Conference Stream
08.15 – 09.00
Registration and Welcome Coffee
09.00 – 09.45: “Beating the Memory Wall: Tile Computing Patterns for Quant Finance”
Ioana Boier:
Ioana Boier:
Ioana Boier: Global Head of Capital Markets Strategy, 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.
09.45 – 10.30: Hedging Exotic Equity Derivatives: From risk profiles to backtesting framework
Candice Yan
Vice President, Quantitative Trading & Research, J.P. Morgan
Candice Yan
Candice Yan: Vice President, Quantitative Trading & Research, J.P. Morgan
Candice is a quantitative researcher within the Equities Derivatives Group at J.P. Morgan, based in New York, where she has been since 2018. She specializes in exotic equity derivatives pricing and risk management, with focus areas including path-dependent payoffs and modelling, numerical methods, and more. Candice holds a Master of Science in Computational Finance (MSCF) from Carnegie Mellon University.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Using Generative Adversarial Networks (GAN) and Transformer Models for Portfolio Construction
Hiteeksha Mathur Ghai:
VP, Quantitative Research, BlackRock ; Founder, ‘Quant’ify Your Career
Hiteeksha Mathur Ghai:
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.
11.45 – 12.30: PANEL: Talent Attraction & Retention
Recruiting/Retaining talent
- What strategies are financial companies using to retain talent? Is there anything else that could be done?
Pipeline & Entry Routes
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How can universities and employers better collaborate to strengthen the pipeline of female quant talent especially at MSc/PhD level?
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Are traditional recruiting criteria (e.g., specific degrees, competition backgrounds) unintentionally narrowing the pool of potential female candidates? What alternatives could broaden access?
Culture & Belonging
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Micro-cultures within quant teams can vary widely across a firm. What cultural markers actually make women stay?
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How can leaders detect and address “invisible tax” issues (e.g., women taking on more non-promotable work, emotional labour, or DEI (Diversity, Equity, and Inclusion) tasks)?
Retention & Managerial Responsibility
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How much of retention is driven by the behaviour of individual line managers versus firm-wide policy?
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Are performance reviews and promotion cycles transparent enough for women in quant roles to feel fairly assessed?
Compensation & Transparency
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Does pay-transparency help retention in quant teams? Where has it worked well?
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Are women in quant finance getting equitable access to high-bonus roles (e.g., model validation vs. trading strategy roles)?
Future-Looking Questions
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How might emerging areas: AI risk, sustainability analytics, digital assets offer opportunities to attract more women into the next generation of quant roles?
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If you were to redesign the “ideal quant career path” from scratch, what would you change to make it more inclusive?
Moderator:
Edith Mandel:
Principal, Greenwich Street Advisors, LLC
Edith Mandel:
Edith Mandel: Principal at Greenwich Street Advisors, LLC
Edith Mandel is a seasoned finance professional with 20 years of experience. She held a number of senior roles both on the sell and buy sides of the Fixed Income business.
Edith has extensive hands-on experience in developing quantitative trading models, and building systematic risk-taking businesses from the ground up.
As a principal at Greenwich Street Advisors, LLC, Edith advises both established participants in the Fixed Income market and those companies considering opportunities for expansion. As an expert in the Fixed Income market, Edith evaluates the opportunity cost, advises on trading infrastructure build-out, electronic and quantitative trading, risk management, alpha research and algorithmic execution.
In the last two-and-a-half years, Edith Mandel was the head of Fixed Income Mid-Frequency Trading at KCG (formerly GETCO). While there, she spearheaded a development of a new quantitative and systematic business within the Global Fixed Income group.
Edith started her professional career at Goldman Sachs, where she held a number of positions in the Fixed Income division. As a Managing Director, Edith ran a team of quantitative strategists responsible for algorithmic trading in US Treasuries and Swaps, for risk management of a broad set of interest rate products, including vanilla and exotic options, and for the development of a toolkit for systematic risk-taking.
Prior to joining KCG, Edith Mandel worked at Citadel as a Managing Director, Head of Fixed Income Quantitative Research. There she was instrumental to a significant revamp and expansion of the Fixed-Income Asset Management business and a development of new profitable systematic trading strategies in liquid rates.
Victoria Averbukh:
Professor of Practice & Director of Cornell Financial Engineering Manhattan
Victoria Averbukh:
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.
Wafaa Schiefler:
Executive Director – Commodities Quantitative Researcher, JP Morgan Chase
Wafaa Schiefler:
Wafaa Schiefler: Executive Director – Commodities Quantitative Researcher, JP Morgan Chase Speaker
Wafaa Schiefler joined J.P.Morgan in 2011 as a Commodities Quant. She leads the Base Metals, Precious Metals and Agricultural Products Quantitative Research, as well as the Commodities Data Science team. Her main responsibilities include the development and support of pricing and risk management models, and the implementation of data-driven tools to help the Trading and Sales desks. Prior to this, Wafaa was a Quant on Energy. Wafaa is also part of the Diversity, Equity and Inclusion Council of J.P.Morgan. She has been encouraging women to join the world of Quantitative Finance since the beginning of her career and mentored many students. She created a large network of Women in Quantitative Research on LinkedIn (860+ members).
Wafaa started her career in finance in 2008. She holds a MSc in Applied Mathematics from Ecole Centrale Paris and a MSc in Quantitative Finance from University Paris VII.
Qingyi Huang:
Managing Director, Head of AlphaWise Quant Research, Morgan Stanley
Qingyi Huang:
Qingyi Huang: Managing Director, Head of AlphaWise Quant Research, Morgan Stanley
Qingyi Huang is a Managing Director of Morgan Stanley in Research based in New York. She is Head of AlphaWise quant team and is responsible for conducting quantamental research and using AI & ML for alpha generation. Qingyi worked as a Fixed Income Strat for three years before she moved to Equity Research. Prior to joining Morgan Stanley, Qingyi was an assistant professor at Guanghua School of Management at Peking University from 2011 to 2014. Qingyi holds a PhD degree from Kellogg School of Business and a BS degree from Fudan University.
12.30 – 13.30: Lunch Break
13.30 – 14.15: “Quantitative Models with Qualitative Scenarios: Simulation with Transformer and Large Language Models”
Irene Aldridge:
CEO and Founder, AbleBlox and AbleMarkets
Irene Aldridge:
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).
14.15 – 15.00: “Digital Ownership: The Tokenization of Real-World Assets.”
We study conditions under which tokenization creates value for indivisible realworld assets (RWAs). We show that tokenization does not create value merely by making an asset transferable, fractional, or digitally scarce. Value arises only when digital ownership records change the economics of ownership by leveraging on asset characteristics and past ownership records on the blockchain. This may generate retained value for past owners, create provenance value for later buyers, separate usage and financial rights, support membership benefits through fractional ownership, or strengthen post-sale incentives through royalties. These forces determine whether tokenized markets are inactive, thin but informative, lemons-like, or liquid but uninformative. We discuss implications for art and luxury tokens, private-equity and venture-capital tokens, real-estate tokens, tokenized claims on a social enterprise, and sustainable-firm tokens.
Katrin Tinn:
Associate Professor of Finance, Desautels Faculty of Management, McGill University
Katrin Tinn:
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.
15.00 – 15.45: Feature-less Engineering: A deep learning pipeline for predictive modeling with intraday market data
Feature engineering could be a tax on alpha generation with intraday data – months of researcher time spent on brittle transformations. We present a framework that eliminates this step entirely: a deep learning pipeline that ingests raw time series data and surfaces directional signals with built-in interpretability, no feature specification required
Edith Mandel:
Principal, Greenwich Street Advisors, LLC
Edith Mandel:
Edith Mandel: Principal at Greenwich Street Advisors, LLC
Edith Mandel is a seasoned finance professional with 20 years of experience. She held a number of senior roles both on the sell and buy sides of the Fixed Income business.
Edith has extensive hands-on experience in developing quantitative trading models, and building systematic risk-taking businesses from the ground up.
As a principal at Greenwich Street Advisors, LLC, Edith advises both established participants in the Fixed Income market and those companies considering opportunities for expansion. As an expert in the Fixed Income market, Edith evaluates the opportunity cost, advises on trading infrastructure build-out, electronic and quantitative trading, risk management, alpha research and algorithmic execution.
In the last two-and-a-half years, Edith Mandel was the head of Fixed Income Mid-Frequency Trading at KCG (formerly GETCO). While there, she spearheaded a development of a new quantitative and systematic business within the Global Fixed Income group.
Edith started her professional career at Goldman Sachs, where she held a number of positions in the Fixed Income division. As a Managing Director, Edith ran a team of quantitative strategists responsible for algorithmic trading in US Treasuries and Swaps, for risk management of a broad set of interest rate products, including vanilla and exotic options, and for the development of a toolkit for systematic risk-taking.
Prior to joining KCG, Edith Mandel worked at Citadel as a Managing Director, Head of Fixed Income Quantitative Research. There she was instrumental to a significant revamp and expansion of the Fixed-Income Asset Management business and a development of new profitable systematic trading strategies in liquid rates.
15.45 – 16.15: Afternoon Break and Networking Opportunities
16.15 – 17.00: “Driving Innovation in Active ETFs: Custom Baskets, Mutual Fund Conversions, and Share Classes.”
Mary Phillips:
Deputy Head of Portfolio Management, North America, Dimensional Fund Advisors
Mary Phillips:
Mary Phillips: Deputy Head of Portfolio Management, North America, Dimensional Fund Advisors
17.00 – 17.45: PANEL: Career Progression
Structural Barriers & Gender Dynamics
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Do you think that being a woman is a significant factor in slowing down career progression in Financial Services?
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Is it still hard to make it to the top positions? If so, why, and what can be done to change the situation?
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Where in the career pipeline do women face the steepest drop-off, and why?
AI in the workforce and its potential impact on women
- How could AI change these dynamics in the workplace?
- More generally, will AI impact the workplace in a similar way for men and women, and minorities?
Maternity Leave, Shared Parental Leave & Return-to-Work Strategies
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Has Shared Parental Leave (SPL) helped equality in this area?
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Discuss the current career return-to-work strategies available.
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Have you benefited from any such schemes?
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What would an ideal returnship programme for quants look like?
Promotions, Pay & Workplace Practices
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How important are:
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Promotions / career opportunities
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Pay-gap elimination
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Agile / flexible working
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Getting the feedback you need (even if you don’t really want it)
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Supporting each other
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Are performance-review systems objective enough for technical roles like quant?
Moderator:
Hiteeksha Mathur Ghai:
VP, Quantitative Research, BlackRock ; Founder, ‘Quant’ify Your Career
Hiteeksha Mathur Ghai:
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.
Jess Stauth:
Chief Investment Officer – Systematic Equity, Fidelity Investments
Jess Stauth:
Jess Stauth: Chief Investment Officer – Systematic Equity: Fidelity Investments
Jess is a seasoned asset management executive with over 15 years of experience in quantitative finance, systematic asset management and fintech. Jess has held a range of leadership positions in both startups (Quantopian, StarMine) and established firms (Fidelity Investments, Geode Capital Management, Thomson Reuters now Refinitiv). Skilled in quantitative finance, risk management, portfolio construction, data science, statistical modeling, and equity research. Jess holds a PhD in Biophysics from University of California, Berkeley.
Natascha Hey:
Postdoctoral Researcher and Adjunct Professor, Columbia Business School
Natascha Hey:
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.
Karen B. Lin:
Executive Director and co-lead of the Credit Quant Trading team, JPMorgan
Karen B. Lin:
Karen B. Lin: Executive Director and co-lead of the Credit Quant Trading team, JPMorgan
Karen B. Lin is an Executive Director and co-lead of the Credit Quant Trading team at JPMorgan, where she has spent 20 years across a uniquely diverse career path. Before moving into quantitative finance, Karen worked in various roles bridging the gaps among technology, structuring, and trading — giving her a rare, cross-functional perspective on the markets. In 2015 she joined the EDG Quantitative Analytics team, and in late 2018 transitioned to Credit Quantitative Research, where she has spent the past seven years deepening her expertise in credit markets. Today, Karen’s work spans mid-price modeling, signals and alpha research, market microstructure, and factor models, applying statistical learning and machine learning techniques to drive innovation across these areas. She holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science from Stony Brook University. A former QR Champion for Mobility and internship mentor, Karen remains passionate about career development and is always happy to connect with anyone looking to discuss their path in quantitative finance.