The Zuckerman Fund for Interdisciplinary Research in Machine Learning and Artificial Intelligence taps into talent at multiple campuses of one of Israel’s AI research leaders, Technion – Israel Institute of Technology, and supports its dedicated center for AI research and implementation, the Technion Center for Machine Learning and Intelligent Systems (MLIS).
The rapidly growing field of Artificial Intelligence (AI) is already changing the world as we know it and this fund leverages the power of U.S.-Israel collaboration to produce ground-breaking interdisciplinary research that will place Israel at the center of the global AI map.
“We are utilizing both the domestic and global resources of Israel’s AI research leader, the Technion, as a game-changing platform for positioning the ‘start-up nation’ at the forefront of the global AI revolution,” said James S. Gertler, Zuckerman Institute trustee.
The Zuckerman Fund for Interdisciplinary Research in Machine Learning and Artificial Intelligence supports as many as six research projects annually. All projects must be conducted between researchers affiliated with at least two faculties at the Technion’s main campus in Israel and the Jacobs Technion-Cornell Institute in New York, with a focus on applied research in those sectors which are increasingly influenced by AI: health and medicine, financial technology, autonomous vehicles, home and industrial robots, smart environments, agricultural technology, and defense and homeland security.
“Supporting tomorrow’s technological solutions through today’s research is a crucial manifestation of our vision that philanthropy’s ultimate goal is the betterment of society as a whole,” said Zuckerman Institute trustee Eric J. Gertler. “Joint innovation in AI and other high-tech areas also forms the basis of the next frontier in the U.S.-Israel relationship. We hope that others follow our lead in paving the way for stronger American-Israeli ties through collaborative research.”
The Technion Center for Machine Learning and Intelligent Systems (MLIS), is co-directed by Profs. Shie Mannor and Assaf Schuster of the Henry and Marilyn Taub Faculty of Computer Science. More than 100 affiliated staff members reflect the“core” of AI-related faculties (Computer Science, Electrical Engineering, and Industrial Engineering) as well as “user” faculties (Biomedical Engineering, Mechanical Engineering, and Aerospace Engineering).
MLIS acts as an umbrella and focal point for all Technion AI activities, research, and collaboration with industrial partners.
MLIS projects are led by the following researchers:
Asst. Prof. Ido Kaminer (Faculty of Electrical and Computer Engineering) designed the Ramanujan Machine, a novel “conjecture generator” that creates mathematical conjectures, which are considered the starting point for developing mathematical theorems.
Dr. Or Aleksandrowicz from the Architectural Research Lab (Faculty of Architecture and Town Planning) led a project based on the collection and analysis of Big Data, using multidisciplinary research related to architecture and urban environments, to support the development of advanced building technologies.
Dr. Yaniv Romano (Faculty of Electrical and Computer Engineering) is leading a machine-learning project focused on design learning and statistical methodologies to effectively identify explanatory features (e.g., genetic variations) linked to a phenomenon under study (e.g., disease risk), while rigorously controlling the number of false positives among the reported features.
Asst. Prof. Daniel Soudry (Faculty of Electrical and Computer Engineering) is carrying out research addressing the core challenges of (1) understanding deep learning and (2) making it more efficient in terms of computational resources.
Prof. Roi Reichart (Faculty of Industrial Engineering and Management) has conducted research into Natural Language Processing (NLP) focused on language learning and design models that integrate domain and world knowledge with data-driven methods.
Assoc. Prof. Vadim Indelman’s Autonomous Navigation and Perception Lab (ANPL, Faculty of Aerospace Engineering) investigates problems related to single and multi-robot collaborative autonomous navigation and perception, with a particular focus on accurate and reliable operation in uncertain environments.
Prof. Assaf Schuster is leading the Asynchronous Distributed Training of Deep Neural Networks Project, which has developed asynchronous versions of data-parallel training and showed them to be faster than their synchronous counterparts, contributing to efficient cloud computing