Meet the Team

Jeroen has been interested in the relation between statistics, truth-finding and society for a long time. When he realized the wide scale adoption of Big Data and ML could lead to the exponential growth of real consequences due to statistics mistakes, he decided to get into the responsible ML field. He currently works as a ML Engineer at ABN Amro. Before that, Jeroen was a Data Science consultant, and obtained his PhD in astrophysics. At ABN Amro, he focuses on implementing MLOps and responsible ML. Jeroen proposes that in the field of applied ML everything is connected: from how ML is organised to how the algorithms work, from the happiness of ML professionals to the safety of end-users.

Daniel has long been interested in the intersection between the law, technology and society. Unsurprisingly, this drew him into the field of data science and law. Daniel currently works as legal counsel for AI & data science at the H&M Group: where his principal focus is on developing and maturing the company’s MLOps (business, governance, and regulatory) capacities. Daniel is also completing his PhD in law, MLOps, & finance at Leiden University. His education is in behavioural science, statistics, and law. Having worked at corporate law firms and as a consultant, Daniel has practical legal and commercial experience in the field. He proposes that responsible ML is centred around two essential themes - (a) a constant appreciation of context, and (b) prudent MLOps & project management.

Patrick Hall is principal scientist at bnh.ai, a D.C.-based law firm specializing in AI and data analytics. Patrick also serves as visiting faculty at the George Washington University School of Business. Prior to co-founding bnh.ai, Patrick led responsible AI efforts at the machine learning software firm H2O.ai, where his work resulted in one of the world's first commercial solutions for explainable and fair machine learning.

Nicholas started working in the field of responsible AI through his twenty years of experience working at the intersection of statistics, law, and regulation. He is currently the CEO of SolasAI and the AI practice leader at BLDS, LLC. Before that, Nicholas studied at the University of Chicago. In his work at SolasAI, he leads a team of data scientists and engineers building software that assesses whether there is evidence of bias or discrimination in an algorithm, and then provides alternative models that are both fairer and highly predictive. Nicholas believes that algorithms can be used as a force for good, but that responsible governance is necessary for that to happen.

Marjolein has been interested in consumer behavior through data analysis since her Masters in Economics at Utrecht University. She currently works as an Insight Strategist at Born05. Before that, she worked in Market Research and at various start-ups. Helping startups to understand their consumers, break through and scale-up is what drives her. Marjolein is excited to use her experience on growing startups to help the FBPML reach its potential.

Iris Clever, Ph.D. is a historian of science, medicine, and technology who researches the history of the life sciences and data sciences from 1800 to the present. Her current book project asks how we have come to trust biometric technologies to operate objectively, while in practice they perpetuate historical biases. She received her Ph.D. from UCLA and currently works as a postdoctoral fellow at the Stevanovich Institute for the Formation of Knowledge at the University of Chicago. Iris is excited to be part of this multidisciplinary team and contribute her expertise on rethinking science, data, and equity to the FBPML.

Leonoor is a machine learning researcher at the Delft University of Technology. She has a MSc in control engineering. Her work focuses on the development of machine learning methodologies for biomarker discovery in molecular imaging data. She has expertise in explainable artificial intelligence and is passionate about algorithmic fairness. Leonoor believes that research in responsible AI would benefit from more collaboration between academia and industry.

Chris is a Data Science Consultant at SolasAI, where he helps clients build fair, highly predictive models using advanced machine learning and explainable AI techniques. Prior to his work with SolasAI and BLDS LLC, Chris analyzed disparities in credit outcomes at the Consumer Financial Protection Bureau to support fair lending actions that resulted in millions of dollars in restitution to consumers. Chris hopes to use his experience on both the regulatory and industry side of fair AI in fields like credit, healthcare, and marketing to help bridge the gap between academic ideals of fairness and how algorithms can be made more equitable in practice. Chris has an MS in Business Analytics.

Gerry spent the last two decades in a Big 4 consulting firm advising clients from various industries on Cybersecurity challenges ranging from strategy to operational setups. He is a firm believer that technology - when put towards good and responsible use - can be a force multiplier for social good. The increasing global interest in Artificial Intelligence and Machine Learning brings great opportunities, but it has also demonstrated how a lack of good practices exacerbates the digital divide, privacy, fairness, and erodes the broader institutional trust. He has been dedicating his time in this area to connect the various communities to collectively design solutions to capture the opportunities while navigating the undesired consequences. An avid learner, he holds a Bachelor's degree in Electrical & Electronics Engineering, and continues pursuing a Masters degree in Artificial Intelligence and Sociology.