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.
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.
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.
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.
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.
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.
James is a Senior Lead Data Engineer, primarily working with distributed systems and big data applications. As part of the Data Engineering team, his goal is to facilitate the development of machine learning pipelines, focusing on scalability, efficiency and repeatability.
James was previously a Ph.D. candidate at Stanford University, where he devised robot motion strategies for the Honda Asimo and Mars rover prototypes. He holds a M.Sc. in Scientific Computing from Stanford and a B.Sc. in Mathematics From Texas A&M University. Prior to joining Insitro, James worked on creating bioinformatics tools at Roche and Helix, and he co-authored a book on big data architectures.
In his free time, he chases after his dog and three cats, enjoys playing board games, travels internationally, and hacks on open-source software projects.
Joe Marrama is a software engineer with background in heathcare data and distributed systems. At Insitro, Joe is focused on ensuring that the large amount of data we generate is effectively processed and stored.
Joe is a native of Oakland. He attended Stanford where he graduated with a BS in Symbolic Systems and a MS in Computer Science. Prior to Insitro, Joe worked at Nuna Health, where he worked with the federal Medicare program to build a system that evaluated healthcare providers on quality of service and cost-effectiveness. He loves to tackle complex engineering problems in the service of helping others.
Outside of work, Joe enjoys surfing, skiing, mountain biking, cooking, and reading.
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.
Matthew is a computer scientist with experience in developing and deploying machine learning models. His focus is on applying deep learning models to gain insights from imaging data.
Prior to insitro, Matthew acquired his M.S. in computer science from Stanford University. His graduate research focused on applying machine learning methods to infer diagnostic information from medical imaging, and applying natural language processing to efficiently gather data from unstructured reports. Additionally, he has extensive experience building scalable data infrastructure to support computationally intensive analysis of large datasets. He has worked in both an academic research setting and industry setting for companies such as Google and several startups.
In his free time, Matthew likes to catch up on reading, spend time with friends and family, and continue the unending quest to find a third hobby.
As VP of Data Engineering, Matt is responsible for leading the development of data pipelines, data storage systems, provenance tracking, and engineering infrastructure to support the high-throughput biology and Machine Learning teams at insitro.
Previously, as VP of Engineering for Myriad Genetics, Matt led engineering teams focused on software automation and genomic data pipelines to make high complexity genetic testing routine in clinical practice. During his time at Counsyl, Matt developed and scaled the software behind several successful prenatal genetic testing products.
Matt holds a Ph.D in Computer Science from MIT, where he developed efficient bioinformatic algorithms with applications in evolutionary genomics and population genetics.
In his spare time, Matt enjoys running, drawing, programming for fun and playing with his kids.
Michael is an MS candidate in computer science at Stanford University, where he has also received a bachelor’s degree in computer science. In his time at Stanford, Michael has completed numerous ML projects, including computer vision work to improve diagnosis from medical images and automated liquid-handling robots. In high school, he was introduced to medical research through internships at a Stanford allergy lab. Michael is excited to combine his passions for computer science and medicine to work on problems with significant human impact at insitro.
In his free time, Michael loves to dance, trail run, learn new things, and joke around with friends.
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 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.
Robin is passionate about working at the interface of biological and computational problems. She studied Bioinformatics as an undergraduate at UC San Diego, and recently completed her doctorate in Biophysics from Stanford, where she was a NSF and NVIDIA Fellow. Her PhD work focused on molecular dynamics simulation of protein-ligand binding, integrating computational methods development with interesting discoveries about specific proteins and drugs. During the course of her research, Robin worked on parallel algorithms, online analysis of large datasets, visualization methods, and contributed to the development of multiple related software packages, including AMBER and VMD.
In her free time, Robin likes to race bicycles, contribute to open source software, and scuba dive.