Jeon Lee, Ph.D. (Principal Investigator)
Since December 2020, Jeon Lee has been appointed as an Assistant Professor in the Lyda Hill Department of Bioinformatics. He received his Ph.D. in Biomedical Engineering from Yonsei University in South Korea (1999–2006), where he trained as a medical device developer and innovator with biomedical signal processing and machine learning (ML) expertise. He did his 1st post-doc training in the Chronic Disease Informatics Program (PI: Joseph Finkelstein, M.D., Ph.D.) at Johns Hopkins School of Medicine (2013-2014). Subsequently, he studied and developed seminal computational algorithms for big data mining while working as a post-doc and later as an Assistant Research Scientist (parallel to Assistant Professor) for the Department of Computer Science and Electrical Engineering (PI: Seung-Jun Kim, Ph.D.) at the University of Maryland, Baltimore County (2014-2015). He joined UT Southwestern Medical Center as a Computational Biologist in 2016 and led the machine learning team at the Bioinformatics Core Facility from 2018 to 2020.
Krishna Kanth Chitta, M.S. (Computational Scientist)
Krishna received his BS in Biomedical Engineering at Vellore Institute of Technology in Vellore, India, and an MS in Biomedical Engineering from Arizona State University. He is formally trained in medical image analysis, deep learning, and computer vision with special emphasis on physics and clinical applications of MRI. After his Master’s, he worked as Senior Research Officer at Agency for Science Technology and Research (A*STAR) in Singapore for 3 Years. He has experience in developing methods for detecting and quantitating structures/lesions in MRI datasets, fluorescence microscopy images, and colonoscopy videos. He is currently developing machine learning algorithms including advanced convolutional neural networks to solve challenges in multi-modal medical image analysis.
Affiong Akpaninyang, M.S. (Computational Biologist)
Affiong received her BS in Biochemistry at The University of Texas at San Antonio in San Antonio, Texas (2016-2020), and an MS in Bioinformatics and Computational Biology from University of South Florida in Tampa, Florida (2021-2022). Her skillset includes bioinformatics, computational biology, biostatistics, R-programming, SAS, Python, and C++. She possesses 4 plus years of experience in clinical environments and is very excited to combine that with her newfound passion for Bioinformatics and Computational Biology.
Ahmed M. Shalaby, Ph.D. (Computational Scientist)
Dr. Shalaby earned his Bachelor's and Master's degrees in Electrical Engineering in 2003 and 2009 respectively. He earned his Doctorate in electrical engineering from the University of Louisville in 2014. He has 12+ years of hands-on experience in the fields of medical image analysis and machine learning in medical application. He has worked on a variety of research projects focusing on machine learning and medical imaging. His research is helping to advance the medical understanding and imaging for Autism Spectrum Disorder, prostate cancer, and spinal cord injuries, among other areas. He has authored or coauthored more than 100 technical articles, and 5 US patents.
Achisha Saikia, M.S. (Computational Biologist)
Achisha earned a Master of Science in Genetics, Genomics, and Bioinformatics from the State University of New York at Buffalo (2021-2023), along with a Bachelor of Engineering in Biotechnology from R.V. College of Engineering, Bengaluru, India (2017-2021). She predominantly worked as a wet lab researcher during her undergraduate days. She has acquired practical expertise in bioinformatics through her professional experiences as a Bioinformatics Analyst at the NYS Center of Excellence in Bioinformatics and Life Sciences during her time at SUNY Buffalo. She has developed expertise in various types of Next-generation sequencing data analyses (e.g., RNA-seq, scRNA-seq, ChIP-seq, metagenomics). Her computational languages are R, Python, and Bash. She is passionate about using computational methods for scientific discovery, especially related to how and why genetic disorders are caused. Beyond her professional pursuits, she has diverse interests in volunteering, hiking, traveling, reading, writing, and filmmaking.
Khushi Ahuja, M.S. (Computational Biologist I) - DSSR
Khushi earned her Master of Science in Bioinformatics from Boston University (2022-2023) and Bachelor of Engineering in Biotechnology at the University School of Biotechnology, GGSIPU, Delhi, India (2018-2022). During her undergraduate years, she undertook a multitude of projects encompassing wet lab experiments and in silico analysis in the fields of Bioinformatics and computational biology. Khushi has expertise in handling Next Generation sequencing data types, including Sc-RNA seq, bulk RNA seq, Chip seq, CUT&RUN seq, and Hi-C seq. She has also demonstrated her proficiency in constructing biological databases and creating user-friendly R shiny applications for data representation and visualization. Her technical skills encompass Python, R, and shell scripting, making her a versatile computational biologist. Her true passion lies in utilizing these tools to address complex challenges in cancer genomics and drug discovery, which she will pursue in her current capacity at DSSR. Outside of her professional endeavors, Khushi has a penchant for exploration, with a genuine love for discovering new places.
Hankyu (Chris) Lee, M.S. (Computational Biologist I) - DSSR
Chris earned his Bachelor's in biomedical engineering from the Engineering Science program at the University of Toronto. He also received a Master's of Applied Science degree from the University of Toronto, following his research into the computational design of de novo protein structures. As a student researcher during his undergraduate years, he contributed to a diverse range of research topics, including computational linguistics, neuro-rehabilitation research, and protein design. His primary programming languages are Python and Bash. He is excited to be part of the DSSR team to support cancer research via computational tools, and has a strong interest in the field of genomics. In his off-time, he likes to listen to music or watch movies and read books to relax.
Jingxuan Chen, Ph.D. (Computational Biologist II) - DSSR
Jingxuan received her Ph.D. in Bioinformatics from The University of Georgia (2018-2023), and Bachelor’s degree in Biological Science from Beijing Normal University in China (2014-2018). Jingxuan has 6+ years experience in handling large-scale next- and third-generation sequencing data (WGS, WES, and bulk RNA-seq) and HPC cluster computing. She is also experienced in developing reproducible bioinformatics workflows and evaluation of bioinformatics tools. She is proficient in scripting languages Python, R and Bash, and experienced in using Snakemake for workflow management. She is passionate about analyzing variant omics data and developing novel computational tools to support the cutting-edge cancer research, as part of DSSR.
Jui Wan Loh, Ph.D. (Computational Biologist II) - DSSR
Jui Wan received her B.S. (in Biotechnology) and Ph.D. (in Microbiology and Molecular Genetics) degrees from Rutgers University in 2015 and 2021 respectively. After her Ph.D. graduation, she worked as a Research Fellow in Singapore for 3 years (at National Cancer Centre Singapore and Duke-NUS Medical School). She has experience in high-throughput data sequencing analysis, particularly in genomics and transcriptomics, both at bulk and single-cell level. In her free time, she enjoys swimming and hiking.
Ermis-loannis Michail-Delopoulos, M.S. (Computational Biologist I) - DSSR
Ermis holds an integrated Bachelor’s and Master’s degree in Biotechnology from the Agricultural University of Athens (2019) and a Master’s degree in Bioinformatics from KU Leuven University in Belgium (2024). Throughout his studies, he focused on NGS analyses (e.g., bulk RNA-seq and scRNA-seq), as well as microbial interactions and microbial association networks, where he developed a Cytoscape plugin to enhance their visualization and exploration. Additionally, as a Computational Biologist at the Lab of Microbial Systems Biology at KU Leuven, he applied machine learning techniques to analyze and predict co-culture abundances in microbial flow cytometry data. His programming skills include Java, Python, R, and Bash. He is passionate about software development and leveraging computational tools in genetic research. In his free time, he enjoys playing video games and basketball.