Academic journey

In recent times, buzz words like machine learning and deep learning have taken our world by a storm. From the lens of a biostatistician, I see this fascinating treasure box of algorithms as a unique blend of mathematical and statistical methodologies that has the power to utilize big data to engender discoveries that are indistinguishable from magic. More specifically, the wonders of these data-driven techniques in the field of biomedical sciences are unparalleled. From identifying quantitative radiomic biomarkers to learning the scope of disease spread, they have done it all. In the past few months, where the entire world was brought to its knees at the hand of a microscopic genius enemy, the need to build scalable, data-driven techniques towards deciphering complex diseases has only gotten stronger. These unprecedented times have proven that the era of biostatisticians and biomedical engineers is here!

My journey as a researcher started during the summers of my junior year in college when I joined Biomedical Informatics Research Lab (BIRL) at the Lahore University of Management Sciences (LUMS). At BIRL, I was part of a team that developed a collaborative web application to equip clinicians with a multi-scale cancer modelling platform for cancer prognosis and therapeutic evaluation. The project was later titled, ‘Theatre for In Silico Oncology (TISON). Concurrently, I was also involved with the development of a MATLAB toolbox for cell fate discovery and reprogramming. Seeing computational and mathematical tools being applied to a multifaceted problem like cancer, I decided to enter the interdisciplinary world of biostatistics. To this end, I applied for Fulbright scholarship to pursue master’s in biostatistics at some of the leading schools in the US, including Duke University and Washington University in St. Louis. The process of applying for Fulbright scholarship was extremely long and exacting. After jumping through various hoops, I got selected as a Fulbright Scholar to pursue my masters at Duke. This was just the beginning of an extremely rewarding academic journey.

Biostatistics at Duke is undoubtedly one of the leading programs in the US. The structure of the master’s program is designed in a way that even as an electrical engineer with very little statistical background, I was able to capture the essence of all the courses. From learning to calculate maximum likelihood estimators to understanding the biology of head and neck cancer, first semester alone made me feel like a biostatistician.

With a set of interdisciplinary research skills acquired during my undergraduate and first year of master’s program, I was now looking to expand my horizon beyond cancer and explore a new territory, a new disease. This time, it was AIDS. I applied for an internship at Duke Center for AIDS Research (CFAR). This internship was very competitive and only two students from my cohort were selected with a total of 12 interns from different universities including University of North Carolina at Chapel Hill and Penn State University. I was assigned to Tomaras Lab under the direct mentorship of Dr. Cesar Lopez (Stanford University & Duke University) and Dr. Georgia Tomaras (Duke University). The internship was supposed to start in summer of 2020 but seeing my enthusiasm, Cesar introduced me to the preliminary project outline in early January and I was set off on the journey to learn about immunology. The project was around investigating the impact of pre- existing CMV immunity on the risk of HIV acquisition for the participants of HVTN505 vaccine efficacy trial. Cesar’s training as a computational immunologist inspired me in a lot of ways. He could discuss the absolute depths of HIV pathology and evaluate computational tools for running statistical analysis with equal amount of ease. Dr. Tomaras on the other hand, inspired me with her expertise in immunology and I grew fond of the way she could always keep her eye on the big picture questions. Under the brilliant mentorship of Cesar and Dr. Tomaras, I was able to design a well-rounded statistical analysis pipeline for answering our main research question. In the end, we were able to conclude that pre-existing CMV immunity was associated with the risk of HIV acquisition in HVTN505 vaccine efficacy trial. After the successful completion of this project, I was the only intern whose abstract was selected for an oral presentation at the 16th Annual CFAR Virtual Fall Scientific Retreat. Currently, Cesar and I are writing a manuscript under the mentorship of Dr. Tomaras to publish our findings.

Given my experience with statistical analysis, I wanted my master’s project to be around statistical method development for immunology. For that, I started working with Dr. Cliburn Chan, CFAR quantitative core director, who introduced me to deep learning approaches being employed on RNA-seq data to remove batch effects – a prevalent problem in high throughput data. Our idea was that similar approaches could be applied to flow cytometry data for removing batch effects and performing efficient clustering. I successfully completed my master’s project, and was awarded the best master’s project in data science category.

Having acquired expertise in the field of biostatistics, I wanted to apply and expand my mathematical and biological knowledge. Towards this, I joined The Reinhardt Biomedical Imaging Lab at University of Iowa in Fall 2021. Here, I plan on acquiring expertise in the field of biomedical image processing, digital image processing, and understanding different mathematical and computational techniques to build diagnostic tools for different lung diseases. For future, I am determined to focus on various complex diseases and plan on becoming an independent researcher with strong collaborations across the world towards building groundbreaking data-driven tools for understanding and deciphering these diseases.