San Francisco Bay Area
Strategic Leader in Computational Discovery & AI Acceleration I bridge the gap between cutting-edge Artificial Intelligence and clinical breakthrough. With over 10 years of leadership across TechBio and Biopharma, I specialize in building the high-performing teams and scalable AI/ML architectures required to turn multi-modal molecular data into a competitive advantage. Throughout my career at organizations like Genentech/Roche, Gilead Sciences, and Tempus AI, I have focused on accelerating oncology drug development—from target discovery through Phase 3 trials. My expertise lies in transforming high-dimensional RWD and clinical trial data into actionable hypotheses that de-risk pipelines and shorten the path to IND. Core Impact Areas: Scaling AI/ML Teams: Leading cross-functional groups of computational biologists and data scientists to deliver production-grade data products. Translational Strategy: Integrating clinical genomics and predictive modeling to drive biomarker discovery and patient stratification. Scientific Thought Leadership: Author of Mastering Scientific Computing with R and co-author of 35+ peer-reviewed publications.
● Platform Leadership & '0 to 1' Execution: Architected the technical vision and system lifecycle for the Lab-in-the-loop (LOOP) research platform, engineering a dynamic closed-loop infrastructure that pairs multi-modal real-world data with custom modeling environments to drive computationally-driven discovery. ● Multidisciplinary Team Leadership: Built and led a high-performing team of AI/ML research scientists and computational biologists, optimizing complex technical roadmaps to align raw algorithmic performance with biological fidelity and research impact. ● AI Foundation Strategy: Spearheaded internal model interrogation initiatives, deploying mechanistic interpretability techniques—including attention mechanism introspection and direct embedding space interrogation—to reverse-engineer feature attribution and track biological signal representation within deep learning foundation models. ● External Innovation & TechBio Ecosystem: Evaluated and benchmarked external virtual cell frameworks and frontier foundation models, establishing rigorous metrics to assess model alignment, generalization capabilities, and emergent biological constraints to complement internal R&D. ● Scientific & Technical Assessment & Target Validation: Validated the accuracy and emergent biological insights of generative AI architectures by cross-referencing model embeddings with ground-truth experimental data, integrating CRISPR functional genomics and patient-derived organoid screens. ● Strategic Collaboration & Matrixed Governance: Steered matrixed collaborations across core modeling labs, R&D engineering units, and external strategic partners, establishing the technical governance required to translate high-dimensional interpretability data into actionable discovery pipelines.
● Scientific Advisory Board Member, CQDM ● Provide strategic oversight for a pharma-based consortium to accelerate the translation of innovative technologies; evaluate collaborative R&D proposals from a global innovation ecosystem. ● Facilitate strategic partnerships between industry and academic stakeholders to drive high-impact research and technological advancement.
● Strategic Partnerships: Served as Data Acquisition Board Representative, managing end-to-end technical due diligence and evaluation for 3+ strategic AI partnerships and data acquisition deals. ● Systems Thinking: Engineered a "0 to 1" machine learning framework integrating clinical trials and real-world data to drive evidence-based decision-making for multiple antibody-drug conjugate (ADC) programs. ● Cross-Functional Influence: Led the implementation of deep learning workflows in Python, generating single-cell atlases from over 1 million cells to identify cell-type-specific drug response signatures.
● Implemented statistical and predictive modeling with R and Python to extract actionable features associated with patient response to targeted therapies and standard of care from complex clinical trial biomarker data including ctDNA from Predicine, Guardant Health, Foundation Medicine (FMI), genomic tissue bulk and single cell assays, cell lines screens, in vivo PDX assays, public datasets (DEPMAP, TCGA, etc.) and real-world evidence from Flatiron Health and FMI, driving evidence-based decision-making. ● Orchestrated and streamlined reverse translation efforts for solid tumor signaling franchise as group leader and bioinformatics lead scientist; achieved breakthroughs in target discovery and combination strategies through successful exploration of PI3K inhibitor clinical trials (INAVO120, Sandpiper, Lorelei, Morpheus) by identifying PI3Ka inhibitor and degrader inavolisib can co-opt FGFR2 to enhance response in hormone receptor-positive breast cancer patients. ● Led 5+ cross-functional collaboration with Research, Translational Medicine, Biomarker, Early Clinical development, and Informatics team leads to drive adoption of bioinformatics data-derived insights, resulting in accelerated decision-making across the organization for improved patient outcomes. ● Presented research findings on the effectiveness of PI3K inhibitors and endocrine therapies in breast cancer at international conferences. ● Spearheaded the recruitment, development, and management of a talented team of 1 senior bioinformatics scientist and 1 contractor, driving the solid tumor signaling franchise's reverse translation capabilities and data derived insights. ● Mentored 1 intern in executing research project leveraging statistics and reverse translation to extract digital pathology features associated with treatment Taselisib response in Sandpiper Phase 3 trial through multi-modal analysis of omics data and digital pathology PathAI extracted features.
● Conducted comprehensive analysis of 10+ large-scale NGS omics bulk and single cell datasets, high-dimensional assay, and drug sensitivity data from diverse sources (internal, public, commercial) to identify optimal drug combinations, biomarkers, and tumor resistance mechanisms; delivered crucial insights to drive precision medicine advancements. ● Led Bioinformatics analysis of 10+ complex datasets to unravel intricate biological pathways and identify novel therapeutic targets; facilitated cross-functional collaboration and knowledge sharing to accelerate drug discovery and development efforts. ● Led 1 research project focused on investigating the impact of ARID1A gene mutations on cetuximab resistance in first-line metastatic colorectal cancer patients, contributing to a deeper understanding of treatment response and paving the way for personalized therapies. ● Developed an innovative gene expression–based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma, leveraging clinical trial datasets representative of glioblastoma clinical trial populations, fostering collaboration, and driving advancements in precision medicine. ● Awarded the prestigious Genentech Innovation Fund in 2019 as co-primary applicant to establish novel single nuclei methylation technology, leveraging cutting-edge research in the field; managed a 1 wet-bench contractor to develop and optimize a new assay, driving our innovation award forward.
● Translated complex molecular biomarker data into diagnostic tools; guided understanding of drug activity in patient tumors and identified subsets of patients for targeted therapeutic interventions, from for 2 gastrointestinal and 3+ breast cancer clinical trial and omics dataset. ● Collaborated on biomarker projects with OBD on breast cancer (Neosphere study) and colorectal cancer (CALGB 80405 trial), and GBM (ARTE, TAMIGA, EORTC 26101, AVAglio, GLARIUS trials). ● Awarded a highly competitive Genentech Innovation Fund in 2018, to support a collaboration between Genentech and the Metrakos Lab at McGill University to adapt the Cao et al., 2017, Science paper to enable the simultaneous measurement of single nuclei RNA and DNA from colorectal cancer patient biopsies.
● Led large-scale data processing and analysis of 10+ projects for researchers Dr. Morag Park, Dr. Russell Jones, and Dr. Peter Siegel at McGill University and international collaborations, using genomics, microarray, and metabolomics data. ● Managed secure storage and regular backups for all NGS sequencing data on our lab servers, ensuring data integrity and availability for analysis processes on Compute Canada Calcul Quebec clusters. ● Created lecture courses for Dr. Nicole Beauchemin's GCRC bioinformatics workshops and EXMD 635 course, covering a wide range of topics such as Microarrays & next generation sequencing pre-processing and analysis, experimental design, interpreting gene expression data, aligning genes to the genome, and pathway analysis.