Yash Patel

Home | Selected Papers | Mentoring | Projects | Blog

Hi! I’m an incoming research engineer at Harmonic. I have previously worked as a research fellow at Anthropic, a senior software engineer at Meta, and a software engineering intern at Waymo and studied at the University of Michigan (PhD) and Princeton University (BA).

I have significant coding experience in C++, Python/PyTorch, OpenGL/GLSL, OpenCL, and Unity and research experience in uncertainty quantification, robust/convex optimization, rare-event detection, control theory, and PDE surrogate modeling. I am most excited in working on using ML for accelerating scientific discovery.

Harmonic Jan 2026 – Present

Research Engineer

Anthropic Nov 2025 – Present

Research Engineer, AI Safety Fellowship

Working on the safety of frontier models on the Frontier Red Team. Selected as one of 32 researchers out of 2,000+ applicants.

University of Michigan Sep 2021 – Dec 2025

PhD in Statistics · Ambuj Tewari

My research focuses during my PhD were on principled uncertainty quantification, robust decision-making, and AI for Science. Given the importance of uncertainty in evaluating scientific hypotheses, my initial work primarily centered around one core question: How can we design principled uncertainty estimates for black-box models and use such uncertainty optimally for decision-making? Some highlights:

Conformally Robust Decision Making [PhD Thesis]
Outstanding First-Year Ph.D. Student Award (2022)
Outstanding GSI Team Award (2022): Helped create the introductory deep learning statistics course
NSF GRFP Honorable Mention (2020, 2022)

Waymo Jun 2025 – Sep 2025

Data Science PhD Intern, Simulations · Aman Sinha

Implemented an ADMM-based distributed convex optimization algorithm in C++ for importance sampling of rare events to achieve a 20x speedup in the simulations pipeline.

Bose Jan 2025 – Jun 2025

Machine Learning Research Co-op · Russell Izadi, Shuo Zhang

Implemented SAC and PPO methods for adaptive-FIR noise cancellation (PyTorch). Developed novel transformer-based approach for Wiener filter adaptation that outperforms FxLMS (10% dB reduction). Performed linear system identification and analyzed transfer functions to assess ML filtering.

Meta Jul 2018 – Sep 2021

Senior Software Engineer (IC5) · Albert Parra Pozo

At Facebook, I worked on a number of projects, generally in 3D rendering and reconstruction. Some highlights:

Designed and implemented novel real-time (72 FPS) novel dynamic object reconstruction algorithm for 300k+ vertex meshes in Unity HLSL/C# based on linear-blend skinning (LBS)
Implemented real-time (72 FPS) point cloud, dense mesh, and TSDFs (KinectFusion) scene reconstruction & rendering on HMDs & lenticular displays with C++/OpenGL/GLES/OpenCL.
Implemented deep learning model (PyTorch) and optimized via Qualcomm SNPE & QAT to run at 30 FPS on Qualcomm SoC for Portal platforms. Added translation support for quantized nodes in PyTorch-JIT to Caffe2.
Added distributed rendering with Docker, RabbitMQ, and Kubernetes to Manifold camera (code). Reduced depth estimation time by 30%.

Princeton University Sep 2014 – May 2018

A.B. in Mathematics · Matt Weinberg
Certificates in Applications of Computing, Statistics & ML

My interests over undergrad meandered through many areas. Some highlights:

HyperLoop Pod Design [Project Report]

Princeton University Project Founder & Lead, 2015-2017

2x Top 30 Team, International SpaceX HyperLoop Pod Design Competition

Deanonymizing Bitcoin Transactions: An Investigative Study On Large-scale Graph Clustering [Senior Thesis]
Tesla Autopilot Analysis [Project Report]
Neural Branch Predictor [Website] [Code]

Polymarket Jan 2018 – Jun 2018

Early-Stage Developer · Shayne Coplan

Worked on core pre-ICO development, integrating Bancor protocol liquidity and exchanges with the primary TokenDAO in Solidity (Truffle.js, testrpc, geth).

Amazon Jun 2017 – Aug 2017

Software Engineering Intern

Built Java Spring MVC debugging service for Kiva Picking Optimization team. Deployed globally via AWS (EC2, S3, SNS).

OpenLoop Jan 2015 – May 2016

Co-founder & Princeton University Lead

Co-founded a coalition of six top universities (OpenLoop), raised >$150,000, and built an 18 ft functional pod selected as one of 30 teams in the International SpaceX HyperLoop Pod Design Competition (Pod).

Columbia University May 2015 – Aug 2015

Research Intern · Abdulrahmen El-Sayed

Developed and simulated agent-based models of self-efficacy dynamics for sexual minority populations enrolled in exercise coach programs (code).

Princeton Plasma Physics Lab Jun 2013 – Mar 2014

Research Intern · Ilya Dodin

Developed FDTD numerical simulations in C++/Python of the Vlasov equation (reference) to study plasma evolution (video).

Rutgers University Jun 2012 – Aug 2012

Research Intern · Michael Shiflett

Prepared brain slices and performed data analysis to investigate the role of axonal guidance in the social withdrawal of mice with NRP2 gene mutations.

Selected Papers

My work has largely focused on developing methods with end-to-end statistical guarantees to create reliable machine learning systems and layering robust decision-making on top of such uncertainty estimates, especially for scientific applications. My projects largely split into three headings: uncertainty quantification methodology, robust decision-making, and AI for Science.

Uncertainty Quantification Methodology

Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation [Code][Poster]
Neural Information Processing Systems (NeurIPS), 2025
Rivera, E.O.* , Patel, Y.* (* equal contribution), Tewari, A.

Variational Inference with Coverage Guarantees in Simulation-Based Inference [Code]
International Conference on Machine Learning (ICML), 2024
Patel, Y., McNamara, D., Loper, J., Regier, J., Tewari, A.

Robust Decision-Making

Conformal Contextual Robust Optimization [Code][Poster]
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 (Oral)
Patel, Y., Rayan, S., Tewari, A.

Conformal Robust Control of Linear Systems [Code]
In Submission
Patel, Y., Rayan, S., Tewari, A.

Non-Parametric Conformal Distributionally Robust Optimization
ICML Workshop on Structured Probabilistic Inference & Generative Modeling, 2024
Patel, Y., Cao, G., Tewari, A.

AI for Science

Continuum Transformers Perform In-Context Learning by Operator Gradient Descent [Code]
In Submission
ICLR AI for Accelerated Materials Design Workshop, 2025
Patel, Y.*, Mishra, A.* (* equal contribution), Tewari, A.

Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization [Code]
In Submission
Patel, Y.*, Subedi, U.* (* equal contribution), Tewari, A.

A Greedy PDE Router for Blending Neural Operators and Classical Methods [Code]
In Submission
Rayan, S., Patel, Y., Tewari, A.

Diffusion Models for Probabilistic Deconvolution of Galaxy Images [Code]
ICML Machine Learning for Astrophysics Workshop, 2023
Li, Y., Xue, Z., Patel, Y., Regier, J.

RL Boltzmann Generators for Conformer Generation in Data-Sparse Environments [Code][Poster]
NeurIPS Machine Learning in Structural Biology (MLSB) Workshop, 2022
Patel, Y., Tewari, A.

Scalable Bayesian Inference for Finding Strong Gravitational Lenses [Code][Poster]
NeurIPS Machine Learning and the Physical Sciences Workshop, 2022
Patel, Y., Regier, J.

Patents

Holographic Calling for Artificial Reality
US Patent App. 17/360,693
AP Pozo, J Virskus, G Venkatesh, K Li, SC Chen, A Kumar, R Ranjan, BK Cabral, SA Johnson, W Ye, MA Snower, Y Patel.

Mentoring

During my PhD, I have also had the opportunity to mentor the following fantastic undergraduate and master’s students on their theses and research projects.

Guyang (Kevin) Cao (Next step: Ph.D. in Computer Science at University of Wisconsin-Madison)
Honors Thesis, 2023-24
Undergraduate Research Program in Statistics, 2023
Non-parametric Conformal Distributionally Robust Optimization
Zhiwei Xue (Next step: Ph.D. in Computer Science at National University of Singapore)
Undergraduate Research Program in Statistics, 2023
Diffusion Models for Probabilistic Deconvolution of Galaxy Images
Yuhang Li (Next step: Master’s in Computer Science at University of Illinois, Urbana-Champaigna)
Undergraduate Research Program in Statistics, 2023
Diffusion Models for Probabilistic Deconvolution of Galaxy Images
Zhong Zheng (Next step: Master’s in Computational Data Science at Carnegie Mellon University)
Undergraduate Research Program in Statistics, 2023
Atomic Maps Reconstruction for Cryo-EM Data with Continuous Heterogeneity