimated COVID-19 Case Fatality Rates and Demographic, Socioeconomic and Health Factors in the US States of Florida and New York. M. Joshi, Y. Di, S. Bhattacharyya, and S. Chatterjee. COVID (2022), vol. 2, no. 10, pages 1417–1434. DOI: 10.3390/covid2100102.

18.(Observational Causal Inference) Observational Study of the Effect of the Juvenile Stay-At-Home Order on SARS-CoV-2 Infection Spread in Saline County, Arkansas. N. Hwang, S. Chatterjee, Y. Di, and S. Bhattacharyya. Statistics and Public Policy (2022), vol. 9, no. 1, pages 74–84. DOI: 10.1080/2330443X.2022.2050326.

17.(Network-based Inference Methods, Community Detection)The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data. N. Hwang, J. Xu, S. Chatterjee, and S. Bhattacharyya. Sankhya A: The Indian Journal of Statistics (2021), vol. 84, no. 1, pages 283–320. DOI: 10.1007/s13171-021-00248-1.

16.(Network-based Inference Methods, Community Detection) Consistent Recovery of Communities from Sparse Multi-relational Networks: A Scalable Algorithm with Optimal Recovery Conditions S. Bhattacharyya and S. Chatterjee. Complex Networks XI (2020), Proceedings of the 11th Conference on Complex Networks CompleNet 2020, pages 92–103.

15. (Network-based Inference Methods, Change-point Detection) Optimal change point estimator for network data. S. Bhattacharyya, S. Chatterjee, and S. S. Mukherjee. Book of Abstract of Complex Networks 2019, the 8th International Conference on Complex Networks and their Applications (2019), pages 375 - 377.

14.(Inference in High Dimensions) Statistical learning based on high-dimensional data: some issues and remedies for high-dimensionality in clustering and classification. S. Chatterjee (supervised by D. Sengupta and P. Chaudhuri). Masters Thesis (2006).

Group 1 Applied and Theoretical Probability

13.(Percolation) Limiting distribution of the chemical distance in high dimensional critical percolation. S. Chatterjee, P. Chinmay, J. Hanson, and P. Sosoe. Under Review. Preprint: arXiv 2509.06236.

12.(Percolation) Robust construction of the incipient infinite cluster in high dimensional critical percolation. S. Chatterjee, P. Chinmay, J. Hanson, and P. Sosoe. Under Review. Preprint: arXiv 2502.10882.

11.(Percolation) Subcritical Connectivity and Some Exact TailExponents in high-dimensional Percola- tion. S. Chatterjee, J. Hanson, and P. Sosoe. Communications in Mathematical Physics (2023), vol. 403, issue 1, pages 83 – 153. DOI: 10.1007/s00220-023-04759-w.

10. (Stochastic spatial model for Epidemics, Interacting System) The effect of avoiding known infected neighbors on the persistence of a recurring infection process. S. Chatterjee, D. Sivakoff, and M. Wascher. The Electronic Journal of Probability (2022), vol. 27, paper no. 109, pages 1-40, DOI: 10.1214/22-EJP836.

9.(Percolation) Restricted percolation critical exponents in high dimensions. S. Chatterjee and J. Hanson. Communications on Pure and Applied Mathematics (2020), vol. 73, no. 11, pages 2370-2429. DOI: 10.1002/cpa.21938.

8.(Applied Probability, Anomaly Detection) Thresholds for detecting an anomalous path from noisy environments. S. Chatterjee and O. Zeitouni. The Annals of Applied Probability (2018), vol. 28, no. 5, pages 2635–2663.

7. (Dynamics on Random Graphs) Phase transition for the threshold contact process, an approximation of heterogeneous random Boolean networks. S. Chatterjee. Probability Theory and Related Fields (2016), vol. 165, no. 3, pages 985–1023.

6.(First-passage Percolation) Multiple phase transitions for long-range first-passage percolation on square lattices. S. Chatterjee and P. S. Dey. Communications on Pure and Applied Mathematics (2016), vol. 69, no. 2, pages 203–256.

5.(Percolation, Social Network) Jigsaw percolation: What social networks can collaboratively solve a puzzle?. C. D. Brummitt, S. Chatterjee, P. S. Dey, and D. Sivakoff. The Annals of Applied Probability (2015), vol. 25, no. 4, pages 2013–2038.

4. (Dynamics on Random Graphs)A first order phase transition in the threshold θ ≥ 2 contact process on random r-regular graphs and r-trees. S. Chatterjee and R. Durrett. Stochastic Processes and Their Applications (2013), vol. 123, no. 2, pages 561– 578.

3.(First-passage Percolation) Asymptotic behavior of Aldous’ gossip process. S. Chatterjee and R. Durrett. The Annals of Applied Probability (2011), vol. 21, no. 6, pages 2447–2482.

2. (Dynamics on Random Graphs)Persistence of activity in threshold contact processes, an “annealed approximation” of random Boolean networks. S. Chatterjee and R. Durrett. Random Structures and Algorithms (2011), vol. 39, no. 2, pages 228–246.

1.(Dynamics on Random Graphs) Contact process on random graphs with power law degree distribution has critical value 0. S. Chatterjee and R. Durrett. The Annals of Probability (2009), vol. 37, no. 6, pages 2322–2356.

Research Grants

• External Grants
  
  
  • Federal Grants
    
    
    10. Co-Investigator of - Grant from the --  (2022-2026). Award # 2022-67015-38059.
    
    
    11. Principal Investigator of  Grant from the probability program (2022-2025). Award # DMS-2154564.
    
    
    12. Principal Investigator of  Grant from the probability program (2018-2022). Award # DMS-18-12148.
  
  
  • NonFederal Grants
    
    
    7. Principal Investigator of  Collaborative Research Grant (2016-2018). Award # 430073.


• Internal Grants
  
  
  • 
    
    6. Co-Principal Investigator for  (2023-2024).
  
  
  • 
    
    5. Principal Investigator of Cycle 52  (2021-2022). Award # 64673-00 52.
    
    
    6. Principal Investigator of Cycle 50  (2019-2021). Award # 62781-00 50.
    
    
    7. Principal Investigator of Cycle 47  (2016-2017). Award # 69842-00 47.
    
    
    8. Principal Investigator of Cycle 46  Traditional-B Research Grant (2015-2016). Award # 68828-00 46.
  
  
  • Others
    
    
    1. Recipient of Cohen Foundation support for research and travel (2014-2016).

Current Students

Doctoral

• Khalid Shafiq

Masters

• Navpreet Kaur
• Rubiel Aquino
• Haider Riaz

Past Students

Masters

• Prama Chowdhury
• Yijia Sun
• Hong Zhuang
• Sharmin Begum (Senior HR data scientist at MTA)
• Neil Hwang (Faculty at Bronx Community College)
• Jared Gallegos (Math Instructor at South College)
• Raghu Siddani (Vice President at Morgan stanley)
• Christopher Hayduk (Machine Learning Engineer, Meta)
• Yuxuan Huang (Statistical Engineer at NASA Langley Research Center)
• Joshua Pedro (Data Scientist at Advanced Science Research Center,Adjunct Instructor at CUNY)
• Arad Namin (IR Manager at Hunter College)
• Elena Redman
• Leonid Fishler (Head of Data Services at Braze)
• Haoxu Li (Litigation Associate at Wilson Elser)
• Brisilda Ndreka (PhD student at University of Connecticut)
• Pavlos Sakoglou (Senior Developer at New York Stock Exchange)
• David Bennett (Instructor at Santa Barbara City College)
• Cong Jiang (Postdoctoral Fellow at Harvard University)
• Shree R Saha (Instructor at NJIT)
• Sergio Palomo (PhD student at Cornell University)

Undergraduate

• Yuxuan Huang (Statistical Engineer at NASA Langley Research Center )
• Xiaolin Zhomg
• Justin Nunez (Product Analyst, New York Times)
• Arnold King
• Jean-Pierre Kassegne (Acturial Associate at MetLife)
• Chun Biao Wang (Treasurer at Enza)
• Yash Bhardwaj (senior Strategic Analyst at Credit Karma)
• Anita Khabir (QA Analyst at Standard Chartard Bank)

Topic Courses

17. Math 83600, Topics in Machine Learning Methods and Data Science
    
    
    • Verbal Description: This course introduces the fundamental concepts and mathematical methods used in data science, modern statistics, and machine learning, including the description and theoretical analysis of several current algorithms, their theoretical basis, and associated mathematical frameworks. Many of the algorithms that will be discussed have been successfully used in various areas of real-world products and services.
    
     


18. Math B7700, Advanced Topics in Probability (Stochastic Processes)
    
    
    • Verbal Description: This course covered Discrete and continuous time Markov chains, Poisson process and renewal theory, Introduction to queueing theory, Introduction to Brownian motion and Stochastic Calculus.
    
     


19. Math 83600, Topics in Probability (Markov Chains, Random Walks, and Brownian Motion)
    
    
    • Verbal Description: This is a topic course for Ph.D. students that covers Markov Chains and their properties, Brownian Motion and its properties, Applications to Random Walk, Random Walk Green's Function, Intersection Probabilities for Random Walks.
    
     
20. Math 83600, Topics in Probability (Nonspatial Random Graphs)
    
    
    • Verbal Description: This is a topic course for Ph.D. students that cover Branching Process and its properties, the story of the Erdos-Renyi random graph and its properties (particularly phase transition resulting in a giant component, connectivity threshold, scaling limits of near-critical graphs), other random graph models including configuration model, small world model, preferential attachment model etc.~and their properties (particularly diameter and local neighborhoods), introduction to dynamics taking place on random graphs such as epidemics, random walks, the voter model, first-passage percolation, competition models, etc.
    
     


21. Math B7800, Advanced Topics in Statistics (Regression Techniques)
    
    
    • Verbal Description: This course covers Multivariate linear regression models and associated statistical inference problems including the classical linear regression model, least square estimation, inference about the regression model, inference from the estimated regression function, model checking, variable selection, multivariate multiple regression, partial correlation, comparing regression models; Introduction to nonlinear regression including simple and multiple logistic regression;Principal component analysis including population principal components, large sample inference, use of principal components to summarize sample variation and quality control;Discrimination and Classification including multiple multivariate normal population, evaluation of classification functions.
    
     


22. Math B7600, Advanced Topics in Statistics (Decision Theory)
    
    
    • Verbal Description: This course covers The general decision problem, Application to hypothesis testing and estimation (including Bayesian methods), Asymptotic evaluations of different inferential procedures, Analysis of variance, Regression models (including logistic regression) and associated inference.
    
     

Graduate Courses

11. Math 70100, Functions Of A Real Variable I
    
    
    • Verbal Description: This is the first course on real analysis for the Ph.D. program.
    
    
    •  
12. Math A7800/47800, Advanced Mathematical Statistics (Multivariate)
    
    
    • Verbal Description: This course covers Multivariate random vectors and multivariate Normal Distribution, Multiple linear regression and associated statistical inferences, Multiple and partial correlation and their interpretations, Analysis of variance and Covariance.
    
     


13. Math A7800, Advanced Mathematical Statistics (Statistical Inference)
    
    
    • Verbal Description: This course covers Theory of estimation, Theory of hypothesis testing, Introduction to multivariate analysis, Introduction to linear models and statistical learning.
    
     


14. ORIE 3510/5510, Introduction to Stochastic Processes
    
    
    • Verbal Description: This course introduces the concept of stochastic process to the undergraduate and M.Eng students, and gives an overview of the basic techniques used to analyze several standard models. I covered discrete time Markov chain, Poisson process, continuous time Markov chain, branching process, renewal theory including regenerative process, some queuing theory and a brief introduction to Brownian motion.

Undergraduate Courses

7. Math 202, Calculus II
   
   
   • Verbal Description: This is a second semester calculus course for students who have previously been introduced to the basic ideas of differential and integral calculus. Topics that are covered include applications and methods of integration, infinite series and the representation of functions by power series, parametric curves in the plane.
   
    


8. Math 203, Calculus III.
   
   
   • Verbal Description: This course covers Vectors, infinite series, Taylor's theorem, solid analytic geometry, partial derivatives, multiple integrals with applications.
   
    


9. Math 346, Linear Algebra.
   
   
   • Verbal Description: This course covers Vector spaces, basis and dimension, matrices, linear transformations, determinants, solution of systems of linear equations, eigenvalues, and eigenvectors.
   
    


10. Math 366, Introduction to Applied Mathematical Computations.
    
    
    • Verbal Description: This course covers A collection of algorithms and pseudo-codes in applied linear algebra, Method of least squares, Introduction to coding in Matlab, Applications in image processing.
    
     


11. Math 375, Elements of Probability Theory.
    
    
    • Verbal Description: This course covers permutations and combinations, conditional probability, independent events, random variables, probability distributions and densities, expectation, moments, moment generating functions, functions of random variables, Central Limit Theorem, sampling, confidence intervals.
    
     


12. Math 376, Mathematical Statistics.
    
    
    • Verbal Description: This course covers Some Special Distributions, Some Elementary Statistical Inferences, Consistency and Limiting Distributions, Maximum Likelihood Methods, Sufficiency, Optimal Tests of Hypotheses.
    
     


13. Math 377, Applied Probability and Statistics.
    
    
    • Verbal Description: This course covered Essentials of the R Language including data input and output, data frame, graphics, tables, and functions; Simulation (using R) of random variables having various distributions ; Organization of data: measures of central tendency, variability, and order statistics; Understanding of hypothesis testing, p-values, and confidence intervals; Basics of classical diagnostic and statistical tests (normality test, t-test, Chi-Squared test, correlation test, rank-based nonparametric tests, Kolmogorov-Smirnov test, etc.) using R.Basics of the linear regression analysis using R; Basics of the contingency table analysis (Kolmogorov's exact test, goodness of fit test, etc.) using R;Basics of the ANOVA (analysis of variance) using R;Basics of the bootstrap and jackknife methods using R.
    
     

Independent Study Courses (Can be offered as Elective Courses)

1. Math A9804, Topics in Markov Chains and Stochastic Processes
   
   
   • Verbal Description:
   
   
   • Reference Books:  By Rick Durrett


2. Math A9802, Introduction to Math Models for Epidemiology
   
   
   • Verbal Description:This course introduces the fundamental mathematical epidemiological models. Topics that will be covered include basic concepts, compartmental models, endemic disease models, epidemic models.
   
   
   • Reference Books: Mathematical Models in Epidemiology By Fred Brauer, Carlos Castillo-Chavez, Zhilan Feng


3. Math B9802, Processes on Networks
   
   
   • Verbal Description:
   
   
   • Reference Books: Random Graph Dynamics By Rick Durrett


4. Math B9802, Network Models
   
   
   • Verbal Description:
   
   
   • Reference Books: Networks By Mark Newman


5. Math B9802, Topics in Statistical Machine Learning (Modern Supervised Learning)
   
   
   • Verbal Description: This course covered Polynomial and nonlinear regression, Tree based inference methods, and Support vector machines.
   
   
   • Reference Books:  by James, Witten, Hastie, and Tibshirani..


6. Math B9802, Topics in Statistical Machine Learning (Regularized and Robust Regression)
   
   
   • Verbal Description: This course covered several linear methods of regression including the basic linear models, Ridge Regression, and the Lasso.
   
   
   • Reference Books:  by Hastie, Tibshirani, and Friedman.


7. Math B9802, Topics in Deep Learning Procedures
   
   
   • Verbal Description: This course covered fundamentals of machine learning and deep learning, implementation of deep learning algorithms for texts and sequences.
   
   
   • Reference Books:  by Chollet; and  by Goodfellow, Bengio, and Courville.


8. Math B9802, Topics in Network Models and Data Analysis
   
   
   • Verbal Description: This course covered several mathematical and statistical models of network graphs, network topology inference, and network sampling methods.
   
   
   • Reference Books:  by Kolaczyk


9. Math 31000, Topics in Linear Statistical Models and Implementations
   
   
   • Verbal Description: This course covered several topics of linear models and their implementations using R. The topics include variable selection, shrinkage methods, block design, factorial design.
   
   
   • Reference Books:  by Faraway.