We are scientists.
We are engineers.
We are drug hunters. We are insitro.

Anna Shcherbina

Avantika Lal

Avantika works with insitro’s Data Science and Machine Learning team, applying computational methods to analyze multidimensional genomic data and uncover disease biology.

Prior to insitro, Avantika was a senior scientist at NVIDIA, where she worked at the interface of deep learning, GPU computing, and genomics. During her postdoctoral fellowship at Stanford, she developed machine learning approaches to analyze multi-modal genomic data from cancer cells.

Outside of work, she enjoys hacking on open-source projects, reading science fiction novels, and discovering new foods.

Bowen Liu

Bowen Liu is a computational chemist with experience working at the interface of chemistry, drug discovery, and machine learning.

Bowen grew up in New Zealand and received his dual BSc/BCom undergraduate degrees at The University of Auckland majoring in Chemistry, Applied Mathematics, Finance and Accounting. Afterwards, he moved to the Bay Area and completed his Ph.D in Chemistry at Stanford under the supervision of Jure Leskovec and Vijay Pande. At Stanford, Bowen focused on developing machine learning methods for problems in small molecule drug discovery and lead optimization, namely molecular property prediction, molecule generation, and chemical reaction prediction.

In his spare time, Bowen enjoys reading, playing video games, and going on staycations.

Cong "Karl" Guo

As Director of Translational Genetics, Karl leads insitro’s efforts to integrate genetics with multiomic data to identify and validate drug targets.

Karl has over 10 years of genetics and genomics experience in industry and academia. Prior to joining insitro, he was a director in the Human Genetics and Computational Biology department at GlaxoSmithKline. He led efforts to identify drug targets by coupling GWAS results from 23andMe and the UK Biobank with sophisticated variant-to-gene mapping methodologies. He is also experienced in designing high-throughput genetic screens and developing computational methods to identify and prioritize targets. Karl received his Ph.D. in genetics and genomics from Duke University as a trainee in Dr. Tim Reddy’s lab where he studied the functional impacts of non-coding genetic variation on disease. He received his B.S. in biomedical engineering from Georgia Tech while performing undergraduate research in Dr. Ravi Bellamkonda’s lab.

Outside of work, Karl enjoys cooking, rock climbing, and playing tabletop games.

Daphne Koller, Founder & CEO, Board Member

Daphne Koller is the CEO and Founder of insitro.

Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years. She was the co-founder, co-CEO and President of Coursera for 5 years, and the Chief Computing Officer of Calico, an Alphabet company in the healthcare space. She is the author of over 200 refereed publications appearing in venues such as Science, Cell, and Nature Genetics, and has an h-index of 130. Daphne was one of TIME Magazine’s 100 most influential people and is a MacArthur Fellow, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences and the International Society of Computational Biology.

In her spare time, Daphne enjoys spending time with her family, especially while traveling to exotic destinations (62 countries so far and counting), where they enjoy hiking, sailing, scuba diving, and eating fresh local food.

Eilon Sharon, Director of Data Science

As Director of Data Science, Eilon is leading the development of cutting edge machine learning, computational biology and statistical genetics approaches to improve drug development.

His team uses machine learning to integrate observations from large population-level studies with results from various high throughput in-vitro assays to identify potential drug targets.

Eilon has extensive experience in applying machine learning to decipher various biological questions. After completing a dual major B.Sc. in biology and computer science at Tel Aviv University, Eilon joined Rosetta genomics, where he worked on discovering miRNA genes in humans and predicting their targets. He then earned a PhD from the Weizmann Institute of Science under the supervision of Prof. Eran Segal. During his PhD, he developed synthetic biology Massively Parallel Reporter Assay (MPRA) and statistical and thermodynamic models, which he applied to decipher the encoding of transcriptional regulation in yeast. Following graduation, Eilon transitioned to a postdoc at Profs Jonathan Pritchard and Hunter Fraser labs in Stanford Medical school department of genetics. At stanford, Eilon worked on a diverse set of projects including: detection and fine mapping of genetic associations with T cell receptor V-genes expression; software for transplant health monitoring using cell-free DNA sequencing (which was commercialized by Stanford); and detection of functional genetic variants using a novel high throughput CRISPR editing. Eilon is the author of over 20 refereed publications appearing in venues such as Cell, Nature Biotechnology and Nature Genetics.

In his free time, Eilon enjoys hiking and camping outdoors with his family.

Gabriel Dreiman

Haoyang Zeng

Hari Somineni

Hervé Marie-Nelly

Jeevaa Velayutham

Michael Bereket

Mohammad "Muneeb" Sultan

Ralph Ma

Shahin Mohammadi

Srinivasan Sivanandan

Sumit Mukherjee

Syuan-Ming Guo

Theofanis Karaletsos, VP, Data Science / Machine Learning

“insitro offers the unique opportunity for computational modelers interested in hard scientific problems of social relevance to combine data generation, analysis, modeling, and decision making under one roof. I am thrilled to have found an incredible amount of intellectual stimulation paired with mission-driven execution here.”

Theofanis Karaletsos is a machine learning scientist by trade seeking to build robust, data-efficient models of complex systems that allow us to understand and control the world around us. Theofanis previously held positions as Staff Scientist at Meta/Facebook working on the interface of probabilistic programming, deep learning, and uncertainty aiming to robustify large scale ML systems, and was a founding member and senior researcher at Uber AI Labs in San Francisco focusing on probabilistic machine learning, deep learning, probabilistic programming (see pyro.ai), and their applications in fields as diverse as simulation, reinforcement learning, healthcare, biology, spatiotemporal modeling, vision, language, and large-scale economics.
In his earlier roles, Theofanis was a researcher at AI-startup Geometric Intelligence (which was acquired by Uber to form Uber AI Labs) and previously at the Sloan Kettering Institute in New York, and did his graduate work at the Max Planck Institute for Intelligent Systems. See more at karaletsos.com.

At insitro, Theofanis is fascinated to work on building computational abstractions spanning the spectrum of internal in vitro and in vivo (i.e. clinical) datasets representing the breadth of the drug discovery process and is excited by the fundamental problem of causal transfer from the lab to the clinic.

Thomas "Tom" Soare

Tommaso Dreossi

Xueya Zhou

Zachary McCaw

Zack Phillips