Vagheesh M. Narasimhan (firstname.lastname@example.org)
Vagheesh Narasimhan is an Assistant Professor in the Departments of Integrative Biology, Statistics and Data Science, as well as Population Health at The University of Texas at Austin. He received his Ph.D. in Mathematical Genomics and Medicine from the University of Cambridge and the Wellcome Trust Sanger Institute and later completed a post-doctoral research fellowship at the Department of Genetics, Harvard Medical School with David Reich and Nick Patterson.
Emily Javan (email@example.com)
PhD Student (primarily advised by Dr. Lauren Ancel Meyers)
Emily Javan is a PhD student in Evolution, Ecology, and Behavior and part of the UT COVID-19 Modeling Consortium. She is currently working to identify recessive variants in the human genome associated with disease using exome sequencing and electronic health record data, but broadly interested in how diseases spread and manifest from genetic and environmental factors. She received her BS in Applied Mathematics from UC Davis with a minor in Evolution, Ecology, and Biodiversity and her MS in Population and Conservation Biology from Texas State University.
Olivia Smith is a PhD student in the Cell and Molecular Biology graduate program (Bioinformatics and Computational Biology track). She received her Bachelor of Science in Biochemistry and Biomedical Engineering (Honors) at Colorado State University. Prior to joining the lab she worked at Ginkgo Bioworks and Joyn Bio developing their next generation sequencing analysis platforms. She is currently working on developing methods for analysis of DNA methylation information from ancient DNA
Alaukik Gupta is an undergraduate majoring in Biomedical Engineering at the University of Texas at Austin. Outside of the lab, he is involved in Texas Engineering World Health (TEWH) as an officer and team lead. Currently, he is working with computer vision to develop an algorithm that can detect Leukemia (ALL) in blood cells. Alaukik is also interested in applying machine learning to genetic editing in the medical field and utilizing ancient DNA to map the spread of hereditary disease.
Devansh Pandey is a pre-final year undergraduate student at Indian Institute of Technology Kharagpur majoring in Biotechnology and Biochemical engineering. His research interests include computational biology and deep learning. He is currently working on discovering selective sweeps in ancient DNA samples to study natural selection.
Megan Le is an undergraduate double majoring Computer Science and Mathematics at The University of Texas at Austin and is a Teaching Assistant for Principles of Computer Systems (CS 439). She is a recipient of the TIDES Summer Research Fellowship. She is working on understanding natural selection in humans from time series data of ancient DNA.
Kevin Li is a 2nd year undergraduate majoring in Computer Science at the University of Texas at Austin. Outside of the lab, he is heavily involved in UT Austin’s Unmanned Aerial Vehicle Team (UAV Austin) as an infrastructure lead. His areas of interest include the applications of deep learning methods in the fields of medicine and human genomics as well as the analysis of ancient DNA to better understand evolutionary history. He is currently working on a generalized algorithm for the automated construction of family trees based on known pairwise relationships and genetic data.
Kushal Vajrala is an undergraduate double majoring in Business Honors and Electrical and Computer Engineering Honors at The University of Texas at Austin. He is working on real time human pose estimation as well as automated cropping tools for pre-processing imaging data
Eucharist Kun (firstname.lastname@example.org)
Eucharist Kun is a PhD student in the Biochemistry graduate program. He received his Bachelor's of Science in Biochemistry from the University of Texas at Austin. Prior to returning to the University of Texas at Austin for the Biochemistry graduate program, he worked at MD Anderson in the Gynecology Oncology and Reproductive Medicine Department focusing on Low Grade Serous Ovarian Cancer. Eucharist is currently working on developing machine learning methods to improve medical imaging diagnostics.