Financial Engineering Practitioners Seminar

Welcome to the Financial Engineering Practitioners Seminar! This is a recurring series hosted by the IEOR Department that features a diverse lineup of speakers from both academia and industry. Speakers share insights on a range of topics in the rapidly evolving world of financial engineering. The goal is to help students of all levels expand their knowledge, explore the latest research and trends, and to connect leading experts with the larger Columbia community. 


Financial Engineering Practitioners Seminar

The seminar presents research and practice in financial engineering and related fields. It is open to Columbia students, attendees from industry and academia, and the general public (space permitting).

To request more information or express interest in becoming a corporate sponsor, please contact us.

Please click on the expandable sections below to see the slides and information about the talks.

We look forward to seeing you at the next FE Practitioners Seminar.


Ali Hirsa

Professor of Professional Practice, Industrial Engineering & Operations Research
Director of the MS in Financial Engineering
Director of Financial Engineering Practitioners Seminar

Fall 2022 Seminars

Nitin Gupta | September 26th

Speaker: Nitin Gupta
Date: Monday, September 26, 2022
Time: 7:00pm to 8:30pm
Location: Davis Auditorium

Title: Using Data to Drive Value in PE

Bio: Based in New York, Nitin Gupta leads US investments, and is a member of the Global Advisory Investment Committee, and the US Investment Committee.

Nitin joined Caspian Private Equity, a predecessor to Flexstone Partners, in 2008. Prior to Caspian, he was a Principal at Westbury Partners, where he was responsible for deal sourcing, due diligence and serving on the board of portfolio companies. Prior to Westbury Partners, Nitin was a Senior Associate at Saunders Karp & Megrue, where he was responsible for due diligence, with a particular focus on healthcare and retail investments. Prior to Saunders Karp & Megrue, he was an Associate at McCown De Leeuw & Company, where he was responsible for due diligence and a buy and build strategy across a number of industries including business services, industrial, and manufacturing. Prior to McCown DeLeeuw & Company, Nitin was an analyst in the M&A group at Merrill Lynch & Company where he completed a number of buy-side transactions for certain Fortune 500 companies.

Nitin earned his BS at New York University and MBA at Harvard Business School. He serves as a Board member/observer for several portfolio companies of funds managed by Flexstone Partners

Amal Moussa | October 3rd

Speaker: Amal Moussa
Date: Monday, October 3, 2022
Time: 7:00pm to 8:30pm
Location: Davis Auditorium

Title: Introduction to Exotic Derivatives Pricing and Hedging

Bio: Dr. Amal Moussa is a Managing Director at Goldman Sachs where she leads the Single Stocks Exotic Derivatives Trading desk. Prior to that, Amal held senior level positions in equity derivatives trading at other leading financial institutions such as J.P. Morgan, UBS and Citigroup.

In addition to her work in Markets, Amal is an Adjunct Professor at Columbia University where she teaches a graduate course on Modeling and Trading Derivatives in the Mathematics of Finance Master's program.

Amal has a Ph.D. in Statistics, obtained with distinction, from Columbia University. Her thesis “Contagion and Systemic Risk in Financial Networks” shed light on the importance of the network structure in identifying systemic financial institutions and formulating regulatory policies, and has been cited by several scholars and industry professionals including former Federal Reserve president Janet Yellen. She was also awarded the Minghui Yu Teaching Award at Columbia University.

Amal is a board member of Teach for Lebanon, an NGO working to ensure that all children in Lebanon have access to education regardless of socioeconomic background, and she is an active member of the Women in Trading network at Goldman Sachs.

Jon Marymor | October 17th

Speaker: Jon Marymor
Date: Monday, October 17, 2022
Time: 7:00pm to 8:30pm
Location: Davis Auditorium

Title: BAM’s Approach to Macro Risk Management

Bio: Jonathan (Jon) Marymor joined the BAM New York office in December 2018 as Director, Macro Risk Management and was then promoted in January 2021 to Head, Macro Risk Americas. Jon joined BAM from Element Capital, where he started as a Vice President in Modeling and Technology and was promoted through to Risk Management, Director, serving as the lead risk analyst across the $15B core fund. His prior professional experience also includes several roles at Morgan Stanley, where he ultimately covered cash and derivatives in the U.S. Interest Rates Strategy group. Jon holds an M.S. in Mathematics from the University of Michigan and a B.S. in Economics from the University of Pennsylvania’s Wharton School of Business.

Reza Gharavi | November 14th

Speaker: Reza Gharavi
Date: Monday, November 7, 2022
Time: 7:00pm to 8:30pm
Location: Davis Auditorium

Title: To Be A Quant...

Bio: Reza is a Director at Citi’s Market Quantitative Analysis team. He joined Citi in 2005. Before that he held a number of quant positions starting from 1998. He holds a B.Sc. from Boston University and a Ph.D. from Cornell University, both in Electrical Engineering.  

Fabrizio Lecci | November 21st

Speaker: Fabrizio Lecci
Date: Monday, November 21, 2022
Time: 7:00pm to 8:30pm
Location: Davis Auditorium

Title: Survival Analysis for Business Applications

Abstract: Survival Analysis is a branch of statistics for analyzing the expected duration of time until one event occurs, such as death in biological organisms and failure in mechanical systems. However, Survival Analysis techniques are often neglected in business analytics in favor of linear regression models or other nonparametric machine learning solutions. In this talk we go over the fundamentals of Survival Analysis and we discuss why it is an effective tool for studying a variety of business events such as: customer churn, product adoption, employee hiring and resignation, engineering ticket events, loan repayment, inventory issues. 

Bio: Fabrizio Lecci is a statistician and a Data Science executive. After receiving a PhD in Statistics from Carnegie Mellon University he worked in tech and fintech companies (Uber, New York Life, Better) in various leadership roles. He has published in peer-reviewed journals and he has taught Statistics & Probability for the MBA program at the Fuqua School of Business. Fabrizio recently co-founded a boutique consulting firm, Data Captains, which partners with organizations that are committed to using data to improve their business and products for their customers. The common goal of his work is to advance the fields of applied Data Science and Machine Learning and help others leverage data tools in a rigorous yet pragmatic way.

MSFE Alumnae Panel | December 5th

Speaker: MSFE Alumni Panel
Date: Monday, December 5, 2022
Time: 7:00pm to 8:30pm
Location: Davis Auditorium

Panel Member 1: Fatme Hourani (December 2020 Graduate)
Organization: Summit Rock Advisors: Outsourced CIO focused on advising U.S. based families and charitable institutions

Panel Member 2: Genesis Anzures (February 2017 Graduate)
Organization: London Stock Exchange (LSEG) - Evaluated Pricing Service

Panel Member 3: Zixuan (Linda) Ding (December 2018 Graduate)
Organization: Financial Industry Regulatory Authority (FINRA)

--Past Seminars--

Spring 2022 Seminars

Francis Troise | February 21st

Speaker: Francis Troise

Organization: PICO
Date: Monday, February 21st, 2021
Time: 7:00pm to 8:30pm

Location: Courseworks Zoom Link

Title: Financial Technology — An Expanding Opportunity Set

Abstract:  Background in capital markets related to the "innovation spiral” (Merton, Bodie) within the global financial system. Case study of quantitative & technical innovation (Investment Technology Group - HBS case N9-310-064). Discussion about management "nuggets" per Peter V. Norden, INFORMS Fellow, and former president of The Institute of Management Sciences (TIMS).

Bio: Frank is an experienced business executive with a successful record in delivering corporate growth through strategic, operational, and technology transformations in global financial services. As Co-CEO of Pico, Frank partners with Chairman, Founder, and Co-CEO Jarrod Yuster, to ensure Pico remains the market leader in financial technology infrastructure provision and analytics. Together they lead the company’s strategy, vision, and culture.

Prior to Pico, Frank served as CEO, president, and board member of Investment Technology Group, previously NYSE: ITG, a global financial technology innovator and brokerage solutions provider offering trading, analytics, and workflow technology to institutional asset managers, hedge funds, investment banks, and broker/dealers worldwide. Earlier in his career, Frank was the Managing Director and global leader of JP Morgan’s Executive Services; Managing Director at Barclays Capital and Lehman Brothers. He gained valuable strategic, operations, and technology consulting experience at Andersen Consulting, now known as Accenture, and Booz Allen & Hamilton.

Frank holds a B.S. in electrical engineering from Boston University, an M.S. in operations research from Columbia University, and an M.B.A. from MIT’s Sloan School of Management. He has held series 3, 4, 7, 24, 55 and 63 securities licenses. He serves on the Boston University College of Engineering Dean’s Leadership Advisory Board.

Giulio Renzi Ricci | March 7th

Speaker: Giulio Renzi Ricci

Organization: Vanguard
Date: Monday, March 7th, 2021
Time: 7:00pm to 8:30pm

Location: Courseworks Zoom Link

Title: The diversification role of fixed income securities and forward-looking implications for asset allocation

Abstract: Investors are concerned about the rising rate environment and the role of bonds in multi-asset portfolios. The primary role of bonds is not to produce high returns, but to act as a shock absorber in times of equity market stress. History suggests that high-quality bonds act as ballast for the portfolio in both high and low interest rate environments. Also, by applying unsupervised learning techniques to periods when rates were low, we show that government bonds have historically acted as intended in an equity-bond portfolio, performing positively when equities have fallen. Although in some periods both equities and bonds fell, this can be considered part of market volatility and distinctly different from recurrent market states. Amid low but rising interest rates in the U.S., investors may benefit from non-U.S. fixed income. This can offer diversification because of imperfect correlations of interest rate movements in other countries. Also, when we use our portfolio construction models to build an optimized portfolio, we find that certain high-yielding assets, such as U.S. high-yield corporates and emerging market bonds, enhance the portfolio’s risk-adjusted return.

Bio: Giulio Renzi-Ricci is head of asset allocation for Europe in the Investment Strategy Group, specializing in multi-asset portfolio construction, single fund solutions and econometric forecasting. His research has covered topics on factor investing, active-passive blending, dynamic portfolio optimization and ESG investing. His research has been published in The Journal of Investing and The Journal of Portfolio Management. Before joining Vanguard, Giulio worked at NERA Economic Consulting where he provided expert analysis on financial derivatives pricing, hedge fund litigation, market efficiency testing and the suitability of trading and investment strategies. Before that, he was a fixed income structuring analyst at Banca IMI in Milan. Giulio holds a MSc in financial economics with distinction from the University of Warwick and a BSc in economics and finance summa cum laude from Bocconi University.

Jane Street Capital | March 21st

Organization: Jane Street Capital
Date: Monday, March 21st, 2021
Time: 7:00pm to 8:30pm

Location: Davis Auditorium

Title: Q&A with a Jane Street Trader

Abstract: TBD

Bio: After receiving a BA in Economics and Mathematics from Columbia College in 2012, Condello worked as a Derivative Trader at Citi for four years in Structured Credit and Volatility Derivatives Trading. He now works as an Options Trader at Jane Street. 

Harvey Stein | April 4th

Speaker: Harvey Stein

Organization: Bloomberg
Date: Monday, March 7th, 2021
Time: 7:00pm to 8:30pm

Location: Davis Auditorium

Title: Model Invariants and Functional Regularization

Abstract: When modeling data, we would like to know that our models are extracting facts about the data itself, and not about something arbitrary, like the order of the factors used in the modeling. Formally speaking, this means we want the model to be invariant with respect to certain transformations.

Here we look at different models and the nature of their invariants. We find that regression, MLE and Bayesian estimation all are invariant with respect to linear transformations, whereas regularized regressions have a far more limited set of invariants. As a result, regularized regressions produce results that are less about the data itself and more about how it is parameterized.

To correct this, we propose an alternative expression of regularization which we call functional regularization. Ridge regression and lasso are special cases of functional regularization, as is Bayesian estimation. But functional regularization gives a framework under which the models become invariant with respect to linear transformations. It is also more flexible, and easier to understand, thus yielding a number of advantages over ridge regression and lasso.

Bio: Dr. Harvey J. Stein was Head of the Quantitative Risk Analytics Group at Bloomberg, responsible for Bloomberg's credit risk and market risk models, but he left Bloomberg in March 2022, and hasn't yet started his next job. Dr. Stein is well known in the industry, having published and lectured on credit risk modeling, financial regulation, interest rate and FX modeling, CVA calculations, mortgage backed security valuation, COVID-19 data analysis, and other subjects. Dr. Stein is on the board of directors of the IAQF, a board member of the Rutgers University Mathematical Finance program, an adjunct professor at Columbia University, and organizer of the IAQF/Thalesians financial seminar series. He's also worked as a quant researcher on the Bloomberg for President campaign. He received his BA in mathematics from WPI in 1982 and his PhD in mathematics from UC Berkeley in 1991.

Alireza Javaheri | April 18th

Speaker: Alireza Javaheri

Organization: Credit Suisse
Date: Monday, April 18th, 2021
Time: 7:00pm to 8:30pm

Location: Davis Auditorium

Title: An Overview of Equity Derivatives 

Abstract: The goal is to go over various challenges in the modelling of Equity Derivatives and in particular for volatility and correlation components, with an emphasis on the sell side demands. These challenges can be conceptual, numeric as well as practical.  

Bio: Alireza Javaheri is an Adjunct Professor at Columbia IEOR. He is the Global Head of Equity Derivatives at Credit Suisse and has more than 25 years of experience in the financial industry, with a focus on Equities asset class.

Matthew Dixon | May 2nd

Speaker: Matthew Dixon

Organization: Illinois Institute of Technology
Date: Monday, May 2nd, 2021
Time: 7:00pm to 8:30pm

Location: Courseworks Zoom Link

Title: Deep Partial Least Squares for Factor Modeling

Abstract: Across hedge funds, asset management, and proprietary trading firms, it is commonplace to use supervised learning for asset allocations and trade signal generation by performing ``feature engineering''. This often leads to a high dimensional input space. We present a high dimensional data reduction technique which uses partial least squares within deep learning. This framework provides a nonlinear extension of PLS together with a disciplined approach to feature selection and architecture design in deep learning. Unlike purely deep learning based data reduction techniques, such as autoencoders, we achieve the best of both worlds: fast and scalable SVD based algorithms for orthogonal projection combined with more parsimonious, yet highly expressive, deep architectures.  Using 3290 assets in the Russell 1000 index over a period of December 1989 to January 2018, we assess a 49 factor model and generate information ratios that are approximately 50% greater than the OLS factor models and around 20-25% greater than deep learning.  Furthermore, we observe that DPLS exhibits superior performance and robustness to outliers compared to OLS and deep learning. This is joint work with Nick Polson (Chicago Booth).

In this talk, we develop a RL approach to goal-based wealth management problems such as optimisation of retirement plans or target-dated funds. We present G-Learner: a generative RL algorithm that is suitable for noisy high dimensional data. In addition to quadratic regulators for G-Learners, which solve the direct RL problem very computationally efficiently, we develop GIRL, a G-learning inverse RL algorithm (GIRL) to infer the investor reward function from the observed trading actions. Examples demonstrating our G-learner and GIRL on high-dimensional historical data are presented. This is joint work with Igor Halperin (Fidelity Investments).

Bio: Matthew Dixon, Ph.D, FRM, began his career in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. His research focuses on mathematical algorithms for prediction, outlier detection, and risk, applying concepts in computational and applied mathematics to industrial modeling, especially in the area of investment management, algorithmic trading, and derivatives. He is the co-author of the 2020 textbook "Machine Learning in Finance: From Theory to Practice" and has written over 40 peer reviewed papers on machine learning, the blockchain, and quantitative finance, is RISK Magazine's Buy-side Quant of the Year (2022), the recipient of an Illinois Tech innovation award and the College of Computing's Dean Award for Excellence in Research (Junior level). He has been PI/co-PI on research funding from Intel, Dell, NASA JPL, and the NSF in addition to being quoted in the Financial Times and Bloomberg Markets. Matthew has recently co-authored the CFA course material on machine learning, serves on the CFA advisory committee for quantitative trading, and is associate editor of the World Scientific Annual Review of Fintech. He holds a PhD in Applied Math from Imperial College and has held visiting academic appointments at Stanford and UC Davis. Most recently, he founded a venture capital backed global stablecoin settlement network startup in Chicago.

Fall 2021 Seminars

Satyan Malhotra | September 27th

Speaker: Satyan Malhotra
Date: Monday, September 27, 2021
Time: 7:00pm to 8:30pm

Roshan Raman | October 18th

Speaker: Roshan Raman
Date: Monday, October 18, 2021
Time: 7:00pm to 8:30pm

Gary Kazantsev | October 25th

Speaker:  Gary Kazantsev, Bloomberg
Date: Monday, October 25th, 2021
Time: 7:00pm to 8:30pm

Title: Machine Learning in Finance

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and non-stationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Bio: Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets. Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University. He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference Links to an external site. at Columbia University.

Leif Andersen | November 8th

Speaker: Leif Andersen, Bank of America
Date: Monday, November 8th, 2021
Time: 7:00pm to 8:30pm

Title: Asset Pricing Pre- and Post-Crisis: A 60-minute Tour through 60 Years of Finance

Abstract: A snappy, and highly subjective, historical survey of the most important developments in the field of asset pricing, with a focus on how the Financial Crisis of 2007-2008 upended a long list of assumptions and conventions, and paved the way for much more nuanced valuation methodologies even for (apparently) simple instruments.

Bio: Leif B. G. Andersen is the Global Head of The Quantitative Strategies Group at Bank of America Merrill Lynch, and is an
adjunct professor at NYU’s Courant Institute of Mathematical Sciences and CMU’s Tepper School of Business.  He holds MSc's in Electrical and Mechanical Engineering from the Technical University of Denmark, an MBA from University of California at Berkeley, and a PhD in Finance from Aarhus Business School.  He was the co-recipient of Risk Magazine’s 2001 and 2018 Quant of the Year Awards, and has worked for more than 25 years as a quantitative researcher in the global markets area.  He has authored influential research papers and books in all areas of quantitative finance, and
is an Associate Editor of Mathematical Finance and the Journal of Computational Finance.

David Chubak | November 22nd

Speaker: David Chubak
Date: Monday, November 22, 2021
Time: 7:00pm to 8:30pm

Mark Higgins | December 13th

Speaker: Mark Higgins
Date: Monday, December 13, 2021
Time: 7:00pm to 8:30pm

Spring 2021 Seminars

David Chubak | February 22nd

Speaker: David Chubak
Date: Monday, February 22nd, 2021

Institution: Citi

Rosham Raman | March 8th

Speaker: Rosham Raman
Date: Monday, March 8th, 2021

Institution: Woodline Partners

Topic: Acceleration of Transformation of the Banking Industry

Mark Higgins | April 5th

Speaker: Mark Higgins
Date: Monday, April 5th, 2021


Topic: Applying Deep Hedging t Variable Annuities

Gary Kazanstev | April 12th

Speaker: Gary Kazanstev

Date: Monday, April 12th, 2021

Institution: Bloomberg

Topic: Machine Learning in Finance

Fall 2020 Seminars

Satyan Malhotra | September 28th

Speaker: Satyan Malhotra

Date: Monday, September 28th, 2020


Topic: AI Aplicationws in Asset Management

Kenneth Goodman | October 12th

Speaker: Kenneth Goodman

Date: Monday, October 12th, 2020

Topic: DeFi Arbitrage, Scalability Issues on Ethereum

Dave Bagget | October 26th

Speaker: Dave Bagget 

Date: Monday, October 26th, 2020

Institution: Inky

Topic: Mail Phising and its Financial Impact

Gary Kazanstev | November 9th

Speaker: Gary Kazanstev 

Date: Monday, November 9th, 2020

Institution: Bloomberg

Topic: Adversarial Machine Learning and Interpretability

Alireza Javaheri/Mehdi H. Sonthonnax | November 23rd

Speaker: Alireza Javaheri/Mehdi H. Sonthonnax 

Date: Monday, November 23rd, 2020

Institution: Credit Suisse

Topic: Application of Deep Learning Techinques to solving high dimentional nonlinear PDEs

Spring 2020 Seminars

Fabio Mercurio | January 27th

Thank you for attending! Please find the slides from the seminar here.


FE Seminar Speaker: Fabio Mercurio

Hosted by IEOR and kindly sponsored by Guzman and Co.

Date: Monday, January 27th, 2020

Time: 6:00 pm to 7:30 pm

Location: Davis Auditorium, CEPSR Building


Title: Looking Forward to Backward-Looking Rates: A Modeling Framework for Term Rates Replacing LIBOR


Abstract: In this talk, we define and model forward risk-free term rates, which appear in the payoff definition of derivatives and cash instruments, based on the new interest-rate benchmarks that will be replacing IBORs globally.  We show that the classic interest-rate modeling framework can be naturally extended to describe the evolution of both the forward-looking (IBOR-like) and backward-looking (setting-in-arrears) term rates using the same stochastic process.


We then introduce an extension of the LIBOR Market Model (LMM) to backward-looking rates.  This extension, which we call generalized forward market model (FMM), completes the LMM by providing additional information about the rate dynamics between fixing/payment times, and by implying dynamics of forward rates under the classic money-market measure.


Our FMM formulation is based on the concept of extended zero-coupon bonds, which proves to be very convenient when dealing with backward-looking setting-in-arrears rates. Thanks to this, not only the bonds themselves, but also the forwards and swap rates, along with their associated forward measures, can be defined at all times, even those beyond their natural expiries.


Bio: Fabio is global head of Quantitative Analytics at Bloomberg LP, New York.  His team is responsible for the research on and implementation of cross-asset analytics for derivatives pricing, XVA evaluations and credit and market risk. Fabio is also an adjunct professor at NYU.  He has jointly authored the book "Interest rate models: theory and practice" and published extensively in books and international journals, including 19 cutting-edge articles in Risk Magazine. Fabio is the recipient of the 2020 Risk quant of the year award.


Martin Fridson | February 10th


Thank you for attending! Please find the slides from the seminar here.


FE Seminar Speaker: Martin Fridson

Hosted by IEOR and kindly sponsored by Guzman and Co.

Date: Monday, February 10th, 2020

Time: 6:00 pm to 7:30 pm

Location: Davis Auditorium, CEPSR Building


Title: The Credit Market:  Analysis, Dynamics, and Outlook


Abstract: U.S credit is a vast and diverse market comprising investment grade corporates, high yield bonds, and leveraged loans.  These instruments’ behavior is driven by interest rates, the business cycle, idiosyncratic changes in default risk, and variations in market liquidity. The talk will address analysis of these factors and how they shape current market levels and prospects. 


Bio: Martin Fridson is “perhaps the most well-known figure in the high yield world,” according to Investment Dealers’ Digest.  At brokerage firms including Salomon Brothers, Morgan Stanley, and Merrill Lynch, he became known for his innovative work in credit analysis and investment strategy.  For nine consecutive years he was ranked number one in high yield strategy in the Institutional Investor All America Research Survey. 


Fridson received his B.A. cum laude in history from Harvard College and his M.B.A. from Harvard Business School.  He has served as president of the Fixed Income Analysts Society, governor of the CFA Institute, director of the New York Society of Security Analysts, and consultant to the Federal Reserve Board of Governors.  


The Financial Management Association International named Fridson the Financial Executive of the Year in 2002.  In 2000, he became the youngest person inducted up to that time in the Fixed Income Analysts Society Hall of Fame.  The CFA Society New York bestowed its Ben Graham Award on Fridson in 2017. A study based on 16 core journals ranked Fridson among the ten most widely published authors in finance in the period 1990-2001.  In 2013 Fridson served as Special Assistant to the Director for Deferred Compensation, Office of Management and the Budget, The City of New York. 


In 2000, The Green Magazine called Fridson’s Financial Statement Analysis “one of the most useful investment books ever.”  The Boston Globe said his 2006 book, Unwarranted Intrusions: The Case Against Government Intervention in the Marketplace, should be short-listed for best business book of the decade.  


Fridson’s commentary on economics and financial markets can be found  


Marcos Lopez de Prado | February 24th


Thank you for attending! Please find the slides from the seminar here.


FE Seminar Speaker: Marcos Lopez de Prado

Hosted by IEOR and kindly sponsored by Guzman and Co.

Date: Monday, February 24th, 2020

Time: 6:00 pm to 7:30 pm

Location: Davis Auditorium, CEPSR Building


Title: Portfolio Construction in the Age of Machine Learning


Abstract: Classical portfolio construction methods (e.g., Markowitz, Black-Litterman) deliver notoriously unstable solutions. This instability has two main sources: noise and signal. In this seminar, we trace back the two sources of instability, and explain machine learning algorithms that address them. Monte Carlo experiments demonstrate that these algorithms outperform out-of-sample the classical solutions.


Bio: Prof. Marcos López de Prado is the CIO of True Positive Technologies (TPT), and Professor of Practice at Cornell University’s School of Engineering. He has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. Marcos launched TPT after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3.


Concurrently with the management of investments, since 2011 Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, is a founding co-editor of The Journal of Financial Data Science, has testified before the U.S. Congress on AI policy, and SSRN ranks him as the most-read author in economics. Among several monographs, Marcos is the author of several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, forthcoming).


Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he is a faculty member. Marcos has an Erdős #2 according to the American Mathematical Society, and in 2019, he received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.


Mark Broadie | March 23rd

FE Seminar Speaker: Mark Broadie

Hosted by IEOR and kindly sponsored by Guzman and Co.

Date: Monday, March 23rd, 2020

Time: 6:00 pm to 7:30 pm

Location: Davis Auditorium, CEPSR Building


Title: Topics in Golf and Sports Analytics


Description: After a brief overview of sports analytics, this talk will focus on data, modeling techniques and recent advances in golf analytics.  Some connections with methods and models for financial modeling will also be mentioned.


Bio:  Mark Broadie is the Carson Family Professor of Business at Columbia Business School. Professor Broadie's research addresses issues in quantitative finance and sports analytics and, more generally, methods for decision making under uncertainty.  His quantitative finance research focused on problems in the pricing of derivative securities, risk management, and portfolio optimization. In his golf research, Broadie developed the strokes gained stats that are used by the PGA Tour. He works with a number of PGA Tour pros and writes a monthly column for GOLF magazine. His New York Times bestselling book Every Shot Counts uses data and analytics to measure and improve golf performance and strategy. Broadie worked with Turner Sports to bring the first live golf win probabilities to the network broadcast of the Woods-Mickelson match. He has been a member of the USGA's handicap research team since 2003. He currently teaches courses on business analytics and sports analytics. Professor Broadie received a BS from Cornell University and a PhD from Stanford University.

Paul Wilmott | March 30th

FE Seminar Speaker: Paul Wilmott

Hosted by IEOR and kindly sponsored by Guzman and Co.

Date: Monday, March 30th, 2020

Time: 6:00 pm to 7:30 pm

Location: Davis Auditorium, CEPSR Building


Title: Quantifying the Pleasure and Pain of Investing


Bio: Paul was a professional juggler with the Dab Hands troupe, and has been an undercover investigator for Channel 4. He also has three half blues from Oxford University for Ballroom Dancing. He makes his own cheese. Its flavour has been described as "challenging."

Paul was the first man in the UK to get an online divorce.

He was recently an "expert" on a TV show, tasked with forecasting a royal baby name and the winner of the Eurovision Song Contest among other things. He got everything wrong.

He played bridge for his school's D team. There was no E team.

And he plays the ukulele.

Victor Haghani | April 6th

FE Seminar Speaker: Victor Haghani

Hosted by IEOR and kindly sponsored by Guzman and Co.

Date: Monday, April 6th, 2020

Time: 6:00 pm to 7:30 pm

Location: Davis Auditorium, CEPSR Building


Title: Puzzles and Paradoxes Posed by Negative Interest Rates


Abstract: For centuries, economists have proposed theories of interest rates which presumed rates will always be positive in the long term. At the same time, while it’s been recognized that long-term financial decisions are best made on the basis of real rather than nominal return and risk, until recently our historical perspective has been almost exclusively framed in terms of nominal, not real, returns. In this talk, we will revisit the historical record in conjunction with conventional theories of interest rates in the light of fresh scholarly research and recent market experience. We will suggest that a future in which negative long-term interest rates may be the “new normal” will have profound implications for our most fundamental models for valuing stocks and bonds under uncertainty.

Bio: Victor founded Elm in 2011 to help himself and his friends manage their savings in an efficient and disciplined manner, and to capture the long-term returns they ought to earn. He has spent nearly 30 years actively involved in markets and financial innovation, having been a Managing Director in the bond-arbitrage group at Salomon Brothers and later a founding partner of Long-Term Capital Management (LTCM).

Spring 2019 Seminars

Marcos Lopez de Prado | January 28

Speaker: Marcos Lopez de Prado
Date: Monday, January 28, 2019
Time: 6:00pm to 7:30pm
Location: Uris 301- location change

Title: Financial Machine Learning
Abstract: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this presentation, we review some of the most important current financial applications of ML.
Bio: Marcos López de Prado is a principal at AQR Capital Management, and its head of machine learning. Concurrently with the management of investments, between 2011 and 2018 Marcos was also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, and SSRN ranks him as one of the most-read authors in economics. Among several monographs, he is the author of the graduate textbook Advances in Financial Machine Learning (Wiley, 2018). Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a financial machine learning course at the School of Engineering. His research website address is

Ole Peters | February 4

Speaker: Ole Peters
Date: Monday, February 4, 2019
Time: 6:00pm to 7:30pm
Location: Davis Auditorium, CEPSR Building

Title: The ergodicity problem in economics

Abstract: The ergodicity problem queries the equality or inequality of time averages and expectation values. I will trace its curious history, beginning with the origins of formal probability theory in the context of gambling and economic problems in the 17th century. This is long before ergodicity was a word or a known concept, which led to an implicit assumption of ergodicity in the foundations of economic theory. 200 years later, when randomness entered physics, the ergodicity question was made explicit. Over the past decade I have asked what happens to foundational problems in economic theory if we export what is known about the ergodicity problem in physics and mathematics back to economics. Many problems can be resolved. Following an overview of our theoretical and conceptual progress, I will report on a recent experiment that strongly supports our view that human economic behavior is better described as optimizing time-average growth rates of wealth than as optimizing expectation values of wealth or utility of wealth.

Bio: Ole Peters is a Fellow at the London Mathematical Laboratory and External Professor at the Santa Fe Institute. He works on different conceptualizations of randomness in the context of economics. His thesis is that the mathematical techniques adopted by economics in the 17th and 18th centuries are at the heart of many problems besetting the modern theory. Using a view of randomness developed largely in the 20th century he has proposed an alternative solution to the discipline-defining problem of evaluating risky propositions. This implies solutions to the 300-year-old St. Petersburg paradox, the leverage optimization problem, the equity premium puzzle, and the insurance puzzle. It leads to deep insights into the origin of cooperation and the dynamics of economic inequality. He maintains a popular blog at that also hosts the ergodicity economics lecture notes.

Adam Grealish | February 25

Speaker: Adam Grealish
Date: Monday, February 25, 2019
Time: 6:00pm to 7:30pm
Location: Davis Auditorium, CEPSR Building

Title: Fintech: Better Investing Through Technology

Bio: Adam Grealish is the Director of Investing at Betterment, the largest independent online financial advisor with over $14 billion in assets under management. Adam and his team are responsible for Betterment's strategic asset allocation, fund selection, automated portfolio management and tax strategies. Before joining Betterment, Adam founded a natural language processing startup that matched individuals with employment opportunities. Prior to that, he was a vice president at Goldman Sachs' FICC division, responsible for structured corporate credit and macro credit trading. Before that, Adam was part of the global quantitative equity portfolio management team at New York Life Investments. 

Abstract: In this talk we will explore how technology can be used to improve investor outcomes. Technology and automation can play a significant role in solving traditional asset management problems, such as risk management and rebalancing, as well as unique problems faced by taxable investors. We will also explore how technology can improve investor behavior.

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Richard Robb | March 4

Speaker: Richard Robb 
Date: Monday, March 4, 2019
Time: 6:00pm to 7:30pm
Location: Davis Auditorium, CEPSR Building

Title: Choice with Reason

Abstract: This talk will draw from Richard Robb’s forthcoming book, Willful: How We Choose What We Do, Yale University Press, Fall 2019. This book identifies a new dimension of behavior that can’t be described by rational choice or behavioral biases: acting willfully on the world. These actions are undertaken for their own sake rather than to obtain a preferred outcome. Exploring this uncharted sphere, we learn to see time as a flow and economic life as a high-stakes game. Beliefs, which constitute our identity, are neither infinitely flexible or easily transmitted, even if every agent is rational, trustworthy and properly incentivized. The theory has far-reaching consequences for institutional investing, opportunities for individual investing, reformulated notions of market efficiency and the fundamental limits to communication that cause markets to seize up.

Bio: Richard Robb is Professor of Professional Practice at SIPA where he directs the Concentration in International Finance and Economic Policy. He is also CEO of fund manager Christofferson, Robb & Company (CRC), with over $4 bn under management. Prior to cofounding CRC, he was the Global Head of the derivatives and securities subsidiaries of the Dai-Ichi Kangyo Bank in New York, London and Hong Kong. He has a B.A. from Duke University and a PhD in Economics from The University of Chicago.



Michael Miller | March 11

Speaker: Michael Miller
Date: Monday, March 11, 2019
Time: 6:00pm to 7:30pm
Location: Davis Auditorium, CEPSR Building

Title: Risk-Based Performance Attribution

Abstract: Traditional performance attribution may work well for long-only strategies, but it can be inaccurate and even misleading when applied to hedge fund strategies. Risk-based performance attribution, while more difficult to perform, provides a more accurate picture of the drivers of hedge fund performance.

This presentation will start with a general overview of performance analysis, before moving on to factor analysis and risk-based performance attribution. 

Bio: Michael B. Miller is the founder and CEO of Northstar Risk. Before starting Northstar, he was the Chief Risk Officer for Tremblant Capital and before that the Head of Quantitative Risk Management at Fortress Investment Group.

Mike is the author of Quantitative Financial Risk Management and Mathematics and Statistics for Financial Risk Management. He is also the co-author, along with Emanuel Derman, of The Volatility Smile. Mike is an adjunct professor at Columbia University and the co-chair of the Global Association of Risk Professional’s Research Fellowship Committee. Before starting his career in finance, he studied economics at the American University of Paris and the University of Oxford.


Vasant Dhar | April 8

Speaker: Vasant Dhar
Date: Monday, April 8, 2019
Time: 6:00pm to 7:30pm
Location: Davis Auditorium, CEPSR Building

Title: Artificial Intelligence and Data Science in modern financial decision making
Abstract: There’s a tremendous amount of interest in the use of machine learning in modern day financial decision making. Much of this interest is fueled by increasing amounts of available data and the general success of machine learning in other domains such as perception. I start by assessing the opportunities and key challenges for machine learning in exchange traded and OTC markets, and how finance problems are uniquely challenging. I describe the conditions in which we should trust automated decision making in these markets by breaking down trust into two key risk factors, namely, how often an automated decision system makes mistakes and the consequences of such mistakes. I use my model of trust to present results that show under what conditions we should trust autonomous learning systems with decision making.
Bio: Vasant Dhar is Professor, Stern School of Business and Center for Data Science at New York University. He is the director of NYU’s PhD program in Data Science. Dhar is also the founder of SCT Capital Management a , a machine-learning-based hedge fund in New York City.
Dhar’s central research question asks when we should trust AI machines that learn and make decisions autonomously based on ongoing data. His research has addressed this question in a number of areas, most notably, in financial markets.  Dhar has authored over 100 research papers, as well as articles for publications such as the Financial Times, Wall Street Journal, Forbes, Wired, and the Harvard Business Review. He has appeared on CNBC, Bloomberg TV, and National Public Radio. 

Fall 2018 Seminars

Fabio Mercurio | October 15

FE Seminar Speaker: Fabio Mercurio
Hosted by Columbia IEOR/Waterloo
Date: Monday, October 15, 2018
Time: 6:00 pm to 7:30 pm
Location: Davis Auditorium, CEPSR Building

Title: SOFR so far

Abstract: We propose a simple two-factor multi-curve model where Fed-fund, SOFR and LIBOR rates are modeled jointly. The model is used to price the newly quoted SOFR futures as well as Eurodollar futures. We then derive pricing formulas for SOFR-based swaps, and show how the valuations of LIBOR-based swaps as well as LIBOR-SOFR basis swaps are impacted by the introduction of a new LIBOR fallback.

Bio: Fabio is global head of Quantitative Analytics at Bloomberg LP, New York. His team is responsible for the research on and implementation of cross-asset analytics for derivatives pricing, XVA valuations and credit and market risk. Fabio is also adjunct professor at NYU, and a former CME risk committee member. He has jointly authored the book 'Interest rate models: theory and practice' and published extensively in books and international journals, including 17 cutting-edge articles in Risk Magazine. Fabio holds a BSc in Applied Mathematics from the University of Padua, Italy, and a PhD in Mathematical Finance from the Erasmus University of Rotterdam, The Netherlands

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Heath Windcliff | November 1

FE Seminar Speaker: Heath Windcliff
Hosted by Columbia IEOR/Waterloo
Date: Thursday, November 1, 2018
Time: 6:00 pm to 7:30 pm
Location: 110 Williams Street, 3rd floor. Manhattan Institute of Management
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Title: Measuring and Using Trading Algorithms Effectively

Abstract: In order to build effective trading algorithms, you need to effectively measure trading algorithms. In this talk we will talk about what factors we look at when measuring trading engine performance in tuning our algorithms.  Specifically we will discuss several common benchmarks and discuss what each of these focus their lens upon, and what these measurements are blind to. We will focus on the precision of these measurements and where these sources of noise and uncertainty come from. We will show a lower bound on the amount of noise expected in these measures so you can determine how precisely one can expect to be able to measure trading performance for a given amount of flow.  This has material implications on the feasibility and applicability of quantitative best-execution measures for many users. Finally we will show how use these methods in our engine tuning process.

Bio: Heath Windcliff, Managing Director, is the head of the Quantitative Research team at MS which is responsible for equity algorithmic trading research and development. He actively works on the design of the optimization tools, the limit order worker and the venue selection models used in our products. He is also responsible for the PostTrade analytics framework that MS uses to design and tune the algorithmic offering for the use cases and needs of users and clients. Heath has a PhD in Computer Science focused on numerical methodologies from the University of Waterloo in 2003 following a Masters and Bachelors in Applied Mathematics

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