Why IATACF Certification Program
Training the quantitative finance community.
The IATACF Certification Program designation is conferred by the Institute of Algorithmic Trading and Applied Computational Finance.
The Institute of Algorithmic Trading and Applied Computational Finance serves as the governing body responsible for the IATACF Certification Program certification. The institute is dedicated to advancing the standards in applied financial engineering. It accomplishes this mission by offering a platform for global quantitative finance professionals, which includes the organization of industry events and the provision of personalized mentorship, quality research and online educational materials. These efforts ensure that its members remain well-informed about the most current industry methodologies and practices.
Founded by three professors at Carnegie Mellon University, the IATACF collaborates with clients to expand their expertise, cultivate essential skills, and elevate professional conduct, ultimately driving favorable business results. We are dedicated to comprehending the intricate requirements of clients within dynamic global financial markets. Our comprehensive offerings include knowledgeable instructors, e-learning platforms, personalized coaching, and a mix of candidate assessments, all designed to enhance individual capabilities and boost overall business performance.
Discover the advantages of enrolling in the IATACF Certification Program.
Prof. Patrick McSharry
Research Professor
Patrick McSharry is a research professor at Carnegie Mellon University Africa, director of the World Bank funded African Centre of Excellence in Data Science (ACE-DS) in Rwanda and a member of the Kigali Collaborative Research Centre. Having served 22 years at Oxford University, McSharry remains affiliated with the Oxford Man Institute of Quantitative Finance, the Mathematical Institute, the Department of Computer Science, and the Oxford Internet Institute. He is a Fellow of the Royal Statistical Society, Senior Member of the IEEE and Senior Academic Member of the Willis Research Network and advises on working groups for insurance, open data and big data.
McSharry was awarded a Royal Academy of Engineering Research Fellowship and two Marie Curie Research Fellowships (UK and Spain). He takes a multidisciplinary approach to developing quantitative techniques for data science, decision-making, and risk management. His research focuses on big data, forecasting, predictive analytics, machine learning, and the analysis of human behaviour. He has published over 100 peer-reviewed papers, three books, and advises national and international government agencies and firms in the insurance, finance, agriculture, energy, telecoms, environment, education, and healthcare sectors.
Prof. Martin Saint
Distinguished Service Professor
Martin Saint is an esteemed professor, researcher, and cyber engineer with over 40 years of experience in the private and public sectors. After joining Carnegie Mellon University’s Africa campus between 2013 and 2023, he was dedicated to teaching and researching in the field of complex systems, focusing on financial trading, engineering and emerging technologies. Notably, he is a former stock broker and has pioneered the first academic financial technology and blockchain courses on the African continent. Martin is highly sought-after for his expertise and consults on engineering, economics, fin-tech, computational finance, business, and policy matters for both public and private organizations. Additionally, he serves as an invited lecturer at the International Centre for Theoretical Physics in Italy and is a research fellow at the Kigali Collaborative Research Centre. He plays a vital role as an advisor for two World Bank-funded African Centers of Excellence, one specializing in the Internet of Things (IoT) and the other in data science.
Martin’s research extends to renowned institutions such as the University of Colorado, where he conducted studies at the Pervasive Communications Laboratory and Digital Energy Lab. Furthermore, he has contributed to the U.S. Federal Emergency Management Agency’s Emergency Management Institute and Idaho National Laboratory. Martin holds an MS and Ph.D. in computer systems networking and telecommunications from the University of Colorado’s Technology, Cybersecurity, and Policy (TCP) Program.
Prof. Ben Racz
Professor of the Practice
Ben is the founder and Managing Director of Algorithmic Sciences, a scientific research and consulting firm with a track record in developing institutional-grade validation procedures and systems for generating and executing portfolios of positive expectancy intraday trading strategies, using proprietary statistical analysis and robustness testing methods.
With a foundation in specialized programming languages, Ben has managed complex software projects since 2001. Ben’s specialties include the validation of manual, semi-automated and automated speculative trading strategies, developing distributed research infrastructure and Execution and Order-Management systems, risk management and monitoring systems, and securely operating and optimizing the underlying infrastructure.
Before focusing on Algorithmic Sciences during his career as an entrepreneur, Ben co-founded a consulting firm dedicated to data-driven methodologies to promote business growth, a data-science based wholesale trading company, and an information security consulting and private counterintelligence firm, where the scope ranged from information security auditing, research and consulting on cyberwarfare, and ISO 27001 auditing, policy development, implementation and management of Sarbanes-Oxley compliant infrastructure operations, and technical consulting and vulnerability testing in line with PCI-DSS requirements for multinational corporations.
Ben has the academic title of Professor of the Practice at the College of Engineering at Carnegie Mellon University, where he teaches Advanced Quantitative Finance and Algorithmic Trading to MSc. students, while remaining a full time researcher as Head of Quantitative Analysis at Algorithmic Sciences.