Eddie Moler brings over 20 years of experience leading pharmaceutical and clinical diagnostics R&D, data-science/algorithm/software intensive platform development, gene-target and biomarker discovery and validation, establishing and scaling high-performing data-science & software teams, and collaboration management.
Previously, Eddie led Quest Diagnostics’ Bioinformatics organization, leading the development and launch of dozens of advanced clinical genomic diagnostic tests while more than quadrupling the size of the team across multiple sites. He has led clinical/biological research projects and the development of production-scale clinical and research platforms at Quest Diagnostics, GE Healthcare, Tethys Bioscience, Novartis, and the US Department of Energy.
Dr. Moler has published over 50 peer-reviewed papers including clinical and molecular medicine, population health, and health economics, and has 7 issued patents on clinical risk algorithms, biomarkers, automated histopathology image analysis, drug targets, and discovery technology. Eddie received a PhD in Chemistry from UC Berkeley and a BS in Chemistry from Texas A&M University.
From whiteboards to datacenters, Roie brings 15+ years of experience developing novel computational methods & deploying them at industry-scale. As an analytic lead of multi-omic prospective studies of unprecedented scale, he has a record of not only discovering insights from biological big-data, but of activating marketable insights for patients from them.
His research spans a breadth of ‘omic studies. Previous accomplishments include the development of DeepMass, a deep-learning model to accelerate & expand the scope of proteomic mass-spectrometry, and the elucidation of microbial feedback system capable of resisting naive dietary intervention. A former bench scientist, he is familiar with the pitfalls of biological data & works with lab-scientists to accelerate their own research.
Dr. Levy has published over 15 peer-reviewed papers ranging from metagenomics & microbial ecology to proteomics in personalized medicine. He received his PhD in Cellular & Molecalar Biology from the University of Washington & conducted a post-doctoral fellowship at the Institute for Systems Biology. In his spare time, he still does comp-bio work, but with a dog in his lap.
Since joining Juvena Therapeutics, Tejaswini Ganapathi has worked on the development of AI models that power Juvena’s protein discovery for targeted in vitro screening, and will be exploring the application of AI to other aspects of the company’s drug pipeline as well.
Tejaswini has extensive experience in machine learning algorithm development both in research and production and towards building end to end ML systems. Her industry experience spans various domains such as healthcare, medical imaging, finance and wireless networks. She has been an early employee at 2 startups with successful exits and holds ~10 patents. She pursued her graduate studies at University of Toronto and University of Texas at Austin, both specializing in machine learning.
Outside of AI and work, Tejaswini is a RYT200 Yoga instructor, is trained in Indian Classical music and tries to contribute her time to animal rescues.
Dr. Rohit Jadhav brings more than 12 years of cross disciplinary experience encompassing the fields of oncology, immune aging and auto-immune diseases. He has extensive experience in algorithm development for diagnostic and prognostic biomarker discovery dealing with diverse datasets investigating the Transcriptome, Epigenome and DNA mutations in clinical cohorts.
Dr. Jadhav earned his B.Tech. in Bioinformatics from India, a M.S. in Bioinformatics from Indiana University, Indianapolis, a PhD in Molecular Medicine from UT Health, San Antonio as a CPRIT pre-doctoral fellow, followed by a postdoctoral fellowship in Immunology at Stanford School of Medicine and Mayo Clinic.
Masters & PhD (2009-2016)
Dr. Jadhav worked on identifying epigenetic biomarkers investigating alterations in DNA methylation across different cancer types including breast, ovarian, endometrial, oral and prostate. He was awarded a Cancer Prevention and Research Institute of Texas (CPRIT) Predoctoral Fellowship, which enabled him to be involved in collaborative projects focusing on non-invasive approaches for biomarker discovery. One such project involved use of single-cell RNA sequencing to study circulating tumor cells and another involved studying DNA methylation in cell free DNA, both from peripheral blood. As a lead bioinformatician for these studies, he implemented classification-based prediction methods to enable identification of a panel of biomarkers to be used in determining treatment approaches for prostate cancer patients belonging to different subtypes. These studies have led to 3 approved patents from his mentor Dr. Tim Huang. During this time, Dr. Jadhav was also a lead author on the study that identified DNA methylation biomarkers capable of identifying predisposition to breast cancer after prenatal exposure to the compound bisphenol A and preventive effects of genistein.
Postdoctoral Fellowship (2017-2022)
Dr. Jadhav’s work at Stanford University and Mayo Clinic with Dr. Jorg Goronzy who is a leading expert in immune aging and auto-immunity, focused on epigenetics in the context of chromatin accessibility and specifically alterations in the immune cell types like CD4 and CD8 T cells during aging and autoimmune diseases like rheumatoid arthritis. He was also a lead author studying epigenetic alterations in exhausted T-cells post checkpoint blockade therapies in collaboration with Dr. Rafi Ahmed from Emory University. During this time Dr. Jadhav built pipelines deployed in on-prem clusters and in the cloud for analyzing and visualizing data from RNA-seq, ATAC-seq, TCR-seq, single cell multiome (RNA+ATAC), single cell Cite-seq (RNA+Protein) among others.
Dr. Jadhav has published over 30 peer-reviewed papers with over a thousand citations.
Munir Yousef joined Juvena Therapeutics as a bioinformatics analyst to contribute to Juvena’s growing platform by developing robust data analysis pipelines for their in silico protein drug discovery platform. After realizing his passion for data-driven discovery of solutions to real world problems, Munir obtained his Masters degree in Applied Data Science from Syracuse University.
Before officially joining the team, Munir worked as a data science consultant and helped develop internal data analysis and visualization tools to support Juvena’s growing R&D platforms.
