About
Rajiv Chaitanya M. CS graduate from DSCE Bengaluru with a minor in Economics and Finance. Most of my work sits at the intersection of reinforcement learning, graph learning, and sequential decision-making, with applications to financial markets, macro-financial systemic risk, and biomedical signal processing.
Seven conference papers, six manuscripts under review, one working paper. One Best Paper Award (ICEI 2025) and one Best Paper Candidate selection (ICEdge 2025). Invited speaker at the IEEE Faculty Development Programme on Data and Decision Sciences (SJEC Mangaluru, Jun 2026). Program Committee for IEEE CIFEr 2026. Reviewer for IEEE WCCI 2026, IEEE AISIIS 2026, ICAIMLC 2026, and IEEE GSCon 2027.
Currently a Machine Learning Engineer at Albertsons Companies India, working on ML Ops and production model deployment.
Research focus
- Reinforcement learning. Policy-gradient methods under non-stationarity (ARISE), reward-aware document selection in edge RAG (SRAS), and inverse RL for decoding black-box trading strategies (RRR).
- Graph learning. Meta-learning GNNs over correlation graphs (MetaGraph) and spatio-temporal forecasting on real grids (GridCast).
- Quantitative finance and macro risk. Drawdown-aware allocation (REBOUND), regime-conditional exposure governance (Beyond REBOUND), meta-learning GNNs with leakage-free evaluation for equity forecasting (MetaGraph), and selection-aware VaR backtesting under ML model classes (Your Backtest Is Lying To You).
Education
BE, Computer Science and Engineering
Dayananda Sagar College of Engineering (DSCE)
Minor in Economics and Finance. CGPA 8.04 / 10.0, Minor CGPA 9.78 / 10.0.
Open to PhD positions and quant or risk-management roles. If any of the above is your problem too, send a note.