M4 Conference Speakers
Nassim Nicholas Taleb
Nassim Nicholas Taleb spent more than 20 years as a derivatives trader before starting a full-time career in research in the field of risk management and applied probability.
Taleb has been involved in risk-based policy making, advising the IMF, and the UK Prime Minister on model error and the detection and mitigation of tail exposures. He has also been hired the RAND corporation and various branches of the U.S. government and has testified twice for the United States Congress.
Taleb holds a PhD from the University of Paris and an MBA from the Wharton School. He is the author of the Incerto, a 4-volume essay on uncertainty (Antifragile, The Black Swan, Fooled by Randomness, The Bed of Procrustes), and Dynamic Hedging (1997), a technical clinical book on derivatives, in addition to Silent Risk, a freely available technical book (and reexpression of the Incerto) in applied probability theory.
Dr Spyros Makridakis is a Professor at the University of Nicosia, the director of the Institute For the Future (IFF) and an Emeritus Professor at INSEAD. He has authored, or co-authored, twenty-two books and more than 250 articles. He was the founding editor-in-chief of the Journal of Forecasting and the International Journal of Forecasting and is the organizer of the M (Makridakis) Competitions.
Professor Armstrong (PhD MIT) has been on the Wharton School faculty since 1968.
He is a cofounder of the Journal of Forecasting, International Journal of Forecasting, International Institute of Forecasters, International Symposium on Forecasting, ForecastingPrinciples.com, and PollyVote.com. The latter has provided the most accurate forecasts for U.S. Presidential elections since its launch in the 2004 election.
In 2007, he was included among the “55 of the Hottest, Smartest, Most Talked About College Professors.” In 2010, he was named one of the “25 Most Famous College Professors Teaching Today.” He received the “Lifetime Achievement Award in Climate Science” from the Heartland Institute in March (2017).
Armstrong has had 24 international visiting appointments at 17 universities. In addition, he has given over 110 invited lectures at universities in 28 countries outside the U.S.
He wrote Long-Range Forecasting (1978, 1985), edited Principles of Forecasting (2001) and has published more than 150 research papers in academic journals. If you were to read only one of his papers on forecasting, he recommends Forecasting Methods and Principles: Evidence-Based Checklists (2018). This paper has been widely read. For example, there have been 15,000 reads on ResearchGate alone.
Andrea Pasqua leads Intelligent Decisions Systems at Uber, a team dedicated to employing data science methods to Forecasting, Anomaly Detection, Infrastructure and Developer tooling. Prior to joining Uber, he was Director of Data Science at Radius, applying Machine Learning to the marketing space, and, before that, he built risk models for MSCI, a financial company. He earned his Ph.D. in theoretical physics from UC Berkeley, where he worked on string theory and particle physics, and later, pursued a postdoc in biophysics.
Slawek Smyl is a Staff Data Scientist at Uber Technologies working in the area of time series forecasting. He holds MSc in Physics from Jagiellonian University, Poland, MEng in Information Technology from RMIT, Australia, and GradD in Legal Studies from UNSW, Australia. Slawek has ranked highly in forecasting competitions: he won Computational Intelligence in Forecasting International Time Series Competition 2016, got a third place in Global Energy Forecasting Competition in 2017, and won the M4 Forecasting Competition in 2018.
Pablo Montero-Manso is a PhD Student in Statistics at the University of A Coruña, Spain.
He holds a degree in Computer Science from the same university and has worked in video processing and visualization before his doctoral studies.
His research interests are in supervised and unsupervised statistical learning of complex data objects, including forecast, clustering and classification of time series, shapes and functional data.
He is an R enthusiast, has authored the TSclust package and contributed to many others.
Maciej Pawlikowski graduated from the University of WrocÅ‚aw in 2018, with MSc in computer science. His forecasting practice began in April 2018, when he started working as a data analyst / programmer at ProLogistica Soft. Two months later, the method he developed took 3rd place in the M4 Competition. His other interests include neural networks and natural language processing.
Robert L. Winkler is James B. Duke Professor in the Fuqua School of Business and Professor in the Department of Statistical Science at Duke University. His primary research areas include decision analysis, Bayesian statistics, forecasting, and risk analysis, and he has published extensively in these areas. He was awarded the Frank P.
Ramsey Medal for significant contributions to decision analysis. Recent work involves probability forecasting, combining forecasts, decision modeling, stochastic dominance, sequential decision making, and multiattribute utility.
Tim Januschowski is a Machine Learning Science Manager in Amazon AI Labs. He has worked on forecasting since starting his professional career. At Amazon, he has produced end-to-end solutions for a wide variety of forecasting problems, from demand forecasting to server capacity forecasting. Tim’s personal interests in forecasting span applications, system, algorithm and modeling aspects and the downstream mathematical programming problems. He studied Mathematics at TU Berlin, IMPA, Rio de Janeiro, and ZuseInstitute Berlin and holds a PhD from University College Cork.
Michael Harris started trading commodity and currency futures 28 years ago. He is the developer of the first commercial software program for identifying patterns in market price action. In the last seven years he has worked on the development of software for hedge funds that identifies features in time-series for use with machine learning models. Michael holds two master’s degrees, one in Mechanical Engineering from SUNY at Buffalo with emphasis in control systems and optimization and another in Operations Research from Columbia University with emphasis in forecasting and financial engineering. Mike has worked for several years in the field of robotics and then in the financial sector where he developed a bond portfolio optimization program and strategies for trading futures and equities. He has also worked as a long/short equity and ETF trader for a Swiss-based hedge fund. He is the author of the books “Short-Term Trading with Price Patterns” (1999), “Stock Trading Techniques with Price Patterns” (2000), “Profitability and Systematic Trading” (2008) and “Fooled By Technical Analysis: The perils of charting,
backtesting and data-mining” (2015). Mike has also published many articles in trading magazines and in his blog, Price Action Lab Blog.
Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance.
Jocelyn began her PhD Biophysics at Stanford as a bench scientist moving around small volumes of liquid, but overtime was drawn to the power of statistical modeling. Her thesis used machine learning to identify subtypes of cancer. Now at Microsoft, she has spent the past 2 years she has been developing quantitative methods for forecasting. Her pipelines forecast 100% or Microsoft’s revenue every quarter and are consumed directly by the CFO, Amy Hood, who described them as “an integral part of our financial planning and budgeting process”. Her research focuses on developing novel methods for forecasting time series using machine learning
Chris Fry leads the Resource Efficiency Data Science team within Google’s Technical Infrastructure division. His team provides data science support for compute and storage resource efficiency initiatives, resource load forecasting and capacity planning, as well as tools and metrics to support the efficiency initiatives. Prior to Google he was Managing Director of Strategic Management Solutions, an analytics and data science consulting firm specializing in forecasting, pricing, and supply chain optimization.
Michael Gilliland is Marketing Manager for SAS forecasting software. Prior to SAS, he spent 15 years in forecasting positions in the food, consumer electronics, and apparel industries, and in consulting. Mike is author of The Business Forecasting Deal (2010), principal editor of Business Forecasting: Practical Problems and Solutions (2015), and writes The Business Forecasting Deal blog. He is column editor covering forecasting practice for Foresight: The International Journal of Applied Forecasting, and co-chaired the 2016 Foresight Practitioner Conference on Worst Practices in Forecasting. Mike serves on the Board of Directors of the International Institute of Forecasters, and received the 2017 Lifetime Achievement Award from the Institute of Business Forecasting. Mike holds a BA in Philosophy from Michigan State University, and Master’s degrees in Philosophy and Mathematical Sciences from Johns Hopkins University. He is interested in issues relating to forecasting process, such as worst practices and Forecast Value Added analysis, and in applying research findings to real-life improvement in business forecasting.
Professor Dimitris Drikakis is the Vice President for Global Partnerships at the University of Nicosia.
He has a joint professor’s appointment in the Schools of Science, Engineering and Medicine.
Prior to that, he has held academic and executive posts as a Professor, Executive Dean, and Head of Department at various UK universities. His expertise is in computational science and he has applied it in several diverse fields, including turbulence, rocket science, and nanoscience.
He has dealt with the reduction of computational uncertainty, as well as the development of physics-based models. He has published 400 journal and conference papers and two books.
He has served on the editorial boards of several engineering, applied mathematics, applied physics,
and interdisciplinary scientific journals.
Maria is the CEO and co-founder of Winningminds.ai. She has a strong professional experience in strategy and innovation based on emerging technologies and scientific trends. Her rich academic background includes a BSc and MSc in Neurobiology and Biotechnology (Reading University), a Policy Making & Lobbying Diploma (Michigan University), a MBA (Liverpool University), and an AI in Business Strategy Executive Program Certificate (MIT). Maria has 9 years’ experience in the corporate environment as an innovation and policy maker manager and 7-years as a co-founder and advisor of tech start-ups. Her work focuses on the application of cognitive sciences in organisational behaviour. Maria is leading the research programmes conducted by WinningMinds.ai in collaboration with Universities and Research Institutes in Canada and Europe. The research covers the fields of neuroeconomics, behaviour analysis, neurolinguistics, embodied cognitive artificial intelligence, learning and emotional psychology. Maria is also an adjunct professor of Neuromarketing courses in UK Universities.
Vassilios Assimakopoulos is a professor at the School of Electrical and Computer Engineering of the National Technical University of Athens. He has worked extensively on applications of Decision Systems for business design and he has conducted research on innovative tools for management support in an important number of projects. He specializes in various fields of Strategic Management, Design and Development of Information systems, Statistical and Forecasting Techniques using time series.
Visiting Associate Professor of Business Administration, Harvard Business School
Associate Professor of Business Administration, Darden School of Business
Associate Professor Yael Grushka-Cockayne’s research and teaching activities focus on data science, forecasting, project management, and behavioral decision-making. Her research is published in numerous academic and professional journals, and she is a regular speaker at international conferences in the areas of decision analysis, analytics, project management and management science. She is also an award-winning teacher, winning the Darden Morton Leadership Faculty Award in 2011, the University of Virginia’s Mead-Colley Award in 2012, the Darden Outstanding Faculty Award in 2013, and the Faculty Diversity Award in 2013 and 2018. In 2015 Yael won the University of Virginia All University Teaching award and has been voted MBA faculty marshal in 2016, 2017 and 2018. In 2014, Yael was named one of “21 Thought-Leader Professors” in Data Science.
At HBS Yael teaches the RC Technology and Operations Management course. At the Darden School, Yael taught the core Decision Analysis course, and elective courses on Project Management and Data Science in Business. Yael’s recent “Fundamentals of Project Planning and Management” Coursera MOOC had over 200,000 enrolled, across 200 countries worldwide.
Mr. Polemitis currently serves as the Chief Executive Officer of the University of Nicosia (UNIC) and EDEX, as a Board member of EDEX and UNICAF, and as a member of the Council of the University of Nicosia. UNIC serves nearly 12,000 students, along with 6,000 additional students in its affiliated academic institutions. It is the largest university in Cyprus and is the largest English language university in southern Europe.
Mr. Polemitis has two decades of experience in private equity, higher education and software development in New York, as a principal or partner at Ledra Capital, ACG Capital and Oliver Wyman (formerly Mercer Management Consulting). He has been a principal or advisor for a broad range of corporate financings in the United States, Europe, Latin America and India, ranging from multi-billion dollar industrial buyouts to early stage venture capital
Mr. Polemitis helped found the world-leading Digital Currency / Blockchain Initiative at the University of Nicosia, co-taught the first university cryptocurrency course in the world, and is regularly quoted in the Wall Street Journal, USA Today, Wired and other publications as an expert on cryptocurrency issues. His research at Mercer on optimal financing strategy in private equity has been published in Buyouts and cited in the Financial Times Mr. Polemitis holds an MBA from Harvard Business School, where he graduated with highest distinction as a Baker Scholar, and a B.S. in International Studies, Accounting and Computer Information Systems from Indiana University, where he graduated with highest distinction as a Wells and Chancellor’s Scholar.
Dr. Tao Hong is Associate Professor and Research Director of Systems Engineering and Engineering Management Department, Director of BigDEAL (Big Data Energy Analytics Laboratory), NCEMC Faculty Fellow of Energy Analytics, and associate of Energy Production and Infrastructure Center at University of North Carolina at Charlotte. He is the Founding Chair of IEEE Working Group on Energy Forecasting, Director at Large of International Institute of Forecasters, General Chair of Global Energy Forecasting Competition (gefcom.org), and author of the blog Energy Forecasting (blog.drhongtao.com). Dr. Hong is an editor of IEEE Transactions on Smart Grid, a department editor or IEEE Transactions on Engineering Management, and associate editor of International Journal of Forecasting. Dr. Hong received his B.Eng. in Automation from Tsinghua University in Beijing and his PhD with co-majors in Operations Research and Electrical Engineering from North Carolina State University
Warren Hatch joined Good Judgment as a volunteer forecaster when it was a research project, became a “Superforecaster,” and is now president of the commercial spinoff. Warren’s prior career was on Wall Street where he started at Morgan Stanley before co-founding a boutique investment firm. He earned his PhD from Oxford University.
Jay is currently the head of Data Science and Operations for Google’s Global Patents Organization. In that role, he oversees machine learning, software, database architecture, and business intelligence for the patents org. Previously, Jay has held data science positions in insurance and social media analytics companies and has consulted for various government agencies and startups. Jay holds a PhD in political science from Penn State University, where he focused extensively on machine learning based forecasting models of political conflict