Mathematical Sciences Archives

Saikatul Haque

Dr. Saikatul Haque is a Postdoctoral Fellow at Harish-Chandra Research Institute, Allahabad since 2021. He obtained his Ph.D. in 2021, under the supervision of Prof. Sandeep Kunnath, at Tata Institute of Fundamental Research – Centre for Applicable Mathematics (TIFR-CAM), Bengaluru. He earned his master’s in 2016 from TIFR-CAM after completing his bachelor’s in 2014 at R K Mission Vidyamandira, University of Calcutta, Kolkata.

Dr. Haque’s research work focuses on the study of well-posedness and regularity for elliptic and time dependent partial differential equations. He has published several research articles in reputed international journals. He also qualified IIT JAM in 2014 and CSIR UGC NET held in December 2015. He has been selected for the INSPIRE faculty fellowships in 2023 by Department of Science & Technology, Government of India.

As a Fulbright-Nehru Postdoctoral Research fellow, Dr. Haque is studying mainly three partial differential equations: the nonlinear Schrödinger equation (NLS), a modified Korteweg-de Vries (mKdV) equation, and a related heat equation. The main focus of this project is to understand the global time behavior of the energy-critical focusing inhomogeneous fractional NLS. This includes research into local and global well-posedness, variational estimates for elliptic problems, linear profile decomposition, and rigidity. For the cubic NLS, mKdV and nonlinear heat equation, Dr. Haqueis investigating well-posedness and ill-posedness in modulation spaces.

Manpreet Singh

Dr. Manpreet Singh is a postdoctoral F ellow at Harish-Chandra Research Institute (HRI), Prayagraj (Allahabad). His research interest is in low dimensional topology. Before joining HRI, he was an Integrated PhD student in the Department of Mathematical Sciences at the Indian Institute of Science Education and Research (IISER) Mohali. He received his master’s degree and PhD under the supervision of Dr. Mahender Singh, in 2021. During his PhD, he worked on algebraic and combinatorial aspects in knot theory. He has published several research articles in reputed international journals.

During the Fulbright-Nehru Postdoctoral Research Fellowship, he will work on exploring connections among algebraic and geometric invariants of knots. He aims at understanding certain colorings of alternating diagrams of prime links using the elements of the first homology groups of cyclic branched coverings of links. In the last two decades, many algebraic structures have been introduced as invariants of (ramified) knots. Dr. Singh is planning to delve into the intricacies of such invariants from a geometric facet.

Suparna Basu

Dr. Suparna Basu obtained BSc (Hons.) in Statistics from University of Calcutta in 2011, M.Sc. in Statistics from Banaras Hindu University in 2013. She obtained PhD in statistics in the 2018 from Banaras Hindu University under the supervision of Prof. Sanjay K. Singh. She was awarded with the BHU-CRET Fellowship, UGC-NET Junior Research Fellowship (JRF) and BSR-JRF during her PhD, although she had availed only the first two. Her thesis work was directed towards estimation techniques for various Time censored situations encountered in life-testing problems for two heavy-tailed distributions. Her research works have been published in journals of international repute. Dr. Basu has served as an Assistant Professor in the University of Burdwan during Feb. 2016 – Dec. 2019 and taught several courses to M.Sc. (Statistics) students, in addition to administrative and research endeavors. Thereafter, she joined Banaras Hindu University and has been actively involved in teaching, research and administration. She was awarded with the IOE-BHU, start-up grant of Rs. 6 lakhs in July, 2020.

Location-shifted Weibull, Gamma and Generalized exponential distributions, fails to meet an important regularity condition of the support being independent of unknown parameters. This impedes derivation of estimation techniques, especially those based on likelihood function like globally consistent estimators for complete/censored situation or any testing criterion to discriminate suitability of two competing models. Dr. Basu would explore some robust statistical estimation techniques for such non-regular distributions and illustrate its applicability on real data sets during her postdoctoral stint supported by the Fulbright-Nehru Postdoctoral Research Fellowship.

Abhijeet Atmaram Ghanwat

Dr. Abhijeet Atmaram Ghanwat is currently an NBHM postdoctoral fellow at the Department of Mathematics, Indian Institute of Technology Madras, Tamil Nadu. He earned his B.Sc. in 2012 from Tuljaram Chaturchand College, Baramati, affiliated with Savitribai Phule Pune University, and completed his M.Sc. in 2014 at Savitribai Phule Pune University. In July 2021, he was awarded a Ph.D. from the Chennai Mathematical Institute, Tamil Nadu. Dr. Ghanwat was a visiting fellow at the Tata Institute of Fundamental Research Mumbai from August 2021 to July 2023.

Dr. Ghanwat’s research interests are primarily in the field of low-dimensional topology, with a focus on the open books of 3-manifolds, Lefschetz fibrations of 4-manifolds, and trisections of 4-manifolds. He has published several research articles in reputed international journals. He has qualified for the CSIR-UGC NET and GATE examinations, and he was also awarded a gold medal by Savitribai Phule Pune University for achieving the first rank in his M.Sc. program.

As a Fulbright-Nehru Postdoctoral Research fellow at the University of Georgia, Athens, GA, Dr. Ghanwat is focusing on trisections of 4-manifolds, a pioneering area of study introduced in 2016 by Prof. David Gay and Prof. Robion Kirby. The trisection theory has quickly become an important tool in understanding the topology of 4-dimensional manifolds. During his fellowship, Dr. Ghanwat aims to explore minimal genus (relative) trisections and investigate their properties.

Arka Banerjee

Arka Banerjee is a Ph.D. candidate at the Indian Institute of Technology Kanpur in the Department of Mathematics and Statistics. Studying Markov Chain Monte Carlo (MCMC) sample quality by computationally efficient estimation of the asymptotic covariance matrices in the multidimensional MCMC setup is the main focus of his doctoral thesis. He has published in reputed journals and has participated in and presented papers at national and international conferences.

Arka holds a bachelor’s and a master’s in statistics from the University of Calcutta. Before joining IIT Kanpur, he was a data analyst at Infosys Limited for a year where he worked on a financial modeling project that dealt with the prediction of default payments in a banking institution.

As a Fulbright-Nehru Doctoral Research fellow at the University of Minnesota, Arka is exploring the computationally efficient and optimized procedures in the estimation of asymptotic covariance matrices in MCMC for a better understanding of MCMC sample quality. During his grant period, he will be studying MCMC sample quality in a high dimensional setting. Arka enjoys travelling and cooking different culinary dishes.

Souvik Chakraborty

Dr. Souvik Chakraborty is assistant professor at the Applied Mechanics Department, Indian Institute of Technology, Delhi. He also holds a joint faculty position at the Yardi School of Artificial Intelligence. His research interest is at the intersection of scientific computing and machine learning, with a focus on developing scalable, interpretable, and trustworthy machine learning algorithms for solving scientific and engineering problems. He is a recipient of the prestigious INAE Young Engineers Award.

Dr. Chakraborty joined IIT Delhi in 2020. Prior to that, he spent two years at the University of Notre Dame. He also spent some time at the University of British Columbia as a postdoctoral researcher. He obtained his PhD from the Indian Institute of Technology, Roorkee.

During his Fulbright-Nehru Academic and Professional Excellence (Research and Teaching) fellowship, Dr. Chakraborty will do a combination of both research with teaching. He is developing novel algorithms and frameworks for seamless fusion of data and physics. It is expected that the developed algorithm will address challenges such as out-of-distribution generalization and interpretability and will be a step towards realizing the dream of digital twins for infrastructural systems. Dr. Chakraborty is also teaching a course on operator learning, with the goal of developing synergy between teaching and research.