About Pythia Biosciences

Eugene Bolotin, Ph.D., is Head of bioinformatics at Santa Ana Bio since joining in 2021. He has been working in the bioinformatics field for 12 years since attaining his Ph.D. at University of California Riverside and his undergraduate degree in Genetics from University of California, Davis.  Prior to Santa Ana Eugene’s industry experience includes Illumina, Cell Design Labs and Kite Pharmaceuticals where he excelled as a leader and performer with increasing levels of responsibility.  Eugene brings deep understanding of biology and data science which he combines with expertise and experience  across many  multi-omics technologies. 

Brandon Jeffy, Ph.D., DABT has been leading the discovery toxicology group since joining Takeda in 2017, and has been working in pharmaceutical toxicology for nearly 20 years.  Prior to joining Takeda, Brandon worked in nonclinical drug safety departments at companies including AstraZeneca, Celgene, and Vertex, providing increasing levels of toxicology support to discovery and development programs across multiple therapeutic areas and modalities.  Dr. Jeffy has extensive experience in working in the subdiscipline of computational toxicology, and is a recognized expert in the areas of toxicogenomics, pathways/network analysis, and use of machine learning algorithms for derivation of muti-omics predictive signatures/biomarkers. Brandon received his Ph.D. in Cancer Biology/Toxicology from the University of Arizona and completed his postdoctoral fellowship at Pfizer.   Brandon is currently serving as President of the Drug Discovery Toxicology specialty section of Society of Toxicology, is Past President of the Southern California Chapter of Society of Toxicology and is co-editor of the first book on Drug Discovery Toxicology.

Dr. Kovacs is the CEO and Founder of Afecta Pharmaceuticals and a practicing obstetrician-gynecologist specializing in high risk maternal and fetal medicine.  He received his medical degree from University of Southern California School of Medicine where he later held several senior executive management positions and was a Clinical Professor and Director of the Molecular & Cytogenetics Laboratory for over 20 years.  While at Afecta he has developed a new pipeline of de-risked, small molecule immune- modulating drugs with fast-to-market potential through the FDA 505(b)(2) pathway.    Crucial to Afecta’s drug and OTC pipeline was the development of the proprietary Artificial Intelligence powered disease/drug targeting platform, PharmetRx™, which combines multiple genomic modeling techniques with cheminformatics and machine learning. 

Dr. Brett Pickett completed his B.S. at Brigham Young University, his Ph.D. at the University of Alabama, and his postdoctoral work at the University of Texas Southwestern Medical Center at Dallas. His subsequent positions at the J. Craig Venter Institute, Thomson Reuters (now Clarivate), and Booz Allen Hamilton enabled him to develop and apply algorithms and bioinformatics workflows to analyze and interpret various types of -omics data in a systems biology context. Since joining the faculty at Brigham Young University, Dr. Pickett has focused on developing and applying big data, artificial intelligence, and drug repurposing methods to predict therapeutic targets, biomarkers and highly accurate diagnostics.

Dr. Christopher Szeto is a Bioinformatics and Machine-learning team leader with over 13 years of industry experience building data-driven solutions for healthcare applications. His expertise is in clinically-focused R&D, gained from contributing to FDA-approved NGS products, ML/AI-based predictive biomarkers, as well as having direct involvement in multiple clinical trials with a molecular diagnostic component. Christopher is currently Sr. Director of Bioinformatics at Exelixis, Inc., a genomics-based oncology drug discovery company located in Alameda. Previously he held roles as Director of Translational Bioinformatics at Zai Lab, a multinational commercial-stage biopharma, and Director of Machine Learning at NantWorks, a healthcare technology company. He obtained his Ph.D. from UCSC in 2013 on a Fulbright scholarship studying under Prof. David Haussler, during which he was involved in coordinating data and predictive analytics for the TCGA project.

Ming "Tommy" Tang serves as the Director of Computational Biology at Immunitas Therapeutics, a clinical-stage biotech company committed to discovering and developing novel therapeutics for patients with cancer. In this role, he guides a skilled computational biology unit in scrutinizing single-cell genomic data to unveil the intricacies of immune cells within human tumors.  Prior to joining Immunitas, Tommy was the lead scientist for the Cancer Immunologic Data Commons at Dana Farber Cancer Institute.  Prior to Dana Farber, Tommy was a Bioinformatics Scientist at Harvard focusing on single cell analysis.  Tommy received his Ph.D. from the University of Florida in Genetics and Genomics and completed his postdoctoral Fellowship at MD Anderson Cancer Center.  He is the author of the book “From Cell Line to Command Line”, has written several book chapters and has over 35 peer-reviewed publications including high-profile papers in Cancer Cell, Nature Genetics and Nature Communications.  More about him can be found at https://divingintogeneticsandgenomics.com/ 

Dr. Vivek Das is a Lead Data Scientist in Integrated Omics department under AI & Analytics in Novo Nordisk A/S in Denmark. He presently works in Clinical Trial Omics & Precision Medicine in Development (Clinical). Lead, design & implement integrative multi-omics & single cell strategies, in-silico analytical frameworks & interpretability in Late-Stage Clinical Trial (CardioRenal) settings. His earlier work involved Target and Biomarker Identification in Chronic Diseases settings via integration of big data e.g. bulk/single-cell multi-omics along with relevant phenotypic measurements from  human observational cohorts and/or preclinical studies. He is leveraging the interdisciplinary knowledge from Biology, Computer Science, Mathematics, and Statistics. He also explores the application of Advanced Statistical, Machine Learning, and Deep Learning models in high-dimensional single-cell multi-omics data for specific problem scopes given the data bandwidth as a part of some of his curiosity-driven research activities. For more on Vivek Das's professional journey, you can follow him on LinkedIn.