ABOUT THIS COURSE
This week-long course will provide a high-level overview of various important components in a canonical AI architecture highlighted in the image below. The focus of the course is to provide a shared vision of what is AI, how it is developed, evaluated and deployed.
The course will begin with a brief AI history, including a survey of representative AI success stories. We will cover topics such as AI data requirements and conditioning; various categories of AI techniques including supervised learning, unsupervised learning and reinforcement learning; applications including computer vision and natural language processing, as well as computing and hardware requirements to support AI and Big Data applications. We will also discuss properties and techniques that lead to robust AI solutions and review effective human-machine teaming principles and requirements. Each of the AI subsystem components will be addressed at a level deep enough to provide a working knowledge of the key technical drivers. Through it all, the course will emphasize an AI system architecture approach applied to engineering prototypes. We will highlight strengths and weaknesses of AI solutions and illustrate the role AI can play in augmenting human intelligence.
REQUIREMENTS
This course is targeted to students that have limited to basic knowledge of AI concepts and technologies.
COURSE LECTURERS
Dr. Danelle Shah
Dr. Danelle Shah is an Assistant Leader of the Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory. She received B.S. degrees in Mechanical and Aerospace Engineering from the State University of New York at Buffalo, and M.S. and Ph.D. degrees in Mechanical Engineering from Cornell University. Her research background is in machine learning, artificial intelligence, and human-machine interaction. Danelle joined Lincoln Laboratory in 2012 and has since been involved in a variety of programs with foci including pattern analysis and anomaly detection; multi-intelligence analytics for anti-access/area denial (A2/AD); cognitive robotics; distributed battle management, command, and control (BMC2); quantitative assessment of intelligence, surveillance, and reconnaissance (ISR) employment effectiveness; humanitarian assistance and disaster response (HA/DR); and open-source data exploitation. Danelle currently helps lead Lincoln’s AI Technology & Systems Group, which specializes in the development of AI systems for the DoD, IC, and Law Enforcement leveraging signal processing, machine learning, graph analytics, and natural language processing.
TSgt Armando Cabrera
TSgt Armando Cabrera is the Artificial Intelligence Flight Chief for the newly established Air Force Artificial Intelligence Accelerator at MIT. As the first DoD graduate of Amazon Machine Learning University, TSgt Cabrera advises the Accelerator Director, MIT Faculty, and MIT Lincoln Laboratory Staff on AI application expertise. These interdisciplinary teams create new algorithms and solutions for rapid deployment into Air Force operations
Dr. Julie Mullen
Dr. Julie Mullen is a member of the technical staff in the MIT Lincoln Laboratory Supercomputing Center (LLSC) where she assists researchers in maximizing their use of high performance computing resources in order to minimize their time to solution. Additionally, Dr. Mullen leads the design and creation of online professional education courseware for the LLSC, where she pursues research in learning analytics for adaptive learning design and the integration of hands-on physical construction and experimentation with MOOC platforms and technologies.
Dr. Ryan Soklaski
Ryan Soklaski is a technical staff member of Lincoln Laboratory’s Intelligence & Decision Technologies group. There, he researches machine learning techniques that are performant under data-restricted circumstances, and works as a core developer for a lab-internal machine learning library. He is also the lead instructor of the CogWorks course at the Beaver Works Summer Institute, and the creator of the educational site “Python Like You Mean It”.
Prior to joining the laboratory, Ryan earned his PhD in theoretical condensed matter physics at Washington University in St. Louis. His doctoral thesis involved conducting physics simulations on high-performance computing clusters to study the physical mechanisms that drive the glass formation process in metallic liquids. His interests include methods of numerical analysis, developing software in Python, and quantum mechanics.
Dr. Virginia Goodwin
Ms. Goodwin is a member of the technical staff in the Homeland Protection Systems Group in the Homeland Protection and Air Traffic Control Division of MIT Lincoln Laboratory. She joined the lab in 2004, in the Air, Missile, and Maritime Defense Technology division. Virginia’s work focuses on implementing novel computer vision and machine learning algorithms for decision support across multiple sensor modalities. She received her bachelor’s degree in Physics from Wellesley College in 2004 and her Master’s degree in Electrical Engineering from Harvard University in 2010.
Dr. Charlie K. Dagli
Dr. Charlie K. Dagli has been a member of the research staff in the Artificial Intelligence Technology and Systems Group (formerly the Human Language Technology Group) at MIT Lincoln Laboratory since January 2010. His primary research interests are in the areas of natural language processing, multimedia understanding and network analysis.
Dr. Dagli received the BS degree from Boston University in 2001, and the MS and PhD degrees from the University of Illinois, Urbana-Champaign, in 2003 and 2009, all in electrical and computer engineering.
Dr.Olga Simek
Dr. Olga Simek is a member of technical staff in the Artificial Intelligence Technology and Systems Group at Lincoln Laboratory. For the past eight years, she has been leading projects, conducting applied research and publishing in the area of text analytics, crowdsourcing, and social networks analysis.
Dr. Simek received the BA degree in Mathematics and Computer Science from Eastern Washington University, the MA degree in Mathematics from Western Washington University, and the PhD degree in Mathematics from the University of Arizona.
Dr. Rajmonda S. Caceres
Dr. Rajmonda S. Caceres is Assistant Group Lead of the AI Technology Group at MIT Lincoln Laboratory. Rajmonda earned her PhD degree in mathematics and computer science from the University of Illinois at Chicago in 2012. Her research background is in network science, data mining and machine learning. Her graduate research work was on characterization and identification of optimal temporal scales for analyzing temporal interaction networks.
Rajmonda has contributed to a variety applications area including anomaly detection in cyber and social networks, open-source data analytics, information retrieval, and AI-driven synthetic biology. Her current work focuses on methods for learning robust representations of relational and sequential data in resource constrained environments, as well as optimization of experimental design of complex systems.
Prof. Mykel J. Kochenderfer
Mykel J. Kochenderfer is an Associate Professor of Aeronautics and Astronautics at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. He is also the director of the SAIL-Toyota Center for AI Research at Stanford and a co-director of the Center for AI Safety. He teaches courses on optimization, decision making under uncertainty, and the validation of autonomous systems. From 2006-2013, he was a member of the technical staff at Lincoln Laboratory in Group 42, where he remains as a consultant. He received a Ph.D. in informatics from the University of Edinburgh and B.S. and M.S. degrees in computer science from Stanford University. He is an author of the textbooks "Decision Making under Uncertainty: Theory and Application" and "Algorithms for Optimization", both from MIT Press.
Ms. Kimberlee Chang
Ms. Kimberlee Chang is member of the Advanced Technologies and Concepts Group in the Air, Missile, and Maritime Defense Division of MIT Lincoln Laboratory. She earned her BS in astrophysics from the University of Wisconsin at Madison and her MS in electrical engineering at Tufts University. Since joining Lincoln, she has supported a variety of mission areas including air and missile defense, space situational awareness, air traffic control and humanitarian assistance and disaster response. Her primary research area is human machine collaboration and her recent work focuses on the use of gaming technology as a platform to train and benchmark human-AI teaming systems.
Dr. Sarah McGuire
Dr. Sarah M. McGuire is a technical staff member in the Cyber Operations and Analysis Technology Group. Her research is in Human Factors in Cyber Operations. Prior to joining Lincoln Laboratory, Dr. McGuire was an Assistant Professor at the University of Pennsylvania, where her research focused on physiological and behavioral sensors. Dr. McGuire received her Ph.D. and M.S. in mechanical engineering at Purdue University in 2012 and 2010 and her B.S. in applied physics at Northern Illinois University in 2006.
Dr. Vijay Gadepally
Dr. Vijay N. Gadepally is a Senior Member of the Technical Staff at the Massachusetts Institute of Technology (MIT) Lincoln Laboratory and works closely with the Computer Science and Artificial Intelligence Laboratory (CSAIL). Vijay holds M.Sc. and PhD degrees in Electrical and Computer Engineering from The Ohio State University and a B.Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur. In 2011, Vijay received an Outstanding Graduate Student Award at The Ohio State University. In 2016, Vijay received MIT Lincoln Laboratory’s Early Career Technical Achievement Award and in 2017, Vijay was named to AFCEA's inaugural 40 under 40 list. Vijay’s research interests are in high performance computing, machine learning, artificial intelligence and high-performance databases. Vijay is a Senior Member of the IEEE.
Lauren Milechin
Lauren Milechin works in Research Computing at MIT. She conducts training for the MIT Supercloud system, and has done research work involving big data, database technology, and machine learning applied to problems in diverse domains. Previously, Lauren worked as Associate Technical Staff at the Lincoln Laboratory Supercomputing Center. Ms. Milechin received an MS degree in industrial mathematics from the University of Massachusetts, Lowell, focusing in computer science applications, such as machine learning and algorithms. She holds a BS degree in mathematical sciences from Worcester Polytechnic Institute, where she explored mathematical modeling of disease and of population dynamics.
Dr. Andrew C. Kirby
Dr. Andrew C. Kirby is a Postdoctoral Research Associate in the Lincoln Laboratory Supercomputing Center. Andrew’s research focuses on High Performance Computing and Supervised Machine Learning algorithms.
Andrew holds a Ph.D. in Mechanical Engineering from the University of Wyoming (2018), an M.S. in Applied Mathematics from Columbia University (2013), and a B.S. in Mathematics from the University of Wisconsin-Madison (2011). At Wyoming, Andrew developed numerical methods for Computational Fluid Dynamics employed on the fastest supercomputer in the world, ORNL Summit.
Dave Martinez
David R. Martinez is a Lincoln Laboratory Fellow at MIT Lincoln Laboratory. In this capacity, he is focusing on research and technical directions in the areas of artificial intelligence, high performance computing and digital transformation. He is also dedicated to AI teaching within MIT, MIT Lincoln Laboratory and to industry and government organizations.
He has been a keynote speaker at both national and international conferences. He co-authored-co-edited the book titled High Performance Embedded Computing Handbook: A Systems Perspective. He was elected IEEE Fellow for “technical leadership in the development of high performance embedded computing for real-time defense systems.” He holds three US patents based on his work in signal processing for seismic applications.
Mr. Martinez was awarded a bachelor’s degree from New Mexico State University, an MS degree from MIT and the EE degree jointly from MIT and the Woods Hole Oceanographic Institution in Electrical and Oceanographic Engineering. He completed an MBA from Southern Methodist University.