About
Leading AI R&D at a stealth AI-for-physics startup. Previously at NVIDIA, IBM Research, and the Department of Energy's national laboratories.
At NVIDIA: led agentic GPU kernel development and optimization for frontier AI lab workloads, and drove performance evaluation strategy for CuTile, a next-generation GPU programming model.
At IBM Research: led IBM's enterprise generative AI coding assistant from research to production — 8-person team within a 60+ person cross-functional effort across IBM and Red Hat. Earlier led HPC systems work on Summit and Sierra (world's #1 and #2 supercomputers at launch), supporting large-scale scientific simulation workloads at DOE national laboratories. Founded NYU Courant's first graduate course on high-performance ML during this period, still taught today.
Before IBM: built distributed runtime systems at PNNL that spun off into Trovares (now RocketGraph).
Ph.D. in Computer Architecture, UPC Barcelona. IPDPS Best Paper Award (2012). 30+ publications, 15 patents.
Education
- Ph.D. in Computer Architecture — Polytechnic University of Catalunya (UPC), Barcelona
- M.Sc. in Computer Engineering — University of Rome Tor Vergata, Rome
- B.Sc. in Computer Engineering — Roma Tre University, Rome
Featured Work
CuTile Programming Model
AI-driven GPU kernel development and next-generation programming model for production workloads.
Watson Code Assistant
Led creation of the first generative model for IBM's AI-powered code assistant product.
Summit & Sierra Supercomputers
System software for the world's #1 and #2 fastest supercomputers (2018 TOP500).
Awards
- IBM Outstanding Technical Achievement Award (2023) — For leading the creation of the first generative model for Watson Code Assistant
- IBM Research Division Award (2022) — For contributions to AI for code
- HPCwire Editors' Choice Award (2018) — For Summit supercomputer
- PNNL Outstanding Performance Award (2015) — For contributions to extreme-scale computing
- IPDPS Best Paper Award (2012) — For research on scaling irregular applications on massively multithreaded systems
- HiPEAC Paper Award (2010) — For research on TLB misses in chip multiprocessors
In the Press
CUDA Tile Programming Model (2025)
- NVIDIA Introduces CUDA 13.1 with CUDA Tile — InsideHPC
- NVIDIA CUDA 13.1 Powers Next-Gen GPU Programming — NVIDIA Developer Blog
AI for Code / Watson Code Assistant (2022–2023)
Summit & Sierra Supercomputers (2018)
- Two DOE Supercomputers Top List of World's Fastest — U.S. Department of Energy
- Sierra Honored as Top Supercomputing Achievement — LLNL / HPCwire
- Summit Supercomputer Is Already Making Its Mark on Science — HPCwire
Graph Analytics (2014–2019)
Selected Papers
For a full list, see Google Scholar.
AI and Machine Learning
- Towards Learning (Dis)-Similarity of Source Code from Program Contrasts
ACL 2022 - VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
SANER 2022 - D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
ICSE-SEIP 2021 - Probing Model Signal-Awareness via Prediction-Preserving Input Minimization
ESEC/FSE 2021 - Exploring Software Naturalness through Neural Language Models
ArXiv Preprint, 2020
High Performance Computing and Systems
- In-Memory Graph Databases for Web-Scale Data
IEEE Computer, 2015 - Scaling Semantic Graph Databases in Size and Performance
IEEE Micro, 2014 - Scaling Irregular Applications through Data Aggregation and Software Multithreading
IPDPS 2014 — Best Paper Award - Evaluating the Impact of TLB Misses on Future HPC Systems
IPDPS 2012 - A Quantitative Analysis of OS Noise
IPDPS 2011
Writing
Tuning Flash Attention for Peak Performance in NVIDIA CUDA Tile
A deep dive into optimizing Flash Attention on Blackwell GPUs using cuTile, achieving 1.60-1.66x speedups through loop splitting, block remapping, and autotuning.
Read on NVIDIA Developer Blog →Generation Is Cheap. Verification Is Not.
Why complex software will survive the AI shock. The hard part was never writing the code — it was knowing whether the code was right.
Read on LinkedIn →The Physics of AI Systems: Four Fundamental Laws
AI systems have their own physics. Not metaphorical: actual constraints rooted in mathematics and the physical properties of silicon, light, and electrons.
Read on LinkedIn →Intelligence Entails Ethics
The paperclip maximizer is a logical contradiction
Read on Substack →Get in Touch
Interested in collaborating on AI systems, GPU computing, or high-performance infrastructure? I'd love to hear from you.