I'm a Founding Engineer at Weco AI, building self-improving AI agents for engineers and scientists (such as aideml, AIDE). My work has been used by OpenAI (MLE-Bench, GPT-4.5, o1, o3-mini), Meta (The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements, AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench), Google (MLE-Star), Microsoft (R&D-Agent), Amazon (MLZero), Sakana AI (AI Scientist-v2), METR (RE-Bench, Measuring AI Ability to Complete Long Tasks), UK AI Security Institute (Inspect AI), CMU (TimeSeriesGym, CodePDE), Stanford (MLE-Dojo), UC Berkeley (IDA-Bench) as well as tranditional media (1, 2 & 3).

Prior to this, I was a researcher at the Robotics Institute at Carnegie Mellon University working on large multimodal models, robotics and time-series for healthcare (such as AutonFeat, BIFURC). I also spent time at TTIC and UChicago Booth working on fairness and alignment for vision models (such as ASAC Guided Curriculum Learning).

I enjoy building high-performance implementations of my work and applying AI to healthcare, climate, scientific problems. Outside of research, I enjoy spending time outdoors hiking, surfing, and playing golf.

• AIDE: AI-Driven Exploration in the Space of Code [code] [readme] [project] [paper]
• Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement [paper]
• AutonFeat: A high-performance Python package for automatic and distributed time series feature extraction and selection [code] [readme] [docs]
• tsfresh: A time series feature extraction package [code] [readme] [docs]
• Denoising Diffusion Probabilistic Models (DDPMs) [code] [paper]
• Resource-conscious high-performance models for 2D-to-3D single-view reconstruction [code] [readme] [paper]
• pynn (Deep Learning Framework with pure NumPy Computation) [code] [readme] [demo]
• VanillaGAN (Vanilla GAN experimentation library) [code] [readme]
• ConditionalGAN (Conditional GAN experimentation library) [code] [readme]
• DCGAN (DC GAN experimentation library) [code] [readme]
• PSNRGAN (Contrastive PSNR GAN experimentation library) [code] [readme]
• Monocular Depth Estimation [code]
• Neural Artistic Recreation (A CLI for Neural Style Transfer) [code] [readme] [demo]

• Bifurcation identification for ultrasound-driven robotic cannulation (Oral) [paper]

• HuggingFace Text Generation Inference [code] [readme] [docs]
• CUDANN (Distributed Deep Learning Framework accelerated on NVIDIA GPUs) [code] [readme]
• FastConv (Multithreaded GPU CUDA kernels for Convolution) [code]
• Ray Tracing (using Monte-Carlo Algorithm on GPU Accelerators) [code] [readme]
• Advection Diffusion Simulation (Multithreaded, Shared Memory Parallelism, Distributed Memory Parallelism) [code] [readme]
• N Body Particle Simulation (Multithreaded, Shared Memory Parallelism, Distributed Memory Parallelism) [code] [readme]
• Distributed Poisson Solver (using Conjugate Gradient Optimization) [code] [readme]
• Image Editor via Convolutions (using Work Stealing and Work Balancing parallel schemes) [code] [readme]
• Sparse Linear Solver (using the Map Reduce parallelism scheme with Conjugate Gradient Optimization) [code] [readme]
• Image Editor via Convolutions (using Pipeline and BSP parallel schemes) [code] [readme]
• Twitter Application (using Producer-Consumer parallelism model) [code] [readme]

• Boosting and Boostability (An understanding through Computational Learning Theory) [code] [paper]
• Financial Forecasting of TSLA (using sentiment analysis, feature engineering, and RNNs) [code] [readme] [report]

• GoLLUM - A GoLang Compiler [code] [readme] [docs]
• Unix Shell [code] [readme]

• Climbing Mount Everest (A text-based adventure game) [code] [readme]

• Useful Algorithms [code] [readme]
• Superconductor Regression (Kaggle Challenge) [code] [report]
• Canvas Web Application [code] [readme]
• Blood Donations Database [code] [readme]