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Rohan Sinha

CS @ Cornell

Machine learning, AI systems, computational research.

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Experience

Feb 2026 - Aug 2026
Rockville, MD

Temple Allen Industries

Machine Learning Intern

  • Architected reinforcement learning control pipeline in Python and OpenAI Gym to minimize actuator motion variance under external disturbances, reducing positional discrepancy by 38%.
  • Evaluated and fine-tuned computer vision detection models for aircraft component recognition using PyTorch and OpenCV, improving classification accuracy by 14% across validation datasets.
  • Developed simulation and inference testing framework with NumPy and real-time sensor feedback analysis to benchmark actuator stability and vision model robustness under dynamic operating conditions.

Apr 2025 - May 2026
Remote

University of Maryland

Computational Number Theory Researcher

  • Co-author of 2 accepted ACM SIGACT News open problems and arXiv paper on congruence monoid and Gaussian primes.
  • Built custom sieving algorithms and modular filters achieving 3% MAPE and derived 2 prime density formulas validated via nonlinear least-squares regression.
  • Optimized Miller-Rabin tests with Numba, cutting per-check latency ~40% on 10⁹+ candidates and verified densities using a Python/NumPy analysis framework.

Mar 2024 - Feb 2026
Hybrid

University of Maryland

Computational Game Theory Researcher

  • First author of University of Maryland-accepted paper analyzing player skill and ideal positions in imperfect Nim through 10⁶+ Monte Carlo simulations in Java.
  • Designed 4 strategy models with up to 100% accuracy using algorithms for move optimization, win detection, and stochastic modeling.
  • Applied NumPy, SciPy, and Matplotlib for data analysis; leveraged virtual CPUs, parallel processing, and Gaussian smoothing to optimize runtime by ~26% and process 10⁹+ 3D points.

Nov 2024 - Jun 2026
Washington, DC

The George Washington University

Computational Linguistics Researcher

  • First author of manuscript quantifying semantic drift during COVID-19 via 6-trait NLP pipeline on 22.2k Reddit Q&A pairs (2019-2023).
  • Implemented GPT-4o few-shot classifier generating 133.3k Likert scores, achieving 0.82 Krippendorff's alpha and 15% higher calibration accuracy using isotonic regression.
  • Applied Bayesian change-point detection, PELT, and causal impact forecasting to identify post-2020 linguistic shifts with NumPy and scikit-learn.

Education

Cornell University

Computer Science · 4.0 GPA

Expected graduation: 2029

Courses: OOP I, OOP II, Calculus I, Calculus II, Data Structures & Algorithms

Working in Cornell’s Co MSAIL Lab on using reinforcement learning agents for fluid dynamics.

Projects

Adaptive gRPC Compression

Go / gRPC / LinUCB / Contextual Bandits
  • Reduced p95 RPC latency 61.7% across 161K calls by training LinUCB to select gzip/zstd from runtime context.
  • Saved 863.7M payload bytes (56.7%) across 161K RPCs by adapting compression to entropy and CPU load.
  • Achieved 1.38 µs decision latency by embedding bounded LinUCB inference in Go and streaming gRPC interceptors.
View on GitHub

ML CI Test Conflict Detection Scheduler

Rust / eBPF / Python / Go / Postgres
  • Instrumented 280 pytest cases for CI conflicts by tracing files, sockets, ports, DBs, and processes with Rust eBPF.
  • Scheduled CI across 8 workers with 3 risk policies using conflict probability, overlap duration, and LPT placement.
  • Built a Rust eBPF/Python/Go/Postgres pipeline to trace 280 tests, predict conflicts, and schedule workers.
View on GitHub

GroundSense

React / TypeScript / Supabase / Deno / Gemini
  • Architected React/TypeScript + Supabase/Postgres platform with RLS, Edge Functions, and a source-audit ledger.
  • Built Deno ETL normalizing 2K+ BLS/EIA/FRED/UN records with Gemini-assisted claim extraction pipelines.
  • Mapped external shocks to calibrated exposure scores and mitigation measures across 5+ supply-chain workflows.
View on GitHub

Federated Learning for Clinical NLP

PyTorch / Flower / Opacus / ClinicalBERT
  • Built PyTorch + Flower (FedAvg) simulation across hospital nodes over 50-100 rounds; applied Opacus differential privacy (ε 1.5-3.0) and secured via TLS 1.3, AES-256, and JWT auth.
  • Trained ClinicalBERT/mBERT (~110M params); reached 80%+ F1 on holdout with under 15% communication overhead vs centralized baseline, robust to client dropout and label noise.
View on GitHub
More projectsFewer projects

Multi-Agent RL for Energy Distribution

Ray RLlib / PyTorch / Gymnasium
  • Developed multi-agent PPO system with a custom 2-3 home + 1 battery environment and peak/off-peak pricing; ran ~1e6 env steps with 8-16 rollout workers and AMP fp16.
  • Delivered 15-20% cost reduction vs baseline across 100 eval episodes with under 1% reward variance; resilient under 2x demand surges at ~25k env-steps/s throughput.
View on GitHub

Cross-Lingual Medical NLP

Transformers / LoRA / Gradio
  • Built a clinical NLP transfer framework using Hugging Face Transformers, PyTorch, LoRA, and Gradio.
  • Fine-tuned mBERT on 10k+ English medical NER samples and adapted it to Spanish with 200 labeled examples for low-resource transfer.
View on GitHub
All repositories

Publications

ACM SIGACT News, 2026

Estimating the Number of Primes in Unusual Domains

Investigates the distribution of primes beyond the natural numbers: congruence monoids, the Gaussian integers, and quadratic extensions of Z, with heuristic conjectures backed by empirical density evidence.

Skills

Languages

  • Python
  • Java
  • C
  • C++
  • JavaScript
  • TypeScript
  • SQL
  • HTML/CSS
  • R
  • Go

ML / AI

  • PyTorch
  • scikit-learn
  • Hugging Face
  • OpenCV
  • Gymnasium
  • Stable-Baselines3
  • Ray RLlib
  • MLflow
  • Opacus

Tools & methods

  • NumPy
  • pandas
  • SciPy
  • Jenkins
  • Maven
  • Git
  • Linux
  • Postgres
  • Deno
  • Vue.js
  • Agile

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