About
I was born in Houston to Argentine immigrants and grew up in Bentonville, Arkansas, before heading to Boston, Massachusetts for college at Harvard University where I'm studying Computer Science.
During my time at Harvard, I've worked in the Ability Lab on campus for assistive devices. I also worked as a data scientist at Massachusetts General Hospital conducting neurological research. After my third year, I had the opportunity to work as a software engineering intern in San Francisco at Netflix.
I'm particularly passionate about software engineering, machine learning, artificial intelligence, and systems programming. I love building scalable software systems, advancing AI/ML research, and creating innovative solutions that make a real-world impact.
In my free time, I enjoy running, playing the viola, violin, and guitar, and sports. Feel free to reach out if you want to collaborate on anything, discuss research opportunities, or to chat!
+1 (479) 531-3651 | Buenos Aires, Argentina
Languages
-
Python -
Java -
JavaScript -
TypeScript -
HTML -
CSS -
Go -
Rust -
C++ -
C -
C#
Technologies
-
React -
Spring Boot -
Next.js -
Node.js -
PyTorch -
TensorFlow -
OpenCV -
Git -
AWS -
Docker -
GraphQL -
gRPC -
Jenkins -
PostgreSQL -
MongoDB -
Firebase -
Supabase
Education
Harvard University
Cambridge, MA Master of Science in Computer Science August 2025–December 2025
Harvard University
Cambridge, MA Bachelor of Arts in Computer Science with Honors August 2022–May 2025
- Coursework: Data Structures & Algorithms, Operating Systems, Distributed Systems, Networks, Databases, Systems, Machine Learning, Deep Learning, Probability & Statistics, Linear Algebra, Computer Vision, Compilers, Optimization, Artificial Intelligence
Experience
Netflix
Los Gatos, CA Software Engineer Intern, Game Systems June 2025–August 2025
- Created GraphQL mutation endpoint using Java and Spring Boot on enterprise edge, integrating with gRPC services to verify test accounts, check push consent, and dispatch test notifications supporting 1,000,000+ daily requests with 99.999% availability
- Added two new fields to existing GraphQL query, fetching data via gRPC, and displaying results with React and TypeScript
- Built full-stack push consent dashboard, adding two GraphQL query and mutation endpoints managing 800,000+ daily requests
- Developed full-stack notification interaction tracking system with five new GraphQL endpoints handling 600,000+ daily requests
Harvard Ability Lab
Allston, MA Software Engineer September 2023–May 2024
- Integrated a mobile computer vision app with embedded hardware to make a self-steering white cane system to avoid obstacles
- Built a self-steering system with a microcontroller, encoder, and motor using SPI communication and PID control in C++
- Implemented Unity script in C# to transmit obstacle and path data from the app to the microcontroller via Bluetooth for steering
Massachusetts General Hospital
Boston, MA Data Scientist Intern, Global Neurology Research Group August 2023–July 2024
- Created data pipeline from patient registry with SQL and Python reducing dimensionality 83% retaining 90% variance with PCA
- Developed polynomial regression model with 94% accuracy in predicting MRI lesion counts enabling 3x faster training
- Co-authored peer-reviewed paper in Neurological Sciences on effects of GLP-1 agonists (e.g., Ozempic) on MS in 49 patients
Publication: Udawatta, M., Fidalgo, N. & Mateen, F.J. Multiple sclerosis patients taking glucagon-like peptide-1 receptor (GLP-1) agonists: a single-institution retrospective cohort study of tolerability and weight loss. Neurol Sci 46, 343–349 (2025).
Projects
- Co-founded and engineered an AI-powered SAT preparation platform serving 10+ paying customers with a 4.9/5 user rating
- Built full-stack web app featuring dynamic skill tracking, personalized quick practice sessions, and real-time AI tutor chatbot
- Created 2,000+ question bank and 15+ full-length practice tests with question and domain typing to enable targeted practice
LSM-Trees with Machine Learning
GitHub Python, PyTorch, NumPy, scikit-learn
- Designed classifier algorithm with gradient boosted trees reducing query latency by 2.3x and 30% fewer Bloom filter checks
- Built Bloom filters with ML and lightweight backup filters reducing memory footprint 70–80% per level and zero false negatives
- Trained and cross-validated models on synthetic and real key-value workloads achieving up to 91% accuracy on level prediction
Publication: Fidalgo, N. Ye, P. (2025). Learned LSM-trees: Two Approaches Using Learned Bloom Filters.
Simulating Evolvability as a Learning Algorithm
GitHub Python, NumPy, Matplotlib
- Conducted first empirical study of evolvability for six Boolean function classes across four distributions with a genetic algorithm
- Discovered majority function is evolvable under uniform, binomial, and biased Bernoulli distributions but not beta distribution
Publication: Fidalgo, N., Ye, P. (2025). Simulating Evolvability as a Learning Algorithm: Empirical Investigations on Distribution Sensitivity, Robustness, and Constraint Tradeoffs. arXiv:2507.18666.
High-Performance LSM-Tree Storage Engine
GitHub C++
- Designed LSM-tree with skip list memtable, variable false positive rate Bloom filters, and hybrid compaction strategy
- Achieved sub-linear latency scaling from 100MB–10GB data and up to 40% higher write throughput under skewed workloads
- Demonstrated near-linear scalability to 16 threads and 32 concurrent clients, reducing latency 12x and increasing throughput 25x
Publication: Fidalgo, N. (2025). Design and Implementation of an LSM-Tree Storage Engine.
Extending U-Net for Semantic Segmentation
GitHub Python, PyTorch, OpenCV, NumPy, Matplotlib
- Evaluated U-Net with residual blocks and batch normalization and hybrid fully convolutional network on CamVid urban dataset
- Improved dominant class accuracy (0.954 Dice score for sky and 0.928 for road) with residuals and combined loss functions
Publication: Fidalgo, N., Nair, J. (2025). Implementing and Extending U-Net for Semantic Segmentation.
Multimodal AI for Forensic Sketch Generation
GitHub Python, PyTorch, CUDA, Hugging Face
- Achieved 21% higher structural similarity and 25% higher peak signal-to-noise ratio over Stable Diffusion v1.5
- Fine-tuned CLIP model on attention heads using LoRA improving text-sketch alignment by 9% and reducing perceptual error 2%
Publication: Fidalgo, N., Contreras, A., Harvey, K., Ni, J. (2025). Gen-AI Police Sketches with Stable Diffusion. arXiv:2507.18667.
Links