Leadership Team

Narayan Ganesan

Partner and Co-Founder

Narayan Ganesan is the Co-Founder and Partner at Data View Partners Inc.. He has more than 15 years of R&D experience in Academia and Industry and has publications in IEEE/ACM engineering journals in High Performance Computing, Machine Learning and Optimization. In his previous role as Assistant Professor of Electrical and Computer Engineering, at Stevens Institute of Technology, Hoboken, NJ, his lab focussed on finding optimal solutions for large scale data & computing problems Machine Learning, Cyber Security and Quantum Computing. His lab designed heterogeneous computing platforms combining application accelerators such as GPUs and FPGAs for Machine Learning and Data Science applications.

He later worked at Wall street firms where he designed and deployed AI based solutions within various domains in institutional finance including, Credit Default Swap, Systematic Market making in Fixed Income and Low-Latency markets, detecting anomalies and predicting asset price movements.

Financial data is rich in variety and volume that demands solutions that are time-critical as well as those that meet throughput, accuracy and latency requirements. He has designed Big Data architecture and Machine learning solutions that enabled deriving timely and actionable insights from real-time and time-sensitive market data.

He has worked extensively in designing end-to-end Machine Learning pipeline from raw data collection, data engineering, ETL, to designing data science solutions and algorithms for real-time inference.


  • Machine Learning and Data Science, for Algorithmic Trading, Market Micro-structure analysis.
  • Deep Learning & AI for alternative data, quantitative finance, Reinforcement learning for portfolio optimization and market making.
  • Microservices, Virtualization/Containerization
  • Big Data processing, schema design, storage and retrieval, data modeling and stream processing.
  • GPU computing, high performance computing architectures, Parallel and Hybrid Architectures
  • Optimization Theory, Geometric Systems Theory, Quantum Control and Decoherence.