Prof. Sherman receives Hrabowski Fund for Innovation award to develop quantum computing teaching material

Prof. Sherman receives Hrabowski Fund for Innovation award to develop quantum computing teaching material

CSEE Professor Alan Sherman received a seed award from UMBC’s Hrabowski Fund for Innovation to develop educational material for quantum algorithms. The project, Evaluation and Enhancement of a Learning Unit on Quantum Algorithms, will involve a multidisciplinary team that will assess and enhance materials for a two-week learning unit on algorithms for quantum computers for use in a general course on algorithms. Some material has already been developed and field-tested in UMBC’s computer science graduate algorithms course, CMSC 641.

The educational unit will introduce the new transformative paradigm of quantum algorithms, which offers tremendous potential for solving important complex problems when executed on a quantum computer. This project will make this learning unit, including its six videos and other materials, freely available after they are revised and enhanced based on reviews by three experts.

The Hrabowski Fund for Innovation exemplifies UMBC’s commitment to investing in faculty initiatives that fuel creativity and enterprise and also create opportunities for student engagement.

New NSF grant to improve human-robot interaction

person interacting with a virtual robot
Professor Ferraro in UMBC’s Pi2 visualization laboratory talking to a virtual robot.

CSEE faculty receive NSF award to help robots learn tasks by interacting naturally with people

UMBC Assistant Professors Cynthia Matuszek (PI) and Francis Ferraro (Co-PI), along with senior staff scientist at JHU-APL John Winder (Co-PI) received a three-year NSF award as part of the National Robotics Initiative on Ubiquitous Collaborative Robots. The award for Semi-Supervised Deep Learning for Domain Adaptation in Robotic Language Acquisition will advance the ability of robots to learn from interactions with people using spoken language and gestures in a variety of situations.

This project will enable robots to learn to perform tasks with human teammates from language and other modalities, and then transfer what they have learned to other robots with different capabilities in order to perform different tasks. This will ultimately allow human-robot teaming in domains where people use varied language and instructions to complete complex tasks. As robots become more capable and ubiquitous, they are increasingly moving into complex, human-centric environments such as workplaces and homes.

Being able to deploy useful robots in settings where human specialists are stretched thin, such as assistive technology, elder care, and education, has the potential to have far-reaching impacts on human quality of life. Achieving this will require the development of robots that learn, from natural interaction, about an end user’s goals and environment.

This work is intended to make robots more accessible and usable for non-specialists. In order to verify success and involve the broader community, tasks will be drawn from and tested in community Makerspaces, which are strongly linked with both education and community involvement. It will address how collaborative learning and successful performance during human-robot interactions can be accomplished by learning from and acting on grounded language. To accomplish this, the project will revolve around learning structured representations of abstract knowledge with goal-directed task completion, grounded in a physical context.

There are three high-level research thrusts: leverage grounded language learning from many sources, capture and represent the expectations implied by language, and use deep hierarchical reinforcement learning to transfer learned knowledge to related tasks and skills. In the first, new perceptual models to learn an alignment among a robot’s multiple, heterogeneous sensor and data streams will be developed. In the second, synchronous grounded language models will be developed to better capture both general linguistic and implicit contextual expectations that are needed for completing shared tasks. In the third, a deep reinforcement learning framework will be developed that can leverage the advances achieved by the first two thrusts, allowing the development of techniques for learning conceptual knowledge. Taken together, these advances will allow an agent to achieve domain adaptation, improve its behaviors in new environments, and transfer conceptual knowledge among robotic agents.

The research award will support both faculty and students working in the Interactive Robotics and Language lab on this task. It includes an education and outreach plan designed to increase participation by and retention of women and underrepresented minorities (URM) in robotics and computing, engaging with UMBC’s large URM population and world-class programs in this area.

Prof. Tülay Adali receives prestigious Humboldt Research Award for advanced data analysis

Prof. Tülay Adali receives prestigious Humboldt Research Award for advanced data analysis

UMBC’s Tülay Adali, professor of computer science and electrical engineering (CSEE) and distinguished university professor, has received the prestigious Humboldt Research Award. The Alexander von Humboldt Foundation describes the award as presented to scholars “whose fundamental discoveries, new theories, or insights have had a significant impact on their own discipline and who are expected to continue producing cutting-edge achievements in the future.”

UMBC Professor Tülay Adali
Tülay Adali. Photo courtesy of Adali.

Adali is the director of UMBC’s Machine Learning for Signal Processing Lab. Her research focuses on developing flexible methods for data fusion. These innovative methods enable researchers to extract powerful features from multi-modal data by letting them fully interact with and inform each other.

A main application area of her work has been medical image analysis, where these features are used in diagnosis as well as treatment planning and evaluation. Adali and her research collaborators are also exploring applications of these methods in remote sensing, misinformation detection, and gesture and video analysis. 

A years-long research collaboration

Humboldt Award recipients spend up to one year conducting collaborative research at institutions in Germany. Adali plans to continue to work with her longtime collaborator Peter Schreier, who is based in Paderborn University. Through a research connection that has spanned many years, Adali says that her lab and Schreier’s continue to have wonderful synergy. 

Together, Adali and Schreier have worked to address problems such as data-driven discovery of relationships in multi-modal data, and in particular, when the sample sizes are small. “This is a key practical problem in many applications, especially in the medical domain,” Adali shares. She notes that this provides an important starting point for their current work. 

“Things are moving along, even though I could not travel this summer, as we started having weekly research meetings between our groups,” Adali says. “This is a valuable experience for my students. In the past, we had hosted Schreier and his students here at UMBC, some of my students had met Schreier and his students at conferences before, and these initial physical connections matter. I am hoping we will all be able to travel again, soon.”

Receiving the award

As a Humboldt Award recipient, Adali was invited to attend a gathering in June with her fellow awardees, hailing from universities around the world. Due to COVID-19, the event was moved online. Awardees had an opportunity to meet the German president virtually as part of the event. 

While she wishes the event could have been held in person, Adali says that it gave her an exciting opportunity to connect with other Humboldt awardees and learn more about scientist and explorer Alexander von Humboldt.

In 2015, Curtis Menyuk, professor of CSEE, received a Humboldt Award.

Adapted from a UMBC News article written by Megan Hanks. Banner image: Tülay Adali, fourth from left, with the members of her lab. Photo courtesy of Adali.

UMBC’s Naghmeh Karimi receives NSF CAREER Award to develop long-lasting security for cryptographic chips


Naghmeh Karimi receives NSF CAREER award to develop long-lasting security for cryptographic chips


Naghmeh Karimi is the most recent UMBC faculty member to receive a prestigious CAREER Award from the National Science Foundation (NSF). The grant, totaling approximately $500,000 over five years, will support her work to investigate how device-aging related risks compromise the security of cryptographic devices.

Karimi explains that cryptographic chips offer continued advances in authenticating messages and devices as well as preserving the integrity and confidentiality of sensitive information. They do so by implementing cryptographic algorithms in hardware. These chips combine the benefits of cryptographic applications with the speed and power advantage of hardware implementations. 

Despite their significant benefits, cryptographic chips can be compromised by adversaries who have gained physical access to the chips. Current protections against such attacks do not consider the aging of devices, which can shift device parameters over time.

Addressing security vulnerabilities 

Aging makes cryptographic chips operate slower and, ultimately, results in their malfunction, says Karimi. She explains that the typical lifetime of integrated circuits is 7 to 8 years. As the devices age, their performance decreases. Karimi is exploring the specific security vulnerabilities of aged devices and how they can be protected.

“We want to preserve the security of devices over their lifetime,” Karimi says.

Karimi and her research team will study whether the success of the side-channel analysis and fault-injection attacks increase in older devices. Karimi will create and test several countermeasures to protect devices against such attacks.

Connecting students with opportunities in tech security

The CAREER Award funding will support several UMBC undergraduate and graduate student researchers working with Karimi to develop long-lasting security solutions for hardware platforms. 

At the same time, Karimi will also develop and launch a new course in UMBC’s computer science and electrical engineering department on cryptography, hardware security, and testing. She will also work with the UMBC Cyber Scholars Program to connect students with internship opportunities focused on hardware security, to give them additional hands-on experience in the field. 

“The success of this project will enable us to develop long-lasting security for trusted hardware platforms,” Karimi says. “This will result in aging-resistant security solutions that benefit society through devices that remain secure over their lifetime.”

Adapted from a UMBC News article by Megan Hanks. Banner image: UMBC’s ITE building. Photo by Marlayna Demond ’11 for UMBC.

CSEE Prof. LaBerge receives USM Board of Regents’ Faculty Award for Excellence in Teaching


CSEE Prof. LaBerge receives USM Board of Regents’
Faculty Award for Excellence in Teaching


E. F. Charles LaBerge, Professor of the Practice in the Computer Science and Electrical Engineering department, has been awarded the 2020 University System of Maryland Board of Regents’ Faculty Award for Excellence in Teaching.

Since joining UMBC in 2008, E.F. Charles LaBerge’s career has been marked by outstanding classroom instruction, innovative teaching methods, and development of active learning spaces on campus. He brings a wealth of industry experience and knowledge to UMBC students enrolled in the range of courses that he teaches. As a professor of the practice in computer science and electrical engineering, LaBerge exposes his students to computer and electrical engineering concepts through real-world examples and multidisciplinary instruction. His extensive connections in industry have benefited his students and helped to prepare them for careers and graduate degrees.

As an instructor for the introduction to engineering course taken by all engineering students, he has impacted the educational careers of students across the College of Engineering and Information Technology (COEIT). He consistently receives high ranks and positive comments from students on course evaluations. Both his students and colleagues acknowledge and appreciate LaBerge’s modern approach to classroom instruction, which incorporates technology and new practices.

LaBerge was instrumental in the development of UMBC’s Active Learning Center, a space that supports collaborative learning to promote student success and retention in computing courses. He is a strong supporter of students across campus, opening his office to students, from those who have questions about classes to those seeking professional advice and mentorship. His teaching extends beyond the classroom, and he supports and mentors students presenting at UMBC’s annual Undergraduate Research and Creative Achievements Day each spring.

He is a very engaged member of COEIT, having served as the undergraduate program director for computer engineering and as the coordinator for computer engineering’s accreditation program, among other roles. His commitment to the College was recognized with the inaugural College of Engineering and Information Technology Award for Teaching Excellence in 2018. This award was presented to him based on feedback from fellow faculty and colleagues.

LaBerge earned his B.S. and M.S. in electrical engineering from Johns Hopkins University, and his Ph.D. in electrical engineering from UMBC.

Adapted from the UMBC faculty awards announcement.

Prof. Naghmeh Karimi receives NSF CAREER award for research on the security of cryptographic chips

Professor Karimi and a custom measurement board developed by her collaborators at the University of Buchum, Germany that is being used to test a 65 nm application-specific integrated circuit sample


Investigating the Impact of Device Aging on the Security of Cryptographic Chips


Professor Naghmeh Karimi received a prestigious NSF CAREER award to support her research on Investigating the impact of device aging on the security of cryptographic chips.

CAREER awards are among NFS’s most prestigious awards and are intended to support early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

Cryptographic chips implement cryptographic functions in hardware for better performance. Despite the significant performance benefits, cryptographic chips can be compromised by the adversaries via monitoring their power-consumption, tampering their logic or placing the chips under stress to generate erroneous outputs to infer sensitive data. The current protections against such attacks do not consider the aging of the devices that can cause a parametric shift of device parameters over time which can compromise device security.

Supported by this five-year award, Professor Karimi and her students will investigate the effects of device aging on the security of cryptographic devices, particularly those with protection against physical attacks, and develop solutions to ensure security when device aging comes into account. Her work will help enable the development of long-lasting security for trusted hardware platforms, and result in aging-resistant security solutions that benefit the society via devices that remain secure over their lifetime.

UMBC faculty poised to help shape national policies in computing

UMBC faculty poised to help shape national policies in computing

Adam Bargteil, assistant professor of computer science and electrical engineering, has been named chair-elect of the Association for Computing Machinery’s (ACM) Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH). He will lead SIGGRAPH as chair starting Fall 2020.

With Bargteil’s election, UMBC’s College of Engineering and Information Technology (COEIT) now has two faculty members serving as leaders of two of the ACM’s largest special interest groups. Helena Mentis, associate dean for academic programs and learning in COEIT and associate professor of information systems, has been president of the Special Interest Group on Computer-Human Interaction (SIGCHI) since July 2018.

In these leadership roles, Bargteil and Mentis will have an opportunity to shape important policy matters, including redesigning computing education guidelines.

Bargteil’s group, SIGGRAPH, is the leading international society for computing professionals and students in computer graphics and interactive techniques, attracting people from academia, industry, and artistic communities. Bargteil helped create the ACM SIGGRAPH Frontiers program, which highlights emerging fields of research, such as machine learning, medical applications of computer graphics, and autonomous vehicles.

Bargteil plans to continue to empower the SIGGRAPH executive committee to create high-impact programming and opportunities for conversation among members when he is at the helm next year. “I’d like to continue to be proactive, and create more value for the members of SIGGRAPH,” he shares.

Over the summer, Bargteil participated in an intensive leadership program, which he says helped him prepare for his upcoming role in SIGGRAPH. Reflecting on the experience, Bargteil says that he found the selected readings, training exercises in public speaking, and opportunities to connect closely with fellow participants to be valuable for his growth as an emerging leader in computing.

SIGCHI, the group Mentis leads, is the world’s largest association for professionals in human-computer interaction. The group’s main conference attracts more than 3,500 attendees each year, and the SIG sponsors 23 specialized conferences.

Mentis is director of UMBC’s Bodies in Motion Lab, and focuses on how technologies can improve collaboration and coordination in healthcare contexts, from empowering patients to helping surgeons utilize interactive imaging. SIGCHI is a highly multi-disciplinary community that includes researchers and students in fields from sociology to mechanical engineering.

Adapted from an article by Megan Hanks. For additional stories, visit the UMBC News site.

Dr. Forno discusses the Baltimore Ransomware attack on Maryland Public TV program

Maryland Public Television’s Charles Robinson reports on how Baltimore continues to recover after city computers were infected with ransomware in the May 2019 Baltimore ransomware attack and interviews Dr. Rick Forno, associate director of the UMBC Center for Cybersecurity and graduate director of UMBC’s Cybersecurity MPS degree program.

From Wikipedia: On May 7th 2019, most of Baltimore’s government computer systems were infected with a new and aggressive ransomware variant named RobbinHood. All servers, with the exception of essential services, were taken offline. In a ransom note, hackers demanded 13 bitcoin (roughly $76,280) in exchange for keys to restore access. The note also stated that if the demands were not met within four days, the price would increase and within ten days the city would permanently lose all of the data.

As of May 13, 2019 all systems remained down for city employees. It is estimated that it will take weeks to recover. According to Mayor Jack Young, US Federal Law enforcement continue to investigate the attack. The attack had a negative impact on the real estate market as property transfers could not be completed until the system was restored on May 20th. However, the restoration of all systems was, as of May 20, 2019, estimated to take weeks more.

Baltimore was susceptible to such an attack due to its IT practices, which included decentralized control of its technology budget and a failure to allocate money its information security manager wanted to fund cyberattack insurance. The attack has been compared to a previous ransomware attack on Atlanta the previous year, and was the second major use of the RobbinHood ransomware on an American city in 2019, as Greenville, North Carolina was also impacted in April.

New NSF award will help robots learn to understand humans in complex environments

Prof. Ferraro in UMBC’s Pi2 visualization laboratory talking to a virtual robot, modeled using a combination of Unity, ROS, and Gazebo. Image from a recent paper on this research..

New NSF project will help robots learn to understand humans in complex environments

UMBC Assistant Professor Cynthia Matuszek is the PI on a new NSF research award, EAGER: Learning Language in Simulation for Real Robot Interaction, with CO-PIs Don Engel and Frank Ferraro. Research funded by this award will be focused on developing better human-robot interactions using machine learning to enable robots to learn the meaning of human commands and questions informed by their physical context.

While robots are rapidly becoming more capable and ubiquitous, their utility is still severely limited by the inability of regular users to customize their behaviors. This EArly Grant for Exploratory Research (EAGER) will explore how examples of language, gaze, and other communications can be collected from a virtual interaction with a robot in order to learn how robots can interact better with end users. Current robots’ difficulty of use and inflexibility are major factors preventing them from being more broadly available to populations that might benefit, such as aging-in-place seniors. One promising solution is to let users control and teach robots with natural language, an intuitive and comfortable mechanism. This has led to active research in the area of grounded language acquisition: learning language that refers to and is informed by the physical world. Given the complexity of robotic systems, there is growing interest in approaches that take advantage of the latest in virtual reality technology, which can lower the barrier of entry to this research.

This EAGER project develops infrastructure that will lay the necessary groundwork for applying simulation-to-reality approaches to natural language interactions with robots. This project aims to bootstrap robots’ learning to understand language, using a combination of data collected in a high-fidelity virtual reality environment with simulated robots and real-world testing on physical robots. A person will interact with simulated robots in virtual reality, and his or her actions and language will be recorded. By integrating with existing robotics technology, this project will model the connection between the language people use and the robot’s perceptions and actions. Natural language descriptions of what is happening in simulation will be obtained and used to train a joint model of language and simulated percepts as a way to learn grounded language. The effectiveness of the framework and algorithms will be measured on automatic prediction/generation tasks and transferability of learned models to a real, physical robot. This work will serve as a proof of concept for the value of combining robotics simulation with human interaction, as well as providing interested researchers with resources to bootstrap their own work.

Dr. Matuszek’s Interactive Robotics and Language lab is developing robots that everyday people can talk to, telling them to do tasks or about the world around them. Their approach to learning to understand language in the physical space that people and robots occupy is called grounded language acquisition and is a key to building robots that can perform tasks in noisy, real-world environments, instead of being pre-emptively programmed to handle a fixed set of predetermined tasks.

Meet Your Professor: Dr. Frank Ferraro, 12-1 Mon 3/25, ITE 239

Prof. Ferraro’s teaching and research areas include AI, language understanding and machine learning

Meet Your Professor: Dr. Frank Ferraro

Find out about Machine Learning and NLP classes

2:00-1:00 Monday, 25 March 2019, ITE 239

Next Monday, March 25th, join the Computer Science Education Club for its second installment of the “Meet Your Professor” Spring 2019 series featuring Professor Frank Ferraro. The series provides students with the opportunity to learn more about their professors, including how they achieved their position, what they believe makes an effective teacher, what research they conduct, and more.

Dr. Ferraro is currently teaching CMSC 678, Introduction to Machine Learning. In the past, Dr. Ferraro has also taught Natural Language Processing (CMSC 473/673) and CMSC 871, Advanced Topics in Artificial Intelligence. Beside NLP, Machine Learning, and AI, Dr. Ferraro also has research experience in semantics, computer vision and language processing.

If you are interested in learning from Dr. Ferraro’s teaching and research experience, stop by ITE 239 on Monday, March 25th at 12pm. Light refreshments will be provided. RSVP here.

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