Conference Stream
08.15 – 09.00
Registration and Welcome Coffee
09.00 – 09.45: Doing more with tick data: a Machine Learning approach
Edith Mandel:
Principal, Greenwich Street Advisors, LLC
Edith Mandel:
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.
09.45 – 10.30: Smartly Managing Risk for Complex Payoffs
Risk managing exotic derivatives in today’s financial markets is challenging due to their intricate structures. Our approach enhances traditional Greek-based hedging by incorporating dynamic risk factors, crucial for managing the variable Vega profiles of exotic payoffs. Further, we employ a data-driven strategy using a Value Function Neural Network (VNN) to identify risk factors that drive PnL, with feature engineering playing a vital role in building an effective exotics learning model. This method uses a componentized approach, integrating a Differentiable Linear Constraint Layer, Gradient Surgery, and the VNN for scalability. This innovative strategy boosts efficiency and effectiveness, offering a strategic edge and demonstrating potential for broad application in complex derivatives.
Nina Wu:
Quantitative Research Associate at J.P. Morgan
Nina Wu:
Nina Wu: Quantitative Research Associate at J.P. Morgan
Nina joined the J.P. Morgan Quantitative Research group in 2022 after she earned her Master’s degree in Computational Finance at Carnegie Mellon University. During her time as a quantitative research associate, she plays a role to assist Equity Derivatives Desk in various end including pricing, risk management, hedging strategies, and developing platforms to aid trading operations and decision making.
Mengdi Wang:
Quantitative Research Vice President, JPMorganChase
Mengdi Wang:
Mengdi Wang: Quantitative Research Vice President, JPMorganChase
Mengdi Wang joined JPMorganChase in 2020 as a quantitative researcher, working closely with Equity Exotic Derivatives trading desk to provide critical analytical support for modeling and risk management. Her expertise lies in leveraging innovative AI solutions to enhance the efficiency and accuracy of quoting and hedging for exotic products.
She holds a Master’s in Financial Mathematics at Columbia University and is a CFA charterholder.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: “This is the Real World, Not Barbie World”
Inflation is not a trade or a macro call about what is going to happen in the future. Inflation is a risk to all investors, especially savers as they move closer to retirement. How to measure and invest in inflation and rate volatility markets will be the topic of this discussion.
Nancy Davis:
CIO and Managing Partner, Quadratic Capital
Nancy Davis:
Nancy Davis: CIO and Managing Partner, Quadratic Capital
Nancy Davis founded Quadratic Capital Management in 2013. Ms. Davis began her career at Goldman Sachs where she spent nearly ten years, the last seven at the proprietary trading group where she rose to become the Head of Credit, Derivatives and OTC Trading.
Prior to starting Quadratic, she served as a portfolio manager at Highbridge where she managed $500 million of capital in a derivatives-only portfolio. She later served in a senior executive role at AllianceBernstein.
Ms. Davis writes and speaks frequently about markets and investing. She has been published in Institutional Investor, Absolute Return and Financial News, and has contributed papers to two books. She has been interviewed by The Economist, The Wall Street Journal, The Financial Times, New York Magazine and Le Figaro. Ms. Davis has also appeared on CNBC, CNN, Reuters, Sina, Bloomberg and MSNBC.
11.45 – 12.30: PANEL: Talent Attraction & Retention:
Recruiting/Retaining talent
- What are QR Financial Services currently doing and what should they be doing to attract more female talent?
- What strategies are financial companies using to retain talent? Is there anything else that could be done?
Career progression
- At more senior levels the number of women is even lower than at entry level which means that the female population retention rate is low or/and women are not being promoted. Discuss
- Are quantitative positions too specialised which prevents women (and men) to move horizontally to different (and possibly more senior) roles?
- Hybrid working model-does it benefit for retaining/advancing women careers?
- Mentoring vs sponsorship programmes that could specifically help Diversity & Inclusion.
- Limitations for progression in challenging times when there is less opportunities available
- Is there difference in diversity approach/success between US vs Euro banks?
- Company diversity targets-good idea?
- Do junior female employees face unique barriers?
- How to overcome unconscious bias:
- Have you personally experienced instances of gender bias in the workplace?
- Has it gotten better over the years?
Moderator:
Christina Qi:
CEO, Databento
Christina Qi:
Christina Qi: CEO, Databento
Christina Qi is the CEO of Databento, an institutional market data provider. She formerly founded Domeyard LP, a hedge fund focused on high frequency trading (HFT) that traded up to $7.1 billion USD per day. Failing to earn a job offer after a Wall Street internship, Christina started Domeyard from her dorm room with $1000 in savings, in 2012. Her fund was a tiny minnow amongst the tigers of the hedge fund world, but after Michael Lewis’s Flash Boys came out in 2014 and HFT firms hid from the spotlight, Domeyard accidentally found itself in the center of the ring. Over the next decade, her company’s story was featured on the front page of Forbes and Nikkei, and quoted in the Wall Street Journal, Bloomberg, CNN, NBC, and the Financial Times as a result of the controversy and fascination with HFT. By a series of accidents, Christina became a voice in her industry, contributing to the World Economic Forum’s research on AI in finance, guest lecturing at dozens of universities, and teaching Domeyard’s case study at Harvard Business School. She is grateful to be able to open up about her mistakes, and to help people turn failures into opportunities. After a decade, Domeyard winded down gracefully after facing increased headwinds to its strategies, scalability, and operational resources.
No amount of therapy has quashed Christina’s impostor syndrome, but she will always be proud of her non-profit volunteer work. Christina is a member of the MIT Corporation Development Committee, a standing committee of the MIT Corporation (the board of trustees), charged with raising critical financial resources to uphold the Institute’s academic rigor, promote student life, and advance global initiatives. From 2018 to 2023, she co-chaired the Development Committee and eventually became Co-Chair of the Board of Invest in Girls, bringing financial literacy education to underserved populations across the US. Christina also sits on the Board of Directors of The Financial Executives Alliance (FEA) Hedge Fund Group, drove entrepreneurship efforts at the MIT Sloan Boston Alumni Association (MIT SBAA), and served on the U.S. Non-Profit Boards Committee of 100 Women in Finance. Although “X Under X” lists are a gimmick, she’ll admit that Forbes 30 Under 30 made a positive impact on her life by giving her a community – friends who dragged her out of bed during the lowest days of her life. Christina holds a Bachelor of Science in Management Science from MIT and is a CAIA Charterholder.
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.
Lily Gu:
Quantitative Researcher, Bloomberg LP
Lily Gu:
Lily Gu: Quantitative Researcher, Bloomberg LP
Ms. Lily Gu joined the Bloomberg Quantitative Research group in 2018. Prior to that, she earned her Master in Finance from Princeton University. At Bloomberg, Ms. Gu’s work focuses on applying innovative quantitative models across all asset classes & using machine learning methods to help reveal embedded signals in financial data.
Mante ZB:
Quantitative Research Executive Director, JPMorganChase
Mante ZB:
Mante ZB: Quantitative Research Executive Director, JPMorganChase
I am a quantitative researcher at JPMorganChase, specializing in counterparty credit exposure models for valuation, risk management, and capital allocation. Since joining the firm in 2014, I have held various finance roles within the commodities and rates businesses, gaining extensive experience in counterparty credit risk management.
In 2021, I transitioned to the Quantitative Research team after earning an MSc in Data Science from University College London. I am currently pursuing a PhD in Bayesian Statistical Modelling with a focus on financial applications. In 2023, I relocated to New York to further my career in quantitative research.
As an active member of the QR Diversity, Equity, and Inclusion Council, I am dedicated to promoting inclusive recruitment practices and talent retention initiatives.
Shulin Liu:
Director Markets Quantitative Analytics, Barclays
Shulin Liu:
Shulin Liu: Director Markets Quantitative Analytics, Barclays
“Shulin began her career in Bloomberg before joining Lehman Brothers’ Structured Credit Trading Analytics in 2007. She joined Barclays’s Credit Correlation IT team in 2008. In 2011, she moved to Credit team of Quantitative Analytics (QA). From 2014 to 2018, Shulin led the QA effort to implement CCAR Global Market Shock (GMS) for all products in scope. She is currently part of Core Engineering Team in QA Markets, where she leads the Analytics Platform Delivery team in New York. Additionally, she co-chairs the committee for QA intern program in US.”
12.30 – 13.30: Lunch Break
13.30 – 14.15: Assess how a combination of the right model, causal feature discovery, and conformal prediction can strengthen market prediction.
- Provide a technical overview of how different models including GLM / GAM / ML are applied for stock market prediction
- Highlight the model challenges present without incorporating causality into research process
- Process and train models to mitigate these inherent risks and maximize prediction accuracy with causality
- Understand the role of conformal prediction in advancing methods of quantitative trading
Judith Gu:
Managing Director, Head of Equities & eFX Quant Strategist, Scotiabank
Judith Gu:
Judith Gu: Managing Director, Head of Equities & eFX Quant Strategist, Scotiabank
14.15 – 15.00: “Introduction to Generative AI managed services on AWS”
Mojgan Ahmadi:
Head of Markets Data Science Platform Architecture, Barclays
Mojgan Ahmadi:
Mojgan Ahmadi: Head of Markets Data Science Platform Architecture, Barclays
15.00 – 15.45: Towards High Performance Large Generative AI Models for Domain-specific Code Generation
Abstract: In recent years, significant advancements in artificial intelligence have been driven by the development of Deep Neural Networks (DNNs) and Transformer-based models, including BERT, GPT-3, and other Large Language Models (LLMs). These technologies have catalyzed innovations in various fields such as autonomous driving, recommendation systems, and chatbot applications. DNNs are increasingly designed with deeper, more complex structures and require larger computational resources. As computational demands escalate, model sparsification has emerged as a promising method to reduce model size and computational load during execution. Given the evolution of high performance computing platforms, particularly advanced GPUs, end-to-end DNNs runtime speedup with model sparsification is an ideal but difficult goal due to the intricacies involved in sparsity which may need the change of matrix and kernel settings.
In this talk, I will present our recent efforts in domain-specific code generation and system optimizations. It mainly focuses on the following innovative aspects: (1) our recent works on using generative AI models for high-quality domain specific code generation and the cooperative competitive (coopetitive) framework design for code generation in general domains; (2) an advanced sparse progressive pruning method which show for the first time that reducing the risk of overfitting can help the effectiveness of pruning; (3) an novel self attention architecture design for Transformer-based models inference acceleration.
Shaoyi Huang:
Assistant Professor at Stevens Institute of Technology
Shaoyi Huang:
Shaoyi Huang: Assistant Professor at Stevens Institute of Technology
Shaoyi is a tenure-track assistant professor in the Computer Science department at Stevens Institute of Technology. She received her PhD degree in Computer Engineering from the School of Computing at University of Connecticut. She was selected as a Machine Learning and Systems Rising Star 2024. She was awarded the Marion and Frederick Buckman Engineering Fellowship for outstanding academic achievement in 2024 and both Predoctoral Prize for Research Excellence, GE Fellowship for Excellence from UConn in 2023 and 2022. She was the recipient of Eversource Energy Graduate Fellowship in 2022, Synchrony Fellowship in 2022, and Cigna Graduate Fellowship in 2021. Her work on FPGA-based language models acceleration through sparse attention and dynamic pipelining won DAC 2022 Publicity Paper Award.
15.45 – 16.15: Afternoon Break and Networking Opportunities
16.15 – 17.00: Lessons Learned from Starting a Hedge Fund
Christina Qi:
CEO, Databento
Christina Qi:
Christina Qi: CEO, Databento
Christina Qi is the CEO of Databento, an institutional market data provider. She formerly founded Domeyard LP, a hedge fund focused on high frequency trading (HFT) that traded up to $7.1 billion USD per day. Failing to earn a job offer after a Wall Street internship, Christina started Domeyard from her dorm room with $1000 in savings, in 2012. Her fund was a tiny minnow amongst the tigers of the hedge fund world, but after Michael Lewis’s Flash Boys came out in 2014 and HFT firms hid from the spotlight, Domeyard accidentally found itself in the center of the ring. Over the next decade, her company’s story was featured on the front page of Forbes and Nikkei, and quoted in the Wall Street Journal, Bloomberg, CNN, NBC, and the Financial Times as a result of the controversy and fascination with HFT. By a series of accidents, Christina became a voice in her industry, contributing to the World Economic Forum’s research on AI in finance, guest lecturing at dozens of universities, and teaching Domeyard’s case study at Harvard Business School. She is grateful to be able to open up about her mistakes, and to help people turn failures into opportunities. After a decade, Domeyard winded down gracefully after facing increased headwinds to its strategies, scalability, and operational resources.
No amount of therapy has quashed Christina’s impostor syndrome, but she will always be proud of her non-profit volunteer work. Christina is a member of the MIT Corporation Development Committee, a standing committee of the MIT Corporation (the board of trustees), charged with raising critical financial resources to uphold the Institute’s academic rigor, promote student life, and advance global initiatives. From 2018 to 2023, she co-chaired the Development Committee and eventually became Co-Chair of the Board of Invest in Girls, bringing financial literacy education to underserved populations across the US. Christina also sits on the Board of Directors of The Financial Executives Alliance (FEA) Hedge Fund Group, drove entrepreneurship efforts at the MIT Sloan Boston Alumni Association (MIT SBAA), and served on the U.S. Non-Profit Boards Committee of 100 Women in Finance. Although “X Under X” lists are a gimmick, she’ll admit that Forbes 30 Under 30 made a positive impact on her life by giving her a community – friends who dragged her out of bed during the lowest days of her life. Christina holds a Bachelor of Science in Management Science from MIT and is a CAIA Charterholder.
17.00 – 17.45: “Measuring Performance of AI-Based Strategies: Advances in Optimization”
So, you have distinct AI strategies. Now what? How do you compare their performance and usability? This talk focuses on the latest techniques for measuring and evaluating AI-based output.
Computer Scientists have made significant inroads in evaluating performance of AI strategies. We will review:
- The state-of-the-art performance measurement of AI models
- Irene Aldridge’s latest research in this space
- Implementation with specific Quant Finance examples
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).
17.45 – 18.30: PANEL: Career Progression:
1. How has the idea of a quant woman changed over time?
2. What were the most memorable times you can think of when you think of being a woman in quant finance?
3. How do you balance family and work?
4. How does raising children factor in with your career?
5. What were the hardest or most interesting career lessons you had to learn?
6. Did your academic experience prepare you for the work life in quant finance? If so, how?
Moderator:
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).
Nancy Davis:
CIO and Managing Partner, Quadratic Capital
Nancy Davis:
Nancy Davis: CIO and Managing Partner, Quadratic Capital
Nancy Davis founded Quadratic Capital Management in 2013. Ms. Davis began her career at Goldman Sachs where she spent nearly ten years, the last seven at the proprietary trading group where she rose to become the Head of Credit, Derivatives and OTC Trading.
Prior to starting Quadratic, she served as a portfolio manager at Highbridge where she managed $500 million of capital in a derivatives-only portfolio. She later served in a senior executive role at AllianceBernstein.
Ms. Davis writes and speaks frequently about markets and investing. She has been published in Institutional Investor, Absolute Return and Financial News, and has contributed papers to two books. She has been interviewed by The Economist, The Wall Street Journal, The Financial Times, New York Magazine and Le Figaro. Ms. Davis has also appeared on CNBC, CNN, Reuters, Sina, Bloomberg and MSNBC.
Joanna Brard:
Executive Director, JPMorgan Chase & Co.
Joanna Brard:
Joanna Brard: Executive Director, JPMorgan Chase & Co.
Joanna works at J.P. Morgan in Quantitative Research, where she plays an instrumental role in supporting the Equity Derivatives desk. Her responsibilities include developing sophisticated models and strategies to optimize risk management, pricing, and trading operations.
Before joining J.P. Morgan, she accumulated over a decade of experience at Deutsche Bank, where she advanced to the position of Director. During her time there, she worked in both New York and Hong Kong, demonstrating her ability to adapt to various markets and regulatory environments.
Joanna began her career at Barclays Capital in London and New York, where she built a strong technical foundation.
She holds a Master’s in Quantitative Finance from the University of Oxford and a Bachelor’s in Engineering Science from the University of Auckland.
Shilpa Akella:
Shilpa Akella: Managing Director, Head of Equities Structuring Americas, Barclays
Shilpa Akella:
Shilpa Akella: Managing Director, Head of Equities Structuring Americas, Barclays