Open to opportunities

Aman Githala

@amangithala

Message

Researcher and software builder focused on cloud-native ML systems.

India

Message

What I'm looking for

I’m looking for a role where I can combine information-theoretic ML with reliable cloud-native systems—shipping experiments end-to-end, scaling workflows, and building production-grade diagnostics and infrastructure.

I’m an information-theoretic ML researcher at the Faculty of Technology, University of Delhi, where I’m developing diagnostics for cross-sensor-group interactions using Partial Information Decomposition. I’ve validated my Synergy Ratio (SR) approach on four datasets (AI4I, C-MAPSS, SMD, Synthetic) to guide model selection with measurable signal structure.

Alongside research, I build production-minded infrastructure. I created a Kubernetes Operator and DAG workflow orchestrator (Go controller, Python ONNX inference engine, C++ scheduler), plus a closed-loop feedback pipeline that records runtime CPU/memory to PostgreSQL and retrains a RandomForest → ONNX model. I’ve also built CodeManthan, a Redis-backed distributed online judge with Docker-sandboxed execution and a real-time Supabase leaderboard, stress-tested at 1,284 concurrent users.

Experience

Work history, roles, and key accomplishments

Current

Information-Theoretic Researcher

Current

Faculty of Technology, University of Delhi

Feb 2026 - Present (4 months)

Developed a Synergy Ratio diagnostic using Partial Information Decomposition to quantify cross-sensor-group interactions, validating it on 4 datasets (AI4I, C-MAPSS, SMD, Synthetic). Built an information-theoretic entropy–KL model and a bi-level stacked SGD classifier achieving up to 0.999 AUC, with bootstrap CI (n=50) and ablation confirming significant entropy feature contribution (p=0.033) when

Partial Information Decomposition Entropy KL Modeling Shannon Entropy KL Divergence Bi Level Stacked SGD XGBoost Paired T Test Ablation

Education

Degrees, certifications, and relevant coursework

Faculty of Technology, University of Delhi

B.Tech, Computer Science & Engineering

Activities and societies: Information-theoretic failure diagnostics (Synergy Ratio) since Feb 2026; validated across AI4I, C-MAPSS, SMD, and Synthetic datasets; builds supporting models and ablation/CI studies.

Pursuing a B.Tech in Computer Science & Engineering at the Faculty of Technology, University of Delhi. Conducts research in information-theoretic failure diagnostics using Partial Information Decomposition and a Synergy Ratio diagnostic validated on multiple datasets.