Many of our former researchers and lab members have built successful careers in academia and industry. Below are profiles of some of our former lab members.

Hao Huang

Haohan is the Director of Enterprise Model Risk Management at Royal Bank of Canada. He is the leader of the Front Office XVA Validation Team and has 11+ years of work experience and 15+ years of research experience in credit risk.

“I was part of the research lab while pursing my Ph.D. degree in Applied Mathematics. As a formal PhD student in Financial Mathematics, this lab had provided me with practical trainings and learning opportunities that cannot be found in the normal research work.”

Nathan Gold

Nathan is the Head of Data Science at Shara Inc. a lending company in Sub-Saharan Africa using peer selection and network financing to offer credit facilities to merchants in local markets. He is responsible for the majority of data-related projects and model development. This includes infrastructure set up for a Modern Data Stack (ELT pipelines and data warehousing), analytics needs for product development and monitoring, and machine learning modelling.

"I was associated with the Health Analytics and Multidisciplinary Modelling Lab from its inception to October 2020. I was able to work on numerous projects and experience a broad mix of areas where mathematical modelling can be applied. I also developed a modelling toolbox and build new models of observed phenomena. I have been with Shara since October of 2021. Prior to that I was working at BFS Capital (now Nuula) as a Machine Learning Researcher where I built a cashflow forecasting algorithm for small businesses based on bank transaction history, multiple credit risk and loan sizing models, and designed a small business floating line of credit product."

Pavan Aroda

Pavan is Senior Manager of Operational Risk Modeling and Artificial Intelligence at the Bank of Montreal leading the execution of the Comprehensive Capital Analysis and Review (CCAR) and Dodd-Frank Act Stress Test (DFAST) exercises for US stress testing.  He is responsible for all aspects of model development and performance monitoring of operational risk models used throughout the bank.  He is also tasked with driving BMO’s digital-first strategy and has applied Natural Language Processing (NLP) to simplify and accelerate business processes and continues to utilize machine learning to uncover patterns and derive insight.  He has previously worked in quantitative teams at the national financial regulator, Office of the Superintendent of Financial Institutions (OSFI), and in the cards product division at Canadian Imperial Bank of Commerce (CIBC) .

“The lab is truly multidisciplinary and that is its core strength.  The problem-solving approach starts off with individuals equipped with a range of tools in the mathematical toolbox so that a diverse set of problems can be tackled.  The mathematics follows naturally from the onset of a proposed problem to solve, defining the scope and boundaries within which to play in the proverbial sandbox, and then investigating and writing down in the most concise manner a solution in a way that is understandable and clear.  There is a joy emanating from this lab when solving real-world problems.  The lab provided an environment for a completion of the PhD degree in addition to contributing to an area used in modern day finance.” 

Sasha Nanda

Sasha is a Senior Data Scientist at Deloitte, Omnia AI , where she builds machine learning models that address complex business needs such as demand forecasting, marketing experiment design, and customer segmentation. Sasha obtained a bachelor's degree in physics and minor in computer science from Caltech, where she specialized in quantum computing. She was a Feynman Quantum Resident at NASA Ames, and a Quantum AI Summer Resident at Google X. She then earned her Master's in Applied Computing at the University of Toronto , specializing in Data Science. Her thesis was on using a temporal convolutional network for demand forecasting in retail. Sasha is particularly interested in applying deep learning architectures to time series problems.