CSEE professor Penny Rheingans has been elected to the Computing Research Association (CRA) Board of Directors. She will serve a three year term.

Dr. Rheingans is a Professor of Computer Science and Electrical Engineering and Director of the Center for Women in Technology (CWIT). As CWIT Director, she oversees a scholarship program for undergraduates committed to increasing gender diversity in the technology fields and develops programs to increase the interest and retention of women in technology programs.  She received a Ph.D in Computer Science from the University of North Carolina, Chapel Hill and an AB in Computer Science from Harvard University. Her current research interests include the visualization of predictive models, visualization of data with associated uncertainty, volume rendering, information visualization, perceptual and illustration issues in visualization, non-photorealistic rendering, dynamic and interactive representations and interfaces, and the experimental validation of visualization techniques.

The CRA was founded in 1972 as an association of more than 220 North American academic departments of computer science, computer engineering, and related fields; laboratories and centers in industry, government, and academia engaging in basic computing research; and affiliated professional societies. Its mission is to enhance innovation by joining with industry, government and academia to strengthen research and advanced education in computing. CRA executes this mission by leading the computing research community, informing policymakers and the public, and facilitating the development of strong, diverse talent in the field.

CRA’s Board of Directors is a distinguished group of leaders in computing research drawn from academia and industry. Its members serve on CRA’s standing committees and lead the organization’s responses as new issues affecting computing research arise and evolve.

Spatiotemporal Data Mining and Analytics:
Issues, Methods, and Applications

Shen-Shyang Ho, Nanyang Technological University

12:00pm Thursday, 3 March 2016, ITE325b, UMBC

The extensive and ubiquitous uses of sensors (e.g., satellites, in-situ sensors) and smartphones have resulted in the collection of huge amount of time-stamped data with location information. These large-scale dynamic datasets present many research challenges and application opportunities. In this talk, I describe my research work on spatiotemporal tasks related to (1) application-specific pattern mining, (2) prediction methods, (3) similarity search, and (4) privacy issue. Moreover, I highlight my new research direction in array-based distributed database for spatiotemporal domains.

Shen-Shyang Ho is a tenure-track assistant professor in the School of Computer Engineering at the Nanyang Technological University in Singapore since January 2012. Before this, he was a researcher at the University of Maryland, College Park from 2010 to 2011. He was a postdoctoral scholar at the California Institute of Technology from 2009 to 2010 and a NASA postdoctoral fellow at the Jet Propulsion Laboratory (JPL) from 2007 to 2009. Shen-Shyang received his Ph.D. in Computer Science from George Mason University in 2007 and his Bachelor (Honors) in Science (Mathematics and Computational Science) from the National University of Singapore in 1999. His research was supported by NASA, JPL, and GSFC between 2007 and 2012. His current research is supported by the Ministry of Education (Singapore), National Research Foundation (Singapore), Rolls Royce (UK), and BMW (Germany). He has two US patents and one pending Germany patent. He has given technical tutorials at AAAI (2011), IJCNN (2011), and ECML (2014).

Host: Cynthia Matuszek

Learning models of language, action and perception
for human-robot collaboration

Dr. Stefanie Tellex
Department of Computer Science, Brown University

4:00pm Monday, 7 March 2016, ITE325b

Robots can act as a force multiplier for people, whether a robot assisting an astronaut with a repair on the International Space station, a UAV taking flight over our cities, or an autonomous vehicle driving through our streets.  To achieve complex tasks, it is essential for robots to move beyond merely interacting with people and toward collaboration, so that one person can easily and flexibly work with many autonomous robots.  The aim of my research program is to create autonomous robots that collaborate with people to meet their needs by learning decision-theoretic models for communication, action, and perception.  Communication for collaboration requires models of language that map between sentences and aspects of the external world. My work enables a robot to learn compositional models for word meanings that allow a robot to explicitly reason and communicate about its own uncertainty, increasing the speed and accuracy of human-robot communication.  Action for collaboration requires models that match how people think and talk, because people communicate about all aspects of a robot’s behavior, from low-level motion preferences (e.g., “Please fly up a few feet”) to high-level requests (e.g., “Please inspect the building”).  I am creating new methods for learning how to plan in very large, uncertain state-action spaces by using hierarchical abstraction.  Perception for collaboration requires the robot to detect, localize, and manipulate the objects in its environment that are most important to its human collaborator.  I am creating new methods for autonomously acquiring perceptual models in situ so the robot can perceive the objects most relevant to the human’s goals. My unified decision-theoretic framework supports data-driven training and robust, feedback-driven human-robot collaboration.

Stefanie Tellex is an Assistant Professor of Computer Science and Assistant Professor of Engineering at Brown University.  Her group, the Humans To Robots Lab, creates robots that seamlessly collaborate with people to meet their needs using language, gesture, and probabilistic inference, aiming to empower every person with a collaborative robot.  She completed her Ph.D. at the MIT Media Lab in 2010, where she developed models for the meanings of spatial prepositions and motion verbs.  Her postdoctoral work at MIT CSAIL focused on creating robots that understand natural language.  She has published at SIGIR, HRI, RSS, AAAI, IROS, ICAPs and ICMI, winning Best Student Paper at SIGIR and ICMI, Best Paper at RSS, and an award from the CCC Blue Sky Ideas Initiative.  Her awards include being named one of IEEE Spectrum’s AI’s 10 to Watch in 2013, the Richard B. Salomon Faculty Research Award at Brown University, a DARPA Young Faculty Award in 2015, and a 2016 Sloan Research Fellowship.  Her work has been featured in the press on National Public Radio and MIT Technology Review; she was named one of Wired UK’s Women Who Changed Science In 2015 and listed as one of MIT Technology Review’s Ten Breakthrough Technologies in 2016.

Why applications are still draining our batteries, and how we can help

Aaron Schulman, Stanford University

12:00pm Tuesday, 01 March 2016, ITE325b

Application developers lack tools to profile and compare the energy consumption of different software designs. This energy-optimization task is challenging because of unpredictable interactions between the application and increasingly complex power management logic. Yet, having accurate power information would allow application developers to both avoid inefficient designs and discover opportunities for new optimizations.

In this talk, I will show that it is possible to accurately measure system-level power and attribute it to application activities. I will present BattOr, a portable, easy-to-use power monitor that provides developers with a profile of the energy consumption of their designs—without modifications to hardware or software. I will show how Google developers are using BattOr to improve Chrome’s energy efficiency. I will also show how fine-grained understanding of cellular power at different signal strengths enables novel energy optimizations. Finally, I will describe my future plans to attribute system-level power to individual hardware components and to investigate opportunities presented by instrumenting every server in a data center with fine-grained power monitoring.

Aaron Schulman is a Postdoctoral Scholar at Stanford working with Sachin Katti; he earned his Ph.D. in Computer Science from the University of Maryland, where he was advised by Neil Spring. His research interests are in low-power embedded systems, wireless communication, and network measurement. Aaron’s research on the BattOr power monitor has been funded by Google, is being commercialized by his startup Mellow Research, and is becoming Google’s de facto standard tool for measuring the energy consumption of the Chrome web browser. For his dissertation, Aaron provided the first observations of fundamental factors that limit the reliability of the Internet’s critical last-mile infrastructure. His dissertation was selected to receive the the 2013 ACM SIGCOMM Doctoral Dissertation Award.

TechRepublic identified CSEE professor Marie desJardins as one of “10 artificial intelligence researchers to follow on Twitter”. Check out her feed at @mariedj17.

“Want to know what’s happening at the epicenter of artificial intelligence? Follow these 10 AI researchers who make the most of their 140 characters on Twitter.”

The Maryland Cyber Challenge, or MDC3, is held in Baltimore each year. The competition, organized by the CyberNEXS division of Leidos, provides a hands-on cybersecurity competition for participants through, among other things, a “king of the hill” style challenge. The event tests not only a team’s ability to break into a target machine, but also its ability to secure that machine from attacks by other competitors. In addition, competitors are given a forensics image of a computer, and tasked with finding hidden flags within the image.

In 2015, about 25 college and university teams qualified for the semi-finals, and eight of those teams made it to the finals.  That eight includes two teams from UMBC: Cyberdawgs 1 and Cyberdawgs 2.  The final round of competition began on October 28, but technical difficulties prevented the contest from finishing.

The contest resumed at noon on Saturday, February 20, 2016.  The Cyberdawgs Team 1,  Tyler Campbell, Josh Domangue, Chris Gardner, Anh Ho, Jacob Rust, and Julio Valcarcel took first place with 18,979 points, outscoring the winning team in the professional division by 5690 points. (No other results or point totals were released.)

The Cyber Defense Team’s meetings are open to all students that have a desire to learn hands-on cybersecurity skills, and no prior cyber experience is needed. The club meets during the Spring 2016 semester on Wednesdays at 7:10PM in ITE 231.  To request access to their mailing list, send an email to Sorry, you need javascript to view this email address. If you have any questions about the club feel free to email Julio Valcarcel (Sorry, you need javascript to view this email address. ) or Anh Ho (Sorry, you need javascript to view this email address. ). The Cyber Defense Team’s faculty advisers are Dr. Charles Nicholas (Sorry, you need javascript to view this email address. ) and Dr. Rick Forno (Sorry, you need javascript to view this email address. ).

Learning from High-Dimensional Data via Transformations

Dr. Hossein Mobahi, MIT

12:00pm Monday 29, February 2016, ITE325b

High-dimensional data is ubiquitous in the modern world, arising in images, movies, biomedical measurements, documents, and many other contexts. The “curse of dimensionality” tells us that learning in such regimes is generally intractable. However, practical problems often exhibit special simplifying structures which, when identified and exploited, can render learning in high dimensions tractable. This is a great prospect however, but how to get there in not trivial. In this talk, I will address two challenges associated with high-dimensional learning and discuss my proposed solution.

First, parsimony (sparsity, low rank, etc.) is one of the most prevalent structures in high-dimensional learning applications. However, its presence is often implicit and it reveals itself only after a transformation of the data. Studying the space of such transformations and the associated algorithms for their inference constitute an important class of problems in high-dimensional learning. I will present some of my work in this direction related to image segmentation. I will show how low-rank structures become abundant in images when certain spatial and geometric transformations are considered. This work resulted in a state of the art algorithm for segmentation of natural images.

Second, important scenarios such as deep learning involve high-dimensional nonconvex optimization. Such optimization is generally intractable. However, I show how some properties in the optimization landscape, such as smoothness and stability, can be exploited to transform the objective function to simpler subproblems and allow obtaining reasonable solutions efficiently. The theory is derived by combining the notion of convex envelopes with differential equations. This results in algorithms involving high-dimensional convolution with the Gaussian kernel, which I show has a closed form in many practical scenarios. I will present applications of this work in image alignment, image matching, and deep learning. Furthermore, I will discuss how this theory justifies heuristics currently used in deep learning and suggests new training algorithms that offer a significant speedup.

Hossein Mobahi is a postdoctoral researcher in the Computer Science and Artificial Intelligence Lab at the Massachusetts Institute of Technology. His research interests include machine learning, computer vision, optimization, and especially the intersection of the three. He obtained his PhD from the University of Illinois at Urbana-Champaign in Dec 2012. He is the recipient of Computational Science & Engineering Fellowship, Cognitive Science & AI Award, and Mavis Memorial Scholarship. His recent work on machine learning and optimization have been covered by the MIT news.

The UMBC Cyber Defense Lab presents

Mini-MAC: Raising the Bar for Vehicular Security with a
Lightweight Message Authentication Protocol

Jackson Schmandt, CSEE, UMBC
11:15am-12:30pm Friday, 26 February 2016, ITE 237

We propose Mini-MAC, a new message authentication protocol that works in existing automotive computer networks without delaying any message or increasing network traffic. Deployed in many vehicles, the CAN bus is a low-speed network connecting electronic control units, including those that control critical functionality such as braking and acceleration. The CAN bus is extremely vulnerable to malicious actors with bus access, including wireless access. Traditionally, Message Authentication Codes (MACs) help authenticate the sender of a message, and variants prevent message replay attacks; however, standard MACs are unsuitable for use on the CAN bus because of small payload sizes. Restrictions of the CAN bus, including the need not to delay messages or increase bus traffic, severely limit how well this network can be protected.

Mini-MAC is based on a counter-seeded keyed-Hash MAC (HMAC), augmented with message history and truncated to fit available message space. It does not increase bus traffic and incurs a very small performance penalty relative to the provably secure HMAC. It is the first proposal to combine these two tenets for vehicle networks. The message history feature protects against all transient attackers, even if they know the keys. Though the CAN bus cannot be properly secured against a dedicated attacker, Mini-MAC meaningfully raises the bar of vehicular security, enhancing the safety of drivers and others.

Jackson Schmandt is a MS student in Computer Engineering in the Mobile Pervasive Sensor System Lab. Joint work with Alan Sherman and Nilanjan Banerjee.

Host: Alan T. Sherman, Sorry, you need javascript to view this email address.

Establishment of Trust and Integrity in Modern Supply Chains

Ujjwal Guin, University of Connecticut

11:30 Thursday, 25 February 2016, ITE325b

With the advent of globalization and resulting horizontal integration, modern supply chain becomes extremely complex and requires immediate solutions for eliminating counterfeit integrated circuits (ICs), which pose a serious threat to the safety and security of our day-to-day lives. The reliability of such ICs could be questionable as they may have many defects and might not go through as much of a rigorous test process as their authentic counterparts. An adversary can also create a backdoor to bypass the security modules in these ICs. In this research, I have systematically addressed the aforementioned issues by risk analysis and assessment of test methods, and by proposing different Design-for-Anti-Counterfeit (DfAC) measures. As a part of risk analysis, I have developed taxonomies for counterfeit IC types, counterfeit defects, and test methods. Based on these taxonomies, I have introduced novel test metrics and developed a comprehensive framework for assessing a set of test methods to maximize test coverage. In the DfAC domain, I have proposed a suite of solutions to detect counterfeit ICs without performing conventional tests. A set of lightweight negative-bias temperature instability (NBTI)-aware ring oscillators have been developed for combating die and IC recycling. In addition, I have developed a comprehensive solution for preventing intellectual property piracy and IC overproduction by assuring forward trust between all entities involved in the system-on-chip design and fabrication process.

Ujjwal Guin is a PhD candidate at the Electrical and Computer Engineering department of University of Connecticut, where he has been working with Dr. Mark M. Tehranipoor. His current research interests include Hardware Security and Trust, Supply Chain Security, Cybersecurity, and VLSI Design and Test. He has developed several on-chip structures and techniques to improve the security, trustworthiness, and reliability of integrated circuits. He has co-authored a book entitled “Counterfeit Integrated Circuits – Detection and Avoidance”. He has published several journal articles and refereed conference papers. He received Best Student Paper Award from the IEEE North Atlantic Test Workshop (NATW’2013). He is an active participant in the SAE International’s G-19A Test Laboratory Standards Development Committee. Mr. Guin received his B.E. degree from the Department of Electronics and Telecommunication Engineering of Bengal Engineering and Science University, Howrah, India in 2004 and the M.Sc. degree from the Department of Electrical and Computer Engineering of Temple University, Philadelphia, PA, USA in 2010.

Host: Chintan Patel

CSEE professor Marie desJardins has received an Undergraduate Research Faculty Mentoring Award from the Computing Research Association (CRA). The award recognizes faculty who have provided exceptional mentorship, undergraduate research experiences, and, in parallel, guidance on admission and matriculation of undergraduate students to research-focused graduate programs in computing. The award will be presented to Dr. desJardins at the CRA Conference at Snowbird in July.

Dr. desJardins is a professor in UMBC’s Computer Science and Electrical Engineering department and Associate Dean of the College of Engineering and Information Technology. Her research is in the area of artificial intelligence, machine learning, and computer science education. Currently, her research group includes six undergraduates and five graduate students. Since 2005, she has mentored over 70 undergraduate students. At least 29 of them have enrolled in graduate programs in computing, with eight of the 29 having pursued a Ph.D. in computer science.

In announcing the award, the CRA noted that

“Many of the undergraduates Marie has mentored never imagined that they would be involved in research and consider graduate education. Marie’s strategy for working with undergraduate majors involves engaging with students in their first two years and building teams in which her more senior research students (graduate and undergraduate) help train and lead the junior students. She is known for her unconditional support, encouragement, and dedication. She encourages students from underrepresented minorities to get involved with national organizations and programs, including CRA-W, NCWIT, Grace Hopper, IJCAI and AAAI. She stays in touch with her mentees beyond their graduation, and she has helped some to become effective mentors themselves.”

This is the first year for the CRA Undergraduate Research Faculty Mentoring  Award, which was also given to Pieter Abbeel of the University of California, Berkeley and Judy Goldsmith from the University of Kentucky.

Founded in 1972, CRA‘s membership includes more than 200 North American organizations active in computing research: academic departments of computer science and computer engineering, laboratories and centers (industry, government, and academia), and affiliated professional societies (AAAI, ACM, CACS/AIC, IEEE Computer Society, SIAM, USENIX). Its mission is to enhance innovation by joining with industry, government and academia to strengthen research and advanced education in computing. CRA executes this mission by leading the computing research community, informing policymakers and the public, and facilitating the development of strong, diverse talent in the field.