🗣️ CyberInnovation Briefing: Global Impact, Promise & Perils of Blockchain

Global Impact, Promise & Perils of Blockchain

UMBC Professors Haibin Zhang (Computer Science and Electrical Engineering) and Karuna Joshi (Information Systems) will be panelists for an event focused on the impact, promise and perils of blockchain technologies at the bwtech@UMBC Research & Technology Park. The event will take place from 8:15 to 11:15am on Tuesday, March 20 2018 at and will include breakfast and time for networking.  They will be joined on the panel by Marcus Edwards, from Northrop Grumman’s Cyber & Intelligence Mission Solutions, Steve Cook of Verizon Enterprise Solutions, Sean Manion of  Science Distributed and moderator Razvan Miutescu of Whiteford, Taylor & Preston, LLP.   For more information and tickets are available on eventbrite.

CyberInnovation Briefing: The Global Impact, Promise & Perils of Blockchain

8:15 – 11:15 Tuesday, March 20, 2018

bwtech@UMBC Research & Technology Park
5520 Research Park Drive, Baltimore, MD 21228

Cryptocurrency market capitalizations have soared over the past year and new innovative blockchain applications are continuing to emerge seemingly by the day. However, the average end-user is left to singularly make sense of a vast and global marketplace that is rapidly converging the core tenets of economics and technology development. The future prospects of traditional business and financial models is uncertain as blockchain technology leaves key decision makers in an untenable position to either adopt and adapt or simply be left behind. Industry and academic experts in the field will discuss the pros and cons and opportunities and challenges of this disruptive technology movement.

talk: desJardins on Planning and Learning in Complex Stochastic Domains, 1pm fri 3/8

UMBC ACM Student Chapter

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 K12 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 K12 schools.

In the op-ed, desJardins writes about why it is important to expose K12 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.

Read the entire piece in The Baltimore Sun, “All Kids Should Have a Computer Science Education.

Adapted from an article in UMBC News by Megan Hanks.

talk: Semi-supervised Learning for Visual Recognition, 1pm Fri 2/23, ITE325, UMBC

ACM Faculty Talk Series

Semi-supervised Learning for Visual Recognition

Dr. Hamed Pirsiavash, Assistant Professor, CSEE

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.

UMBC’s Haibin Zhang shares tips to secure data in the cloud

UMBC’s Haibin Zhang shares tips to secure data in the cloud

As more consumers rely on cloud-based data storage for everything from family photos to financial information, both experts and general users have voiced concerns about cloud security. In a new Conversation article recently published by Scientific American, Haibin Zhang, assistant professor of computer science and electrical engineering, explains precautions consumers can take to protect their files in the cloud.

Zhang explains that data stored and secured using commercial cloud storage systems is encrypted, which means that without the key, the information looks like a series of meaningless characters. Encryption keys have the potential to be misused, if they end up in the wrong hands, which can compromise the security of files stored in a cloud.

“Just like regular keys, if someone else has them, they might be stolen or misused without the data owner knowing,” says Zhang. “And some services might have flaws in their security practices that leave users’ data vulnerable.”

Zhang notes that some cloud services allow customers to maintain their encryption key themselves, which give the consumer the control in ensuring that their data remains safe. Other services keep the encryption keys internally and manage the security for their customers. He says that while each option has benefits, it is important to recognize that “some services might have flaws in their security practices that leave users’ data vulnerable.”

To keep data secure in the cloud, Zhang suggests using enhanced security features offered by cloud storage companies and taking additional precautions that are available to individual customers. He recommends that people use a cloud storage service that allows customers to encrypt their data before uploading it for storage, and to rely on services that have been “validated by independent security researchers.”

Read “How secure is your data when it’s stored in the cloud?” in The Conversation for Zhang’s additional recommendations on securing data on the cloud. The piece also appeared in Scientific American, and has so far been read nearly 20,000 times.

Adapted from a UMBC News article by Megan Hanks. Photo by Yuri Samoilov, CC by 2.0.

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.

Dr. Sleeman’s award is part of an inaugural set of 35 grants in more than ten countries for access to Microsoft Azure and AI technology platforms, services and training.  In an post on Monday, AI for Earth can be a game-changer for our planet, Microsoft announced its intent to put $50 million over five years into the program, enabling grant-making and educational trainings possible at a much larger scale.

More information about AI for Earth can be found on the Microsoft AI for Earth website.

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.

Gymama Slaughter: The Art of Powering Implantable Electronics

The Art of Powering Implantable Electronics

UMBC professor Gymama Slaughter give a short talk at the Grit-X event on her recent research on powering implantable devices for medical applications.

The number of smart implantable devices is on the rise, especially as we approach the ramping up of the “internet of things.” A key challenge for implantable electronic devices has been keeping these devices properly and conveniently powered. Current battery technologies are sealed within these devices, thereby forcing the surgical replacement of the device once the battery is depleted. We need an inconspicuous means of powering implantable electronics with imperceptible methods that moves us toward new innovative solutions to the power challenge in implantable devices. A lightweight bio-solution that leverages the biochemical energy from human biological fluids is a step forward for powering these smart implantable technologies.

Marie desJardins receives UC Berkeley Distinguished Alumni Award in Computer Science

Prof. Marie desJardins receives UC Berkeley Distinguished Alumni Award in Computer Science

CSEE professor Marie desJardins has been selected for the 2018 Distinguished Alumni Award in Computer Science by the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Professor desJardins received a Ph.D. degree in Computer Science from UC Berkeley in 1992.

Each year since 1991, the award has recognizes two distinguished alumni of Berkeley who have made valuable contributions to the field of computer science. Past recipients can be seen here The award will be presented during the Berkeley EECS Annual Research Symposium (BEARS ) on in February 2018.

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