AI · ML · Data Science
Alejandro Rodriguez
AI Research Scientist / Data Scientist working at the intersection of artificial intelligence, machine learning, and their application to scientific fields.
About
Who I am
I am a data scientist and machine learning engineer with a fascination for the way the world works. In today's world, we are flooded with data about every aspect of our lives — but data alone tells us nothing. I find immense enjoyment in unveiling relationships between data and capturing new insights from studying it. With multiple years of AI research and software engineering experience, as well as several publications and certifications, I work at the frontier of machine learning applications in scientific fields, such as marketing science and physics.
Education
M.S. Computer Science (Data Science Specialization)
University of Massachusetts, Amherst, 2023
B.S. Mathematics — Computer Science
University of California, San Diego, 2022
Experience
Work history
Aug 2025
— Present
Data Scientist - Incubation Lab
NIQ (Nielsen IQ) · Madrid, Spain
Researching new methodologies to apply to NielsenIQ's product offerings. Developing new internal tools for NielsenIQ consultants and Data Scientists. Performing personalized case studies for clients that require methodologies not offered by NielsenIQ's core product. Clients include: Google, The Coca-Cola Company, Meta.
Jun 2025
— Present
Research Scientist (part-time)
University of California, San Diego · Remote
Assisting Professor Floor Broekgaarden in applying machine learning methodologies to gravitational wave paleontology efforts. Also providing software development support for tools and projects. Projects include STROOPWAFEL 2.0: testing and documenting a rebased version of STROOPWAFEL (Broekgaarden et al.), a simulation system for identifying rare outcomes from astrophysical populations related to gravitational wave sources.
Jun 2023
— Jan 2025
Machine Learning Engineer
Dioscuri Capital · Remote (California, USA)
Developed a cutting-edge few-shot semi-supervised learning (SSL) model for detecting volatility contraction patterns in trading data over variable time scales (4-64 weeks), achieving superior detection accuracy. Prototyped these models in an algorithmic investment bot achieving a 40% gain over benchmarks in Alpha and Sharpe ratio. Also prototyped a multi-modal RAG system built on LlamaIndex & GPT-4o to support quant researchers in financial analysis tasks.
Sep 2023
— Dec 2023
Teaching Assistant
UMass Amherst — Manning College of Information & Computer Sciences · Amherst (Massachusetts, USA)
Held office hours and graded assignments for Prof. Matthew Rattigan in the "Data Analytics with Python" class for 50 students. Topics included Python, web APIs, SQL, Pandas, visualization, Scikit-Learn, supervised & unsupervised learning, causality and model evaluation.
Jun 2022
— Jan 2023
Data Scientist Intern
Cleat Street · San Diego (California, USA) + Remote
Led algorithm design and software development of a fully-automated end-to-end data science workflow for prediction of NBA player performance, across 5 million data points per season. Contributed to a 3.0x increase in YoY trading volume and 1.7x increase in trading ROI, with a 30% reduction in trading volatility.
Sep 2020
— Jun 2022
Undergraduate Research Assistant
UC San Diego — Computer Science & Engineering (CSE) Department · La Jolla (California, USA)
Conducted research in Spatio-Temporal Multi-Resolution Tensor Learning algorithms from spatio-temporal data. Led development of an ST-MRTL model generating dynamic shooting profiles of NBA players. Presented findings at the MIT Sloan Sports Analytics Conference.
Oct 2019
— Jun 2022
Software Developer
Triton Software Engineering · San Diego (California, USA)
Developed pro-bono websites for nonprofits including UWEAST Community Kitchen and Sakyadhita. Functionality included customer-facing interface, administrator tools for content creation, and back-end infrastructure.
Oct 2018
— Jun 2019
Associate, Tenderloin Technology Lab
St. Anthony's Foundation · San Francisco (California, USA)
Helped homeless and low-income individuals in San Francisco's Tenderloin district acquire technology skills for employment and access to essential community resources.
Projects
Selected work
Gravitational Wave Dectection
Repository →Developed a machine learning model that detected anomalous gravitational waves and achieved the best true negative rate in the event while maintaining a true positive rate above 90%. Awarded with the Best Performing Model in the Gravitational Wave Detection category at the UCSD SMASH & NSF HDR ML Hackathon.
Multi-Resolution Tensor Latent Factor Models for Automatic Performance Profiling of Basketball Players
Repository →Developed machine learning models (multi-resolution tensor latent factor models with tensor learning) to produce NBA players shooting profiles using spatio-temporal tracking data. Advised by professor Rose Yu, at the Computer Science and Engineering (CSE) department of University of California, San Diego. Results were published at the 2022 MIT Sloan Sports Analytics Conference.
BACON: AI-Powered Poetry Generation with Author Linguistic Style Transfer
Repository →A basic prototype of an automatic poetry generator with linguistic style transfer. It combines concepts and techniques from finite state machinery, probabilistic models, artificial neural networks and deep learning, to write original poetry with rich aesthetic qualities in the style of any given author. Extrinsic evaluation of the output generated by BACON shows that participants were unable to tell the difference between human and AI-generated poems in any statistically significant way. Results were published at the 2018 Contra Costa County Science and Engineering Fair, and qualified for presentation and the2018 California Science and Engineering Fair.
Publications
Research
Understanding why shooters shoot - An AI-powered engine for basketball performance profiling
2022 MIT Sloan Sports Analytics Conference
Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan, Frank Rodriz, Rose Yu
DOI: 10.48550/arXiv.2303.09715
BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer
2018 California Science & Engineering Fair
Alejandro Rodriguez Pascual
DOI: 10.48550/arXiv.2112.11483
Contact
Get in touch
I'm always open to research collaborations, interesting projects, or just a conversation about AI and its impact.
alexrodpas01@gmail.com