I'm a passionate technologist and researcher at the intersection of quantitative finance, machine learning engineering, and artificial intelligence research. Currently pursuing my Master's degree, I specialize in developing cutting-edge systems that transform theoretical breakthroughs into production-ready solutions for financial markets.
My fascination with quantitative research began with a simple question: How can we harness the power of data and algorithms to understand and predict complex market behaviors? This curiosity has driven me to explore the fascinating convergence of advanced mathematics, computer science, and financial theory.
As a Machine Learning Engineer at Deep Forest Sciences, I've been building scalable inference pipelines and optimizing neural network architectures for real-world applications. My work involves designing systems that can process massive datasets in real-time while maintaining the precision and reliability required for production environments. Through this role, I've gained deep expertise in distributed computing, GPU optimization, and the challenges of deploying AI systems at scale.
My journey in open-source development as a Software Engineer with Google Summer of Code has taught me the importance of building robust, maintainable systems. Contributing to DeepChem's ecosystem, I've developed production-grade code that serves researchers worldwide, reinforcing my belief that great software engineering is the foundation of impactful research.
I'm particularly drawn to systematic trading and quantitative research because it represents the perfect synthesis of my interests: rigorous mathematical modeling, innovative algorithm development, and real-world impact. The challenge of extracting meaningful signals from noisy financial data while building systems that can operate under the pressure of live markets is both intellectually stimulating and practically rewarding.
My research interests span several key areas:
Quantitative Strategy Development: I'm passionate about developing systematic approaches to alpha generation, from traditional factor models to cutting-edge machine learning techniques. I believe the future of quantitative finance lies in the intelligent combination of domain expertise with advanced computational methods.
AI Research for Finance: The application of modern deep learning architectures—particularly transformers and attention mechanisms—to financial time series presents fascinating research opportunities. I'm exploring how recent advances in natural language processing can be adapted for financial sequence modeling and multi-modal market analysis.
High-Performance Computing: Building systems that can process market data with microsecond precision requires deep understanding of computer architecture, networking, and optimization techniques. I find immense satisfaction in squeezing every bit of performance from hardware while maintaining code clarity and reliability.
I believe in research-driven engineering—where every system design decision is informed by rigorous analysis and empirical validation. Whether I'm optimizing a GPU kernel for faster model inference or designing a backtesting framework for strategy evaluation, I approach each challenge with scientific rigor and attention to detail.
My experience spans the full technology stack, from low-level C++ optimization for latency-critical applications to high-level Python frameworks for rapid research prototyping. I'm particularly passionate about building infrastructure that empowers researchers—tools that make it easier to test hypotheses, validate models, and translate insights into production systems.
Right now, I'm diving deep into several exciting areas:
The intersection of AI and quantitative finance is evolving rapidly, and I'm excited to be part of this transformation. I'm particularly interested in how advances in foundation models, reinforcement learning, and causal inference can be applied to systematic trading and risk management.
I'm always eager to collaborate with fellow researchers, engineers, and practitioners who share my passion for pushing the boundaries of what's possible in quantitative finance. Whether it's discussing the latest developments in transformer architectures, debating the merits of different backtesting methodologies, or tackling the engineering challenges of building resilient trading systems, I believe the best innovations emerge from collaborative exploration.
When I'm not immersed in code or research papers, I enjoy staying current with developments in both academic finance and practical trading strategies. I'm an active participant in the quantitative finance community, contributing to open-source projects and engaging in discussions about the future of systematic trading.
I believe that the most impactful work happens at the intersection of disciplines, and I'm constantly seeking to expand my understanding of both the theoretical foundations and practical applications of quantitative finance. Every day presents new challenges and opportunities to learn, whether it's mastering a new optimization technique, understanding a novel market phenomenon, or building a more elegant system architecture.
If you're working on interesting problems in quantitative research, machine learning infrastructure, or AI applications in finance, I'd love to connect and explore potential collaborations.
Find my quant resume here
Find my AI research resume here
Find my SDE resume here
