Aneesh Pappu
Founding Research Advisor at Stealth Startup
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolAneesh Pappu is a PhD student at Stanford and Knight-Hennessy scholar who researches AI safety, privacy, and security while advising and angel-investing across deep tech startups. He brings a rare blend of academic rigor and industry impact from roles as a Research Scientist at Google DeepMind and contributions to open-source scientific ML (e.g., work on DeepChem for drug discovery). His background spans machine learning, public policy (Marshall Scholar at UCL and Cambridge), and hands-on engineering at companies like Slack and Sisu Data, enabling him to bridge technical, product, and regulatory perspectives. Aneesh has influenced policy work cited by regulators and published multiple DeepMind papers on LLM privacy and security. He’s active in early-stage investing and advising where technical guidance materially accelerates teams, and he often applies low-data ML techniques from computational chemistry to practical ML problems. Based in Palo Alto, he combines deep research credentials with operator experience across startups, labs, and policy institutions.
11 years of coding experience
8 years of employment as a software developer
Running Start Student, Running Start Student at Washington State UniversityUniversity College LondonMaster of Philosophy - MPhil, Public Policy, Master of Philosophy - MPhil, Public Policy at University of CambridgePullman High SchoolDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Stanford University
Telugu, Spanish
Github Skills (10)
scikit-learn10
machine-learning10
drug-discovery10
deep-learning10
random-forest10
python10
data-science10
scikit10
biology9
pandas9
Programming languages (7)
Github contributions (5)
deepchem/deepchem
Jul 2016 - Nov 2016
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Role in this project:
Data Scientist
Data ScientistContributions:64 commits, 5 PRs, 6 comments in 3 months
Contributions summary:Aneesh focused on developing and implementing machine learning models for the NCI dataset. Their work involved creating scripts to train and evaluate Random Forest and DNN models using the deepchem library. They also made changes to the data loading process, and implemented a stratified splitting strategy, demonstrating a focus on data preprocessing, model training, and performance evaluation within the context of drug discovery and related fields.
deep-learningquantum-chemistrydrug-discoverybiologymaterials-science
apappu97/W-Me
May 2016 - Jun 2016
Contributions:39 commits, 6 pushes, 1 branch in 1 month
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