Dr Nick Firoozye

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About Me

NBF Headshot Dr. Nick Firoozye is a mathematician and finance professional with over 20 years of experience in research, structuring, and trading across buy and sell-side firms, including Lehman Brothers, Deutsche Bank, Nomura, Goldman Sachs, and Citadel. He specialises in areas ranging from Quant Strategy, RV Trading, to Asset Allocation. Currently, Nick works at a mid-frequency trading prop firm based in Chicago.

As an Honorary Professor at University College London, Nick developed the Algorithmic Trading Strategies course, which he has taught PhD and MSc students since 2016. He has also created and taught several successful online versions of the class. He has supervised eight PhD students researching machine learning for algorithmic trading and finance, with several now working in AI, systematic trading, and quant research. Over 600 students have successfully completed the Master’s and online courses.

Nick co-authored the book Managing Uncertainty, Mitigating Risk, which addresses uncertainty in modelling financial crises. He holds a PhD from the Courant Institute, NYU, with postdoctoral positions at the University of Minnesota, Heriot-Watt University, the University of Bonn, and NYU. Before moving to Wall Street, Nick held a tenure-track Assistant Professorship at the University of Illinois, Urbana-Champaign.

Academic Information

  • Institution: University College London (UCL)
  • Department: Computer Science
  • Research Group: Financial Computing and Analytics Group
  • Position: Honorary Reader, Lecturer (Teaching)
  • PhD Students:
    • Anna-Helena Mihov - DWS & UCL
    • Zhicheng Xu - UCL
    • Chloe Taysom - Oxford University Endowment Management & UCL
    • Konark Jain (expected 2025) - JP Morgan & UCL
    • Dr Gabriel Borrageiro (2023) - Laser Digital / Nomura
    • Dr Wilbur Zhu (2021) - Lumen Research
    • Dr Denis De Montigny (2020) - Fund Guardian
    • Dr Adriano Koshiyama (2019) - Holistic AI
    • Oxford University PhD Student : Dr Vincent Tan (2024) - Cubist
    • University of Sao Paolo PhD Student: Daniel Cunha-Oliveira - Itau BBA

Research Interests:

I am interested in ML, Computations Statistics, and AI, applied to Finance and especially to Algorithmic Trading. This necessarily involves time-series forecasting, using methods from Statistics, (Adaptive) Signal Processing, Econometrics, and Machine Learning. Also I have interests in portfolio allocation, and stochastic control / reinforcement learning for optimal execution. Recent areas of interest have been in random matrix theory for portfolio analysis, RL for optimal execution, bagging and boosting over-parameterised shallow nets for online learning to forecast prices in near real-time.

In summary, I am interested in some of the following topics, but only if they are applicable to Algorithmic Trading, primarily in medium or high frequency(!):

  • Financial Time-Series Analysis
  • Time-series forecasting for low SNR, highly non-stationary signals.
  • Change-point detection / regime-switching
  • Data Augmentation for better model training
  • Over-parameterised Models and Ensemble methods
  • Adaptive forecasting and State-space models, primarily for low-latency forecasting
  • Kernel methods and DNN for time-series forecasting
  • Online Learning & Adaptive Filtering, Fast Algorithms for mid to high-frequency trading
  • Optimal Allocation (e.g., Stats Arb) and Optimal Trade Placement
  • Order book simulation, Optimal Market Making
  • Algorithmic Trading, High Frequency Trading, Market Microstructure,
  • Reinforcement Learning / Stochastic Control for Algo Trading
  • Machine Learning, Deep Learning, Statistical Learning, Bayesian Methods for Algo Trading
  • Other (vague and older) interests:
    • Calculus of Variations and Nonlinear PDE, Elasticity
    • Image Processing
    • Islamic Law and Economics
    • Eurozone Crisis, Financial Crises, and the role of uncertainty in financial markets
    • Fixed Income derivatives, RV strategies, Macro Trading.

Background:

Financial Experience:

  • TradeLink Wordwide (2024-Present)
  • Exos Securities (2019-2024)
  • Exodus Point (Consultant, 2018)
  • Symmetry Investments (2017-2018)
  • Nomura (2009-2017)
  • Citadel (2007-2009)
  • Goldman Sachs (2006-2007)
  • Deutsche Bank (2001-2006)
  • Sanford Bernstein & Alliance-Bernstein (1999-2001)
  • Lehman Brothers (1995-1999)

Academic Experience:

  • University College, London (2006-Present) Honorary Professor and Lecturer (Teaching), Computer Science.
  • University of Illinois at Urbana-Champaign (1994-1995) Assistant Professor (tenure-track), Mathematics.
  • Postdocs: (1990-94) Inst for Math and its Applications, Univ of Minn; Heriot-Watt University; University of Bonn; Courant Institute, NYU.

Awards and Grants

  • NSF Mathematical Sciences Postdoctoral Fellowship, 1990-1993
  • Courant Institute 50th Jubilee Fellow, 1986
  • Wolfson Prize in Economics, Short-listed, 2012 - Planning for an orderly break-up of the European Monetary Union. Note: Despite the obvious agenda of the prize, our conclusion was there was no such thing as an orderly breakup! see also Wolfson Economics Prize Shortlist Announcement (2012)
  • FSA Dear CRO letter of 2010 asking for planning around risk-scenarios for Eurozone breakup and Greek Exit, prompted by my paper on the legal aspects of Eurzone breakup Currency risk in a Eurozone break-up: Legal Aspects (“Scariest paper we have ever seen. Went round the FSA like wildfire’’)).
  • Global Derivatives Research and Strategy House of the Year, Global Capital, Nomura, 2015
  • Third Place All Europe Research Team, Institutional Investor, Deutsche Bank, 2005

Education:

  • PhD, Mathematics, Courant Institute, NYU, 1990, advisor: Prof Robert V Kohn, Nonlinear Elasticity, Calculus of Variations.
  • MS, Mathematics, Courant Institute, NYU, 1987.
  • BS, Applied Mathematics, Harvey Mudd College, 1986, advisor: Prof Stavros Busenberg.

Courses - Live, Hybrid, and Online

University Teaching & Advising

  • COMP0051: Algorithmic Trading MSc Course : Based on my previous online courses and further extended, this course covers the foundations of algorithmic trading, including market microstructure, trading strategies, and risk management. Offered at UCL for MSc Students every Spring Term since 2018. Co-instructor: Dr Paolo Barucca.

  • Note that this class was devised initially as an PhD reading course in 2015, and later taught online pre-recorded and live versions in Experfy, with live classes at WBS Training and at QuantsHub. I then attempted to launch as an MSc course in 2017 with Dr Julian Bonart. After his departure for Citadel, it was launched with Paolo Barucca in 2018. We have run this course for over five years running - 2018/2019-2023/24.

  • PhD Advising Please note that, despite my having advised some eight PhD students, all my advisory work is voluntary. As a consequence, I do not take many students and generally only do so if the topics they wish to pursue are directly in line with my current research in mid-frequency and high-frequency trading. Most pure finance topics (investing, long-only, long-horizon outlook) are only of passing interest and I would do more harm than good if I was asked to advise. Please note as well that UCL has exceptionally few faculty in this area, and despite a concentration in Computational Finance, few if any people do either computation or finance. Oxford University Mathematics, Oxford Man Institute, and Oxford University School of Engineering all have specialists who, like me, do active research in various aspects of algorithmic trading and quant finance, while Imperial College Mathematics and Finance Departments both have experts in quant finance and in algorithmic trading.

Online Hybrid and Pre-recorded Teaching

  • Algorithmic Trading Certificate(ATC): A Practitioner’s Guide in WBSTraining. Co-taught with Dr Brian Healy (UCL). Online course offered in medium sized cohorts. This course is based on my MSc course and covers the practicalities and foundations of algorithmic trading, including market microstructure, trading strategies, and risk management, and other information needed to set up quant functions in a fund. Students who have taken this course have gone on to new roles in sell-side and buy-side quant and algo trading functions, or used their new skills to manage their PA.

  • Fundamentals of Algorithmic Trading: An introductory self-paced course, covering the industry and the basics of algorithmic trading. This is a new and exciting course which fills the gap that many new STEM graduates have - not knowing what type of Quant Finance they would be interested in, and in particular, whether Algo Trading, would be their cup of tea. I also describe the Algo Trading Process and give a case study (work in progress) on Crypto Trading. Access to a github repo is inclusive.

Publications and Current Work

Books

  • Managing Uncertainty, Mitigating Risk: A book on the role of uncertainty in financial markets, covering the Eurozone crisis, financial crises, and the impact of uncertainty on trading strategies.
    Coauthored with Fauziah Ariff. Wiley 2017.
  • Quantitative Trading: A Systematic Approach (Underway) - A comprehensive guide to algorithmic trading, covering the foundations of quantitative trading strategies across styles and timescales. Together with Dr Brian Healy, under contract, CRC Press, expected publication in 2027.

Publications: recent (Algo Trading) and older (Nonlinear PDEs)

Some repeats amongst these. Earlier academic papers were on Nonlnear PDE, Calculus of Variations, and some Image Processing. Intermediate papers are on Islamic Finance, the Eurozone crisis and uncertainty in finance. Later papers are entirely on Algorithmic Trading and Machine Learning for Finance.

  • ORCID Official Citation Lists
  • Google Scholar Very Accessible Citations compiled on Google. Not always accurate.
  • SSRN Social Sciences Research Network - we submitted some here.
  • arXiv arXiv - we submitted almost all preprints here.
  • ResearchGate

Publications: Sell-Side Research

Sell-side research on the EZ crisis, Quant methods in Finance, RV trades, and Islamic Finance.

Current Projects / Ongoing Collaborations

  • Project 1: LOB Simulation and Reinforcement Learning in a Discrete Time setting for optimal market making and order placement (with Konark Jain, UCL and JP Morgan).
  • Project 2: Hawkes Processes with Exogenous Variables for Algorithmic Trading - causal relationships modelled explicitly (with Konark Jain, UCL and Daniel Cunha-Oliveira, Universidad Sao Paolo).
  • Project 3: Robust Portfolio Optimization and Tuning using Bootstrap. Robust tuning of Strategies for Algorithmic Trading (with Daniel Cunha-Oliveira - Universidad Sao Paolo).
  • Project 4: Time-series Prediction in the presence of Regime Shifts using SSMs, RNNs & Multi-modal models (with Zhicheng Xu, UCL).
  • Project 5: Fast Adaptive Benign Overparmeterisation (FABO) - Double descent in online learning. Ongoing work with MSc students since 2022. Fast updates of RFF models capturing nonlinearity (using 100+ time-steps but 3k+ or more features with sub-second updates)

Contact Information

Notes for Prospective Students

Funding

  • Please Note: UCL does not have funding for PhD studentships in this area.
  • All my students are either self-funded, funded by their employers, or are part-time PhD students and fund their own studies.
  • If you need funding, please do not contact me; I cannot help you.

Advising

  • UCL in its wisdom, does not compensate me for taking PhD students so I rarely take any.
  • I will only consider PhD students whose research interests directly align with my own, are published and/or already work in the industry.
  • I am not taking any new PhD students at this time.
  • Again, if you are not doing Algo Trading or Something directly related, please do not contact me. (I get far too many requests from people whose interests are really far from my own.)
  • I get a lot of unsolicited requests. I really don’t have that much time to give due consideration for a response, so there is no point in writing if you do not have a strong interest and it directly aligns to one of mine listed above

General Advice

  • In order to qualify for the PhD program at UCL, you must have a really stellar background (sorry - I don’t make the rules), and preferably have some work experience in finance. The PhD proposal is crucial, but I will help out on it. I am not entirely happy helping out on research proposals for students who wind up going somewhere else. Happy to discuss the matter first though.
  • Note that at UCL, it is a fantastic CS dept, but there are almost no profs working on Algo Trading aside from me. Just a little on options pricing in an online setting, and some LOB sims, but this does not make a major research theme.
  • Other places in the vicinity to consider are:
    • UCL Mathematics Dept (one prof working on mid to hft - I don’t know him but he seems to do some good work),
    • Imperial Mathematics Dept (some great profs mostly working in pricing, but a little interest in trading as well),
    • Imperial Finance Dept (a few profs doing microstructure),
    • Oxford Mathematics Dept (one senior prof at least, highly diverse interests including DNNs for trading),
    • Oxford Man Institute (focused more on Stoch Control and RL for trading),
    • Oxford Engineering Dept (a few profs working on ML and DNN for trading applications)
  • Other schools may have funding. Look for a CDT (Centre for Doctoral Training) in Quant Finance or similar. If you think you can do mainstream ML/AI with an application to finance, then you might have more funding opportunities, but you have to make it interesting to mainstream ML/AI people, not just some application.
  • I take MSc students every summer as per my contract with UCL, but I do not have research projects. You must have an industrial project or one of your own.
  • If you are an undergrad at UCL, from time-to-time we do have a need to pair you with a PhD student on a project. This can be very rewarding as I have some excellent PhD students and the undergrads have done well with the interaction.

Social Media and Online Presence

Last updated: [17-09-2025]