Suraj Rajendran

About Me

I am a Ph.D. student in the Tri-Institutional PhD Program in Computational Biology at Weill Cornell Medical College, specializing in computational biology and bioinformatics. My research interests include the impact of data heterogeneity in federated learning, predicting the ploidy status of embryos using deep video classification, clinical trial emulation, and deep learning in genomics. I am honored to be supported by the National Science Foundation through a Graduate Research Fellowship. Currently, I am co-mentored by Dr. Fei Wang and Dr. Iman Hajirasouliha.

In the past, I have had the opportunity to work as a Bioinformatics Researcher at Wake Forest School of Medicine under the guidance of Dr. Umit Topaloglu. My projects there included the identification of immunotherapy-related adverse events (irAEs), predicting COVID-19 diagnosis using televisits and progress notes, and federated learning using cloud computing. I also worked on detecting smoking status using natural language processing.

During my undergraduate studies at Georgia Institute of Technology, I majored in Biomedical Engineering with a minor in Computing and Intelligence. I was awarded the President’s Undergraduate Research Award (PURA) Fellowship.

In my free time, I enjoy exploring the latest advancements in machine learning and artificial intelligence. I am passionate about using technology to solve real-world problems and make a positive impact on society.

Outreach

I have been actively involved in various outreach activities. Since March 2023, I have been a Research Mentor at Lumiere Education in New York City, where I have mentored students on machine learning in healthcare. I have also led a team for the Dept. of Health & Humans Services Blood Donation Campaign from October 2022 to February 2023, where we developed a winning proposal for the “Giving=Living” campaign aimed at promoting blood donations to address shortages. I have also led a team addressing inequities in academic recognition for disadvantaged groups from December 2021 to April 2022. We proposed a policy to ensure that students with disabilities get academic recognition for completed courses at Georgia Tech, and presented our findings to the Georgia Tech College of Engineering Diversity & Inclusion Council.

GRFP Help

The Graduate Research Fellowship Program (GRFP) has opened numerous doors for me, and I strongly urge all eligible individuals to consider applying. I’m more than willing to discuss the application process and provide insights based on my experience. If you’re interested, I can also share my research and personal statement upon request.

News

• 1/24/2024: We published a review paper on Cross-Cohort Cross-Category Learning (C4) and its importance in the future of healthcare AI. Read it at Cell Patterns here.
• 12/11/2023: Our team developed a curriculum for learning genomics for middle and high school students that the NIH plans on implementing at select schools across the United states, see here for details.
• 11/2/2023: Check out the podcast episode where I talked about my research with Dr. Carina Minardi on STEM From.
• 10/25/2023: I gave a talk at Instituto de Salud Carlos III in Dr. Biagio Mandracchia’s Introduction to Biomedical AI course. I talked about the burgeoning field of Cross-Cohort Cross-Category Learning (C4) and its applications in healthcare.
• 9/19/2023: Our team placed 3rd in NASA’s Pushback to the Future Challenge! See the blog post written about the winners and our approach here.
• 6/27/2023: Gave a talk at European Society of Human Reproduction and Embryology (ESHRE 2023) on “Predicting Embryo Ploidy Status Using Time-lapse Images”. Our abstract was published in Human Reproduction.
• 5/30/2023: Our paper, “Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative Study”, was published in the Journal of Medical Internet Research.
• 4/10/2023: Our team were quarterfinalists in the Advanced Research Projects Agency for Health (ARPA-H) Dash to Accelerate Health Outcomes. Our idea was a Bionic Pancreas system for diabetes monitoring.
• 3/21/2023: Our paper, “Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH)”, was published in iScience.
• 3/15/2023: We published “Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care” in PLOS Digital Health.
• 3/11/2023: Our team placed in the finals for the University of Texas Southwestern (UTSW) Case Competition, proposing the development of Pterostilbene as a synthetic compound
• 2/15/2023: Led a team that won the US Dept. of HHS Giving=Living Blood & Plasma Innovation Challenge. Our proposal, “Giving=Living”, aimed at promoting blood donations to address shortages.
• 2/4/2023: Gave a guest lecture for Cornell Tech’s INFO 5375: Health Tech Oriented Machine Learning on aspects of federated learning in the presence of data heterogeneity
• 1/4/2023: Our paper, “Development of non-invasive artificial intelligence models for the prediction of human blastocyst ploidy”, was published in Lancet Digital Health.
• 11/5/2022: Worked with a team of graduate students to create plans for marketing a bispecific antibody therapy for multiple myeloma. Placed as Finalists at the Penn Healthcare Case Competition.