talk: Building Resilience against Cyberattacks, 12pm ET, Dec 15

ArtIAMAS Seminar Series, Co-organized by UMBC, UMCP, and the Army Research Lab

Building Resilience against Cyberattacks

Aryya Gangopadhyay, UMBC

12-1 PM ET Wednesday, 15 December 15, 2021
Online via webex

In this talk, we will address the issue of building resilient systems in the face of cyberattacks. We will present a defense mechanism for cyberattacks using a three-tier architecture that can be used to secure army assets and tactical information. The top tier represents the front-end where autonomous sensing and inferencing through AI models take place by UAVs, UGVs, etc. We will illustrate how models can be defended against data poisoning attacks. In the middle tier, we focus on building cyber defense against attacks in federated learning environments, where models are trained on a large corpus of decentralized data without transferring raw data over a communication channel. The bottom tier represents back-end servers that train deep learning models with large amounts of data that can subsequently be pushed to the edge for inferencing. We will demonstrate how adaptive models can be developed for detecting and preventing various types of attacks at this level.

Dr. Aryya Gangopadhyay is a Professor in the Information Systems department at the University of Maryland, Baltimore County. Dr. Gangopadhyay has a courtesy appointment as a Professor in Computer Science and Electrical Engineering at UMBC. He is also the Director of the Center for Real-time Sensing and Autonomy (CARDS) at UMBC. His research interests include adversarial machine learning at the edge, cybersecurity, and smart cities. He has graduated 16 Ph.D. students and is currently mentoring several others at UMBC. He has published over 125 peer-reviewed research articles and has received extramural support from ARL, NSF, NIST, the Department of Education, and IBM.

talk: Top-K Ranking Deep Contextual Bandits for Information Selection Systems, 12pm ET 12/8

The multi-armed bandit problem arises when allocating a fixed limited set of resources between competing choices to maximize expected gain when each choice’s properties are only partially known but may become better understood as time passes

ArtIAMAS Seminar Series, co-organized by UMBC, UMCP & Army Research Lab

Top-K Ranking Deep Contextual Bandits for Information Selection Systems

Dr. Jade Freeman, Army Research Lab

12-1pm ET Wed. 8 Dec. 2021, Online via Webex

In today’s technology environment, information is abundant, dynamic, and heterogeneous in nature. Automated filtering and prioritization of information is based on the distinction between whether the information adds substantial value toward one’s goal or not. Contextual multi-armed bandit has been widely used for learning to filter contents and prioritize according to user interest or relevance. Learn-to-Rank technique optimizes the relevance ranking on items, allowing the contents to be selected accordingly. We propose a novel approach to top-K rankings under the contextual multi-armed bandit framework. We model the stochastic reward function with a neural network to allow non-linear approximation to learn the relationship between rewards and contexts. We demonstrate the approach and evaluate the performance of learning from the experiments using real-world data sets in simulated scenarios. Empirical results show that this approach performs well under the complexity of a reward structure and high dimensional contextual features.

Dr. Jade Freeman is the Chief of the Battlefield Information Systems Branch, DEVCOM U.S. Army Research Laboratory (ARL), overseeing military information systems and analysis research projects. Prior to joining ARL, Dr. Freeman served as the Senior Statistician for the Chief of Staff at the Department of Homeland Security, Office of Cybersecurity and Communications, currently known as The Cybersecurity and Infrastructure Security Agency (CISA), Dr. Freeman obtained her Ph. D. in Statistics from George Washington University

talk: Shadow IT in Higher Ed: Survey & Case Study for Cybersecurity, 12-1 Fri 12-3

Shadow IT is the use of information technology systems, devices, software, applications, and services without explicit IT department approval.

The UMBC Cyber Defense Lab presents

Shadow IT in Higher Education: Survey and Case Study for Cybersecurity

Selma Gomez Orr, Cyrus Jian Bonyadi, Enis Golaszewski, and Alan T. Sherman
UMBC Cyber Defense Lab

Joint work with Peter A. H. Peterson (University of Minnesota Duluth), Richard Forno, Sydney Johns, and Jimmy Rodriguez

12-1:00 pm, Friday, 3 December 2021, online via WebEx

We explore shadow information technology (IT) at institutions of higher education through a two-tiered approach involving a detailed case study and comprehensive survey of IT professionals. In its many forms, shadow IT is the software or hardware present in a computer system or network that lies outside the typical review process of the responsible IT unit. We carry out a case study of an internally built legacy grants management system at the University of Maryland, Baltimore County that exemplifies the vulnerabilities, including cross-site scripting and SQL injection, typical of such unauthorized and ad-hoc software. We also conduct a survey of IT professionals at universities, colleges, and community colleges that reveals new and actionable information regarding the prevalence, usage patterns, types, benefits, and risks of shadow IT at their respective institutions.

Further, we propose a security-based profile of shadow IT, involving a subset of elements from existing shadow IT taxonomies, that categorizes shadow IT from a security perspective. Based on this profile, survey respondents identified the predominant form of shadow IT at their institutions, revealing close similarities to findings from our case study.

Through this work, we are the first to identify possible susceptibility factors associated with the occurrence of shadow IT-related security incidents within academic institutions. Correlations of significance include the presence of certain graduate schools, the level of decentralization of the IT department, the types of shadow IT present, the percentage of security violations related to shadow IT, and the institution’s overall attitude toward shadow IT. The combined elements of our case study, profile, and survey provide the first comprehensive view of shadow IT security at academic institutions, highlighting the tension between its risks and benefits, and suggesting strategies for managing it successfully.

Dr. Selma Gomez Orr ( ) received her Ph.D. from Harvard University in the field of decision sciences. She also holds Masters degrees in applied mathematics, engineering sciences, and business administration, also from Harvard. She has worked in the private sector in the fields of cybersecurity and data analytics. Most recently, as a CyberCorps Scholarship for Service (SFS) Scholar, Dr. Orr completed a Master’s of Professional Studies in both cybersecurity and data science at UMBC.

Cyrus Jian Bonyadi ( ) is a computer science Ph.D. student and former SFS scholar studying consensus theory at UMBC under the direction of Alan T. Sherman, Sisi Duan, and Haibin Zhang.

Enis Golaszewski ( ) is a Ph.D. student at UMBC under Alan T. Sherman where he studies, researches, and teaches cryptographic protocol analysis. A former SFS scholar, Golaszewski helps lead annual research studies that analyze and break software at UMBC.

Dr. Alan T. Sherman () is a professor of computer science, director of CDL, and associate director of UMBC’s Cybersecurity Center. His main research interest is high-integrity voting systems. Sherman earned the Ph.D. degree in computer science at MIT in 1987 studying under Ronald L. Rivest.

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 12-1 pm. All meetings are open to the public. Upcoming CDL Meetings: Feb 4, Filipo Sharevski

Webinar on NSA Codebreaker challenge and student opportunities, Sept 9

NSA Codebreaker challenge and student opportunities Webinar

4-6 pm EDT Thursday, 9 September 2021, Online

Register Here

NSA will hold an NSALive Adobe Webinar on Thursday, September 9, 2021, from 4-6 pm EDT to learn about the National Security Agency and Student Program opportunities, as well as a deep dive into the 2021 Codebreaker Challenge. Register for the online session here.

The Codebreaker Challenge is the NSA’s annual cybersecurity and cryptanalysis challenge with a realistic, NSA mission-centric scenario open to U.S-based academic institutions. The 2021 challenge is open now and runs through December 31, 2021.

While the challenge is intended for students, faculty are encouraged to participate as well. Furthermore, the site was designed to make it easy for those faculty interested in incorporating the challenge into their courses (see the additional FAQ entries below.)

The 2021 Codebreaker Challenge consists of a series of tasks worth a varying amount of points based upon their difficulty. Schools will be ranked according to their students’ total number of points with the current ranking shown on a leaderboard. Solutions may be submitted at any time for the duration of the Challenge.

While not required, it is recommended that participants solve tasks in order since they flow with the storyline. Later tasks may rely on artifacts or inputs from earlier tasks. Each task in the 2021 challenge will require a range of skills. You will need to call upon all of your technical expertise, intuition, and common sense.

UMBC’s Donna Ruginski and bwtech@UMBC finalists for CAMI’s Maryland Cybersecurity Awards

Donna Ruginski and bwtech@UMBC finalists for CAMI’s Maryland Cybersecurity Awards

Congratulations to UMBC’s Donna Ruginski and bwtech@UMBC Research and Technology Park for their selection as finalists in the Cybersecurity Association of Maryland’s Fifth Annual Maryland Cybersecurity Awards.

Donna Ruginski is a finalist for the Cyber Warrior Woman Award, which honors a woman doing extraordinary or exemplary work in Maryland’s cybersecurity industry. She is UMBC’s Executive Director for Cybersecurity Initiatives in the Office of the Vice President for Research. She is responsible for the strategic positioning and growth of UMBC’s cybersecurity partnerships, research, and programs.

The bwtech@UMBC Research and Technology Park is a finalist for the Cybersecurity Industry Resource Award, which celebrates a non-cybersecurity business, organization, academic institution, or government agency that has significantly contributed to Maryland’s cybersecurity industry through its products, services, or mission.

Finalists were selected by an independent panel of judges represented by leaders in a variety of fields. One winner from each category will be announced at the Maryland Cybersecurity Awards Celebration on September 22, 2021, 5 PM – 8 PM at Maryland Live! Casino.

All finalists are automatically entered into the People’s Choice Award category. The public is invited to vote online to determine who will receive the coveted Cybersecurity People’s Choice Award. The winner will be announced during the virtual Awards Celebration on September 22, 2021. Vote for your choice here.

The Cybersecurity Association of Maryland, Inc. (CAMI) is a statewide nonprofit organization established in 2015. It is Maryland’s only organization dedicated 100% to the growth of Maryland’s cybersecurity industry. 

UMBC’s 25th Undergraduate Research & Creative Achievement Day had a global audience

A scene from the game Recurring Moment by Kristian Mischke. Image courtesy of Mischke.

UMBC’s 25th Undergraduate Research and Creative Achievement Day had an expanded global audience

UMBC’s 25th Undergraduate Research and Creative Achievement Day (URCAD) reached more viewers than ever before, with visitors connecting online from as far away as Spain, Indonesia, Nigeria, Brazil, Bhutan, Germany, and the U.K.. Audiences logged more than 11,000 visits (compared with 8,000 in 2020) and posted more than 3,500 comments over the course of the week-long event. 

For UMBC’s video game designers, going virtual was not new. Marc Olano, associate professor of computer science and electrical engineering, mentored four projects presented at URCAD, each led by a group of about four students. They include Sword Shibe; Recurring Moment – A Time Travel Puzzle Platformer; Jump Starters, and the two-player Android and PC strategy game Hamster Toaster Checker. Students in UMBC’s computer science game development track collaborated with students in animation and interactive media to envision and begin developing the new games.

“The beauty of the CMSC 493 class is that it brings artists and programmers together and the management of the project is completely led by us,” says Kristian Mischke ‘21, computer science, the game designer for the Recurring Moment project.

In Sword Shibe, players take a dog with a sword through different paths. The student team that created it drew inspiration for its concept designs from Japanese culture, folklore, and legends. The dog in the game is also inspired by a Shiba Inu, which is a breed of hunting dog from Japan. 

Olano worked to model the students’ project experience on the structure of the game design and development industry. “Students began working through ideas in small teams and worked their way through prototypes and onto a bigger team,” he explains. “In the game industry, you have to work collaboratively or you fail.” 

Mischke explains how he would bounce ideas off the artists for visual appeal or about the game’s narrative arc. With the other programmers, he talked through implementation feasibility. “We all would give feedback and discuss adaptations together,” says Mischke. “Everyone on the team was able to be part of the process and apply their unique skill set.”

This post was adapted from a UMBC News article written by Catalina Sofia Dansberger Duque.

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.

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


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, )

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.  

ACM career talk: career opportunities in data privacy

Continuing with our Innovation, Collaboration, Job Search, and Career help theme, the ACM UMBC chapter is back again, hosting another session on the coming Friday with Sameer Ahirrao, a Founder and CEO of Ardent Privacy. He will be talking about Innovation, Collaboration, and Career Opportunities in Data Privacy. Find more on how you can get a part-time off-campus or full-time internship under MIPS (Maryland Industrial Partnership) Program with Ardent Privacy.

Join us for insights from him and a Q&A session with Sameer.
See you on Friday, March 16 at 3:00 pm EST on WebeX.  For more information, contact:  .

1 2 3 32