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• Discriminant Analysis
• Inference based on Data Depth
• Characterization of Multivariate Distributions
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Subhajit Dutta is an Associate Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. He has established himself as a notable researcher in specialized statistical methodologies with publications in top-tier statistical journals. Dr. Dutta completed his PhD in Statistics from the Indian Statistical Institute (ISI), Kolkata in 2013 under the supervision of Prof. Probal Chaudhuri. His academic journey includes an M.Sc. in Statistics from IIT Kanpur (2007) and a B.Sc. in Statistics from Presidency College, University of Calcutta (2005). He also pursued post-doctoral research at KAUST with Prof. Marc G. Genton. His research focuses on advanced statistical methodologies, particularly in Discriminant Analysis, Inference based on Data Depth, Characterization of Multivariate Distributions, and Classification of Sequence Data. His work bridges theoretical statistics with practical applications, developing robust methods for complex data analysis problems across various scientific domains. Dr. Dutta's publication record shows consistent progression from foundational properties of statistical depth functions to practical applications in classification and sequence analysis, demonstrating both theoretical depth and practical relevance in his scholarly contributions. As a faculty member at IIT Kanpur, one of India's premier technical institutions, Dr. Dutta contributes to both teaching and research in the Department of Mathematics and Statistics, helping to advance statistical science education and methodology development.

• Entropy Estimation
• Theory of Stochastic Orders
• Estimation in Restricted Parametric Space
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Neeraj Misra is a Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur (IIT Kanpur), where he has held faculty positions since 1996. He earned his PhD (1991) and M.Sc. (1985) in Statistics from IIT Kanpur, and a B.Sc. from Kanpur University (1982). His research focuses on theoretical statistics and applied probability. Research Interests: His work spans entropy estimation, stochastic ordering theory, estimation under parameter restrictions, and ranking/selection methodologies, with applications in reliability engineering and molecular sciences. Key themes include: Development of nonparametric entropy estimators Stochastic comparisons for system reliability Optimization in statistical decision theory Inference for restricted parameter spaces Publication Trends: His recent articles (2003–2015) demonstrate a consistent focus on reliability modeling, stochastic ordering, and entropy estimation. Works frequently involve gamma distributions, redundancy allocation in engineering systems, mixture models, and multivariate circular distributions, blending theoretical rigor with practical applications. Awards & Honors: Jacob Wolfowitz Award (2003) for theoretical advances C. L. Chandna Mathematics Award (2003) for research/teaching excellence All India Rank #2 in Mathematics (National Entrance Exam, 1985) Outstanding Teaching Recognition at IIT Kanpur He has held visiting positions at West Virginia University, Marquette University (USA), and KU Leuven (Belgium).

• Bayesian Econometrics
• Quantile Regression
• Machine Learning
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Mohammad Arshad Rahman is an Associate Professor in the Department of Economic Sciences at IIT Kanpur. His research spans Bayesian econometrics, quantile regression, machine learning, and applied econometrics. He has held positions at Zayed University and University of California, Irvine, and currently teaches econometrics and finance courses. Education: Ph.D. Economics, University of California, Irvine (2013) M.S. Statistics, UC Irvine (2011) M.A. Economics, UC Irvine (2009) M.A. Economics, Delhi School of Economics (2006) B.Sc. Economics Hons., St. Xavier's College, Kolkata (2004) Research focuses on developing Bayesian methods for econometric problems, with applications in energy economics, finance, and social policy. Research areas include: Bayesian inference techniques, quantile regression models, machine learning applications in economics, discrete choice modeling, and time series analysis. Awards and Honors: Social Science Merit Fellowship Multiple Summer Research Fellowships All India Post-Graduate Scholarship Analyst Accolade Award

• Asymptotic Statistics
• Bayesian Variable Selection
• Nonparametric Inference
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Minerva Mukhopadhyay is an Assistant Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur. She earned her PhD in Statistics from the Indian Statistical Institute (ISI), Kolkata, and completed postdoctoral work at Duke University under Professors David Dunson and Sandeep Dave. Her research focuses on Asymptotic Statistics, Bayesian Variable Selection, and Nonparametric Inference, with applications to high-dimensional data analysis. Her academic work includes developing distribution-free tests for high-dimensional data, consistency analysis of Bayesian variable selection methods, and novel approaches for sparse linear models. She has contributed to journals like Biometrika , Annals of the Institute of Statistical Mathematics , and Statistica Sinica , reflecting her expertise in theoretical and computational statistics. Research Trends: Her recent articles emphasize scalable Bayesian methods for ultra-high-dimensional data, random projections for predictive modeling, and nonparametric extensions using Gaussian processes. Scientific Awards: Best Student Paper Award (IISA 2015) Pillar Iglesias Travel Award (ISBA 2016) O’Bayes Travel Award (2017) Academic Roles: Previously taught at Bethune College (Kolkata) and Duke University, with experience in interdisciplinary statistical research at ISI. Skills: C, R, MATLAB, and journal reviewing for TEST , JSPI , Stat , and CSDA .

• Machine Learning
• Bayesian Statistics
• Latent Variable Models
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Piyush Rai is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He also holds an Adjunct Assistant Professor position in Electrical and Computer Engineering at Duke University. His academic journey includes postdoctoral research at Duke University and the University of Texas at Austin, following his PhD from the University of Utah. His research interests focus on Machine Learning and Bayesian Statistics, with specializations in Latent Variable Models, Probabilistic Modeling, Approximate Inference, and Nonparametric Bayesian Methods. His work bridges theoretical foundations with practical applications in artificial intelligence and data science. Dr. Rai's publication record shows a consistent focus on tensor factorization, Bayesian nonparametrics, and scalable algorithms for large datasets. His work spans conferences including NIPS, ICML, UAI, and AISTATS, demonstrating strong contributions to both theoretical and applied machine learning. Best Student Paper Award at ECML-PKDD (2015) National Science Foundation (USA) EAGER Award (2015) Dr. Deep Singh and Daljeet Kaur Faculty Fellowship at IIT Kanpur (2015) NIPS 2013 Reviewer Award Sheldon Ekland-Olson Postdoctoral Fellowship (2012) He teaches advanced courses in Machine Learning and Probabilistic Machine Learning at IIT Kanpur, mentoring the next generation of researchers in statistical machine learning techniques. His collaborative work with Lawrence Carin and other researchers demonstrates strong interdisciplinary connections between institutions.