is led by world class pioneers at the intersection of data science and the life sciences, with extensive experience in applying machine learning to a range of biological problems and data sets.
Adam Riesselman is a computational biologist with experience in developing powerful, interpretable machine learning models for complex biological data. At insitro, Adam is focused on integrating high-throughput measurements with new scalable algorithms to understand disease.
Adam received a BA in Biochemistry: Cell and Molecular Biology from Drake University and his PhD in Biomedical Informatics at Harvard University with Debora Marks as a Department of Energy Computational Science Graduate Fellow. There he developed new statistical models for unsupervised mutation effect prediction from evolutionary data, de novo protein structure prediction via simulation, protein library design with improved biomolecular properties, and small molecule production optimization utilizing biosynthetic pathway engineering.
When not at the computer, Adam likes to cook and enjoy the outdoors by hiking, gardening, and biking.
Alice Starr is a software engineer, with experience designing and architecting scientific research applications. At insitro, Alice is focused on laboratory information and data architecture, making sure we make the most of our generated data. Prior to insitro, she was at Genentech, designing data solutions for the translational research and pathology organizations. She loves to tackle data challenges to advance science, which provides constant opportunities to learn.
Alice received a BS and MS in Mathematics and Computer Science from EPF, France and ITESM, Mexico.
When not working or playing with her two daughters, Alice likes to read and be outdoors as much as possible, walking or running on the beautiful California trails.
Chris Probert is a computational biologist and computer scientist with extensive experience building deep learning models for genomic data. His current work at insitro is focused on enabling large-scale deep learning on functional genomic data produced by the high throughput biology platform.
Chris is a PhD candidate in Computational Biology (Genetics) at Stanford working with Anshul Kundaje and Christina Curtis where he was an NSF Fellow and an Accel Innovation Scholar. His PhD work focused on large-scale machine learning analysis of functional genomics datasets, including imputation and superresolution of genome-wide epigenomic signals, unsupervised methods for learning differentiation lineages in single-cell RNA-seq, and tissue of origin inference from cell-free DNA fragmentation patterns. He has extensive engineering experience building scalable infrastructure and data architectures to support distributed training of deep learning models from petabyte-scale functional genomic datasets. He also holds an MS and BS in Computer Science, and experience working in both research and product focused software engineering roles at Google, Illumina, and Counsyl.
Outside of work, Chris enjoys running, cycling, backpacking, and backcountry skiing.
Dave did his PhD in biophysics at UCSF and his postdoc at UC Berkeley. He spent 5 years as a high-performance research computing expert at Lawrence Berkeley Labs and at Genentech, where he performed research into Computational Grids including grid system administration and scientific workflow management, to enable scale-out of large computational biology workflows. This was followed by a decade as a Staff Software Engineer at Google, where he was the Founder and Tech Lead of Google Cloud Genomics, and the Founder and Tech Lead of Exacycle, a high-performance platform that enabled scientists to leverage Google’s extensive computational resources to execute and publish state-of-the-art research in protein design, drug discovery, and genomics. He is an author on 27 papers (Nature Chemistry, Biochemistry) and 4 patents.
In his spare time, Dave likes to build cool devices, including microscopes.
Eilon Sharon is a senior data scientist and computational biologist with extensive experience in applying machine learning to decipher various biological questions. Eilon’s work at insitro integrates observations from large population-level studies, such as GWASs, with results from various high throughput in-vitro assays to identify potential drug targets.
After completing a dual major B.Sc. in biology and computer science at TAU, Eilon joined Rosetta genomics, where he worked on discovering miRNA genes in human 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 outdoor with his family.
At Insitro, Paolo works within the Data Science and Machine Learning Team, where he applies his academic training in statistical genetics, computational biology and machine learning to identify and characterize functional mechanisms in human disease.
Previously, he was a postdoctoral researcher at Microsoft Research New England, working on automated machine learning and on deep learning models for imagining genetics. Before that, he obtained a PhD in statistical genetics from the University of Cambridge and the EMBL-European Bioinformatics Institute, where he developed new methods for genetic association studies and contributed to international projects such as the last phase of the 1000 Genomes Project and the Blueprint initiative. Previously, he obtained a bachelor’s and master’s degree in physics from the University of Naples, Italy.
In his spare time, Paolo enjoys playing soccer, powerlifting, motorcycling and travelling.
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.
Kathryn is a data engineer with a background in the microbiome and cloud computing. At insitro Kathryn is working with the data engineering, and data science and machine learning teams to build and scale data analysis pipelines in the cloud.
Before joining insitro, Kathryn worked at Second Genome building metagenomics pipelines and analyzing microbiome data. Kathryn received her Master’s in Bioinformatics from the University of Michigan and worked in Pat Schloss’ lab on projects in microbial ecology, clostridium difficile, and colorectal cancer.
In her free time, Kathryn enjoys playing and watching hockey, traveling, and scuba diving.
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.