machine learning · quantitative finance · low-level systems
class Srisailesh:
def __init__(self):
self.role = "Engineer & researcher"
self.works_on = ["machine learning", "quantitative finance", "systems"]
self.languages = ["Python", "C++", "JavaScript"]
self.principle = "build it, then try to break it before reality does"
def what_i_care_about(self):
return "code that's fast, results that are honest, and ideas that survive testing"- 🤖 Machine learning & AI — reinforcement learning, neuro-symbolic reasoning, applied ML on messy real-world data
- 📈 Quantitative finance — market microstructure, execution, and systematic strategies
- ⚙️ Systems & software — low-latency C++, simulation engines, and full-stack apps
| Project | Domain | What it is |
|---|---|---|
| 🎯 execlab | Quant / RL | Optimal-execution research lab — L2 replay simulator, TWAP / VWAP / Almgren–Chriss, and a PPO agent from scratch, with honest, ablation-tested findings |
| ⚡ nanolob | Systems / C++ | Low-latency C++20 limit order book & matching engine with Binance L2 replay and a market-making simulator |
| 🧩 Neuro-Symbolic RCA | ML / NLP | Hybrid neuro-symbolic approach to root-cause identification in customer-service conversations |
| 🧭 ADAPT | Quant | Regime-aware portfolio framework for Indian equities — market classification, multi-sleeve allocation, factor optimization vs NIFTY 50 TRI |
| 💳 Riskon | Fintech / ML | Dynamic credit-scoring algorithm |
💡 Start here → execlab's README walks through how a promising ML result turned out to be an artifact — and how a fair baseline exposed it. That mindset (breaking my own results before trusting them) is what I bring to everything.
⚡ "Build it, measure it, and try to break it — the market (and reality) will if you don't."