WILKO SCHWARTING - Key Persons
Job Titles:
- Delta Electronics Professor of Electrical Engineering
- Professor
- Research Areas
Antonio Torralba is a Delta Electronics Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and Head of the AI+D faculty in the EECS department. He received a degree in telecommunications engineering from Telecom BCN, Spain in 1994 and a Ph.D. degree in signal, image, and speech processing from the Institut National Polytechnique de Grenoble, France in 2000. From 2000 to 2005, he spent postdoctoral training at the Brain and Cognitive Sciences Department and the Computer Science and Artificial Intelligence Laboratory, MIT, where he is now a professor.
Job Titles:
- Associate Director
- COO
- Member of the Senior Leadership Team
- Professor
- Research Areas
- Research Group / Computation Structures Group
Job Titles:
- Fellow of IEEE
- Professor
- Research Areas
Arvind is the Johnson Professor of Computer Science and Engineering at MIT. Arvind's group, in collaboration with Motorola, built the Monsoon dataflow machines and its associated software in the late eighties. In 2000, Arvind started Sandburst which was sold to Broadcom in 2006. In 2003, Arvind co-founded Bluespec Inc., an EDA company to produce a set of tools for high-level synthesis. In 2001, Dr. R. S. Nikhil and Arvind published the book "Implicit parallel programming in pH". Arvind's current research focus is on enabling rapid development of embedded systems.
Arvind is a Fellow of IEEE and ACM, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences
Job Titles:
- Senior Research Scientist
- Research Areas
- Research Group / Vision Group
Job Titles:
- Senior Research Scientist
- 3Com Founders Senior Research Scientist
- Founder of the Center for Democracy
- Leader in Internet
- Research Areas
- Research Group
- Research Group / Data Systems Group
Daniel J. Weitzner holds the 3Com Founders Senior Research Scientist chair at CSAIL, is Founding Director of the MIT Internet Policy Research Initiative, and teaches Internet public policy in MIT's Electrical Engineering and Computer Science Department. Weitzner's research pioneered the development of Accountable Systems to enable computational treatment of legal rules.
Weitzner was United States Deputy Chief Technology Officer for Internet Policy in the White House. where he led initiatives on privacy, cybersecurity, copyright, and digital trade policies promoting the free flow of information. He was responsible for the Obama Administration's Consumer Privacy Bill of Rights and the OECD Internet Policymaking Principles.
Weitzner has been a leader in Internet public policy from its inception, making fundamental contributions to the successful fight for strong online free expression protection in the United States Supreme Court, and for laws that control government surveillance of email and web browsing data.
Weitzner has a law degree from Buffalo Law School, and a B.A. in Philosophy from Swarthmore College. His writings have appeared in Science magazine, the Yale Law Review, Communications of the ACM, the Washington Post, Wired Magazine and Social Research.
Weitzner is a founder of the Center for Democracy and Technology, led the World Wide Web Consortium's public policy activities, and was Deputy Policy Director of the Electronic Frontier Foundation. He is a Fellow of the National Academy of Public Administration, recipient of the International Association of Privacy Professionals Leadership Award (2013), the Electronic Frontier Foundation Pioneer Award (2016), a member of the Council on Foreign Relations and a Senior Fellow at the German Marshall Fund.
Job Titles:
- Associate Director
- Member of the Senior Leadership Team
- Professor
- Professor of Computer Science at MIT
- Research Areas
Daniel Jackson is professor of computer science at MIT, and associate director of CSAIL. For his research in software, he won the ACM SIGSOFT Impact Award, the ACM SIGSOFT Outstanding Research Award and was made an ACM Fellow.
He is the lead designer of the Alloy modeling language, and author of Software Abstractions. He chaired a National Academies study on software dependability, and has collaborated on software projects with NASA on air-traffic control, with Massachusetts General Hospital on proton therapy, and with Toyota on autonomous cars.
His most recent book, Essence of Software, offers a fresh approach to software design, and shows how thinking about software in terms of concepts and their relationships can lead to more usable and effective software.
Job Titles:
- Director
- LAB DIRECTOR
- Member of the Senior Leadership Team
Job Titles:
- Senior Research Scientist
Job Titles:
- Professor
- Research Areas
- Research Group
David Karger (A.B. Summa cum laude in Computer Science, 1989, Harvard University, Ph.D., 1994, in Computer Science, Stanford University) is a Professor of Computer Science at MIT.
Karger splits his research between algorithms and information retrieval. His work in algorithms has focused on applications of randomization to optimization problems and led to significant progress on several core problems. He has also researched applications of theoretical ideas to applied areas such as compilers and networks.
His dissertation received the 1994 ACM doctoral dissertation award and the Mathematical Programming Society's 1997 Tucker Prize. His research in information retrieval has focused on new interfaces and algorithms for helping people sift effectively through large masses of information. His work on the Scatter/Gether browsing system at Xerox PARC led to two patents.
More recently he has been researching retrieval systems that personalize themselves to best fit their individual users' needs and behaviors. He recently received the National Academy of Science's 2004 Award for Initiative in research.
Karger leads CSAIL's Haystack group, which researches many facets of information management including capture, organization, retrieval, sharing, and visualization.
Job Titles:
- Executive Director
- Managing Director
Job Titles:
- Professor
- Professor in the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology
Fredo Durand is a professor in the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology, and a member of the Computer Science and Artificial Intelligence Laboratory. He received his PhD from Grenoble University (France) in 1999. He worked with Claude Puech and George Drettakis on both theoretical and practical aspects of 3D visibility. From 1999 till 2002, he was a post-doctoral researcher in the MIT Computer Graphics Group with Julie Dorsey, where he is now an associate professor.
His research interests span most aspects of picture generation and creation. This includes realistic graphics, real-time rendering, non-photorealistic rendering, as well as computational photography. His recent emphasis is on the use of tools from signal processing and inspiration from perceptual sciences. He received an inaugural Eurographics Young Researcher Award in 2004, an NSF CAREER award in 2005, an inaugural Microsoft Research New Faculty Fellowship in 2005 and a Sloan fellowship in 2006.
Job Titles:
- Graduate Student
- Research Areas
Job Titles:
- Professor of Electrical Engineering at the Massachusetts Institute of Technology
- Research Areas
- Research Group
- Research Group / Sussman Lab
Gerald Jay Sussman is the Panasonic (formerly Matsushita) Professor of Electrical Engineering at the Massachusetts Institute of Technology. He received the S.B. and the Ph.D. degrees in mathematics from the Massachusetts Institute of Technology in 1968 and 1973, respectively. He has been involved in artificial intelligence research at M.I.T. since 1964. His research has centered on understanding the problem-solving strategies used by scientists and engineers, with the goals of automating parts of the process and formalizing it to provide more effective methods of science and engineering education. Sussman
Job Titles:
- Professor of Computer Science
- Research Areas
As much as computing is ubiquitous in our lives, it is still effectively out of reach for most people: It's hard to understand, hard to shape to personal needs, and hard to accommodate into legal and policy framework. Professor Hal Abelson's research in MIT CSAIL involves democratizing computing - making information technology a tool for everyone, from children in school to leaders in government.
Prof. Abelson is a Professor of Computer Science and Engineering in the EECS department at MIT and a Fellow of the IEEE. He has received many awards for teaching computer science, including the Bose Award, the Taylor L. Booth Education Award, the ACM Special Interest Group on Computer Science Education Award for Outstanding Contribution to Computer Science Education, and the ACM Karl Karlstrom Outstanding Educator Award.
Throughout his impressive career, he has played key roles in fostering MIT institutional educational technology initiatives including MIT OpenCourseWare and DSpace, and he has served as co-chair of the MIT Council on Educational Technology.
His focus on both education and democratizing culture and intellectual resources has made him a leader in this field. He is a founding director of Creative Commons, Public Knowledge, and the Free Software Foundation. Within CSAIL he is also involved with the Internet Policy Research Initiative (IPRI), which collaborates with policy-makers and technologists to improve the trustworthiness and effectiveness of interconnected digital systems like the internet. He is also a co-author of the 2008 book Blown to Bits, which talks about the cultural and political disruptions caused by the information explosion.
Prof. Abelson is pursuing projects with this overall theme of making information technology more accessible for all.
Hal Abelson - the Class of 1922 Professor of Electrical Engineering and Computer Science at MIT, a principal investigator at CSAIL and co-chair of the MIT Council on Educational Technology - has been at the forefront of not only computer science education, but also teaching in general for much of his storied career. In the past year, he has been honored with both the Association for Computing Machinery's (ACM) Karl V. Karlstom Outstanding Educator Award and the SIGCSE Award for Outstanding Contributions to Computer Science Education for his work in advancing computer science education.
Job Titles:
- Assistant Professor of EECS at UC Berkeley
- Assistant Professor of Electrical Engineering & Computer Science at MIT
Job Titles:
- Assistant Director, Infrastructure
Job Titles:
- Senior Research Scientist
- Research Areas
- Senior Research Scientist at the Massachusetts Institute of Technology
James Glass is a Senior Research Scientist at the Massachusetts Institute of Technology where he leads the Spoken Language Systems Group in the Computer Science and Artificial Intelligence Laboratory. He is also a member of the Harvard University Program in Speech and Hearing Bioscience and Technology. Since obtaining his S.M. and Ph.D. degrees at MIT in Electrical Engineering and Computer Science, his research has focused on automatic speech recognition, unsupervised speech processing, and spoken language understanding using machine learning. He is an IEEE Fellow, and a Fellow of the International Speech Communication Association, and is currently an Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence.
Job Titles:
- Senior Research Scientist
- Research Areas
- Research Group / Vision Group
- Senior Research Scientist at CSAIL
John Fisher is a a Senior Research Scientist at CSAIL where he runs the Sensing, Learning, and Inference Group analyzing complex, high-dimensional data sources in a variety of application domains. His group develops scalable algorithms with theoretical performance guarantees utilizing physics-based sensor models by combining elements of Bayesian inference, information theory, optimization, and machine learning.
His research considers fundamental problems in probabilistic inference, information planning, scene understanding, and Bayesian structural inference. Applications areas include multi-modal data fusion, distributed inference under resource constraints, multi-object tracking, and accelerated materials design.
He received a BS and MS in Electrical Engineering at the Univsersity of Florida in 1987 and 1989, respectively. He earned a PhD in Electrical and Computer Engineering in 1997.
Job Titles:
- Research Areas
- Research Group
- Research Group / Spoken Language Systems Group
- Research Group / Vision Group
Job Titles:
- Assistant Professor
- Research Areas
- Research Group
- Research Group / Computer Graphics Group
- Research Group / Medical Vision Group
- Research Group / Vision Group
Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and assistant professor of EECS at UC Berkeley. He works on high-efficiency visual computing, including systems, compilers, and architectures for image processing, vision, 3D rendering, simulation, and machine learning. He is a recipient of the ACM SIGGRAPH Significant New Researcher award, NSF CAREER award, Intel Outstanding Researcher award, and two CACM Research Highlights. He was previously a visiting researcher at Google, a postdoc in Computer Science at Stanford, and earned his PhD in Computer Science from MIT in 2014. He co-created the Halide language and has built more than a half-dozen other DSL and compiler systems, the first of which was a finalist for an Academy technical achievement award.
Job Titles:
- Research Areas
- Research Group
Job Titles:
- Managing Director, Global Strategic Alliances
Job Titles:
- Graduate Student
- Research Areas
Job Titles:
- Adjunct Professor
- Research Areas
Job Titles:
- Assistant Director Planning and Initiatives DIRO
Job Titles:
- Research Areas
- Research Group
Job Titles:
- Professor
- Research Areas
- Research Group / Vision Group
Job Titles:
- Contributor
- Professor
- Research Areas
Randall Davis received his undergraduate degree from Dartmouth, graduating summa cum laude, Phi Beta Kappa in 1970, and received a PhD from Stanford in artificial intelligence in 1976. He joined the faculty of the Electrical Engineering and Computer Science Department at MIT in 1978 where he held an Esther and Harold Edgerton Endowed Chair (1979-1981).
He has been a Full Professor in the Department since 1989. He has served as Associate Director of MIT's Artificial Intelligence Laboratory (1993-1998), as a Research Director of CSAIL from 2003-2007, and as Associate Director of CSAIL from 2012-2014.
Dr. Davis has been a seminal contributor to the fields of knowledge-based systems and human-computer interaction, publishing some more than 100 articles and playing a central role in the development of several systems. He and his research group are developing advanced tools that permit natural multi-modal interaction with computers by creating software that understands users as they sketch, gesture, and talk.
He is the co-author of Knowledge-Based Systems in AI. In 1990 he was named a Founding Fellow of the Association for the Advancement of AI and in 1995 was elected to a two-year term as its President. From 1995-1998 he served on the Scientific Advisory Board of the U. S. Air Force, earning the USAF Decoration for Exceptional Civilian Service.
Dr. Davis has also been active in the area of intellectual property and software. In 1990 he served as expert to the Court in Computer Associates v. Altai, a case that produced the abstraction, filtration, comparison test now widely used in software copyright cases.
From 1998 to 2000 he served as the chairman of the U.S. National Academy of Sciences study on intellectual property rights and the information infrastructure entitled The Digital Dilemma: Intellectual Property in the Information Age, published by the National Academy Press in February, 2000.
Job Titles:
- Distinguished Professor of Computer Science at MIT
- Professor
- Research Areas
Rob Miller is a Distinguished Professor of Computer Science at MIT and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He earned bachelors and masters degrees in computer science from MIT (1995) and PhD from Carnegie Mellon University (2002). He has won an ACM Distinguished Dissertation honorable mention, NSF CAREER award, seven best paper awards (UIST, USENIX, HCOMP and CHI), and two lasting-impact awards (VL/HCC and UIST). He has been program co-chair for UIST 2010, general chair for UIST 2012, associate editor of ACM TOCHI, associate director of MIT CSAIL, education officer of MIT EECS, and education officer of the MIT Schwarzman College of Computing.. He has won two department awards for teaching, and was named a MacVicar Faculty Fellow for outstanding contributions to MIT undergraduate education. His research interests lie at the intersection of programming and human computer interaction, including online education, software development tools, and end-user programming.
Job Titles:
- Graduate Student
- Research Areas
Job Titles:
- Professor
- Research Areas
- Research Group
- Research Group / Advanced Network Architecture Group
- Research Group / Computation Structures Group
- Research Group / Data Systems Group
Professor Samuel Madden, a Professor of Electrical Engineering and Computer Science and principal investigator in MIT CSAIL, leads the BigData@CSAIL initiative and the Data Systems Group. He investigates issues related to systems and algorithms for data that is high rate, massive, or complex. The goal of his research is to help people access different types of data through building interfaces that let them interact with or query that data.
As a prototypical example, a relational database system runs SQL queries over tables of data to look up a fact. In business, this type of query might be the earnings over the last quarter or the number of employees in a company. Prof. Madden and his research group take this kind of simple abstraction provided by these database systems and apply it to much more complicated types of data.
One such project with more complicated data that Prof. Madden is working on is a system that takes the input from people's smartphones that capture GPS traces or satellite imagery, and generates digital maps as the output. This system is especially important for regions of the world that are less developed, where maps might not be high-quality or up-to-date. The combination of both satellite imagery and GPS traces allows for more accurate digital maps because the data accounts for the interconnectivity of roads at complicated interchanges that are difficult to determine from one type of data alone. Projects like this one have required the researchers to apply a number of different techniques from the data-processing community and sophisticated technologies, including machine learning.
Machine learning is especially useful for learning the critical components of complex systems. Inside of a database system, there are various data structures that are critical to the operation of these systems. What Prof. Madden has observed in his research in CSAIL is that these internal components, which are traditionally algorithms or data structures that are hand-engineered, can actually be synthesized or tuned through machine learning, making the whole system much more efficient.
Innovative techniques for these types of complex data management systems are not limited to just software. In Prof. Madden's PhD work at the University of California, Berkeley, he built a data-management system for networks of sensor devices. The system, called TinyDB, took a collection of tiny wireless connected sensors and treated them as though they are database systems. Instead of running a query over a database system asking for the salary of employees working at a company, TinyDB allowed you to run a query over a collection of sensors to ask for a property, such as how the temperature is varying throughout a building.
When Prof. Madden first came to CSAIL, he worked on column-oriented databases, which focused on how data is stored and represented inside of a database system. Typically, database systems lay out data into tables. A table could represent for example, all of the employees who work at an organization, and that table would have one row per employee with fields such as hiring date and salary. Prof. Madden found that instead of laying out the data physically in computer memory by row, if the data was laid out column by column (e.g., all the names were stored together and all the salaries stored together), this arrangement led to running more efficient queries over larger amounts of data.
Another technique that has made querying more efficient for Prof. Madden is a recent project for running queries over large archives of video. He developed a machine-learning algorithm that avoids looking at every frame of video, which is very computationally intensive, and enables users to ask questions about certain regions of the video that will satisfy the predicate that the user asked about.
Through this ongoing data management research, Prof. Madden continues to find new ways to help people query and discover relevant data in more user-friendly, faster, and efficient ways.
BIO:
Professor Madden's research is in the area of database systems, focusing on database analytics and query processing, ranging from clouds to sensors to modern high performance server architectures. He joined the faculty in January, 2004 receiving his Ph.D. in 2003 from the University of California, Berkeley.
Job Titles:
- Professor
- Research Areas
- Webster Professor of Electrical Engineering
Srini Devadas is the Webster Professor of Electrical Engineering and Computer Science and has has been on the MIT EECS faculty since 1988. He served as Associate Head of the Department of Electrical Engineering and Computer Science, with responsibility for Computer Science, from 2005 to 2011.
Devadas's research interests span Computer-Aided Design (CAD), computer security and computer architecture and he has received significant awards from each discipline. In 2015, he received the ACM/IEEE A. Richard Newton Technical Impact award in Electronic Design Automation. He received the IEEE Computer Society Technical Achievement Award in 2014 for inventing Physical Unclonable Functions and single-chip secure processor architectures. Devadas's work on hardware information flow tracking published in the 2004 ASPLOS received the ASPLOS Most Influential Paper Award in 2014. His papers on analytical cache modeling and the Aegis single-chip secure processor were included as influential papers in "25 Years of the International Conference on Supercomputing." In 2017 he received the IEEE W. Wallace McDowell Award for contributions to secure hardware. He is an IEEE and ACM Fellow.
Devadas has taught widely in EECS, lecturing classes in VLSI, discrete mathematics, computer architecture, algorithms and software engineering. He is a MacVicar Faculty Fellow and an Everett Moore Baker teaching award recipient, considered MIT's two highest undergraduate teaching honors.
Job Titles:
- Assistant Professor
- Associate Professor
- Research Areas
- Research Group / Algorithms Group
- Research Group / HCI Engineering Group
- Research Group / Multimodal Understanding Group
- Research Group / Software Design Group
- Research Group / Usable Programming Group
- Research Group / Vision Group
- Systems
Stefanie Mueller is an assistant professor in the MIT EECS department and a member of the Computer Science and Artificial Intelligence Laboratory. In her research, she develops novel hardware and software systems that advance personal fabrication technologies. Stefanie publishes her work at the most selective HCI venues CHI and UIST and has received a best paper award and two best paper nominees in the past. She is also serving on the CHI and UIST program committees as an associate chair. In addition, Stefanie has been an invited speaker at universities and research labs, such as Harvard, Stanford, UC Berkeley, CMU, Microsoft Research, Disney Research, and Adobe Research.
Stefanie directs the HCI Engineering group at CSAIL and is actively recruiting Postdocs, PhD students, and interns interested in helping to kickstart this new lab. Interested Postdocs can email her directly. For a PhD position please apply through MIT's PhD admission page.
Job Titles:
- Associate Professor
- Associate Professor of Electrical Engineering
- Research Areas
Professor Tim Kraska, an Associate Professor of Electrical Engineering and Computer Science in MIT CSAIL, aims to dramatically increase the efficiency of data-intensive systems and democratize data science through machine learning. In CSAIL, he co-leads the Data Systems Group and is part of the Systems Community of Research (CoR), and he is the founding co-director of the Data System and AI Lab (DSAIL) at MIT and co-founder of Einblick Analytics, Inc. Much of his work in systems has significantly impacted both academia and industry.
His research using machine learning in systems takes two main approaches:
Systems for ML: Building systems to make the recent advances in machine learning more accessible
ML for Systems: Leveraging machine learning to improve systems
The goal of Systems for ML is to make it easier for people to use the complex analytics techniques of machine learning, as well as enabling a broader audience to do more with the data and make data-driven decisions. For example, the Northstar project explores new user interfaces and infrastructure to help experts and non-experts alike to become citizen data scientists, enabling visual, interactive, and assisted data exploration and model building. More recently, he is interested in exploring natural language interfaces for analytic tasks.
In the second category of applying ML for Systems, Prof. Kraska and the Data Systems Group are looking into instance-optimized systems; systems that self-adjust automatically to the data and the workload. For example, SageDB is a new type of system that aims through machine learning and other optimization techniques to significantly increase the efficience of data processing systems.
With Moore's law ending, data continues to increase at an unprecedented pace. Prof. Kraska is interested in investigating new methods to account for the increase in data and still be able to efficiently analyze that data. Instance-optimized systems, applying machine learning to improve systems, and tailoring them for workloads and data distributions are potential ways to address these issues.
Job Titles:
- Principal
- Research Scientist
- Leader of the AnyScale Learning for All ( ALFA ) Group
- Research Areas
- Research Group
To make our lives and systems more secure, Dr. Una-May O'Reilly's research goals are to understand adversarial intelligence and to computationally model it with machine learning. Dr. O'Reilly hopes that by developing Artificial Adversarial Intelligence it will help reveal the dynamics of conflicting behavior and how adaptation drives it, leading to more secure systems.
Dr. O'Reilly, who holds a PhD from Carleton University in Ottawa, Canada and joined CSAIL in 1996, was drawn to this area of research when pursuing her highly regarded and award-winning work on evolutionary algorithms. These algorithms are a good fit to model the evolving arms race between tax avoidance and tax loophole remediation. They supported an approach to identifying vulnerabilities in a subsection of the Internal Revenue code while avoiding auditing, even under auditing co-adaptation. Now one of the main areas Dr. O'Reilly is investigating is cybersecurity and how to stop destructive and escalating arms races between cyber attack actors and cyber defenders.
Another area she is exploring is the security of software. Dr. O'Reilly, who is interested in understanding computer programs in general, conducts neuroscience experiments to reveal how humans read code and uses machine learning models to encode software. Her research also dives into deep learning techniques for program representations. Typically, code varies in length, obeys syntax, and expresses semantics. To automatically detect bugs or code malware, a computer-friendly representation of code that encompasses all of these properties is required. Such representations can be used by software enterprises in machine learning detectors and classification training.
Most recently Dr. O'Reilly, who is passionate about addressing climate change, is studying disinformation flows through social media platforms supported by computer and AI/ML technology that enables its amplification at very accelerated rates. In the context of climate disinformation, she investigates "organic" influence across social media, how a disinformation campaign expresses a narrative, and how disinformation is seeded and disseminated around critical events.
Dr. O'Reilly's security-driven, adversarial-focused approach to AI and machine learning will lead to more secure systems, better program comprehension, and improved disinformation awareness.
BIO
Una-May is leader of the AnyScale Learning For All (ALFA) group at CSAIL. ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. The group has projects in cybersecurity, healthcare, and online education.
Una-May was awarded the EvoStar Award for Outstanding Contribution of Evolutionary Computation in Europe in April, 2013. She is also is a Fellow of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO.
Una-May co-founded ACM SigEVO in 2004. She serves as Vice-Chair of ACM SigEVO. In 2013 she inaugurated the Women in Evolutionary Computation group at GECCO.
Una-May served as chair of the largest international Evolutionary Computation Conference, GECCO, in 2005. She has served on the GECCO business committee, co-led the 2006 and 2009 Genetic Programming: Theory to Practice Workshops and co-chaired EuroGP, the largest conference devoted to Genetic Programming.
Una-May serves as the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), as editor for Evolutionary Computation (MIT Press), and as area editor for ACM Transactions on Evolutionary Learning and Optimization.
Una-May has a patent for an original genetic algorithm technique applicable to internet-based name suggestions.
Una-May holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada. She joined the Artificial Intelligence Laboratory, MIT as a Post-Doctoral Associate in 1996.
Job Titles:
- Chancellor for Academic Advancement at MIT
- Research Areas
W. Eric L. Grimson is Chancellor for Academic Advancement at MIT, Professor of Computer Science, and the Bernard M. Gordon Professor of Medical Engineering. He also serves as Interim Vice President for Open Learning. As Chancellor for Academic Advancement, he represents the Institute to alumni/alumnae, parents and others, and is a central advisor to MIT's president on issues related to MIT's Campaign for a Better World. As Vice President, he oversees MIT's efforts in online education, including Open Courseware, MITx, and xPro. A faculty member since 1984, he previously served as Head of the Department of Electrical Engineering and Computer Science, and as Chancellor for MIT. In addition to research in computer vision and medical image analysis, Professor Grimson teaches introductory Computer Programming courses, including an online MITx course. In all, he has taught some 15,000 MIT undergraduates and served as the thesis supervisor to almost 50 MIT PhDs. A Fellow of AAAI, of ACM, and of IEEE, he earned a BSc in mathematics and physics from the University of Regina in Saskatchewan, Canada and a PhD in mathematics from MIT, and holds a Doctor of Letters (honoris causa) from Dalhousie University and a Doctor of Science (honoris causa) from University of Saskatchewan, and from the University of Regina.