Infrastructure that lets an advisory firm run like a larger one.

Abbas Shah is the Chief Product Officer of AllocateRite, where he is responsible for digital wealth management strategies, data science, risk analytics, portfolio management and optimization solutions. He brings more than two decades of experience on Wall Street to the company, starting at Lehman Brothers where he spent eight years developing risk analytics, optimization models and global trading systems while holding a number of positions in quantitative research, trading systems and zero coupon trading.

UBS Securities then asked Abbas to manage the zero coupon trading desk in addition to the long end of the U.S. Treasury market. Abbas then went to the proprietary trading group at Deutsche Bank where he was responsible for setting up and executing the global macro trading strategy, after which he joined Moore Capital Management as a PM, where he managed a global macro portfolio in fixed income, equities and currency markets.

Subsequently, when widespread adoption of the Internet became a phenomenon, Abbas founded IsoSpace, an Internet social collaboration and cloud computing start-up venture, where he became its Chairman and CEO, raised millions of dollars in venture capital and announced it on CNN. During his five-year tenure there, he was responsible for acquiring IsoSpace’s largest clients, including the United States Army, Royal Bank of Canada and Daimler Chrysler.

Abbas holds a B.S. degree in Mathematical Economics and Biology from Columbia University.

Quanzhen Ding is a quantitative data scientist and AI systems architect with expertise in non-linear optimization, machine-learning pipelines, and large-scale financial data systems. He has led teams building real-time analytics engines, portfolio-optimization workflows, scalable APIs, and autonomous reporting across equities, fixed income, and alternative assets.

He has directed external data-science initiatives in financial and real-estate markets. At Honely, he led development of predictive valuation models, market-forecasting algorithms, and large-scale ingestion pipelines. At Swopx, he oversaw digital-asset analytics and prediction models, building end-to-end data infrastructure. He also served as a core data architect on the firm’s earlier retail multi-asset trading and portfolio-management app, focused on execution-layer analytics and personalized recommendations.

Quanzhen holds a Ph.D. in Physics from Stevens Institute of Technology, where he researched stochastic quantum systems and propagator-based algorithms that reduced computational complexity from factorial to linear time.

At AllocateRite, he leads quantitative research and data architecture behind Newt, the firm’s AI wealth-management assistant powering real-time reporting, attribution analysis, tax-loss harvesting diagnostics, IPS monitoring, and household-level analytics.