Kathryn Zhao: Global Head of Electronic Trading, Cantor Fitzgerald

Kathryn Zhao is the Global Head of Electronic Trading at Cantor Fitzgerald. She has successfully rolled out the algorithmic trading suite (the Precision Algo Platform) cross asset classes, including equity, foreign exchange and fixed income. Now it is expanding into cryptocurrencies.

Kathryn joined Cantor Fitzgerald from JPMorgan where she was the Global Head of Algorithmic Quantitative Research. She started at JPMorgan as the Head of Linear Quantitative Research in Asia where she spent about five years before relocating to New York to take on the role as Global Head of Algorithmic Quantitative Research. She was leading quantitative research efforts for Electronic Trading, Delta-One, ETF Market Making, Automated Market Making, Cash, Central Risk Book, as well as Block Trading.

Prior to JPMorgan, Kathryn worked at Deutsche Bank, based in Hong Kong, where she started as a portfolio trader before she took over the Electronic Trading team for APAC. Her portfolio trading experience, deep knowledge in Asian markets microstructure as well as her strong quantitative background builds a unique and strong foundation for her electronic trading career.

Strong research professional graduated from Cornell University, Kathryn is an experienced quant with a demonstrated history in the financial services industry, skilled in Electronic Trading across Asset Classes, Market Microstructure, Data Analysis and Portfolio Management.

Kathryn graduated from Cornell University and Purdue University with double degrees in Statistics and Finance.

Ioana Boier: Senior Principal Solutions Architect, NVIDIA

I have a Ph.D. in Computer Science from Purdue University. In addition, I have completed graduate coursework in Financial Mathematics at NYU and Big Data at Harvard University. Prior to joining Citadel, I was a Director in the Global Markets Division at BNP Paribas where I managed the Interest Rate Options & Inflation quantitative research team. Before transitioning into Finance, I was a research staff member at the IBM T. J. Watson Research Center.

Irene Aldridge: CEO and Founder, AbleBlox and AbleMarkets

Irene Aldridge is CEO of AbleMarkets and Able Blox, as well as Adjunct Professor at Cornell University Operations Research and Information Engineering Department, Cornell Financial Engineering Manhattan (CFEM) program. Irene separately teaches at Cambridge University Master in Finance program. To date, Aldridge started 6 companies, and has previously successfully brought to market 2 platforms, Able Alpha and Able Markets, both in the Financial Services space. Aldridge holds a BE in Electrical Engineering from Cooper Union (NYC), M.S. in Financial Engineering, MBA from INSEAD (France) and has studied in two PhD programs: Industrial Engineering and Operations Research and Finance. She is a noted researcher, having authored or co-authored six books (all published by Wiley), including Real-Time Risk (2017, with Steve Krawciw, Wiley, ISBN: 9781119318965), High-Frequency Trading (2nd ed., 2013, Wiley, ISBN: 9781118343500) and multiple research articles. Over the years, Irene has held various senior positions in most aspects of financial services, beginning with back office with large scale integration of financial systems and mission-critical security implementations, through system architecture for distributed web-secure applications, through risk management where she ran quantitative teams, through front office and trading, where she developed and built out cutting-edge quantitative financial tools for designing, trading and marketing of financial products.

Irene’s most recent publications include: “Synthetic KYC: Detecting Irregularities and Money Laundering on Blockchains” (patent pending, presented at Columbia Math Finance Seminar, Economic and Computation Conference 2024 (poster), INFORMS Security 2024, University of Florida Blockchain conference), “The AI Revolution: From Linear Regression to ChatGPT and Beyond and How It All Connects to Finance” (2023, Journal of Portfolio Management) and Big Data Science in Finance (2021, with Marco Avellaneda, Wiley, ISBN: 9781119602989).

Katrin Tinn: Associate Professor of Finance, Desautels Faculty of Management, McGill University

Katrin Tinn’s research focuses on theoretical modelling, technological innovation, financial economics, information economics. Her most recent work is on FinTech: the role of crowdfunding and distributed ledger technologies (blockchain) in raising external capital and facilitating learning about future demand, and on central bank digital currency design. Her research has been published in prominent journals, such as the American Economic Review, Management Science and Journal of Economic Theory. She has also written book chapters for editions focused on alternative finance, blockchain, and digital currencies (Palgrave-MacMillan, World Scientific Publishers, and VoxEU’s e-book series).

Prior joining McGill in 2019, she worked as an Assistant Professor of Finance at Imperial College Business School, London, and has been a member of the Centre for Global Finance and Technology, and the Imperial College multidisciplinary Fintech Network. She is a member of the Centre for Economic Policy Research (CEPR). In addition to academic positions, she has also worked in commercial banking and asset management, at the European Central Bank, the International Monetary Fund, and the European Bank for Reconstruction and Development. Katrin holds a MSc in Economics from University College London and a PhD in Economics from London School of Economics.

Hiteeksha Mathur Ghai: VP, Quantitative Research, BlackRock & Founder, ‘Quant’ify Your Career

Hiteeksha is a Quantitative Researcher in the Multi-Asset Strategies division at BlackRock and the founder of ‘Quant’ify Your Career, an educational and mentorship platform for quant finance aspirants.

Her experience spans financial risk management, quantitative retirement research and analytics, applied AI across different asset classes, systematic fixed income investing, derivatives trading and sales, product management, and risk consulting.

Hiteeksha has worked across applied AI and quantitative engineering, including building investment recommendation systems using knowledge graphs and NLP, developing machine learning pipelines for peer clustering and risk outlier detection, and deep learning frameworks using computer vision to reduce manual work at scale. Her research work includes designing and backtesting relative value strategies and leading utility-based optimization research for retirement portfolios.

She holds an MBA in Finance, Bachelors in IT Engineering, and the Financial Risk Manager (FRM) Charter. In addition to her full-time roles, she mentors students & quant aspirants on Quantitative Finance study and career preparation through her platform: ‘Quant’ify Your Career (https://www.youtube.com/@QuantifyYourCareer) and has led master classes on Data Science in Finance, as well as preparation for quant interviews at university programs.

Victoria Averbukh: Professor of Practice & Director of Cornell Financial Engineering Manhattan

Victoria Averbukh is a Professor of Practice at the School of Operations Research and Information Engineering and the Director of Cornell Financial Engineering Manhattan (CFEM). Victoria received her B.A. in Mathematics from NYU in 1993 and her M.S. and Ph.D. from Cornell ORIE in 1997.

After completing her Ph.D., Victoria worked in Fixed Income Research at Salomon Brothers (now Citi) as a strategist covering U.S. Treasury futures, and later Mortgage-Backed Securities. In 2004 she joined Deutsche Bank, where she became the Head of Structured Residential Mortgage-Backed Securities Research. During her Wall Street career, Victoria focused on transaction-oriented research in fixed income. She has been quoted by the Wall Street Journal, the New York Times, and Bloomberg Radio.

CFEM was established in 2007 to serve as a satellite Manhattan campus for ORIE M.Eng. students interested in careers in quantitative finance.  As a director of CFEM, Victoria leverages her knowledge of financial markets and broad relationships within the financial industry to ensure that students receive the practical and hands-on education needed to start their careers.

Agathe Sadeghi: Research Fellow, Uniswap Labs

Agathe Sadeghi is a quantitative researcher and financial engineer whose work sits at the intersection of market microstructure, statistical learning, and digital asset markets. She is currently a Research Fellow at Uniswap Labs in New York, where she focuses on research related to decentralized markets and modern trading mechanisms.

Alongside her industry work, Agathe spent nearly six years at Stevens Institute of Technology, serving as an Instructor, Graduate Research Assistant, and Graduate Teaching Assistant. She taught and supported courses in portfolio theory, statistical learning, and market microstructure, while conducting academic research in financial engineering. Earlier at Stevens, she also worked as a Hanlon Financial Systems Lab Assistant.

Agathe has industry research experience from internships at Bloomberg, where she worked in Quant Analytics and Quant Research roles, contributing to data driven financial analysis and modeling.

She is highly active in academic leadership and service. She co founded the Business Doctoral Students Association at Stevens and serves as Vice President. She is also a board member of the Doctoral Student Advisory Group and previously held leadership roles within the Stevens Society of Financial Engineering, including Treasurer and Vice President. In addition, she has mentored Armenian undergraduate students through AGBU financial internship programs in New York.

Agathe holds a PhD in Financial Engineering from Stevens Institute of Technology, along with an MS in Financial Engineering. She also earned an MS in System Engineering and a BS in Industrial Engineering from Iran University of Science and Technology, where she previously served as Editor in Chief of the department’s first scientific magazine.

Natascha Hey: Postdoctoral Researcher and Adjunct Professor, Columbia Business School

Natascha Hey is a postdoctoral researcher and adjunct professor at Columbia Business School, where she teaches optimization theory and studies market microstructure in both traditional and decentralized financial markets. Her research focuses on optimal execution and the design of trading and liquidation mechanisms, including recent work on auto-deleveraging systems in crypto derivatives markets. She completed her PhD at École Polytechnique in collaboration with Capital Fund Management in Paris. Her work has been published in Risk and Operations Research and recognized with several academic and industry awards.