What brought you to the UAB School of Public Health?

I was impressed by the spatial genomics research at UAB. There are many professors working in this cutting-edge domain, and I look forward to exploring several collaborative opportunities. I believe I will be able to make a meaningful contribution in data science driven genomics research here at UAB. I also liked the balance of methodological and collaborative research in the Department of Biostatistics. Moreover, a strong mentoring structure in the department and School was very appealing to me, and it will be certainly helpful as an early-career faculty. My colleagues are very friendly and supportive. Overall, I find the environment at UAB to be highly conducive for my career growth.

What is the broad focus of your research?

My broad research interest is situated at the intersection of statistics, spatial biology and precision medicine. I am focused on developing methodologies for a broad range of areas including graphical models, hierarchical models and functional data analyses. These methods are motivated by state-of-the-art biomedical technologies generating high-dimensional and high-throughput datasets in genomics, transcriptomics and proteomics particularly within the realms of cancer and neuroscience. My research centers around developing hierarchical Bayesian models along with scalable and reproducible algorithms for high-dimensional complex biomedical data, where recovering the underlying low-dimensional structure is of key interest. A fundamental objective of my research strategy is to develop precise probabilistic representations of applied problems using novel data-driven, robust and adaptable statistical frameworks while integrating heterogeneous data from different sources leads to elevating the scientific impact of the research.

Where did you receive your training and degrees?

I received my bachelor’s degree from St. Xavier’s College, Kolkata followed by my master’s degree from Indian Statistical Institute. Then I earned my Ph.D. in Statistics from Texas A&M University. After that, I worked as a postdoctoral student in the Department of Biostatistics at University of Michigan.

What is the most exciting project you are currently working on?

I am currently focused on developing methods for spatial omics data, an exciting and rapidly evolving field that combines the power of omics technologies with spatial resolution. Spatial omics combines the spatial information with molecular data, enabling us to see the spatial organization of molecules within the tissue micro-environment. In this context, network models allow us to map out the complex interactions between different molecular species, such as genes, proteins and metabolites, and to understand how these interactions vary across different spatial regions of a tissue. By constructing and analyzing these networks, we can gain deeper insights into the functional organization of tissues, identify key regulatory hubs and uncover spatially localized molecular processes. The method development involves several key steps such as data integration, network analysis, representation and visualization. By advancing these methods, I aim to provide researchers with powerful new tools for understanding the spatial dimensions of molecular biology. The potential applications are vast, ranging from basic research to clinical diagnostics and therapeutic development. For example, in cancer research, spatial network models could help identify how tumor microenvironments influence disease progression and response to treatment.

What is your favorite self-authored manuscript?

My favorite self-authored manuscript is “SpaceX: gene co-expression network estimation for spatial transcriptomics” which was published in Bioinformatics in 2022. To the best of my knowledge, SpaceX is the first computationally efficient method to jointly estimate shared and cluster specific gene co-expression networks in spatial transcriptomics. The proposed study is motivated by dearth of statistically principled tools in spatial biology. I developed a statistical framework to detect gene regulation patterns in a spatially structured tissue consisting of different supervised clusters in the form of cell classes or tissue domains. This will help the biomedical researchers to detect transcription factors and study the spatial variation of intercellular signaling in tissues, which may underlie disease etiology.

What professional accomplishment are you most proud of so far in your career?

I was very happy to receive the Early Career Paper award from the Biometrics Section of the American Statistical Association (ASA) in 2022. I also received Student Paper award under Section on Bayesian Statistical Science of ASA, 2019. Another significant achievement was the Postdoctoral Fellows grant from the Rogel Cancer Center of the University of Michigan. This grant was instrumental in advancing my research on spatially varying gene networks for single and multiple sample-based spatial transcriptomics data from genomics research.

What is the coolest training or program you've been a part of, or your favorite conference you've attended?

One of the most impactful experiences in my career has been my postdoctoral training at University of Michigan. It was a rigorous program that provided both methodological and application-focused training, which significantly deepened my expertise and broadened my perspective on research. This experience not only motivated me to explore innovative and complex problems, but it also offered valuable exposure to the intricacies of grant writing. The training equipped me with the skills and confidence needed to pursue a career in academia.

What kind of research would you like to be doing that you haven’t yet had the opportunity to do?

In recent years, high dimensional and multiple sample spatial omics datasets are being sequenced. However, there is a pressing need to develop scalable tools to analyze these complex, high-dimensional datasets. I am eager to use machine learning and/or artificial intelligence-based methods to address pertinent questions in biomedical research such as differential expression analysis, data integration, image segmentation and cell phenotyping. This will help to the biomedical researchers to identify potential targets for therapy.

If you weren’t in academia, what would your career be?

A doctor.