Quietly building AI + Quantum systems for crypto treasury risk.
Systematic hedging with futures
Dynamic hedge construction across venues and instruments to manage drawdowns and basis risk in volatile markets.
AI-driven risk models
Regime detection, stress testing, and scenario analysis informed by machine learning on multi-venue market data.
Quantum-inspired optimization
Portfolio and hedge optimization using quantum and quantum-inspired methods for complex, constrained problems.
Institutional-grade engineering
Low-latency data, robust execution, and full observability designed for professional risk operations.
Founders
Two builders with complementary depth :: a quantum/algorithms/AI computer scientist and a neuroscience/ML systems engineer :: guided by a quiet, techno‑anarchic bent :: Operable tools, not op-eds. We believe deAI will meet deFI, and that AI‑first communities will own their risk. Today the focus is pragmatic: regime‑aware risk modeling and systematic hedging, grounded in years of Monte Carlo and Markov‑chain work (classical and quantum).
Avah Banerjee, PhD
Founder, CEO
Avah blends theory and systems. She has worked across graph algorithms, high‑performance computing, and quantum computation—building compilers for quantum circuits, exploring quantum walks, and designing resource‑efficient chaos generators. Her recent focus applies classical and quantum Markov chains and Monte Carlo methods to risk, execution, and optimization under real constraints. Former faculty with a track record of funded research and industry collaboration. At semiqlassical, she steers architecture and the math that keeps our hedges disciplined.
Emily Hsiang, PhD
Co-founder
Emily turns noisy signals into decisions. Trained in chemical engineering and neuroscience, she has built advanced optical imaging systems, crafted ML pipelines to decode visual pathways, and shipped deep‑learning models that separate signal from artifact. She cares about operability, latency, and measurement—what it takes to move from the lab to live markets. At semiqlassical, she leads data, modeling, and the human factors of systems that must work every day.
We’re supported by a small circle of prominent scientists who collaborate with us quietly.