Saksham Sharma Tower Research Capital Director Quantitative Research Technology
Columbia Mathematics of Finance (MAFN) & Department of Statistics 2026 Future of Portfolio Management & Artificial Intelligence Conference
Saksham Sharma
Director, Quantitative Research Technology, Tower Research Capital
Saksham Sharma is a quantitative technology leader specializing in the design of ultra-low-latency trading infrastructure and large-scale research systems that power systematic investing. As Director of Quantitative Research Technology at Tower Research Capital, he architects the core software platform behind Limestone, the firm’s largest quantitative trading team, enabling hundreds of global algorithmic strategies across equities, futures, options, and other electronic markets.
His expertise spans the intersection of high-performance C++ engineering, quantitative research infrastructure, compiler design, distributed systems, and machine learning platforms. Sharma has become widely recognized for applying advanced programming language techniques to financial systems, optimizing both execution latency and researcher productivity in some of the world’s most demanding trading environments.
Before joining Tower, Sharma was a software engineering intern at Google, where he helped develop Kubernetes’ industry-first envelope encryption framework for securing cluster databases at rest. His work contributed to the open-source Kubernetes platform and introduced scalable key management and rotation capabilities adopted by cloud infrastructure users worldwide.
Earlier, he conducted research at the Max Planck Institute for Software Systems, developing genetic algorithm techniques that improved floating-point numerical accuracy by nearly 50%, research that was later published at the ACM/IEEE International Conference on Cyber-Physical Systems.
A graduate of the Indian Institute of Technology Kanpur, Sharma earned a perfect 10.0/10 CGPA in Computer Science and Engineering and was awarded the university’s prestigious President’s Gold Medal as the top graduating student. He remains active in the broader software community as a member of the ISO C++ (WG21) Standards Committee and the Boost Foundation, contributing to the evolution of modern C++ used throughout high-performance computing and quantitative finance.
His research interests include low-latency trading architecture, programming languages, scalable AI and machine learning research systems, distributed infrastructure, and quantitative systems engineering.
Homepage: https://sakshamsharma.com
Github: https://github.com/sakshamsharma
Google Scholar: https://scholar.google.co.in/citations?user=M1Nz-qsAAAAJ&hl=en
