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

Anna Shcherbina

Anna works with insitro’s Data Science and Machine Learning team, developing machine learning approaches to study the role of noncoding variation in disease. Her current work focuses on building deep learning capabilities to uncover additional candidate drug targets from imputed GWAS studies across several cohorts.

Anna recently completed her PhD in Biomedical Data Science from Stanford, where she was a BioX and NVIDIA Fellow. Her PhD work focused on developing deep learning models to study gene regulation and to functionally characterize non coding variation. She also worked on statistical analysis methods for wearable and mobile health data, such as accelerometry data. Prior to completing her PhD, Anna worked on metagenomic data analysis at MIT Lincoln Laboratory.

In her free time, Anna likes to read science fiction and fantasy novels, spend time with her cat, and go hiking.

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.

Baris Ungun

Baris works within insitro’s Data Science and Machine learning team, where he applies his dual training in medicine and convex optimization to problems in bioinformatic modeling of disease states and scalable algorithmic approaches to interpreting petabyte-scale genomic and imaging datasets.

Baris grew up in the Bay Area. In a previous life, he studied Chemical Engineering at Princeton and worked in research and development at Gilead Sciences. He then spent the majority of the last decade in an MD-PhD program at Stanford, where he worked with Lei Xing and Stephen Boyd on large-scale computing problems in radiation therapy treatment planning. His research focus was on developing high-performance convex approximations to components of the massive, nonconvex problems arising in medicine and biomedicine.

Outside of work life interests include: the ocean, reading (mostly fiction), and seeing as much live music as possible.

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

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

Gabriel grew up in Boulder, Colorado. He received his BS in Biological Engineering from Cornell University, and his MSc in Machine Learning from University College London. He completed his master’s thesis at Alzheimer’s Research UK’s Drug Discovery Institute where he studied applications of ML to High Throughput Screening.

In his free time, Gabriel competes in triathlons in addition to skiing and climbing. He also enjoys cooking and backpacking.

Haoyang Zeng

Haoyang Zeng is a computational biologist with extensive experience in building machine learning models for functional genomics and therapeutic design.

Haoyang grew up in Sichuan, China, home to most of the panda bears in the world. He earned his BE in Electrical Engineering at Tsinghua University, and his MS and Ph.D. in Computer Science at the Massachusetts Institute of Technology under the supervision of David Gifford. His Ph.D. research focused on developing statistical and deep learning methods for learning the regulatory function of DNA sequences, predicting the binding affinity of peptides to the Major Histocompatibility Complex (MHC) for effective neo-antigen vaccine formulation, and designing novel antibody sequences with improved binding affinity and specificity. Haoyang has co-authored 16 publications appearing in venues such as Nature Biotechnology, Nature Genetics, Cell Systems, Genome Research, and Nucleic Acid Research.

During his free time, Haoyang enjoys playing acoustic guitar, drone photography and traveling.

Hari Somineni

Hari Somineni is a human geneticist by training interested in utilizing a multidisciplinary approach that combines the new tools of statistical genetics and genomics with classic population science and molecular biology to impact human health.

Prior to joining insitro, Hari was a statistical geneticist at Goldfinch Bio, leveraging whole-genome sequencing data to understand disease pathogenesis and identify novel targets for kidney diseases. Before that, he obtained a PhD in human genetics and molecular biology from Emory University, where he worked on genetics and genomics of inflammatory bowel disease. He obtained a master’s degree in Pharmacology and Toxicology from Wright State University, and then worked at Cincinnati Children’s Hospital Medical Center on genetics and genomics of asthma.

In his spare time, Hari enjoys hiking, long walks across the Charles River, and playing cricket.

Hervé Marie-Nelly

Hervé is a very enthusiastic applied mathematician with strong research interests in molecular and cell biology. His current work at insitro is focused on the development of new computational methods to generate and extract valuable insight from imaging experiments.

Hervé earned a Master of Machine Learning, Computer Vision and applied mathematics from the Ecole Normale Supérieure in Paris before pursuing a PhD at the Pasteur Institute, where he spearheaded the development of FISH-Quant, a software to quantify 3D microscopy images of single RNA molecules, and GRAAL, a Bayesian genome assembler using HiC data. As a postdoc in Robert Tjian’s lab at UC Berkeley, he developed computational methods to improve the quantification of high resolution microscopy data for insight into transcription regulation and spatial genomic organization.

During his free time, Hervé enjoys hiking and giving autographs pretending he is a famous NBA player.

Jeevaa Velayutham

Jeevaa is a Computer Science masters student from University of Toronto. At insitro, Jeevaa focuses on building machine/deep learning models to improve the effectiveness of our high-resolution cellular microscopy platform.

He completed a BSc. in Applied Physics at the University of Toronto before continuing MSc. in Applied Computing at the same institution. He developed an interest in ML during his bachelors which motivated him to pursue it further. During his masters, he worked on various deep learning projects ranging from biomedical image quality enhancement to accent style transfer.

Jeevaa enjoys playing and watching soccer, traveling and spending time with friends.

Kara Liu

Kara Liu is a computer scientist with an interest in applying machine learning methods towards biology and healthcare systems. At insitro, her research focuses on generalizable modeling and uncertainty estimation.

She obtained her BA in Computer Science at UC Berkeley, where she conducted research at Berkeley Artificial Intelligence Research (BAIR) Laboratory under Professor Pieter Abbeel. Her prior research experience was in robotic manipulation using visual planning, data generalization, and deep generative models.

When she’s not coding, Kara enjoys going on runs, exploring the outdoors, reading, and learning to play the guitar.

Michael Bereket

Michael is a computer scientist with a strong interest in the development and application of computational techniques for scientific discovery. His current work at insitro is focused on deriving insights from patient data to help guide disease modeling efforts.

Michael studied computer science at Stanford University, where he had the opportunity to contribute to research across machine learning and medicine.

In his free time, Michael loves to dance, run, learn new things, and joke around with friends.

Mohammad "Muneeb" Sultan

Mohammad “Muneeb” Sultan is a computational chemist with experience working at the interface of computational biophysics, free-energy methods, machine learning, and statistical mechanics. His current work at insitro is focused on building up the machine learning platform, and designing novel methods for analyzing the outputs of various high throughput assays.

Muneeb is a native of Pakistan and grew up in the city of Rawalpindi. He got his undergraduate degree in Chemistry at Yale, and his PhD in Physical Chemistry at Stanford under Vijay Pande. At Stanford, Muneeb focused on studying oncogenic kinases using the Folding@home distributed computing platform, collecting and analysing some of the largest simulation datasets of their kind. Simultaneously, he also worked on developing new Machine learning algorithms for accelerating free-energy calculations and molecular simulations. Muneeb has co-authored 17 publications appearing in venues such as PRE, PNAS, Nature Scientific Reports, and Nature Chemistry.

During his free time, Muneeb likes to powerlift, do yoga, explore the bay area, create digital art, cook, and listen to music.

Ralph Ma

Ralph Ma is a software engineer experienced in developing, maintaining and serving machine learning models. His current work at insitro is focused on improving experimentation and serving platforms to better accommodate the scale and uniqueness of the biological data collected.

After graduating with a B.S. in Computer Science from Stanford, Ralph worked at Google using machine learning to improve labeling of places with low impressions. Also at Google, he constructed embedding models to assist efforts in automating web actions by better understanding semantics of UI elements.

In his free time, Ralph enjoys rock climbing, fishing, and winning his fantasy football league.

Shahin Mohammadi

Shahin is passionate about the challenges and promises of interdisciplinary research. He is a firm believer in collaborative research and teamwork, and he advocates for the open-science/open-source movement. His primary interest is to build robust, reproducible, and interpretable models inspired by, grounded in, and driven by core biological knowledge. He develops computational models and methods, mathematical formulations, and rigorous statistical techniques for problems at the intersection of computational, biological, and clinical sciences, including single-cell analysis.

Shahin is an LGBTQIA right activist and works hard to educate himself and others on gender expression, identity, and equality. Additionally, he seeks to challenge the current, unfair, view(s) on mental health issues and improve support and understanding for affected individuals.

Srinivasan Sivanandan

Srinivasan is an MSc in Applied Computing candidate in the Department of Computer Science at the University of Toronto. His research interests are machine learning and image processing for computer vision and healthcare applications. His work at insitro involves developing learning models for the efficient analysis of our high-throughput microscopy platform.

During his Master’s program at the University of Toronto, he has worked on various machine learning projects, such as early prediction of sepsis onset and adversarial imitation learning by planning. Prior to this, he was a Lead Engineer in the Medical Imaging team at Samsung Research, India working on ultrasound imaging applications. Srinivasan holds a dual degree (Bachelors + Masters) in Biotechnology and Biochemical Engineering from the Indian Institute of Technology, Kharagpur.

In his free time, Srinivasan enjoys singing, hiking and reading books.

Sumit Mukherjee

Sumit is an applied machine learning researcher with academic training in computational biology. At Insitro, Sumit works in the statistical genetics team as a Senior Machine Learning Engineer.

Sumit’s expertise spans a broad range of topics in machine learning for healthcare, including computational method development for biological assays, multi-modal data integration, and development of methods for privacy preserving generative modeling.

Prior to Insitro, Sumit was a Senior Applied Scientist in the AI for Good Research Lab at Microsoft. Sumit did his PhD in Electrical & Computer Engineering at the University of Washington, focusing on algorithm development for single cell RNA-Seq data.

Outside of work, Sumit enjoys hiking, improv, and spending time with family.

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

Tom is a statistical geneticist and data scientist excited about integrating genomic and deep
phenotypic data to impact human health.

Prior to joining insitro, Tom was a statistical geneticist and computational biologist at Goldfinch
Bio, conducting genetic association studies in large-scale sequencing cohorts to identify
common and rare variants that associate with a rare kidney disease, focal segmental
glomerulosclerosis (FSGS). To enable target identification and validation at Goldfinch, he also
analyzed scRNAseq data of human organoids and tissues, and analyzed image data for cellular
assays. Before that, he examined the effect of early-life adversity on psychopathology and DNA
methylation, as well as conducted genetic association studies of depression and cortical
thickness at the Center for Genomic Medicine at the Massachusetts General Hospital. Tom
holds a PhD in Psychology with a focus on Population Genetics from the University of
Washington where he studied the effect of satellite image-derived landscape features on
dispersal of an ant species.

In his spare time, Tom enjoys hiking, camping, sailing, and traveling with his family.

Tommaso Dreossi

Tommaso Dreossi is a computer scientist with a strong passion for applied machine learning and theoretical computer science.

Prior to joining insitro, Tommaso was an Applied Scientist at Amazon Search building machine learning models for Amazon’s search engine and a postdoctoral researcher at UC Berkeley working on reliable computer vision systems for autonomous vehicles.
He obtained his Ph.D. in Computer Science in joint supervision from the University Joseph Fourier, Grenoble, France and the University of Udine, Italy with a thesis on the analysis of dynamical systems.

In his free time, Tommaso enjoys rock climbing, sailing, riding bicycles and motorcycles.

Xueya Zhou

Xueya is a computational biologist with particular interests in making sense out of human genetic variations and linking them to diseases. At insitro, Xueya applies his expertise in human genetics to guide the in vitro disease model development and drug targets discovery.

Prior to joining insitro, he was a postdoc in Columbia University, developing computational approaches and bioinformatics workflows to analyze large cohorts of developmental disorders. Before that, he worked on applying quantitative approaches to study complex traits in the University of Hong Kong. Xueya received a BS in engineering and PhD in bioinformatics from Tsinghua University, China.

During his free time, Xueya enjoys outdoor activities with his family and friends.

Zachary McCaw

Zachary is a biostatistician whose research interests include statistical genetics, ML-phenotyping, and clinical trials. At Insitro, he works with the Data Science and Machine Learning Team.

He completed his Ph.D. in Biostatistics with Professor Xihong Lin at Harvard. His dissertation developed “Transformation and Multivariate Methods for Improving Power in Genome-Wide Association Studies.” He has conducted research with Professor Hilary Finucane at the Broad Institute on cross-population fine-mapping, with Professor Lee-Jen Wei at Harvard on clinical trials, and with Professor Steven Kleeberger at the NIEHS on respiratory genetics. Prior to joining Insitro, he spent 2 years as a Data Scientist at Google, where he worked on causal inference and genomic discovery for ML-derived phenotypes.

In his free time, Zachary enjoys running, biking, swimming, and playing volleyball.

Zack Phillips

Zack is an energetic imaging system builder, with expertise spanning mechanical engineering, optical system design, and inverse problems for computational imaging. His current work at Insitro focuses on creating programmable hardware which allows dynamic high-content imaging of the cellular microenvironment, and leveraging these to maximize performance of ML pipelines.

Zack earned his BS with Honors in Applied Science (Biomedical Engineering) at UNC Chapel Hill before pursuing a PhD in Applied Science with Laura Waller at UC Berkeley. Working closely with Lei Tian and other collaborators, Zack’s imaging research has spanned smartphone microscopy (Computational CellScope), high-throughput fluorescence imaging using motion deblurring, super-resolution microscopy (Fourier Ptychography), and quantitative phase microscopy in 2D and 3D (Differential Phase Contrast). He prides himself on creating harmonious computational models and physical hardware, which drives rapid development and deployment of novel imaging systems. Prior to joining Insitro, Zack worked as an optical engineer in Apple’s Exploratory Design group where he contributed to several exploratory projects, and co-founded SCI Microscopy as a PhD Student.

In his free time, Zack enjoys surfing, traveling, and woodworking.