High Performance Scientific Computing
Parallel algorithms, distributed workflows, MPI-based numerical methods, Dask pipelines, and batch execution on HPC systems.
University of New Mexico Computer Science
Saif Ryan Gangaram
Data Scientist 2 at LANL and UNM Computer Science Ph.D. student focused on HPC, AI/ML, Big Data, signal processing, and reliable scientific software.
- Current Role
- Data Scientist 2, LANL
- Research Areas
- HPC, Big Data, AI/ML, signal processing
- Ph.D. Program
- CS, UNM
About
Scientific computing for large, noisy, consequential data.
I am a Computer Science Ph.D. student at the University of New Mexico with a background in mathematics, data science, software engineering, and applied machine learning. My work focuses on building computational tools that make complex sensor, simulation, and experimental data easier to validate, analyze, and use responsibly.
I am advised by Dr. Amanda Bienz at UNM. Professionally, I currently work as a Data Scientist 2 at Los Alamos National Laboratory, after previously working as a Software Engineer in Automation and AI/ML at Space Dynamics Laboratory and as a Teaching Assistant at the University at Buffalo.
I am especially interested in scalable algorithms, high-throughput data systems, time-frequency analysis, robust ML inference workflows, and the engineering practices that turn research code into dependable software. This public site intentionally keeps project descriptions technical, high level, and suitable for open audiences.
Research
Research Themes
Big Data and Scalable Analytics
Current research includes Big Data methods for large scientific datasets, distributed processing, scalable storage formats, and analytics workflows for terabyte-scale data collections.
Signal Processing and Time Series Analysis
Denoising, spectral analysis, stationarity testing, transient detection, and quality checks for high-volume sensor data.
AI/ML for Scientific Data
Generative AI, supervised and unsupervised learning, clustering, regression, deep learning, anomaly detection, segmentation, forecasting, and validation for large scientific datasets.
Reliable Data Systems
Reproducible ingestion, schema design, workflow hardening, data validation, and visualization for large experimental collections.
Selected Work
Projects
Hyperion
Phase 2 complete: a self-hosted, local-first agentic AI operations console that runs OpenAI and Anthropic agents in parallel, streams tokens and tool events live, and provides file context, persistent memory, terminal control, AI-assisted email drafting, and a no-key mock mode.
View open-source repositoryConductor
Developing an agentic AI dashboard for software development and workflow optimization, with a focus on coordinating tasks, surfacing project state, supporting debugging workflows, and improving how developers move from intent to verified changes.
Parallel Principal Component Analysis
Implemented distributed PCA with rank-local data sharding, collective covariance aggregation, eigenpair broadcast, optional standardization, and scalability logging for large datasets.
Large-File Inference Workflow
Built a slabbed and microbatched inference path for large scientific files, replacing dense full-file processing with overlap-aware chunking, manifest tracking, and fast validation outputs for high-throughput and near-real-time workloads.
Real-Time Emotion Recognition
Trained and evaluated CNN-based models using large public image datasets, applying augmentation, regularization, feature extraction, and real-time video processing techniques.
Financial Risk Modeling
Developed supervised learning pipelines for probability-of-default estimation with feature engineering, hyperparameter tuning, stress testing, and error analysis.
Scientific Data Access Tools
Designed database-backed analysis tools for large scientific collections, with searchable metadata, plotting, export workflows, and reproducible input generation for modeling studies.
Segmentation and Detection Models
Applied CNNs, U-Net style segmentation, clustering, and classical image processing to improve detection and denoising in scientific imagery and signal-derived products.
Reproducible Pipeline Hardening
Built instrumentation, validation checks, runtime tracing, and portable execution paths for scientific data workflows, improving reproducibility across local, Linux, and HPC-style environments.
Background
Experience and Education
2026 - Present
Data Scientist 2, Los Alamos National Laboratory
Develops data acquisition, analysis, automation, signal processing, supervised and unsupervised ML, generative AI, and HPC-integrated workflows for engineering test and validation environments using public-safe scientific computing methods.
2023 - 2026
Software Engineer, Space Dynamics Laboratory
Built data pipelines, database-backed web tools, scientific analysis workflows, and automation software for aerospace and remote-sensing research settings.
2024 - 2025
Technical Recognition and Presentation
Received internal recognition for user-centered engineering, data tooling, documentation, and technical communication; presented telemetry anomaly-detection methods at a public technical venue.
2019 - 2020
Teaching Assistant, University at Buffalo
Supported lab and recitation instruction, grading, exam administration, and student mentoring for undergraduate computer science coursework.
University of New Mexico
Ph.D. in Computer Science, expected 2028; advisor: Dr. Amanda Bienz
University at Buffalo
M.P.S. in Data Sciences and Applications, 2023
University at Buffalo
B.A. in Mathematics, Computing and Applied Mathematics, 2021
Methods
Technical Areas
Python
C++
C#
SQL
MATLAB
Julia
PyTorch
TensorFlow
Scikit-learn
NumPy
Pandas
Dask
Slurm
MPI
PostgreSQL
SQLite
NetCDF
HDF5
Docker
Linux
Git
Time Series
Spectral Analysis
Visualization
Contact
Open to research conversations and technical collaboration.
For public research, academic, or professional communication, email is the best way to reach me.
Public Information Note
The descriptions on this site are intentionally limited to public-safe topics. They omit sensitive access details, restricted system names, operational parameters, non-public datasets, and program-specific details.