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CTO at Transcripta Bio | Building AI-powered drug discovery at unprecedented scale | Ex-Google (Computer Vision/AI) | Stanford MS CS
I am Chief Technology Officer at Transcripta Bio, where we're building an omics-first drug discovery platform using hyperscaled transcriptomics and AI to discover novel biological mechanisms and small molecule drugs that can provide treatment. We've architected a closed-loop platform that can screen billions of compounds computationally, predict gene expression changes, and validate predictions with exceptional hit rates, all at unprecedented speed. Our infrastructure processes 100 million single cells in under two hours, enabling us to iterate on scientific hypotheses in days rather than months. Previously, at Google I built production ML systems as a technical leader in computer vision; and at Yahoo/Flickr I was a founding member of the computer vision team and built one of the largest vector search systems at the time, querying billions of images with subsecond latency. I've authored publications with citations totalling over 1,000+ and hold several patents. Large-scale machine perception of biology across diverse experimental modalities will define our future understanding and control of biological systems. I'm building the infrastructure to make that future real. Motivated by personal experience, I was diagnosed ten years ago with a rare genetic disorder that results in tumors developing on nerves. This journey has only strengthened my belief that breakthrough machine learning and high-throughput biology technologies have the potential to transform human health.
Stanford University
Master of Science, Computer Science, Dual focus in Human-Computer Interaction and Artificial Intelligence
January 1, 2010 – January 1, 2012
Stanford University
Bachelor of Science, Symbolic Systems - Cognitive Science
January 1, 2007 – January 1, 2011
Transcripta Bio
Chief Technology Officer (formerly VP Engineering)
April 1, 2022 – Present
Staff Software Engineer, Computer Vision Lead
March 1, 2017 – March 1, 2022
San Francisco Bay Area
Yahoo
Software Engineer
October 1, 2013 – March 1, 2017
San Francisco
LookFlow (Employee #2)
Software Engineer
July 1, 2012 – October 1, 2013
San Francisco, CA
Stanford University
Symbolic Systems Program Student Advising Fellow
January 1, 2010 – July 1, 2012
Introduction to Deep Learning with Python Course
March 1, 2016 – July 1, 2016
I designed, wrote, and instructed a hands-on weekend-long course introducing people with basic programming skills to the core concepts of deep learning. Students started by learning the basics of logistic regression and ended the course having designed their own CNNs and trained them on AWS GPU instances. I have personally instructed the course three times and a revised version of the course is occasionally offered through www.dataweekends.com. Most students leave very satisfied having gotten hands-on experience in applying deep learning.
Programmed cell death
Coursera
June 24, 2026 – Present
Epigenetic Control of Gene Expression
Coursera
June 24, 2026 – Present
Computational Molecular Evolution
Coursera
June 24, 2026 – Present
Introduction to Systems Biology
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background shows a strong inclination towards innovation, leading technical teams, and building scalable systems. Their experience spans startups (LookFlow, Transcripta Bio) and large corporations (Yahoo, Google), demonstrating adaptability to different organizational cultures. The focus on AI and data-driven solutions aligns well with a modern tech-centric environment. However, the target role of 'Data Analyst' might be a step down from their CTO/Staff Engineer roles, which could impact long-term cultural fit if the role does not offer sufficient technical depth and leadership opportunities.
Soft Skills & Operational Fit
The candidate's experience as a CTO and Staff Software Engineer at major tech companies, coupled with their role in leading product development and customer-driven innovation, suggests strong leadership, problem-solving, and collaboration skills. Their ability to bridge cutting-edge research with real-world commercial deployment indicates a practical and results-oriented approach. The project on instructing a deep learning course also highlights communication and mentorship abilities.