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Who We Are

Founded by a group of noted scientists and leading machine learning pioneers, the team at Geometric Intelligence is redefining the boundaries of machine learning through innovative, patent-pending techniques that learn more efficiently from less data.

Gary Marcus

CEO and Co-Founder

Gary is a scientist, bestselling author, and entrepreneur. His published works include The Algebraic Mind: Integrating Connectionism and Cognitive Science and The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought. He is also Professor of Psychology and Neural Science at NYU.

Zoubin Ghahramani

Chief Science Officer, Co-Founder

Zoubin is a world leader in the field of machine learning and Professor of Information Engineering at the University of Cambridge. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian nonparametric approaches to machine learning systems, and to the development of approximate variational inference algorithms for scalable learning.

Doug Bemis

CTO and Co-Founder

Doug has served as CTO for several other startups, including Syntracts LLC and Windward Mark Interactive. Doug also received a PhD from NYU in neurolinguistics, for work using magnetoencephalography to investigate the neural bases of semantic composition. Subsequently, he worked with Stanislas Dehaene at Neurospin in France.

Ken Stanley

Chief Science Officer, Co-Founder

Ken is an associate professor of computer science at the University of Central Florida. He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.

Jeff Clune

Scientific Advisor

Jeff is an Assistant Professor in Computer Science at the University of Wyoming. His lab (http://EvolvingAI.org) focuses on robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. In the last two years, his robotics paper was on the cover of Nature, he won an NSF CAREER award, he received the Distinguished Young Investigator Award from the International Society for Artificial Life, and his papers were awarded oral presentations at NIPS, CVPR, ICLR, and an ICML workshop.

Noah Goodman

Scientific Advisor

Noah is Associate Professor of Psychology, Computer Science, and Linguistics at Stanford University, where he runs the Computation and Cognition Lab. He studies the computational basis of natural and artificial intelligence,merging behavioral experiments with formal methods from statistics and programming languages. His research topics include language understanding, social reasoning, and concept learning, as well as applications of these ideas and enabling technologies such as probabilistic programming languages. Professor Goodman has published more than 150 papers in fields including psychology, linguistics, computer science, and mathematics.

Jason Yosinski

Machine Learning Researcher

Jason is a machine learning researcher who, besides participating in the never-ending quest for better models, builds "AI Neuroscience" tools to enable better understanding of the training and inner workings of neural network models. Before joining GI, he was a PhD student and NASA Space Technology Research Fellow at the Cornell Creative Machines Lab, the University of Montreal, Google DeepMind, and the Caltech Jet Propulsion Laboratory. His work on AI has been featured on NPR, Fast Company, Wired, the Economist, TEDx, and BBC.

Rosanne Liu

Machine Learning Researcher

Rosanne is living her dream life of being an AI researcher. Before joining GI she was a PhD student at Northwestern University, where she worked towards the intersection of machine learning and high performance computing in the Center of Ultra-scale Computing and Information Security, and has led several major cross-lab research projects funded from NSF, DOE, AFOSR, and NIST. She has a diverse background in green energy, driving dynamics control, materials informatics, quantitative finance and social media mining. She loves reading, standup comedies and playing with dogs.

Jon Berliner

Machine Learning Researcher

Jon has been a machine learning researcher at Geometric Intelligence since August 2015. Before Geometric, he was a graduate student at Princeton University under Dr Matt Botvinick studying computational decision making, and under Dr Jordan Taylor researching human motor planning and adaptation. He earned his BA from Vassar College in Mathematics and Cognitive Science. He loves to laugh, to eat, enjoys the occasional craft brew, bouldering on the weekends, and, of course, the Philadelphia 76ers. When he's not developing decision making models, you can find him writing blurbs for Geometric.

Eli Bingham

Machine Learning Researcher

Eli is a research scientist working on probabilistic programming, approximate Bayesian inference, and grounded language understanding. He has previously worked on condensed matter physics, computational biology, climatology, multiscale dictionary learning, and deep learning for computer vision. In his spare time he hangs out in a lab tinkering with his nanopore DNA sequencer.

Joel Lehman

Machine Learning Researcher

In addition to working with Geometric Intelligence, Joel Lehman is an assistant professor at the IT University of Copenhagen researching neural networks, evolutionary algorithms, and reinforcement learning. Several of his papers have been nominated for best paper awards, and his work has been cited over 1,000 times.

Piero Molino

Machine Learning Researcher

Machine learning researcher with focus on language. Completed a PhD on Question Answering at the University of Bari, Italy, were he founded a startup, QuestionCube. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence.

Theo Karaletsos

Machine Learning Researcher

Theofanis Karaletsos spends his time thinking about probabilistic modeling and unsupervised learning. He has applied his ideas to domains ranging from pure ML and computer vision down to Biology and Medicine at his previous positions at the Sloan Kettering Institute in New York, the Max Planck Institute For Intelligent Systems in Tübingen and the Technical and LMU Universities of Munich. Theofanis has received various fellowships, such as a Microsoft Research PhD Scholarship and the Ministerial Scholarship from the Bavarian Ministry Of Science, Research and the Arts.

Sebastian Risi

Machine Learning Researcher

Sebastian Risi is an Associate Professor at the IT University of Copenhagen where he co-directs the Robotics, Evolution and Art Lab (REAL). His interests include computational intelligence in games, neuroevolution, evolutionary robotics and deep learning. Risi has a PhD in computer science from the University of Central Florida and was a postdoctoral fellow at Cornell University. He has won several best paper awards at GECCO, EvoMusArt and IJCNN.

Anh Nguyen

Machine Learning Intern

Anh is a PhD student at the University of Wyoming working with professor Jeff Clune. He has invented an finger-worn device for Virtual Reality, fooled machines and recently taught them to generate images.

Paul Szerlip

Machine Learning Researcher

Paul Szerlip earned his PhD with Dr. Kenneth Stanley at the University of Central Florida focusing on open-source infrastructure for collaborative evolutionary software. This open-source platform enables researchers to quickly integrate crowd-sourced human contributions with automated algorithms, while making the results easily accessible online. His later research highlighted new ways to integrate neuroevolutionary techniques like HyperNEAT and Novelty Search into deep learning frameworks.

Martin Jankowiak

Machine Learning Consultant

Martin is a consultant for Geometric Intelligence. After obtaining a PhD in particle physics from Stanford and and postdoctoral work in Heidelberg and NYU, he has seen the light and works with Geometric Intelligence on modern Bayesian Inference techniques.

Eric Frank

Machine Learning Research Engineer

Eric joined Geometric when it was still a wee research startup. Before Geometric he invented AI oriented toys for Kite and Rocket Research. He was also a research assistant at the University of Rochester and paints in his free time.

Press Contact:

Geometric Intelligence
137 Varick Street 2nd Floor, New York, NY 10013