Prashant’s mission is to fight information complexity by scaling human expertise through the automation of specialized analytical work across industries. Prior to starting Accrete, he spent over a decade automating order processing tasks traditionally performed by exchange floor brokers in the domain of high frequency trading, consistently producing alpha by finding arbitrage opportunities in the market microstructure. Eventually, Prashant recognized that complexity caused by the digital explosion was creating new types information processing and reasoning inefficiencies that speed could not capture.
As a result, Prashant shifted focus to automating cognitive tasks typically performed by human analysts. His early work involved static machine learning. The first use-case applied static natural language processing to curated market-on-close imbalance indications collected by NYSE brokers using voice recognition and improved the forecasting accuracy of price predictions at the close, generating significant alpha.
Prashant came to believe that there exists an ocean of predictive insights buried in increasingly complex dynamically changing unstructured datasets. The challenge was that static machine learning approaches to extract predictive insights from constantly changing noisy unstructured datasets would quickly decay in terms of performance thus requiring disproportionate amounts of human effort to constantly retrain models.
Accordingly, Prashant spent years experimenting with various approaches to overcoming sparse training data problems to build dynamic learning models that improve in terms of performance with increased autonomy. Prashant launched Accrete to apply dynamic learning to build smart analytical tools capable of reading, understanding, learning and adapting at scale to reduce bias in decision making. Accrete’s platform is built upon a proprietary mathematical framework that represents language contextually in the way humans use memory to correlate observations through time to construct narratives and define context.
Prashant has won an IBM Beacon Award for Best New Application by an Entrepreneur and has been recognized as an IBM Champion as well as an IBM Wild Duck. Prashant was also the first entrepreneur to have been featured in an IBM Watson tv ad campaign and the only one to have demonstrated quantifiable results. He has been invited to present his work at several conferences around the world including various JP Morgan and IBM conferences and has been interviewed on Nasdaq, CNBC, Cheddar and Silicon Angle's the Cube. Prashant lives in Lower Manhattan with his wife and two small children.
With almost 20 years of experience in financial analysis, investment research and portfolio management, Josh has held various senior level positions in the hedge fund industry, where he has been a portfolio manager, Director of Research and analyst. After almost 2 years at Goldman Sachs, Josh was an equity analyst and senior portfolio manager for one of the largest hedge funds in the world, London-based GLG Partners, LP. Josh was also an analyst and portfolio manager at SAC Capital. Josh is currently an investor in and advisor to a portfolio of private companies.
Mayank is a hedge fund veteran and brings two decades of practical experience running organizations to our team. As a former fund manager, he recognized the accelerating need for market participants to adapt traditional methods for extracting alpha to the exponential growth in unstructured data. His desire to help facilitate that change led him to co-found Accrete.
Prior to Accrete, Mayank’s experience includes launching a successful hedge fund focused on global convertible arbitrage, co-managing a convertible bond sales and trading desk for a broker-dealer, building and co-managing an Asia-focused capital markets business and managing large arbitrage portfolios for three hedge funds. Mayank began his career at GE Capital where he gained significant financial and operating experience across multiple business lines. Mayank also holds a Bachelor of Science from the University of Connecticut and an MBA from New York University.
Adam leads our Knowledge team that works with our analyst, AI research, engineering and development teams to create semantically rich domain-specific training data for proprietary mathematical learning models. The Knowledge team also provides feedback to these models and helps establish learning curves and accuracy measurements. Adam is a graduate of Hamilton College and Columbia University.
Dr. Doganata has been conducting research, initiating, developing and delivering projects in broad research areas of computer science for more than 30 years. Prior to joining Accrete.AI, Dr. Doganata has worked as a Research scientist and as a Solutions Technical lead at IBM T. J. Watson Research and IBM Watson Health. His responsibilities included planning, architecting, delivering complex, technology-based, innovative data driven solutions and prototypes using data analytics, information management techniques, NLP, machine learning, predictive modeling and cognitive technologies. His current focus is to extract insight from structured and unstructured data using artificial intelligence to make timely and accurate decisions in financial domain.
As the Chief Technical Officer at Accrete.AI, Dr. Doganata monitors and assesses new technologies as well as existing technology choices, aligns technology-based projects with the company strategy, drives innovative solutions for future offerings, improves existing offerings. He also acts as the Chief Innovation Officer to develop and foster innovation culture, manage the innovation process, identify and protect Accrete.AI’s intellectual property.
Dr. Doganata received his Ph.D. degree from the California Institute of Technology, Pasadena, California, B.S. and M.S. degrees from the Middle East Technical University, Ankara, Turkey, all in electrical/electronics engineering. He has 48 patents, more than 40 peer-reviewed publications in scientific journals and conferences and a number of innovation and excellence awards. He is the recipient of IBM Eminence and Excellence Award in 2014 for his contribution to build an innovation center for a bank in Spain.
Dr. Agarwal is the Director of AI Research at Accrete. His past work used data-driven tools inspired from non-equilibrium Statistical Physics to analyze, model and understand Nonlinear Dynamics and Stochastic Processes. He worked on examining the dynamics and predictability of Arctic sea ice extent from satellite observations, which in turn affects the lives of all living beings through its impact on global climate. This study encompassed combining the concepts of Multifractals, Noise and Chaos to understand the variability of sea ice extent on multiple time scales. In an extension of this, he developed a stochastic model that explains the statistical structure of the very complex and non-linear behavior of Arctic sea ice velocity fields.
Dr. Agarwal received a PhD in Applied Mathematics from Yale University and a Bachelor of Technology from the Indian Institute of Technology.
Dr. Pant is an AI Scientist at Accrete. His experience involves developing high-fidelity physics-based and data-driven models to study the complicated non-linear interaction between turbulence, chemical kinetics, acoustics and molecular diffusion which is typically observed in aircraft engines, rocket engines, and scramjets. He developed a generalized compressible turbulent combustion solver which uses a stochastic model, the transported probability density function (PDF) method, to study turbulent reacting flows ranging from low-speed subsonic flows to high-speed supersonic flow problems. He used this solver to develop a deep understanding of the effect of transient flame dynamics on thermo-acoustic instability in a rocket engine. He also worked on developing data-driven modeling approaches to study thermo-acoustic instability.
Dr. Pant received a Ph.D. in Aeronautics and Astronautics Engineering from Purdue University, a Masters degree in Mechanical Engineering from Purdue University and a Bachelor of Technology degree in Mechanical Engineering from Visvesvaraya National Institute of Technology, Nagpur, India.
Andrés is a trading veteran with 15 years of experience as an active investor across the capital structure. His experience as both a discretionary and quantitative trader gives him a keen understanding of how markets move and where opportunities for cognitive products are greatest. He designed and developed five fully automated black-box trading systems and co-founded Titus Securities which was subsequently acquired by T3 Trading Group in 2011. Andrés is also a graduate of the Wharton School with a degree in economics, entrepreneurship, information systems, and marketing.
Junyu is a user experience designer with a background in digital media and human-computer interaction. He explores the fields of design, technology, and behavioral science to apply interdisciplinary perspectives to design products that are useful, intuitive, and influential.
Junyu recently finished his master's program in the Human-Computer Interaction Institute at Carnegie Mellon University, where he worked on design tools for adaptive user interfaces, data-informed calculus learning experience, interactive machine learning web application, assistive robot for seniors, etc.
As the Head of Business Development, Alex leads initiatives to grow the business, develop new partnerships and build the presence of Accrete. As a strategy and analytics consultant and API Economy researcher at IBM, he gained his background in Fintech and helped enable startups to compete with enterprise companies. Alex also has degrees in Psychology and Markets & Management from Duke University, where he merged his experience together to search for cognitive biases in markets as well as in life.
As Director of Accrete’s R&D in India, Rahul brings an extensive background in data analytics and quantitative trading, rich technical management experience and nearly two decades of programming experience. He works closely with our director of AI Research team on the implementation of various deep learning and machine learning models.
Prior to his time at Accrete, Rahul spent nearly a decade working closely with our CEO, Prashant Bhuyan, in previous fin tech endeavors. Rahul also spent 3 years at Infosys Technologies Limited as a software engineer in its research department (SETLabs) where he worked on a patent-pending EII tool. He holds a Bachelor of Engineering in Computer Science from Rajiv Gandhi Institute of Technology and has been very passionate about markets and investing since a young age.
David Magerman is a co-founder and managing partner at Differential Ventures.
Previously, he spent the entirety of his career at Renaissance Technologies, widely recognized as the best quantitative hedge fund management company in the world. He helped found the equities trading group at Renaissance, joining the group in its earliest days, playing a lead role in designing and building the trading, simulation, and estimation software. On an extended garden leave from quantitative finance, he is looking to use his data science, software development, and statistical modeling expertise to help Israeli startups succeed in the global marketplace.
David holds a Ph.D. in Computer Science from Stanford University where his thesis on Natural Language Parsing as Statistical Pattern Recognition was an early and successful attempt to use large-scale data to produce fully-automated syntactic analysis of text. David also earned a Bachelor of Arts in Mathematics and a Bachelor of Science in Computer Sciences and Information from the University of Pennsylvania.