Arya Lyngdoh Lakshmanan

• Location: Brooklyn, NY
• Links: Email, LinkedIn, Personal Website, GitHub

Education

New York University / May 2026

• M.S. Scientific Computing
• Focus: High-performance modeling/forecasting
• Courses: Numerical Methods, Machine Learning, Deep Learning, GPU Architecture & Programming

Rutgers University / May 2023

• B.S. Astrophysics & Financial Economics
• Awards: Magna Cum Laude, Highest Honors, Aryabhata Endowed Award
• Thesis: Statistical modeling of dark matter and supernovae

Skills

• Languages: Python, C++, CUDA, Rust, JavaScript, GLSL
• ML: PyTorch, Transformers, Model Evaluation, Burn (PyTorch equivalent)
• Systems: Docker, Linux, Bash, Git
• Data: SQL, Pandas, NumPy, R

Work Experience

AI and LLM Consultant / WordsworthTech Inc

Dec 2023 – Apr 2024, May 2025 – Aug 2025

• Architected a Node.js API to orchestrate low-latency interactions between the StreamAlive platform and LLMs (Gemini/OpenAI), optimizing for high-concurrency live session environments
• Developed custom parsing logic and validation layers to handle stochastic LLM outputs, ensuring strict type-safety and reliability for downstream application logic
• Established quantitative metrics to monitor token usage and response quality, implementing caching and prompt-tuning strategies that reduced operational costs while maintaining model performance

Undergraduate Research Assistant / Rutgers University

Aug 2021 – May 2023

• Researched the effect of frequent supernovae on the dark matter within dwarf galaxies under Dr. Kristen McQuinn (NASA Roman Space Telescope Mission Head)
• Developed regression models in Python to estimate dark matter distributions in dwarf galaxies
• Engineered automated testing routines for galaxy simulations, reducing computation time and minimizing error margins in large-scale dataset analysis

Projects

Multi-GPU Performance Analytical Model | CUDA, C++, Python

Sep 2025 – Dec 2025

• Quantified compute vs communication tradeoffs to identify scaling limits under PCIe constraints, informing when multi-GPU parallelism degraded performance
• Implemented micro-benchmarks in CUDA to calibrate hardware parameters (PCIe throughput, FLOPs) on arbitrary Nvidia architectures
• Validated the model against bottlenecked algorithms (N-Body, Conjugate Gradient), successfully identifying hardware saturation points and scaling limits

Rust/CUDA Simulation Engine | Rust, wgpu, CUDA

Sep 2025 – Present

• Re-engineered a solar system simulation engine in Rust, migrating from my prior JavaScript WebGL/GLSL implementation to a Rust WebGPU API
• Accelerated procedural texture generation by implementing a specialized version of Perlin Noise in CUDA

Rust-Based Deep Learning Audio Classifier | Rust, Burn

Aug 2024 – Present

• Engineered an asynchronous, multi-threaded deep learning pipeline in Rust using the Burn framework to classify audio features with Transformer-based attention mechanisms
• Implemented a classification batcher with dynamic padding masks to efficiently process variable-length sequential data, optimizing memory usage during inference

Societies and Extracurriculars

President and Treasurer / Rutgers Astronomical Society

Sep 2021 – May 2023

• Managed budget and led weekly public seminars for 100+ attendees; served as department liaison for university events.
• Created and maintained an LGBTQ+ safe space in collaboration with Rutgers minority advocacy groups.