Dhruv Agrawal

Dhruv Agrawal

Analytical problem-solver with a deep interest in technology-driven systems and capital markets. I study how complex systems behave under stress, how models fail in production, and where structured thinking creates the most leverage.

Selected Work

Projects

Simulation & Agent-Based Systems

Post-AGI Digital Economy Simulation

Built a large-scale agent-based simulation modeling autonomous economic actors in post-AGI tokenised economies. The framework introduces reputation currencies and tokenized attention markets as coordination primitives, then explores policy interventions for wealth distribution dynamics.

result: −34% wealth concentration under targeted policy

stack: Agent-Based Modeling, Simulation Design, Tokenomics

Autonomous Robotics & Edge AI

AI Perception for Agricultural Robotics

Developed the full AI perception stack for an autonomous agricultural robot — instance segmentation for individual plant identification, active viewpoint optimisation for multi-angle canopy observation, 3D spatial tracking using fused camera and LiDAR data, and plant-level risk prediction for targeted intervention. Real-time inference on edge hardware.

mentorship: 50+ teammates trained in ML & Edge AI

achievement: FRC (Farm Robotics Championship), USA 2026

stack: Python, Instance Segmentation, LiDAR, Edge AI, Multi-Sensor Fusion

Statistical Arbitrage

Cross-Currency FX Strategy

Built and validated a market-neutral statistical arbitrage strategy on a 14-year, multi-currency FX dataset sampled at 5-second frequency. Isolates residual mispricings after removing dominant USD exposure, then applies systematic mean-reversion rules across currency pairs. All evaluation followed strict walk-forward, out-of-sample protocols.

sharpe: 3.81 gross / 1.99 net (1bp)

drawdown: Max −13.6% · Profitable 12/14 years

recognition: IISc Podium

Embedded Systems & Firmware

Deterministic Firmware Migration Framework

Designed a reproducible framework for cross-architecture firmware migration using semantic hardware abstraction layers that decouple application logic from platform-specific peripherals. Automates code translation across microcontroller families, validates correctness through simulation-based testing, and produces deterministic builds with full traceability.

approach: Semantic HAL + automated code translation

pipeline: Simulation → HIL verification → deterministic build

stack: C/C++, Microcontrollers, Build Systems, Toolchain Integration

Risk Modeling & Early Warning

Hybrid Crash Forecasting

Developed a multi-signal crash forecasting system for Bitcoin, fusing EGARCH-based volatility regime detection, LPPL bubble diagnostics, sentiment-derived features, and LSTM sequence modeling into a unified early-warning framework. Generates probabilistic crash alerts validated through rolling-window cross-validation with strict temporal separation.

roc_auc: 0.97 · Precision 89%

lead_time: 26-day average warning

Portfolio Construction

Cross-Asset Portfolio Optimization

Built a regime-aware, cross-asset allocation framework combining Hierarchical Risk Parity with dynamic regime-switching filters. Rebalances allocations based on detected market regimes — risk-on, risk-off, and transitional — validated through strict walk-forward protocols on multi-year data spanning equities, fixed income, and commodities.

return: 148% vs 97.5% benchmark

sharpe: 0.41 → 0.68 · HRP + Regime Switch