Education

• Ph.D. in Statistics, University of Pittsburgh (April 2023).
• M.A. in Statistics, University of Pittsburgh (December 2021).
• M.Sc. in Statistics, Indian Institute of Technology, Kanpur (May 2017).
• B.Sc. in Statistics, St. Xavier's College, Kolkata (May 2015).

Publication

• Mitra, A., Prasad, K. and Cape, J. On inference for modularity statistics in structured networks, Journal of Computational and Graphical Statistics, 2024+. 1-26
• Mitra, A. and Mitra, A. Robust Laplace periodogram and its asymptotic properties for frequency estimation of 2-dimensional superimposed sinusoidal model, DEStech Transactions on Engineering and Technology Research, 2017.
• Mitra, A., Hore, G., Pal, S. and Ghosh, S. Association of air pollution parameters and natural calamity with Covid-19: A study from metropolitan Kolkata, West Bengal, India, Journal of Applied Zoological Researches, 2021. 32 (1): 22-39. (link of article to be uploaded).
• Theis, N., Dash, K., Patel, N., Muldoon, B., Iyengar, S., Rubin, J., Mitra, A., Li, Z., Gur, R., Gur, R., Roalf, D., Pogue-Geile, M., Almasy, L., Nimgaonkar, V. and Prasad, K. Multiplex Network Pattern Analysis for Structure-Function Connectivity Coupling in Psychosis Risk, Biological Psychiatry, 2020. 87 (9): S201-S202.
• Prasad, K., Rubin, J., Mitra, A., Lewis, M., Theis, N., Muldoon, B., Iyengar, S., Cape, J. Structural covariance networks in schizophrenia: A systematic review Part I, Schizophrenia Research, 2022. 240 : 1-21.
• Prasad, K., Rubin, J., Mitra, A., Lewis, M., Theis, N., Muldoon, B., Iyengar, S., Cape, J. Structural covariance networks in schizophrenia: A systematic review Part II, Schizophrenia Research, 2022. 239 : 176-191.

Work experience

• Graduate Student Researcher, Department of Statistics, University of Pittsburgh (2021-2023).
• Andrew W. Mellon pre-Doctoral Fellow, Department of Statistics, University of Pittsburgh (2020-2021).
• Givens Associate, Mathematics and Computer Science, Argonne National Laboratory (Summer 2019).
• Graduate Student Researcher, Department of Statistics, University of Pittsburgh (2019-2020).
• Consultant at Statistics Consulting Center, Department of Statistics, University of Pittsburgh (2019).
• Teaching Assistant for STAT 1000 course, Department of Statistics, University of Pittsburgh (2018-2019).
• Arts and Sciences pre-Doctoral Fellow, Department of Statistics, University of Pittsburgh (2017-2018).

Volunteer Experiences

• Vice-President, Pitt ASA Student Chapter (2020-2021).
• Event Coordinator, Pitt ASA Student Chapter (2019-2020).
• Member of National Service Scheme, India (2014).

Certificates

• Design and Conduct of Clinical Trials, Johns Hopkins University on Coursera. (see certificate)
• Short course on quarto, NISS GSN. (see certificate)
• TensorFlow Developer Professional Certificate, DeepLearning.AI. (see certificate)
• Sequences, Time Series and Prediction, DeepLearning.AI. (see certificate)
• Natural Language Processing in TensorFlow, DeepLearning.AI. (see certificate)
• Convolutional Neural Networks in TensorFlow, DeepLearning.AI. (see certificate)
• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, DeepLearning.AI. (see certificate)
• Deep Learning, DeepLearning.AI. (see certificate)(see credly badge)
  • Sequence Models, DeepLearning.AI. (see certificate)
  • Convolutional Neural Networks, DeepLearning.AI. (see certificate)
  • Structuring Machine Learning Projects, DeepLearning.AI. (see certificate)
  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, DeepLearning.AI. (see certificate)
  • Neural Networks and Deep Learning, DeepLearning.AI. (see certificate)
• Applied Plotting, Charting & Data Representation in Python, University of Michigan. (see certificate)
• Introduction to Data Science in Python, University of Michigan. (see certificate)

Workshops

• NIH STRIDES workshop (Pitt Information Technology, Center for Research Computing and Department of Biomedical Informatics, 2020).
• AI for Science workshop (Argonne National Laboratory, 2019).
• Parallel Programming with MPI one-day tutorial seminar (Argonne National Laboratory, 2019).

Achievments and Awards

• Mihaela Serban Award at ASA Pittsburgh Chapter Spring Banquet (2021) for poster titled "Community Detection and Inference in Multi-Layer Networks with Applications to Schizophrenia".
• Dean's Research Scholarship (2018).
• INSPIRE Scholarship, Ministry of Science and Technology, India (2012-2017).
• All-India rank 4 in Joint Admission test for M.Sc., India (2015).
• Rank 4 in M.Sc. at IIT Kanpur (2017).
• Rank 4 in B.Sc. at St. Xavier's College, Kolkata (2015).

Conferences

• MDS-RELY Spring meeting, August 2022, Case Western Reserve University.
• 8th Annual Data Science in Engineering and Life Sciences Symposium, August 2022, Case Western Reserve University.
• Presented talk entitled "On Modularity Asymptotics in Large Structured Networks" at JSM 2021.
• Presented poster entitled "Community Detection and Inference in Multi-Layer Networks with Applications to Schizophrenia" at JSM 2020.
• MDS-RELY planning meeting, September 2019, Case Western Reserve University.

Past Projects

• On the stability of Relative Growth Rate of size variable: An application to individual and interactive dynamics. (2016.)
  • Supervisor: Dr. Sabyasachi Bhattacharya, ISI Kolkata.
  • Studied asymptotic distributions of estimators for Relative Growth Rate and Lotka-Volterra prey-predator model and studied their approximate asymptotic behaviors by extensive simulation in R.
• Time Series Analysis of Air-quality data. (2016.)
  • Supervisor: Dr. Amit Mitra, IIT Kanpur.
  • A group project where we learnt extensive application of statistical packages on time series in R.
• Analysis of Income inequality in the USA from 1947 to 2012. (2016.)
  • Supervisor: Dr. Sharmistha Mitra, IIT Kanpur.
  • A group project where we applied regression techniques, tested for criteria like multicollinearity, auto-covariance, etc.
• Students’ Future Plans and the Reasons behind. (2015.)
  • Supervisor: Dr. Shalabh, IIT Kanpur.
  • A group project where we designed a sample survey with a selfmotivated question, implemented it and studied the data to verify theory learnt in the course.