Mehra-Prescott with Rare Disasters (Equity Premium Puzzle)
Published:
Extends the Mehra-Prescott consumption-based asset pricing model with a mathematical rare disasters framework calibrated to U.S. data.
Posts by Collection
portfolio
RobustSight: AI Safety & Alignment Framework
Published:
Comprehensive CV + LLM safety framework investigating adversarial robustness, interpretability, and human-guided alignment across frontier models.
Traffic Flow Optimization
Published:
Urban traffic management embedding graph theory, linear algebra, and calculus into C++ and JAX to optimize signal timing for city planners.
Volatility Alchemist: Quantitative Options Trading
Published:
End-to-end quant options trading system with real-time data integration, ML volatility models, interactive dashboards, and automated signal generation.
GameNGen: Diffusion World Model for Game Simulation
Published:
Trained a latent diffusion world model on 737K+ Super Mario Bros frames with VAE encoder, CNN reward model, and PPO agents.
Market Microstructure Forecasting with Deep RL
Published:
DRL agent predicting short-horizon price movements from order book snapshots and executing trades via event-driven backtesting.
NFL Veteran Transition: Causal Inference on Player Performance
Published:
Hierarchical mixed-effects models and ML ensembles to isolate the effect of team transitions from individual skill trajectories in NFL player data.
ACL Injury Risk Predictor
Published:
ML pipeline predicting ACL injury risk from biomechanical and performance data, combining sports science domain knowledge with gradient boosting and interpretability tools.
MOSAIC: Calibrated Multi-Stream Outbreak Early Warning
Published:
Fuses wastewater, genomic, and outbreak-text surveillance into one calibrated probability that transmission is growing right now, and proves the number is trustworthy.
VeriGrad RL: Mechanistic Interpretability for Safety RL
Published:
An open-source mechanistic interpretability and AI-safety lab for RL post-training: train policies to choose activation-level interventions, then verify they’re behaviorally safe, useful, and mechanistically faithful.
Popper: Falsifying Specifications Before You Prove Them
Published:
An open-source tool that hunts for counterexamples to mathematical and logical statements, catching wrong specifications rather than just mismatched proofs.
RallyScope: Tennis ML & Computer Vision in the Browser
Published:
36,342 ATP/WTA matches turned into an unsupervised playstyle map, an exactly-explainable win-probability model, and an in-browser ball tracker, trained at build time and inferred client-side.
PodBench: Agent Evaluation at Fleet Scale
Published:
Deterministic, resettable task environments for LLM agents with a programmatic verifier, per-run token/cost metering, and Kubernetes autoscaling: pod health and model behavior on one pane.
Context Forge: The Pareto Frontier of Prompt Compression
Published:
An open, reproducible benchmark of context compression (tokens saved vs. quality retained vs. latency) across the tokenizers production apps actually pay for, on real public data.
Shannon’s Gambit: A Chess Engine That Learns at an Honest Rating
Published:
An end-to-end chess RL system that routes each position to the method that owns it, retrains itself nightly by gated self-play, and reports only the Elo Stockfish says it can actually play.
Covera: An Insurance Marketplace That Texts You the Right Plan
Published:
A multi-agent concierge you text like a person: it simulates thousands of years of your real care against every real plan, ranks on risk-adjusted all-in cost, and stays on as a year-round advocate.
publications
Multiple Myeloma Gene Signatures
Published in The Oncologist, 2024
Statistical analysis of mutation, pathway, and survival data to identify gene signatures associated with disease progression in multiple myeloma.
Recommended citation: Kannappan, A. et al. (2024). "Multiple Myeloma Gene Signatures." The Oncologist.
Agentic AI Systems for Clinical Reasoning
Published in Book Chapter, 2026, 2026
Frameworks for integrating clinical and social data through multi-agent LLM orchestration to improve decision-making and patient-centered care.
Recommended citation: Kannappan, A. et al. (2026). "Agentic AI Systems for Clinical Reasoning." Book Chapter.
research
AI Safety Gridworlds: The Reward You Optimize vs the One You Meant
Published:
DeepMind’s 2017 suite turns abstract alignment worries into tiny testable RL environments, built around one idea I still use: the visible reward is not the hidden performance function you actually care about.
Path Patching: Turning ‘This Circuit Explains the Behavior’ Into a Testable Claim
Published:
Goldowsky-Dill et al. give interpretability a quantitative language for localization: express a hypothesis as a set of paths, patch the rest with a counterfactual input, and measure exactly what is left unexplained.
Capability Is Not Propensity: Evaluating Models for Extreme Risk
Published:
The DeepMind-led paper that crystallized the distinction I use constantly: what a model can do versus what it is inclined to do, and why safety needs to measure both and embed the results in governance.
Safe RLHF: Helpfulness and Harmlessness as a Lagrangian
Published:
Peking University’s Beaver decouples ‘is it helpful’ from ‘is it safe’ into two models, then balances them with a Lagrange multiplier that moves during training instead of a fixed weight you guess in advance.
ACDC: Teaching the Computer to Find the Circuit for You
Published:
Conmy et al. automate the slowest step of mechanistic interpretability by pruning a model’s computational graph edge by edge, and are refreshingly honest about where the automation breaks.
Doubly-Efficient Debate: Judging a Superhuman Prover in Constant Time
Published:
Brown-Cohen, Irving, and Piliouras give debate a complexity-theoretic backbone: two competing polynomial-time provers let a limited verifier check an arbitrary computation with a constant number of human judgments.
Sleeper Agents: When Safety Training Teaches the Model to Hide
Published:
Anthropic builds deceptive models on purpose and shows that standard safety training does not remove the deception. Adversarial training can even teach the model to hide it better.
The Doubling Clock: Measuring How Long a Task an AI Can Finish
Published:
METR replaces saturating benchmarks with a human-anchored metric: the length of task, in human time, that a model completes half the time. It has been doubling roughly every seven months.
A Field Guide to Mechanistic Interpretability
Published:
Rai et al. organize the whole subfield around what you are trying to learn rather than which tool you happen to know, and the map is more useful than any single technique on it.
From Reward Hacking to Sabotage: How a Cheat Becomes a Character
Published:
Anthropic shows that when a production model learns to game its reward on real coding tasks, it generalizes to alignment faking and sabotage, and that how you frame the cheating matters as much as whether it happens.
Bridging the Gap: What LLM Judges See That Humans Don’t
Published:
A statistical framework that models an LLM judge as a shared human preference plus a linear bias on interpretable features, so you can both correct the judge and formally test where it diverges from people.
OpenAgentSafety: What Agents Do When You Give Them Real Tools
Published:
CMU and AI2 put LLM agents in a sandbox with a real shell, browser, file system, and manipulative coworkers, and find unsafe behavior in half to three-quarters of vulnerable tasks, often on perfectly benign requests.