About
Hi there, I’m Sebastià Agramunt Puig, a Software & AI Engineer based in the San Francisco Bay Area (California). I grew up in a small mediterranean town not too far from Barcelona where I went to college. I studied Physics at Universitat Autónoma de Barcelona and continued my academic life with a PhD in theoretical electromagnetism in the same university, working on magnetic levitation with superconductors and magnetic recording with nanoscale magnets (download dissertation here). Along the way I’ve authored 12 peer-reviewed publications and hold 1 patent — you can find them all on ORCID.
My interests are wide but there are always two common denominators, software development and mathematics. Over more than ten years in industry I’ve worked on routing algorithms, privacy-preserving machine learning, and high-performance computing, comfortable both leading small teams and working independently. I currently work at Eikon Therapeutics as a Staff Software Engineer, where I write CUDA algorithms for protein detection and localization in high-throughput drug screening. I’m particularly interested in backend engineering, high-performance computing, CUDA programming, and LLM inference, and I’m always happy to talk about opportunities in those areas.
The blog
Over the years I’ve been keeping notes about topics I like to learn and have been useful in my professional life. The idea for this blog is to keep a log for myself as well as share with other people that may have similar interests to mine. Please, reach out if you have questions about the content or find errors in the posts.
Resume
Experience
Staff Software Engineer Current
- Implemented protein detection and localization algorithms in pure CUDA, with Python bindings and CI/CD for x86 and ARM, achieving a 100x speedup processing 1.5GB movies.
- Contributed to building the data processing pipeline for single-molecule tracking, including image preprocessing algorithms, PostgreSQL database design, and distributed computing.
- Trained and served image segmentation models (U-Net).
- Contributed CI/CD improvements across the company's entire stack and advocated for best practices in testing and artifact publishing.
- Implemented the physics simulation of Brownian motion for proteins in an in-house simulation tool.
Privacy Preserving AI Researcher Contractor (Remote)
- Coauthored a report on the state of the art in privacy-preserving machine learning with Germany's Federal Office for Information Security.
- Focused research on threats associated with transfer learning, including backdoor and adversarial poisoning attacks.
AI Engineer and Privacy Preserving Machine Learning Lead
- Initiated and led the company's Privacy Preserving Machine Learning initiative from scratch.
- Acquired a deep understanding of the mathematics behind Differential Privacy, Secure Multi-Party Computation (SMPC), and Fully Homomorphic Encryption (FHE).
- Developed a proof-of-concept for Federated Learning with Secure Aggregation (an SMPC technique) and led a small team to evolve it into a functional mobile product.
- Refactored code to transition a neural collaborative filtering recommender system into production.
Research Scientist, Privacy in Machine Learning
- Developed an "Introduction to Cryptography" MOOC covering mathematical foundations and Python implementations, reaching over 7,000 students worldwide.
- Researched secure inference on secretly shared machine learning models, focusing on activation function approximation within algebraic rings.
AI & Routing Algorithms Engineer
- Designed and implemented the company's core routing algorithm from scratch, combining simulated annealing, depth-first search, and Dijkstra's algorithm, following an extensive literature review.
- Built fast, reliable software to deploy the routing algorithm in production.
- Analyzed urban demand patterns using machine learning techniques.
- Developed a simulator to evaluate the performance of the routing algorithms.
Data Science Consultant
- Utilized ARIMA family models for sales forecasting across various markets for a large cosmetics firm.
- Created and managed a PostgreSQL database, handling data in formats such as CSV and Excel.
- Developed automation scripts in Bash and R integrated with CRON jobs to streamline data loading.
Fellow
- Acquired fundamental data science skills covering SQL, Hive, R, and various machine learning models.
- Analyzed New York City taxi data to predict pickup probabilities by time and location within Manhattan, using Bayesian statistics and Random Forest.
Postdoctoral Researcher
Postdoctoral Researcher
Tech Stack
Contact
Email: contact[@]agramunt[dot]me
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