Dan is an applied scientist with a background in neuro-symbolic machine learning and software engineering. Dan has 8 years experience in the Emerging Technology turned Applied Research team at IBM UK, where he worked on applying symbolic learning techniques to material discovery problems. Dan has also developed a software solution for a Bayesian Optimization library, and led the technical demonstration work for a generative policy project within a large UK-USA collaborative research program. Dan is nearing completion of a PhD from Imperial College London which focuses on extending inductive logic programming systems to learn from raw data.

Fernando is co-founder and CTO at Durable, and brings a wealth of experience building large-scale AI systems in the real world. Most recently, he led the visual perception effort for the development of autonomous mobile robots at Canvas Technology (and later at Amazon after Canvas was acquired). His broad experience integrating deep learning and traditional components of complex AI systems directly translates to the work of building Durable’s neuro-symbolic AI system. He holds a PhD in Computer Science from the University of Colorado Boulder.

Kia is an applied scientist with an extensive background in programming languages theory and artificial intelligence. Before joining Durable, Kia served as a postdoctoral fellow at the University of Texas at Austin, collaborating with Professor Isil Dillig on neurosymbolic machine learning and program synthesis. During his internship at Microsoft's PROSE team, Kia developed one of the earliest program synthesis algorithms using large language models under the guidance of Dr. Sumit Gulwani. Kia holds a PhD in Computer Science from Purdue University, where his doctoral thesis focused on concurrency theory in modern database systems.

Liam McInroy is a machine learning engineer at Durable. He has previous research experience in various groups across Harvard, MIT, and Intel spanning topics such as higher category theory, provable reinforcement learning, inferring goals of humans through probabilistic programming, and supercomputing on distributed systems. In more consumer facing roles, Liam designed data validation systems for Sunshine Contacts, and he has released several mobile games with over 50,000 downloads. Liam holds a Master’s in Computer Science and a Bachelor’s in Mathematics, both from Harvard.

Mike Kasper is an applied scientist with broad and deep experience combining deep learning and traditional AI systems. Before joining Durable, he built neuro-symbolic perception systems at Amazon to power autonomous robotic navigation in complex unstructured environments. As part of training and validating the necessary models, Mike explored the use of high fidelity simulation for data generation. He holds a PhD from the University of Colorado Boulder, where his research focus was visual perception in challenging illumination environments.

Nima is co-founder and CEO at Durable. Before Durable, he was co-founder and CTO of Canvas Technology, a startup in Boulder CO developing vision-based autonomous mobile robots for manufacturing and logistics.After helping to transition the team at Amazon following the acquisition of Canvas Technoology, Nima joined Xplorer Capital helping the team source and assess early-stage startup deals in deeptech, AI, and sustainability. Nima holds a PhD from the University of Colorado Boulder, where his research focus was visual perception and control for agile autonomous vehicles.

Kevin Ellis is Assistant Professor of Computer Science at Cornell University working on artificial intelligence and program synthesis, with the goal of better combining reasoning and learning. His research is motivated by the goals of building machine learning systems that generalize strongly (extrapolating rather than interpolating); while requiring less data (greater sample efficiency); and which acquire interpretable knowledge that humans can understand and build on. He draws on ideas and techniques from machine learning, artificial intelligence, programming languages, and cognitive science. He received his Ph.D. in Computer Science from MIT.

Noah D. Goodman is Associate Professor of Psychology, Linguistics (by courtesy), and Computer Science (by courtesy) at Stanford University. He studies the computational basis of human thought, merging behavioral experiments with formal methods from statistics and logic. Specific projects vary from concept learning and language understanding to inference algorithms for probabilistic programming languages. He received his Ph.D. in mathematics from the University of Texas at Austin in 2003. In 2005 he entered cognitive science, working as Postdoc and Research Scientist at MIT. In 2010 he moved to Stanford where he runs the Computation and Cognition Lab.