UMBC’s Grand Challenge Scholars Program, apply by May 3


UMBC’s Grand Challenge Scholars Program, apply by May 3

virtual informational session 5:00 pm, Wednesday, April 28


UMBC’s Grand Challenge Scholars Program is designed for students from all majors who are interested in solving important societal problems. The program fosters a vibrant interdisciplinary community to help tackle the National Academy of Engineering’s (NAE) Grand Challenges and gives students experiences and skills to create solutions to some of the most pressing challenges of the 21st century.

The Grand Challenges are 14 broad problems in the areas of sustainability, health, security, and knowledge. Solutions to these issues require interdisciplinary teamwork and years of sustained effort.

The program aims to recruit a cohort of 20 undergraduates from a diverse pool of disciplines for Fall semester 2021. Ideal candidates are students starting their junior year in order to complete the requirements of the program during their last two years at UMBC. Although there is no financial support provided, the students will have the opportunity to incorporate five experiences into their undergraduate studies that will give them valuable interdisciplinary experiences they can bring to the workplace or graduate school, as well as recognition from the National Academy of Engineering upon successful completion of the program.

Read more about the program and find out how to join at the UMBC GCSP site and via a virtual informational session at 5:00 pm on Wednesday, April 28.

talk: MeetingMayhem: Teaching Adversarial Thinking through a Web-Based Game, 12-1 ET 4/9

The UMBC Cyber Defense Lab presents

MeetingMayhem:  Teaching Adversarial Thinking through a Web-Based Game


Akriti Anand, Richard Baldwin, Sudha, Kosuri, Julie Nau, and Ryan Wunk-Fink
UMBC Cyber Defense Lab

joint work with Alan Sherman, Marc Olano, Linda Oliva, Edward Zieglar, and Enis Golazewski

12:00 noon–1 pm ET, Friday, 9 April 2021
online via WebEx


We present our progress and plans in developing MeetingMayhem, a new web-based educational exercise that helps students learn adversarial thinking in communication networks. The goal of the exercise is to arrange a meeting time and place by sending and receiving messages through an insecure network that is under the control of a malicious adversary.  Players can assume the role of participants or an adversary.  The adversary can disrupt the efforts of the participants by intercepting, modifying, blocking, replaying, and injecting messages.  Through this engaging authentic challenge, students learn the dangers of the network, and in particular, the Dolev-Yao network intruder model. They also learn the value and subtleties of using cryptography (including encryption, digital signatures, and hashing), and protocols to mitigate these dangers.  Our team is developing the exercise in spring 2021 and will evaluate its educational effectiveness.


Akriti Anand () is an MS student in computer science working with Alan Sherman.  She is the lead software engineer and focuses on the web frontend. Richard Baldwin () is a BS student in computer science, a member of Cyberdawgs, and lab manager for the Cyber Defense Lab. Sudha Kosuri () is a MS student in computer science.  She is working on the frontend (using React and Flask) and its integration with the backend. Julie Nau () is a BS student in computer science.  She is working on the backend and on visualizations. Ryan Wunk-Fink () is a PhD student in computer science working with Alan Sherman. He is developing the backend.


Host: Alan T. Sherman,  Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681. The UMBC Cyber Defense Lab meets biweekly Fridays.  All meetings are open to the public.

 Upcoming CDL Meetings: April 23, Peter Peterson (Univ. of Minnesota Duluth), Adversarial thinking; May 7, Farid Javani (UMBC), Anonymization by oblivious transfer

talk: Human-in-the-Loop Entity Mining from Noisy Web Data, 1-2 4/6


Human-in-the-Loop Entity Mining from Noisy Web Data

Professor Eduard Dragut, Temple University

1-2 pm, Tuesday, 6 April 2021
online via WebEx


Recognizing entities that follow or closely resemble a regular expression (regex) pattern is an important task in information extraction. Due to a vast diversity of web documents and ways in which they are generated, even seemingly straightforward tasks such as identifying mentions of date in a document becomes very challenging. It is reasonable to claim that it is impossible to create a regex that is capable of identifying such entities from web documents with perfect precision and recall. Rather than abandoning regex as a go-to approach for entity detection, we present methods to combine the expressive power of regexes, the ability of deep learning to learn from large data, and the human-in-the-loop approach into a new integrated framework for entity identification from web data. The framework starts by creating or collecting the existing regexes for a particular type of entity. Those regexes are then used over a large document corpus to collect weak labels for the entity mentions and a neural network is trained to predict those regex-generated weak labels. Finally, a human expert is asked to label a set of documents and the neural network is fine-tuned on those documents.

While human effort is critical to build an entity recognition model, surprisingly little is known about how to best invest that effort given a limited time budget. Should a human’s effort be spent on writing a regex recognizing an entity or on manually label entity mentions in a document corpus? When a user is allowed to choose between regex construction and manual labeling, we discover that (1) if the time budget is low, spending all time for regex construction is often advantageous, (2) if the time budget is high, spending all time for manual labeling seems to be superior, and (3) between those two extremes, writing regexes followed by manual labeling is typically the best approach. I will also give an overview of the ongoing and future projects.


Eduard Dragut is an Associate Professor in the Computer and Information Sciences Department at Temple University. He received his Ph.D. degree in Computer Science from the University of Illinois at Chicago. He previously was a Postdoctoral Research Associate at Purdue University, Discovery Park, Cyber Center. His main area of research is Web data management, e.g., retrieval, extraction, representation, cleaning, analysis, and integration. He is actively pursuing projects in  Data Cleaning, Social  Media Mining (e.g., user behavior and fake news), the Future of Work, and Cyber-Infrastructure for Scientific Research. He is co-author of a book on Deep Web data integration, Deep Web Query Interface Understanding, and Integration.

UMBC Cyber Dawgs win 2021 Mid-Atlantic Collegiate Cyber Defense Competition

Photo by Marlayna Demond ’11 for UMBC

Congratulation to the UMBC Cyber Dawgs team, which took first place in the 2021 Mid-Atlantic Collegiate Cyber Defense Competition (MACCDC) finals. UMBC’s team was one of eight teams out of an initial 23 that qualified for the final competition. UMBC’s Cyber Dawgs will move on to compete in the National Collegiate Cyber Defense Competition (NCCDC), which will be held April 23-25, 2021.

The 2021 MACCDC regional final took place online April 1-3 and had teams fighting to protect their networks efficiently and effectively from simulated cyber threats and attacks using a scenario based on the COVID-19 global pandemic for its competition events.

The National Emergency Response Division (N.E.R.D.) is a data science-focused group within the Big Time Health Organization (BTHO), a multinational entity headquartered in Bethesda, Maryland. N.E.R.D. employees have been exceptionally busy dealing with the global health pandemic. As such, they have had to not only shift to work from home, but also expand the number of employees to support the inordinate amounts of data that is flooding each of its eight geographic locations throughout the U.S. Protecting the integrity of the data is critical, but when the data affects the delivery of health services to the public, the job of N.E.R.D. becomes even more mission critical.

The student teams will stand on the front lines of technology, alongside various healthcare providers. The main task at hand will be to ensure that pandemic-related data from state departments of health are accurate and delivered quickly. Information on outbreak locations, promising interventions, efficacy of testing, mortality rates, and other related statistics are critical so physicians, public health officials, and government entities can make informed decisions about resource allocations. Loss or inaccurate information can lead to tragic consequences. Vigilance is a must – be smart, be strong, be safe.

These regional and national competitions attract leading collegiate cybersecurity teams from across the nation. They put teams in situations that mimic scenarios they might encounter working to secure and protect online systems for government agencies and companies. Throughout each challenge, teammates work together to protect their systems from hackers and cyber attacks. At the same time, they keep their networks accessible to the users relying on them. 

The UMBC Cyber Dawgs team won the MACCDC regionals last year and were national champions in 2017. In this year’s MACCDC, George Mason placed second and Liberty University third. Good luck to the Cyber Dawgs as they compete with the winners of nine other regional competitions in the National Collegiate Cyber Defense Competition later this month.

talk: Mining social media data for health, public health & popular events, 1-2pm ET 4/2


Mining social media data for health, public health, and popular events

Anietie Andy, University of Pennsylvania

1:00-2:00 pm ET, Friday, 2 April 2021

online via WebEx


Increasingly, individuals are turning to social media and online forums such as Twitter and Reddit to communicate about a range of issues including their health and well-being, public health concerns, and large public events such as the presidential debates. These user-generated social media data are prone to noise and misinformation. Developing and applying Artificial Intelligence (AI) algorithms can enable researchers to glean pertinent information from social media and online forums for a range of uses.  For example, patients’ social media data may include information about their lifestyle that might not typically be reported to clinicians; however, this information may allow clinicians to provide individualized recommendations for managing their patients’ health. Separately, insights obtained from social media data can aid government agencies and other relevant institutions in better understanding the concerns of the populace as it relates to public health issues such as COVID-19 and its long-term effects on the well-being of the public. Finally, insights obtained from social media posts can capture how individuals react to an event and can be combined with other data sources, such as videos, to create multimedia summaries. In all these examples, there is much to be gained by applying AI algorithms to user-generated social media data.

In this talk, I will discuss my work in creating and applying AI algorithms that harness data from various sources (online forums, electronic medical records, and health care facility ratings) to gain insights about health and well-being and public health. I will also discuss the development of an algorithm for resolving pronoun mentions in event-related social media comments and a pipeline of algorithms for creating a multimedia summary of popular events. I will conclude by discussing my current and future work around creating and applying AI algorithms to: (a) gain insights about county-level COVID-19 vaccine concerns, (b) detect, reduce, and mitigate misinformation in text and online forums, and (c) understand the expression and evolution of bias (expressed in text) over time. 


Anietie Andy is a senior data scientist at Penn Medicine Center for Digital Health. His research focuses on developing and applying natural language processing and machine learning algorithms to health care, public health, and well-being. Also, he is interested in developing natural language processing and machine learning algorithms that use multimodal sources (text, video, images) to summarize and gain insights about events and online communities.

talk: Enabling Computation, Control, and Customization of Materials with Digital Fabrication Processes, 1-2pm 3/31


Enabling Computation, Control, and Customization of Materials with Digital Fabrication Processes

Michael Rivera, Carnegie Mellon University 

1:00-2:00 pm Wednesday, 31 March 2022

via WebEx


Low-cost digital fabrication technology, and in particular 3D printing, is ushering in a new wave of personal computing. The technology promises that users will be able to design, customize and create any object to fit their needs. While the objects that we interact with daily are generally made of many types of materials—they may be hard, soft, conductive, etc.—current digital fabrication machines have largely been limited to producing rigid and passive objects. In this talk, I will present my research on developing digital fabrication processes that incorporate new materials such as textiles and hydrogels. These processes include novel 3D printer designs, software tools, and human-in-the-loop fabrication techniques. With these processes, new materials can be controlled, customized, and integrate computational capabilities—at design time and after fabrication—for creating personalized and interactive objects. I will conclude Research this talk with my vision for enabling anyone to create with digital fabrication technology and its impact beyond the individual.


Michael Rivera is a Ph.D. Candidate at the Human-Computer Interaction Institute in the School of Computer Science at Carnegie Mellon University where he is advised by Scott Hudson. He works at the intersection of human-computer interaction, digital fabrication, and materials science. He has published papers on novel digital fabrication processes and interactive systems at top-tier HCI venues, including ACM CHI, UIST, DIS, and IMWUT. His work has been recognized with a Google – CMD-IT Dissertation Fellowship, an Adobe Research Fellowship Honorable Mention, and a Xerox Technical Minority Scholarship. Before Carnegie Mellon, he completed a M.S.E in Computer Graphics and Game Technology and a B.S.E in Digital Media Design at the University of Pennsylvania. He has also worked at the Immersive Experiences Lab of HP Labs, and as a software engineer at Facebook and LinkedIn.

UMBC grad students present new ideas at GEARS Ideathon: 9 April 2021


UMBC GEARS Ideathon

11:30-1:30 Friday, April 9, 2021


GEARS, UMBC’s Graduate Experience, Achievements, and Research Symposium, bring you its first-of-a-kind event IDEATHON that invites graduate students to describe how new or existing problems can be better tackled by using their new idea. Participants will present their ideas to the jury and fellow graduate students in UMBC.  You can participate either individually or in a group of up to three people.

This event will highlight your creative skills and the uniqueness of your idea, which can be social, environmental, IT technology, medical field related, etc. These ideas can be real or hypothetical. You create a three-minute presentation showcasing your idea and how unique it is. Up to $1000 in prize money will be available for the winning ideas. All the participants are eligible for a free UMBC logo Mask, and the first ten participants will get a chance to win UMBC merchandise T-shirts.

Sign up here.


We welcome all department’s graduate students to come to participate and celebrate Graduate week with us on the event day i.e.  9th April 2021. For any queries contact Sulabh Sharma (+14438504311, ) or Jhansi Sankaramaddi (+14109006743, )

UMBC researchers work to advance neurotechnology through emerging consortium

CSEE’s Ramana Vinjamuri is proposing that UMBC join the Building Reliable Advances and Innovation in Neurotechnology (BRAIN) Center, a consortium led by Arizona State University and the University of Houston. His vision is to establish an East Coast BRAIN research hub to develop technologies that can help scientists better understand the nervous system and aid people with disabilities.

For more information, click HERE for the full article.

UMBC offers new Research Experiences for Undergraduates in Smart Computing, Big Data


Research Experiences for Undergraduates (REUs) are critical for students in that they serve to bring the world of research to anyone interested in research at UMBC. Nirmalya Roy, an associate professor of information systems at UMBC, is principal investigator leading a new REU in Smart Computing and Communications funded by the National Science Foundation (NSF). The program is accepting applications through March 31 for this summer from students nationwide.

The program will bring together ten undergraduate students in a paid 10-week, full-time research experience from June 7 to August 13. While the summer 2021 program will be remote, each student will work closely with a research group and mentor. They will receive guidance from Roy and co-PI and CSEE faculty member Dmitri Perkins,as well as other information systems and computer science and electrical engineering (CSEE) faculty.

For more information about this program, click here for the full article.

talk: Forward & Inverse Causal Inference in a Tensor Framework, 1-2 pm ET, 3/29


Forward and Inverse Causal Inference in a Tensor Framework


M. Alex O. Vasilescu
Institute of Pure and Applied Mathematics, UCLA

1-2:00 pm Monday, March 29, 2021
via WebEx

Developing causal explanations for correct results or for failures from mathematical equations and data is important in developing a trustworthy artificial intelligence, and retaining public trust.  Causal explanations are germane to the “right to an explanation” statute, i.e., to data-driven decisions, such as those that rely on images.  Computer graphics and computer vision problems, also known as forward and inverse imaging problems, have been cast as causal inference questions consistent with Donald Rubin’s quantitative definition of causality, where “A causes B” means “the effect of A is B”, a measurable and experimentally repeatable quantity. Computer graphics may be viewed as addressing analogous questions to forward causal inference that addresses the “what if” question, and estimates a change in effects given a delta change in a causal factor. Computer vision may be viewed as addressing analogous questions to inverse causal inference that addresses the “why” question which we define as the estimation of causes given a forward causal model, and a set of observations that constrain the solution set.  Tensor algebra is a suitable and transparent framework for modeling the mechanism that generates observed data.  Tensor-based data analysis, also known in the literature as structural equation modeling with multimode latent variables, has been employed in representing the causal factor structure of data formation in econometrics, psychometric, and chemometrics since the 1960s.  More recently, tensor factor analysis has been successfully employed to represent cause-and-effect in computer vision, and computer graphics, or for prediction and dimensionality reduction in machine learning tasks.   


M. Alex O. Vasilescu received her education at the Massachusetts Institute of Technology and the University of Toronto. She is currently a senior fellow at UCLA’s Institute of Pure and Applied mathematics (IPAM) that has held research scientist positions at the MIT Media Lab from 2005-07 and at New York University’s Courant Institute of Mathematical Sciences from 2001-05.  Vasilescu introduced the tensor paradigm for computer vision, computer graphics, and machine learning. She addressed causal inferencing questions by framing computer graphics and computer vision as multilinear problems. Causal inferencing in a tensor framework facilitates the analysis, recognition, synthesis, and interpretability of data. The development of the tensor framework has been spearheaded with premier papers, such as Human Motion Signatures (2001), TensorFaces (2002), Multilinear Independent Component Analysis (2005), TensorTextures (2004), and Multilinear Projection for Recognition (2007, 2011). Vasilescu’s face recognition research, known as TensorFaces, has been funded by the TSWG, the Department of Defenses Combating Terrorism Support Program, Intelligence Advanced Research Projects Activity (IARPA), and NSF. Her work was featured on the cover of Computer World and in articles in the New York Times, Washington Times, etc. MIT’s Technology Review Magazine named her to their TR100 list of honorees, and the National Academy of Science co-awarded the Keck Futures Initiative Grant.  

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