John Riskas
About John Riskas
John Riskas serves as the Head of Machine Learning at Overjet, bringing a strong background in both finance and machine learning to his role. He has previously held positions at Deutsche Bank and DigitalGlobe, and he holds multiple degrees in Physics, Finance, and Computational Finance.
Current Role at Overjet
John Riskas serves as the Head of Machine Learning at Overjet, a position he has held since 2020. In this role, he leads initiatives that integrate machine learning technologies into the company's operations. His responsibilities include overseeing the development and implementation of advanced algorithms that enhance the company's data-driven decision-making processes.
Previous Experience in Machine Learning
Prior to his current role, John Riskas worked as an Applied Deep Learning Researcher at DigitalGlobe from 2018 to 2020. In this capacity, he focused on applying deep learning techniques to various projects. He also served as an AI Research Fellow at Insight Data Science from 2017 to 2018, where he gained valuable experience in artificial intelligence research.
Finance Background
John Riskas has a substantial background in finance, having worked as a Vice President in Equity Derivatives Trading at Deutsche Bank from 2016 to 2017. His role involved trading complex financial instruments and leveraging his financial expertise to navigate market dynamics. This experience complements his technical skills in machine learning.
Educational Qualifications
John Riskas holds a Bachelor of Applied Science (B.A.Sc.) in Physics from the University of California, Santa Barbara, which he completed from 2004 to 2009. He furthered his education at Carnegie Mellon University - Tepper School of Business, where he earned both a Master of Business Administration (M.B.A.) and a Master of Science (M.S.) in Computational Finance from 2011 to 2013.
Expertise in Machine Learning Techniques
John Riskas possesses expertise in advanced machine learning techniques, including domain adaptation, generative adversarial networks (GANs), and transfer learning. He has hands-on experience with semi-supervised learning and graphical neural networks, which are critical in developing sophisticated machine learning models.