Software engineer, AI/ML · UT Austin
Hi, I’m Varun.
I study computer science at UT Austin. I co-founded DUIBL, where I built Stilez, an app that rates your outfit with AI, and I interned at Cox Automotive in 2025.
Experience
Newest first. Each one links to a page with more detail.
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– now
Co-founder & lead engineer Stilez Closed source
DUIBL
I co-founded DUIBL and built Stilez, an iOS app that rates your outfit with AI. I took it from the first architecture sketch to the App Store.
- −67%image-gen spend
- 2×rating throughput
- 68%cache hit rate
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Software engineer intern vAuto Proprietary
Cox Automotive
A summer on vAuto, software used by 7,500+ dealerships. I worked on vehicle sourcing, deploy tooling and an internal auditing app.
- −90%load time
- −80%blue/green shift time
- 1,000+inconsistencies surfaced
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2024
Earlier
A summer in UT Dallas's VR lab building a wind-tunnel simulation in Unity, and co-founding Growth Prep Academy, an AI test-prep platform that reached 500 beta users.
Projects
Side projects on LLM reliability, MLOps and distillation. Each one is measured against a baseline.
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LLM reliability harnesses Open source
Aegis
Reliability harnesses for LLM apps, each tested under a protocol fixed in advance. In the latest study, a 140 ms decision model beat an LLM judge at catching prompt injection, at 1/18 the cost.
- 0.994injection AUROC (judge: 0.980)
- 4×faster than the LLM judge
- 1/18the cost per check
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Self-healing MLOps platform Open source
Demand Forecasting Platform
Forecasts retail demand, notices when its accuracy drifts, and retrains and redeploys itself.
- −46%forecast error (WMAPE)
- +71%accuracy after a demand shock
- 206automated tests
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Agentic computer-vision pipeline Open source
DermAssist
A LangGraph pipeline that takes a skin-lesion photo through preprocessing, a ViT classifier, Claude interpretation, and PubMed literature grounding, and never finalizes a report without human review.
- 8pipeline stages
- 7lesion classes (HAM10000)
- 0reports without human review
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Research Open source
Distillation Reasoning Transfer
Can a small local model learn to reason from a frontier model using only its outputs? Trained on a consumer GPU.
- 30→46%GSM8K accuracy
- ~46%of the gap to the teacher closed
- 3,000filtered reasoning traces
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Early 2026
Multi-agent research system Open source
Stock Advisor
A market-research system on Google ADK: an orchestrator delegates to data, technical, fundamental, and news-sentiment agents, then synthesizes a weighted recommendation in a streaming Streamlit UI.
- 5cooperating agents
- 10+technical indicators
- 4analysis lenses, weighted
GitHub activity
Toolkit
- Languages
- Python, Java, C++, C, C#, Go, JavaScript, TypeScript, SQL, HTML/CSS
- AI / ML
- PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, OpenCV, NumPy, Pandas
- Agents & LLMs
- LangChain, LangGraph, Google ADK, RAG pipelines, multi-agent systems, vector databases (pgvector, Chroma, Qdrant), fine-tuning, distillation, evaluation harnesses
- Infra & DevOps
- AWS (S3, Lambda, RDS, ECS), Docker, Git, CI/CD, GitHub Actions, REST/Express APIs, React/Next.js, PostgreSQL
- Education
- The University of Texas at Austin, B.S. Computer Science.
Data Structures & Algorithms · Operating Systems · Computer Architecture · Machine Learning · Artificial Intelligence · Natural Language Processing · Computer Vision · Generative Visual Computing
Say hello.
If you want to talk about any of this, email me.
varunvsaran@utexas.edu