Department of Computer Science and Electrical Engineering
Meet the Staff: Rebecca Dongarra
Name: Rebecca Dongarra
Educational Background: Bachelor of Arts in Biology from St. Mary’s College of Maryland, currently pursuing a master’s degree in Instructional Systems Development through UMBC
Hometown: West Friendship, Maryland
Current role: Academic Affairs Manager
Rebecca moved from the College of Natural and Mathematical Sciences as the Data and Events Coordinator to join CSEE. She was a team member on the STEM BUILD at UMBC Initiative grant supported by NIH from 2016 to 2018. Prior to working in higher education, Rebecca established herself as a small business owner and local community leader. When not working, Rebecca enjoys hiking, volunteering with YMCA and USA swim teams and keeping busy with gardening.
Prof. Cynthia Matuszek named one of AI’s 10 to Watch
Cynthia Matuszek named one of AI’s 10 to Watch
UMBC CSEE Professor Cynthia Matuszek was named as one AI’s 10 to Watch by IEEE Intelligent Systems. The designation is given every two years to a group of “10 young stars who have demonstrated outstanding AI achievements”. IEEE Intelligent Systems accepts nominations from around the world, which are then evaluated by the the publication’s editorial and advisory boards based on reputation, impact, expert endorsement, and diversity. Dr. Matuszek was recognized for her research that “combined robotics, natural language processing, and machine learning to build systems that nonspecialists can instruct, control, and interact with intuitively and naturally”.
Professor Matuszek joined UMBC in 2014 after receiving her Ph.D. in Computer Science from the University of Washington. At UMBC, she established and leads the Interactive Robotics and Language Lab that integrates research on robotics and natural language processing with the goal of “bringing the fields together: developing robots that everyday people can talk to, telling them to do tasks or about the world around them”.
Here is how she describes her research in the IEEE Intelligent Systems article.
Robot Learning from Language and Context
As robots become more powerful, capable, and autonomous, they are moving from controlled industrial settings to human-centric spaces such as medical environments, workplaces, and homes. As physical agents, they will soon be able help with entirely new categories of tasks that require intelligence. Before that can happen, though, robots must be able to interact gracefully with people and the noisy, unpredictable world they occupy.
This undertaking requires insight from multiple areas of AI. Useful robots will need to be flexible in dynamic environments with evolving tasks, meaning they must learn and must also be able to communicate effectively with people. Building advanced intelligent agents that interact robustly with nonspecialists in various domains requires insights from robotics, machine learning, and natural language processing.
My research focuses on developing statistical learning approaches that let robots gain knowledge about the world from multimodal interactions with users, while simultaneously learning to understand the language surrounding novel objects and tasks. Rather than considering these problems separately, we can efficiently handle them concurrently by employing joint learning models that treat language, perception, and task understanding as strongly associated training inputs. This lets each of these channels provide mutually reinforcing inductive bias, constraining an otherwise unmanageable search space and allowing robots to learn from a reasonable number of ongoing interactions.
Combining natural language processing and robotic understanding of environments improves the efficiency and efficacy of both approaches. Intuitively, learning language is easier in the physical context of the world it describes. And robots are more useful and helpful if people can talk naturally to them and teach them about the world. We’ve used this insight to demonstrate that robots can learn unanticipated language that describes completely novel objects. They can also learn to follow instructions for performing tasks and interpret unscripted human gestures, all from interactions with nonspecialist users.
Bringing together these disparate research areas enables the creation of learning methods that let robots use language to learn, adapt, and follow instructions. Understanding humans’ needs and communications is a long-standing AI problem, which fits within the larger context of understanding how to interact gracefully in primarily human environments. Incorporating these capabilities will let us develop flexible, inexpensive robots that can integrate into real-world settings such as the workplace and home.
You can access a pdf version of the full IEEE AI’s 10 to Watch article here.
Prof. Milton Halem receives UMBC Research Faculty Excellence Award
Professor Milton Halem receives UMBC Research Faculty Excellence Award
Dr. Milton Halem, Research Professor in the Computer Science and Electrical Engineering Department, has been selected as the inaugural recipient of the UMBC Research Faculty Excellence Award. The award recognizes overall excellence in research, and where appropriate, significant contributions to teaching and service/leadership while at UMBC.
Dr. Halem joined UMBC in 2003, after retiring from a highly successful career at NASA Goddard Space Flight Center, where he still holds an Emeritus position as Chief Information Research Scientist to the Director of the Earth Sciences Directorate.
Upon joining UMBC, Dr. Halem served as the founding Director of UMBC’s Center for Hybrid Multicore Productivity Research (CHMPR) and today continues to serve as the UMBC Site Director for this major NSF-supported multi-institutional center that works closely with government and private sector sponsors in the areas of big-data computation, next generation computing and software tool development.
In 2013, Dr. Halem was instrumental in negotiating and securing a major equipment donation from NASA Goddard that significantly enhanced our high-performance computing capacity through the donation of a 512 -node supercomputer to the UMBC campus.
Dr. Halem’s scholarly achievements include more than 150 scientific publications in the areas of atmospheric and oceanographic sciences and computational and information sciences. He is most noted for his groundbreaking research in simulation studies of space-observing systems and for development of four-dimensional data assimilation for weather and climate prediction.
Over the years, Dr. Halem’s achievements have earned him numerous awards including the NASA Medal for Exceptional Scientific Achievement, the NASA Medal for Outstanding Leadership, and NASA’s highest award – the NASA Distinguished Service Medal – in 1996.
Keith J. Bowman, Dean of the College of Engineering and Information Technology, comments: “Dr. Halem’s exceptional vision and his unrelenting drive for excellence continue to serve our UMBC community well. His dedication to pushing the scientific and engineering boundaries serves as a model for our campus and beyond.”
Karl V. Steiner, Vice President for Research, adds: “ I could not think of a more deserving member of our UMBC research community to receive this inaugural Research Faculty Excellence Award than Milt Halem. UMBC is the academic home to over 180 Research Faculty who contribute their expertise and personal commitment to making UMBC a destination for cutting-edge research while providing our students with remarkable insights and opportunities. Milt Halem is clearly one the leaders in the field of high-performance computation and his energy and expertise have been a major factor in UMBC being recognized as a major contributor to high performance computation and data analytics.”
UMBC recognizes Marie desJardins for lasting commitment to inclusive computing education
UMBC recognizes Marie desJardins for lasting commitment to inclusive computing education
Marie desJardins, associate dean of the College of Engineering and Information Technology (COEIT) and professor of computer science and electrical engineering, will be leaving UMBC to take up a new position as founding dean of the College of Organizational, Computational, and Information Sciences at Simmons College in Boston.
“What I will remember most about my 17 years here is UMBC’s collaborative spirit. Because of the open environment and commitment to diversity, I’ve been able to work with colleagues across the university on a wide range of initiatives,” desJardins says.
During her tenure at UMBC, desJardins has applied her passion and expertise to implementing programs for students across all disciplines and majors, explains Keith J. Bowman, dean of the College of Engineering and Information Technology (COEIT). “She brought her passion and expertise to UMBC, and has changed the lives of faculty, students, and staff through her work,” Bowman says. “As COEIT’s founding associate dean, she has played a crucial role in establishing how the College operates, with a focus on supporting students at all levels. She has set an incredibly high bar in all areas of her work.”
One of desJardins’ many accomplishments was the development and launch of UMBC’s Grand Challenge Scholars Program, based on the National Academy of Engineering’s (NAE) Grand Challenges for Engineering. The program is open to students who are interested in working on interdisciplinary teams to address pressing challenges facing society. UMBC’s program is distinct because it is open to all majors, bringing together students studying everything from computing and mechanical engineering to the life sciences, social sciences, humanities, and the arts. The Grand Challenge Scholars Program is “a great match with so many things that UMBC and UMBC students are already doing: applied, project-based learning; service learning; entrepreneurial explorations; global involvement; and undergraduate research,” desJardins said of the program when it launched in 2016.
desJardins also reached students across the university through her work with the Honors Colleges, as an Honors Faculty Fellow. This role enabled her to teach a seminar called “Computation, Complexity, and Emergence,” where students from a range of majors shared their perspectives on interdisciplinary topics and learned how subjects they had not previously explored were relevant to their lives. desJardins also served as a chair of the Honors College Advisory Board during her tenure at UMBC.
Beyond her passion for expanding computer science education at UMBC, desJardins has also been steadfast in her work to increase access to computing education for K – 12 students. She has served as the lead principal investigator of CE21-Maryland, a series of projects implemented to increase opportunities for high school students to access computer science education. She was also instrumental in the creation of How Girls Code, an afterschool program and a summer camp at UMBC where girls in elementary and middle school develop computer science skills through engaging activities and learn about careers in the field.
In addition to her writing for academic and technical audiences, desJardins has written numerous articles for the public, including pieces for The Conversation and The Baltimore Sun about the need for computing education for students of all ages. She is particularly passionate about engaging girls and women in computer science.
In a recent op-ed in The Baltimore Sun, desJardins discussed the importance of computer science education in K – 12 schools, both to expand career opportunities for students of all backgrounds and identities and to make sure the world has a chance to benefit from a diverse talent pool in computing fields. “The need for computer science and computational thinking skills is becoming pervasive not just in the world of software engineers, but in fields as varied as science, design, marketing, and public policy,” she wrote.
desJardins has also worked to support new faculty in her College, as they work to advance their careers, inviting assistant professors and lecturers in COEIT to participate in the Junior Faculty Initiative. The program introduces participants to university resources through units like the Faculty Development Center and Office of Student Disabilities Services. It also supports junior faculty through a series of workshops addressing topics such as time management, mentoring relationships, and conflict management, to acclimate faculty to UMBC.
Across the nation and the world, desJardins has been recognized as a leader in the field of artificial intelligence (AI). Earlier this year, she was named a fellow of the Association for the Advancement of Artificial Intelligence. In 2017, she was included on Forbes’ list of women advancing AI research. UC Berkeley, desJardins’ alma mater, also recently recognized her work to advance her field by presenting her with the Distinguished Alumni Award in Computer Science.
“UMBC has given me so many opportunities to learn, grow, and give back to the community around me, I will be forever grateful,” says desJardins. “No matter where I go from here, I will always consider myself to be part of the UMBC community.”
Adapted from a UMBC News story by Megan Hanks. All photos by Marlayna Demond ’11 for UMBC.
talk: desJardins on Planning and Learning in Complex Stochastic Domains, 1pm fri 3/8
Planning and Learning in Complex Stochastic Domains: AMDPs, Option Discovery, Learning Transfer, Language Learning, and More
Dr. Marie desJardins, University of Maryland, Baltimore County 1-2pm Friday, March 9th, 2018, ITE 456, UMBC
Robots acting in human-scale environments must plan under uncertainty in large state–action spaces and face constantly changing reward functions as requirements and goals change. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level “flat” MDP. AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct.
I will present empirical results in several domains showing significantly improved planning speed, while maintaining solution quality. I will also discuss related work within the same project on automated option discovery, abstraction construction, language learning, and initial steps towards automated methods for learning AMDPs from base MDPs, from teacher demonstrations, and from direct observations in the domain.
This work is collaborative research with Dr. Michael Littman and Dr. Stefanie Tellex of Brown University. Dr. James MacGlashan of SIFT and Dr. Smaranda Muresan of Columbia University collaborated on earlier stages of the project. The following UMBC students have also contributed to the project: Khalil Anderson, Tadewos Bellete, Michael Bishoff, Rose Carignan, Nick Haltemeyer, Nathaniel Lam, Matthew Landen, Keith McNamara, Stephanie Milani, Shane Parr (UMass), Shawn Squire, Tenji Tembo, Nicholay Topin, Puja Trivedi, and John Winder.
Dr. Marie desJardins is a Professor of Computer Science and the Associate Dean for Academic Affairs in the College of Engineering and Information Technology at the University of Maryland, Baltimore County. Prior to joining the faculty at UMBC in 2001, she was a Senior Computer Scientist in the AI Center at SRI International. Her research is in artificial intelligence, focusing on the areas of machine learning, multi-agent systems, planning, interactive AI techniques, information management, reasoning with uncertainty, and decision theory. She is active in the computer science education community, founded the Maryland Center for Computing Education, and leads the CS Matters in Maryland project to develop curriculum and train high school teachers to teach AP CS Principles.
Dr. desJardins has published over 125 scientific papers in journals, conferences, and workshops. She will be the IJCAI-20 Conference Chair, and has been an Associate Editor of the Journal of Artificial Intelligence Research and the Journal of Autonomous Agents and Multi-Agent Systems, a member of the editorial board of AI Magazine, and Program Co-chair for AAAI-13. She has previously served as AAAI Liaison to the Board of Directors of the Computing Research Association, Vice-Chair of ACM’s SIGART, and AAAI Councillor. She is a AAAI Fellow, an ACM Distinguished Member, a Member-at-Large for Section T (Information, Computing, and Communication) of the American Association for the Advancement of Science, the 2014-17 UMBC Presidential Teaching Professor, a member and former chair of UMBC’s Honors College Advisory Board, former chair of UMBC’s Faculty Affairs Committee, and a member of the advisory board of UMBC’s Center for Women in Technology.
Prof. Marie desJardins, new AAAI fellow, advocates for CS education in K–12 schools
Prof. Marie desJardins, new AAAI fellow, advocates for CS education in K–12 schools
Marie desJardins, associate dean of the College of Engineering and Information Technology and professor of computer science, recently wrote a piece for The Baltimore Sun about the importance of computer science education in K–12 schools. She is a leader in the artificial intelligence field and has been nationally recognized for her commitment to mentoring, work increasing diversity in computing, and success expanding computer science education in K–12 schools.
In the op-ed, desJardins writes about why it is important to expose K–12 students to computer science, for both their benefit (in terms of expanded career options) and the benefit of fields that rely on STEM talent. “The need for computer science and computational thinking skills is becoming pervasive not just in the world of software engineers, but in fields as varied as science, design, marketing, and public policy,” she writes.
desJardins describes in the Sun her work with “CS Matters in Maryland,” an initiative that seeks to ensure all students across the state have access to computer science education as part of their regular curriculum. “Our ‘CS Matters in Maryland’ project has trained high school teachers in all of the state’s school systems, emphasizing equity and inclusion for all student demographics and all school systems,” she says.
While this particular project focuses on the state of Maryland, desJardins has been honored across the U.S. for her work in the field. In the past month alone, she has received the Distinguished Alumni Award in Computer Science from UC Berkeley, her alma mater and was formally recognized as a fellow of the Association for the Advancement of Artificial Intelligence.
“I was absolutely overwhelmed when I learned that I had been named one of UC Berkeley’s two Outstanding Alumni in Computer Science for 2018, joining a group of computer scientists for which I have immense respect and admiration,” desJardins said. “It is hard to put into words how much it meant to me to have received this award in the same week that I was inducted as a Fellow of the Association for the Advancement of Artificial Intelligence, a recognition that only a handful of AI scientists receive each year. It is especially meaningful to me that the citations on both awards refer equally to my research and to my mentoring, teaching, and diversity efforts.”
A recent interview with Iridescent brings together desJardin’s research on “intelligent learning” — how robots can learn to solve complicated tasks in complex settings — with her work with students from diverse backgrounds, across all majors. In describing UMBC’s Grand Challenge Scholars Program, she highlights how technology matters, but can’t stand alone — how combining the perspectives of people from all backgrounds and all fields is essential to solving the world’s problems.
“Getting these students together from really different perspectives and having them talk about some of these hard problems is initially really exciting and also very hard,” she explains. “Then, it gets easier. The initial barrier is often just one of language and perspective.”
desJardins continues to work to bridge those divides through her teaching, advocacy, and research, and is now recognized by both Forbes and TechRepublic as a top artificial intelligence expert to follow online.
1:00-2:00pm Friday, February 23, 2018, ITE 325, UMBC
We are interested in learning representations (features) that are discriminative for semantic image understanding tasks such as object classification, detection, and segmentation in images. A common approach to obtain such features is to use supervised learning. However, this requires manual annotation of images, which is costly, time-consuming, and prone to errors. In contrast, unsupervised or self-supervised feature learning methods exploiting unlabeled data can be much more scalable and flexible. I will present some of our efforts in this direction.
Hamed Pirsiavash is an assistant professor at the University of Maryland, Baltimore County (UMBC). Prior to joining UMBC in 2015 he was a postdoctoral research associate at MIT and he obtained his PhD at the University of California Irvine. He does research in the intersection of computer vision and machine learning.
This talk is sponsored by the UMBC Student Chapter of the ACM. Contact with any questions regarding this event.
Jennifer Sleeman receives AI for Earth grant from Microsoft
Jennifer Sleeman receives AI for Earth grant from Microsoft
Visiting Assistant Professor Jennifer Sleeman (Ph.D. ’17) has been awarded a grant from Microsoft as part of its ‘AI for Earth’ program. Dr. Sleeman will use the grant to continue her research on developing algorithms to model how scientific disciplines such as climate change evolve and predict future trends by analyzing the text of articles and reports and the papers they cite.
AI for Earth is a Microsoft program aimed at empowering people and organizations to solve global environmental challenges by increasing access to AI tools and educational opportunities, while accelerating innovation. Via the Azure for Research AI for Earth award program, Microsoft provides selected researchers and organizations access to its cloud and AI computing resources to accelerate, improve and expand work on climate change, agriculture, biodiversity and/or water challenges.
UMBC is among the first grant recipients of AI for Earth, first launched in July 2017. The grant process was a competitive and selective process and was awarded in recognition of the potential of the work and power of AI to accelerate progress.
As part of her dissertation research, Dr. Sleeman developed algorithms using dynamic topic modeling to understand influence and predict future trends in a scientific discipline. She applied this to the field of climate change and used assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. Her custom dynamic topic modeling algorithm identified topics for both datasets and apply cross-domain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time.
CSEE Professor Marie desJardins interviewed for Voices in AI podcast
Voices in AI – Episode 20: A Conversation with Marie desJardins
Byron Reese interviewed UMBC CSEE Professor Marie desJardins as part of his Voices in AI podcast series on Gigaom. In the episode, they talk about the Turing test, Watson, autonomous vehicles, and language processing. Visit the Voices in AI site to listen to the podcast and read the interview transcript.
Here’s the start of the wide-ranging, hour long interview.
Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. Today I’m excited that our guest is Marie des Jardins. She is an Associate Dean for Engineering and Information Technology as well as a professor of Computer Science at the University of Maryland, Baltimore County. She got her undergrad degree from Harvard, and a Ph.D. in computer science from Berkeley, and she’s been involved in the National Conference of the Association for the Advancement of Artificial Intelligence for over 12 years. Welcome to the show, Marie.
Marie des Jardins: Hi, it’s nice to be here.
I often open the show with “What is artificial intelligence?” because, interestingly, there’s no consensus definition of it, and I get a different kind of view of it from everybody. So I’ll start with that. What is artificial intelligence?
Sure. I’ve always thought about artificial intelligence as just a very broad term referring to trying to get computers to do things that we would consider intelligent if people did them. What’s interesting about that definition is it’s a moving target, because we change our opinions over time about what’s intelligent. As computers get better at doing things, they no longer seem that intelligent to us.
We use the word “intelligent,” too, and I’m not going to dwell on definitions, but what do you think intelligence is at its core?
So, it’s definitely hard to pin down, but I think of it as activities that human beings carry out, that we don’t know of lower order animals doing, other than some of the higher primates who can do things that seem intelligent to us. So intelligence involves intentionality, which means setting goals and making active plans to carry them out, and it involves learning over time and being able to react to situations differently based on experiences and knowledge that we’ve gained over time. The third part, I would argue, is that intelligence includes communication, so the ability to communicate with other beings, other intelligent agents, about your activities and goals.
Well, that’s really useful and specific. Let’s look at some of those things in detail a little bit. You mentioned intentionality. Do you think that intentionality is driven by consciousness? I mean, can you have intentionality without consciousness? Is consciousness therefore a requisite for intelligence?
I think that’s a really interesting question. I would decline to answer it mainly because I don’t think we ever can really know what consciousness is. We all have a sense of being conscious inside our own brains—at least I believe that. But of course, I’m only able to say anything meaningful about my own sense of consciousness. We just don’t have any way to measure consciousness or even really define what it is. So, there does seem to be this idea of self-awareness that we see in various kinds of animals—including humans—and that seems to be a precursor to what we call consciousness. But I think it’s awfully hard to define that term, and so I would be hesitant to put that as a prerequisite on intentionality.
talk: Ferraro on Understanding What We Read and Share, 1pm Fri 11/10, ITE325, UMBC
ACM Faculty Talk Series
Understanding What We Read and Share:
Event Processing from Text and Images
Dr. Frank Ferraro, Assistant Professor, CSEE
1:00-2:00pm Friday, 10 November 2017, ITE 325, UMBC
A goal of natural language processing (NLP) is to design machines with human-like communication and language understanding skills. NLP systems able to represent knowledge and synthesize domain-appropriate responses have the potential to improve many tasks and human-facing applications, like virtual assistants such as Google Now or question answering systems like IBM’s Watson.
In this talk, I will present some of my work—past, on-going, and future—in developing knowledge-aware NLP models. I will discuss how to better (1) encode linguistic- and cognitive science-backed meanings within learned word representations, (2) learn high-level representations for document and discourse understanding, and (3) how to generate compelling, human-like stories from sequences of images.
Frank Ferraro is an assistant professor in the CSEE department at UMBC. His research focuses on natural language processing, computational event semantics, and unlabeled, structured probabilistic modeling over very large corpora. He has published basic and applied research on a number of cross-disciplinary projects, and has papers in areas such as multimodal processing and information extraction, latent-variable syntactic methods and applications, and the induction and evaluation of frames and scripts.