Amir Barati Farimani
Professor Farimani joined the Department of Mechanical Engineering at Carnegie Mellon University in the fall of 2018. He was previously a postdoctoral fellow at Stanford University. He received his PhD in Mechanical Engineering in 2015.
His lab at CMU focuses on the problems at the interface of Mechanical Engineering, data science and machine learning. His lab uses the state of the art deep learning and machine learning algorithms and tools to learn, infer and predict the physical phenomena pertinent to mechanical engineering. Currently, he is teaching AI and ML to a large class of graduate students at CMU.
He received the Stanley I. Weiss best thesis award from the University of Illinois in 2016 and was recognized as an Outstanding Graduate Student in 2015. During his post-doctoral fellowship at Stanford, Dr. Barati Farimani has developed data-driven, deep learning techniques for inferring, modeling, and simulating the physics of transport phenomena and for materials discovery for energy harvesting applications.
Peter Allen is Professor of Computer Science at Columbia University, and Director of the Columbia Robotics Lab. He has been building robotic systems for over 35 years, including the GraspIt! grasping simulator, the IREP surgical robot, and the Visibot disposable laparoscope. He is the recipient of the CBS Foundation Fellowship, Army Research Office fellowship, the Rubinoff Award for innovative uses of computers, and the NSF PYI award. His current research interests include robotic grasping, medical robotics and Brain-Computer Interfaces for Human-Robot interaction.
Alán Aspuru-Guzik’s research lies at the interface of computer science with chemistry and physics. He works in the integration of robotics, machine learning and high-throughput quantum chemistry for the development of materials acceleration platforms. These “self-driving laboratories” promise to accelerate the rate of scientific discovery, with applications to clean energy and optoelectronic materials. Alán also develops quantum computer algorithms for quantum machine learning and has pioneered quantum algorithms for the simulation of matter. He is jointly appointed as a Professor of Chemistry and Computer Science at the University of Toronto. Previously, he was a full professor at Harvard University. Alán is also a co-founder of Zapata Computing and Kebotix, two early-stage ventures in quantum computing and self-driving laboratories respectively.
Kevin Carlberg is a Distinguished Member of Technical Staff at Sandia National Laboratories in Livermore, California. He was the President Harry S. Truman Postdoctoral Fellow in National Science and Engineering from 2011 to 2014, and a Principal Member of Technical staff from 2014 to 2019. He received his PhD in Aeronautics and Astronautics from Stanford University in 2011 with a PhD minor in Computational and Mathematical Engineering. He leads a research group of PhD students, postdocs, and technical staff whose work combines concepts from machine learning, computational physics, and high-performance computing to drastically reduce the cost of simulating nonlinear dynamical systems at extreme scale. Current national-security applications include a range of problems in mechanical and aerospace engineering such as hypersonic vehicles, turbulent flows over store-in-cavity configurations, and high-speed gas-transfer systems.
Maxime is a Montreal native, currently working as a research engineer at Mila, an academic research institute created by Yoshua Bengio which is focused on the study of artificial intelligence. Her research interests are centered around reinforcement learning, simulation and general-purpose robotics.
Amir-massoud Farahmand is a faculty member, research scientist, and Canada CIFAR AI Chair at the Vector Institute in Toronto, Canada. His research interests are in reinforcement learning and machine learning with a focus on developing theoretically-sound algorithms for challenging industrial problems. He received his PhD from the University of Alberta in 2011, followed by postdoctoral fellowships at McGill University (2011–2014) and Carnegie Mellon University (CMU) (2014). Prior to joining the Vector Institute, he worked as a principal research scientist at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, USA for three years.
Mike Haley is the Senior Director of AI and Robotics at Autodesk Research where they identify, evaluate, and develop disruptive technologies that improve the practice of imagining, designing, and creating a better world. His team combines research, development, and user experience in coupled iterative cycles to develop new products and foundational technology. For the last several years Mike’s team has been focused on bringing geometric shape analysis and large scale machine learning techniques to 3D design information with the intent to make software a true partner in the design process.
Formerly, Mike led the move of Autodesk products from the desktop to the cloud by driving the adoption of scalable distributed compute and data technology. Prior to joining Autodesk, Mike performed research and product development in the fields of volumetric graphics, distributed multimedia, computer vision, and embedded systems. He is drawn to areas where he can combine his 25 years of experience in computer graphics, distributed systems, and mathematical analysis. Mike holds an MS in computer science from the University of Cape Town, South Africa.
Assistant Professor of Computer Science and Mathematics at the University of Toronto
Levent Burak Kara
Professor of Mechanical Engineering at Carnegie Mellon University
Nathan Kutz is the Yasuko Endo and Robert Bolles Professor of Applied Mathematics at the University of Washington, having served as chair of the department from 2007-2015. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control.
Dr. Rahul Rai is an Associate Professor of Mechanical & Aerospace Engineering at the University at Buffalo-SUNY. He earned his doctoral degree in Mechanical Engineering from The University of Texas at Austin in 2006. He is the recipient of 2017 ASME IDETC/CIE Young Engineer Award. By combining engineering innovations with methods from machine learning, AI, statistics and optimization, and geometric reasoning, his research strives to solve important problems in manufacturing, engineering design, and complex system design domain. He has authored over 100 papers to date in peer-reviewed conferences and journals covering a wide array of problems.
Jason Riordon is a Research Associate at the Department of Mechanical and Industrial Engineering at the University of Toronto (Sinton Lab), where he develops microfluidic technologies that address grand challenges in health and energy, including infertility diagnostics and treatment, industrial fluid analysis and biofuels development. Most recently, Jason has focused on applying deep learning methodologies to single-cell analysis in male fertility – a prime candidate for AI-optimization and standardization. More broadly, he is interested in how AI stands to disrupt microfluidic technologies, from device design to data analysis. Jason earned his doctoral degree in Physics at the University of Ottawa in 2014.
Graham Taylor is a Canada Research Chair and Associate Professor of Engineering at the University of Guelph. He directs the University of Guelph Centre for Advancing Responsible and Ethical AI and is a member of the Vector Institute for Artificial Intelligence. His research aims to discover new algorithms and architectures for deep learning: the automatic construction of hierarchical algorithms from high-dimensional, unstructured data. He is especially interested in time series, having applied his work to better understand human and animal behaviour, environmental data (climate or agricultural), audio (music or speech) and financial time series. His work also intersects high performance computing, investigating better ways to leverage hardware accelerators to cope with the challenges of large-scale machine learning. He has co-organized the annual CIFAR Deep Learning Summer School, and has trained more than 60 students and research staff on AI-related projects. In 2016 he was named as one of 18 inaugural CIFAR Azrieli Global Scholars. In 2018 he was honoured as one of Canada's Top 40 under 40. In 2019 he was named a Canada CIFAR AI Chair. He spent 2018-2019 as a Visiting Faculty member at Google Brain, Montreal.
Graham co-founded Kindred, which was featured at number 29 on MIT Technology Review's 2017 list of smartest companies in the world and CB Insights AI 100 list, highlighting the most innovative artificial intelligence companies for 2018. He is the Academic Director of NextAI, a non-profit accelerator and founder development program for AI-focused entrepreneurs.
Masayuki Yano is an assistant professor at the University of Toronto Institute for Aerospace Studies (UTIAS). His research focuses on the development of computational methods for problems in aerospace sciences and engineering. Specifically, his research interests lie in numerical methods, scientific computation, and numerical analysis for partial differential equations with applications in continuum mechanics with an emphasis on aerodynamics. His current research topics include adaptive high-order methods, reduced-order modeling, and data assimilation. He obtained his PhD in Aeronautics and Astronautics from MIT in 2012, and was a post-doctoral associated in the Department of Mechanical Engineering at MIT before joining UTIAS in Fall 2015.
Michael Yip is an Assistant Professor of Electrical and Computer Engineering at UC San Diego, IEEE RAS Distinguished Lecturer, Hellman Fellow, and Director of the Advanced Robotics and Controls Laboratory (ARCLab). His research lab investigates learning-based representations for robots that enable robots to explore and adapt control to new environments and conditions, enabling responsive artificial intelligence, planning, and execution in dynamic environments. These representations are trained using a variety of local and global model-free learning strategies, and when implemented are comparatively significantly faster, more consistent, and more power and memory efficient. Current focus is towards solving data-efficient and computationally efficient robot control and motion planning representations via deep imitation learning and reinforcement learning strategies. His lab applies these ideas to surgical robotics and the automation of surgical procedures. Previously, research has investigated different facets of model-free control, planning, haptics, soft robotics and computer vision strategies. His work has been recognized through several best paper awards at ICRA, including the 2016 best paper award for IEEE Robotics and Automation Letters. Dr. Yip has previously been a research associate with Disney Research in Los Angeles involved in animatronics design, and most recently held a visiting research position with Amazon Robotics Machine Learning and Computer Vision group in Seattle. He received a B.Sc. in Mechatronics Engineering from the University of Waterloo, an M.S. in Electrical Engineering from the University of British Columbia, and a Ph.D. in Bioengineering from Stanford University.