about me

About Me

Jack Warren

Jack Warren

ML Research Engineer · CS @ Georgia Tech

Atlanta, GA

[email protected]
View Resume

Bio

I'm an ML research engineer and Computer Science student at Georgia Tech (B.S. 2026, M.S. 2028), with hands-on experience in model fine-tuning, training pipeline design, and neural architecture research. At GTRI I designed and validated novel training techniques that improved a physics-informed neural network's MSE by 12%, and at Equifax QLoRA fine-tuning across an ~8-billion-transaction dataset raised LLM classification accuracy from 75% to 99%. I'm comfortable across the full research stack: model internals, quantization, HPC scheduling, GRPO-RL, and agentic system design.

Experience

Data & Analytics Intern

May 2026 – Aug 2026

Equifax · Atlanta, GA

  • Fine-tuned and evaluated Gemma 3 4B and Gemma 4 E4B via QLoRA on Vertex AI Workbench to classify ~8B financial transactions across 30 primary and ~300 total categories.
  • Adapted Gemma 4 E4B for legacy infrastructure by modifying model internals to support older library versions.
  • Tuned quantization and dtype configurations for NVIDIA T4 GPUs, raising test accuracy from 75% to 99%.
  • Used contrastive learning to fine-tune ModernBERT and fit a linear classifier, achieving the same results with ~100x speedup.

Student Assistant Researcher

Jan 2026 – May 2026

Georgia Tech Research Institute · Atlanta, GA

  • Extended a physics-informed neural network surrogate modeling rocket and flare infrared intensity sequences for military use.
  • Designed and A/B-tested an intermediary-value loss module anchoring the physics loss to mid-network intermediate predictions.
  • Implemented push-forward training — feeding model predictions back as inputs during training — improving test MSE by over 12%.
  • Contributed to a YOLO-based object-detection pipeline for moving vehicles, using GPS coordinates to assign global position labels.

Team Lead, Automated Algorithm Design Researcher

Aug 2024 – Dec 2025

Georgia Tech VIP · Atlanta, GA

  • Applied multi-objective genetic programming with LLMs to evolve a CNN image classifier; benchmarked island configurations, finding multi-LLM setups yielded greater early diversity, with certain combinations degrading gene validity over time.
  • Designed a multi-LLM island architecture supporting multi-population evolution, migration, and checkpointing.
  • Built a reproducible HPC pipeline on PACE-ICE using SLURM sbatch, H100/A100 allocation, and shared model storage.

Software Developer Intern

May 2024 – Aug 2024

MessageGears · Atlanta, GA

  • Standardized Angular/JSP actions UX with confirmation modals and shipped multiple UI fixes.
  • Rewrote Groovy/Spock API tests and split suites by endpoint, cutting runtime from minutes to seconds and accelerating builds.

Projects

Spectral Text Encoder

  • Built a spectral text encoder mapping tokens to sine triples summed into a fixed-length waveform embedding.
  • Raised in-batch contrastive validation accuracy from 56% to 78% using frequency-separation and per-channel InfoNCE losses.

Wave Scalar Field Sequence Generator

  • Built a PyTorch video transformer learning 2D wave dynamics from height frames via block-causal attention and RoPE.
  • Reduced rollout error via a pushforward horizon schedule, velocity-supervision aux head, and residual conv refinement.

Agent-Powered Creator-Game

  • Built an open-ended game converting spoken ideas into explorable 3D scenes using an agentic Gemma 4 loop with custom tools.
  • Ran fully local inference on Apple Silicon using quantized models, FLUX for image generation, and TRELLIS for 3D mesh.

Zero External Data LLM Reinforcement Learning (IP)

  • Designed a creator/solver self-play curriculum with verifiable rewards and a difficulty-calibration reward, using GRPO.
  • Extended it to multi-turn RL (20 Questions) with a custom per-turn reward, then A/B tested periodic role-reversal.

Education

Georgia Institute of Technology

B.S. Computer Science (May 2026) & M.S. Computer Science (expected May 2028) · Minor in Japanese · GPA 3.77 / 4.0

Relevant coursework: Deep Learning, Machine Learning, Probability & Statistics, Knowledge-Based AI, Algorithm Analysis, Computer Systems.

Languages

Python SQL C Java JS/TS Groovy

Libraries & Tools

PyTorch Transformers TRL PEFT Unsloth DEAP/GP scikit-learn Pandas NumPy OpenCV Streamlit CUDA Hugging Face SLURM (HPC) GCP Vertex AI BigQuery AWS S3

Interests

Machine Learning LLMs Graphics Game Dev Systems Mountain biking Japanese language & culture

Elsewhere