Teresa M. Donnellan

Innovative approaches propel STAT 250 to new heights at George Mason �鶹��

Teresa Donnellan — Mon, 31 Mar 2025 14:40:51 +0000

Innovative approaches propel STAT 250 to new heights at George Mason �鶹�� Teresa Donnellan Mon, 03/31/2025 - 10:40

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Many university students taking a general education statistics course might be tempted to shirk attendance. But George Mason �鶹��’s STAT 250 Introductory Statistics I boasts an impressive attendance rate of 80% across its sections. With between eight and ten sections per semester, the course is offered in-person and online both synchronously and asynchronously. It serves as one of the most popular quantitative reasoning courses in Mason Core, the university's general education program, with enrollments of nearly one thousand students per semester.

The course’s success can be attributed in part to Associate Professor Kenneth Strazzeri, the course coordinator. Strazzeri tweaks the curriculum each semester to optimize student engagement.

Kenneth Strazzeri, Photo by Office of �鶹�� Branding

“I want students to succeed. That's been my number one goal since taking this type of role at Mason,” said Strazzeri. “When exposed to statistics again, I want them to think back and say, ‘Oh, I remember that. I took stat, and I had a good handle on these basic concepts.’”

Strazzeri’s rich background in statistics education makes him ideal for his role. He holds a PhD in mathematics education and an MS in statistics from the �鶹�� of Virginia.

In STAT 250, Strazzeri focuses on increasing student engagement through various motivational strategies. Attendance bonuses and small incentives for completing course evaluations were introduced to encourage students to actively participate. Another key improvement was the restructuring of assignments. Previously, students were required to complete three major assignments, but these have now been split into six smaller assignments, a change that allows for more frequent feedback and helps students stay on track throughout the semester.

One course innovation that excites Strazzeri has been the introduction of simulation-based methods to teach statistical inference, a process used to draw conclusions about a population based on a sample of data taken from that population.

"We are teaching inference at an informal level first to then get to probability and modeling," Strazzeri explained.

This approach helps students grasp complex concepts related to statistical inference such as the interpretation of a confidence interval by using simulation methods such as bootstrapping—a technique used to estimate the variation of a sample statistic using only the original sample selected—at an informal level before delving into more formal methods.

Looking ahead, Strazzeri is excited about the future of STAT 250, particularly with the transition to the Canvas learning management system. He is working with Instructional Designer Abigail Smith in George Mason’s Stearns Center for Teaching and Learning to make the course even more interactive and user-friendly.

"The future of STAT 250 is bright because of the new design in Canvas," Strazzeri said. Their plans include embedding questions in instructional videos and using those questions live during in-person classes to enhance student interaction and understanding.

Overall, the efforts led by Strazzeri have significantly improved the learning experience in STAT 250. By incorporating modern teaching methods, restructuring assignments, and increasing student engagement, the Department of Statistics is ensuring students not only succeed in the course but also develop a strong foundation in statistical concepts.

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Cyber sweep: George Mason students take top three spots in national transportation cybersecurity competition

Teresa Donnellan — Thu, 27 Mar 2025 14:42:35 +0000

Cyber sweep: George Mason students take top three spots in national transportation cybersecurity competition Teresa Donnellan Thu, 03/27/2025 - 10:42

"I'm going to teach you how to hack a modern car," said George Mason �鶹�� professor Tanvir Arafin to the fall 2024 cohort of CYSE 465 Transportation Systems Design. Arafin not only introduced his students to such exciting concepts in transportation security, but he also incorporated the opportunity to participate in a nationwide cybersecurity competition into his course.

The entire class of 12 from the Department of Cyber Security Engineering chose to participate in the optional assignment and several won first, second, and third place at the National Transportation Cybersecurity Competition (NTCC) in November 2024. George Mason’s Finn Schaefer won first place; the team of Noor Mohammed, Sonia Shaukat, Caleb Hughston, and Kayleigh Batchos won second place; and Navraj Singh Gill won third place.

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The national competition was open to all university students, both undergraduate and graduate, who could participate individually or in teams of up to four, guided by a faculty advisor. This year’s competition was titled “CYBER-CARE Challenge 2024: Securing the Future of Transportation.”

Participants faced a series of cybersecurity challenges, including password cracking, traffic management system exploitation, Vigenère cipher decryption, SQL injection vulnerability identification, cross-site scripting (XSS) attacks, and vehicle trajectory anomaly detection. They had a few weeks to complete the challenges remotely.

Arafin developed CYSE 465 by integrating real-world scenarios and cutting-edge research findings from his lab. He has been working in hardware and cyber-physical systems security with a key focus on cybersecurity issues of autonomous and transportation systems. CYSE 465 covers understanding data, communication, operating systems, control, and security primitives used in today's vehicles for safe and intelligent control and navigation.

“The course offers a unique perspective on the cyber vulnerabilities that emerge from the security-oblivious modernization of transportation systems,” said Arafin.

While he has taught the course since 2022, this was the first time he incorporated the competition into the course. Arafin noted that such competition participation can greatly enhance students’ resumes, and first-place winner Schaefer, a busy student, greatly appreciated the chance to participate.

“This was actually the first competition that I've competed in,” he said, explaining that it’s hard to find time for such activity outside of school and work. Schaefer’s favorite challenge at the NTCC involved assessing vehicle datasets to detect problems or attacks. He drew from his personal interest in machine learning to solve the challenge.

“I've worked with machine learning models a lot in my own personal projects, but I've never had an applicable use for it,” said Schaefer. “So, it was interesting to actually see my work have real meaning to it for once. I was able to do something pretty cool.”

Through courses like CYSE 465, which bridge the gap between theoretical knowledge and practical application, the Department of Cyber Security Engineering is fostering a new generation of cybersecurity professionals who are well-equipped to secure the future of transportation systems.

"The students’ competition performance is a very compelling argument on the benefits of bringing the results and lessons learned in the research lab directly to the classroom, something many CYSE faculty are doing," said Paulo Costa, the department's faculty chair.

The achievements of George Mason students at the NTCC highlight the importance of this approach. By bringing state-of-the-art research directly to the classroom, the Department of Cyber Security Engineering is preparing students to become leaders in the field. As the cybersecurity landscape continues to evolve, the department's innovative educational strategies will ensure that its graduates are ready to meet the challenges of tomorrow.

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Tanvir Arafin

Paulo Costa

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Nearly 500 high schoolers get expert advice at Virginia CyberSlam 2025

Teresa Donnellan — Mon, 17 Mar 2025 16:20:56 +0000

Nearly 500 high schoolers get expert advice at Virginia CyberSlam 2025 Teresa Donnellan Mon, 03/17/2025 - 12:20

Nearly 500 high school students from across the commonwealth gathered at George Mason �鶹�� for Virginia CyberSlam 2025, where cybersecurity experts from both the public and private sectors shared industry insights during a day of activities aimed at inspiring the next generation of cybersecurity professionals.

Dolores Garza addresses CyberSlam 2025 attendees. Photo by Teresa Donnellan/CEC

“What an event it was March 10, with 475 students representing 22 schools and six school districts from around the commonwealth enjoying hands-on workshops and activities led by cyber experts from industry, government, and higher education,” said CyberSlam founder Chris Starke, a teacher for Loudoun County Public Schools who co-directed the event with the �鶹�� of Alabama in Huntsville’s Jennifer Marden. “In all, 28 presenters combined to run 12 workshops—6 for each of our novice and advanced students. It was truly rewarding to see students enjoy learning about cybersecurity.”

Students heard from keynote speaker Dolores Garza, Branch Chief of the Technical Operations Group at the United States Marshals Service, among others. Garza oversaw the creation of a cyber investigation unit at the U.S. Marshals and described the evolution of cybersecurity as a professional field. She encouraged attendees to stay open to new technological developments and emphasized the need for a hybrid set of skills that combines criminal investigatory techniques with technical expertise.

“Diversity in technology is not just an asset; it is a necessity,” said Garza. “The more perspectives we bring to problem-solving, the stronger and more effective our solutions become.”

Student navigates drone during CyberSlam 2025. Photo by Ron Aira/Office of �鶹�� Branding.

Throughout the day, professionals led hands-on workshops in various aspects of cybersecurity.

David Raymond from Virginia Cyber Range led a session on advanced Capture the Flag challenges, focusing on Wireshark.

Greg Crabb from 10-8, LLC, conducted a tabletop exercise on incident response and deep fake technology, and Randy Benn from Claroty introduced students to the Internet of Things.

Other experts leading workshops represented Vertex11, OpusType Advisors, the Idaho National Laboratory, the Drug Enforcement Administration, CYBER.ORG, George Mason, and Shenandoah �鶹��, leading workshops on drones, deep fakes, digital forensics, and more.

Students participate in a CyberSlam workshop. Photo by Ron Aira/Office of �鶹�� Branding

“I would like to thank our co-hosts, George Mason’s Department of Cyber Security Engineering and Loudoun County Public Schools, as well as the Commonwealth Cyber Initiative Northern Virginia Node (CCI NoVA Node), Virginia Cyber Range, Vertex11, CACI International Inc., Claroty, CodeHS, Widelity, Inc., and GT Edge AI for sponsoring this wonderful event that in four years has impacted more than 1,600 students,” said Starke. “And, thank you to our exhibitors the Cybersecurity and Infrastructure Security Agency, Mason Competitive Cyber (MCC), George Mason’s College of Engineering and Computing, GT Edge AI, and the Virginia National Guard 91^st Cyber Brigade.”

CyberSlam attendees navigate drones in a workshop. Photo by Jennifer Marden

One event highlight was a panel featuring George Mason students from the MCC club—Dylan Charnick, Dylan Knoff, and Danyaal Shaozab. MCC is known for its inclusivity, welcoming George Mason students from all majors and levels of experience. The club meets twice weekly, hosting impressive speakers, running workshops on cybersecurity skills, and forming teams to participate successfully in various CTF events throughout the year.

During the panel, the MCC students fielded questions from the audience about degree programs at George Mason, the benefits of technical certifications, and how to best prepare for a career in cybersecurity. “Most cyber jobs are here in the D.C. area,” noted Knoff, pointing out one benefit of pursuing cybersecurity at George Mason.

CyberSlam fosters the cultivation of cybersecurity talent in Northern Virginia, where the industry is booming. This year’s event particularly underscored the opportunities in both public and private sectors that are preparing the next generation of cybersecurity professionals, ensuring they are equipped with the skills and knowledge needed to thrive in this rapidly growing field.

“I would like to thank CCI NoVA Node, specifically Liza Durant and Jordan Mason for sponsoring the amazing raffle prizes and their support for the Virginia CyberSlam, and also George Mason’s Elizabeth Dean for making this event possible,” said Marden. “We could not have done this with the support of George Mason, CCI, our presenters, sponsors, and of course our wonderful Virginia cybersecurity educators and students.”

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Chef’s KISS: Statistics PhD candidate creates new recipe for complex spatial data analysis

Teresa Donnellan — Thu, 06 Mar 2025 20:40:50 +0000

Chef’s KISS: Statistics PhD candidate creates new recipe for complex spatial data analysis Teresa Donnellan Thu, 03/06/2025 - 15:40

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Ben Seiyon Lee

Jin Hyung Lee, a PhD candidate in the Department of Statistics, in collaboration with his advisor, Assistant Professor Ben Seiyon Lee, has developed novel statistical methodology for analyzing large-scale complex data that can be up to 3,600 times faster than the current standard. He will receive the Korean International Statistical Society (KISS) 2024 Outstanding Student Paper Award for his work and present about it at the American Statistical Association’s Joint Statistical Meetings (JSM) 2025 in Nashville, Tennessee.

The primary application of Lee’s methodology is in the analysis of high-resolution satellite image data, which is inherently complex and high-dimensional. A major challenge lies in predicting values at unobserved locations within the satellite imagery. While traditional spatial modeling methods are computationally intensive and time-consuming, Lee's novel approach offers a solution that is significantly faster. This development has significant implications for various fields, such as atmospheric sciences, ecology, public health, and medical imaging.

Jin Hyung Lee, photo provided

Lee’s research focuses on the application of variational inference to massive spatial datasets using a unique machine learning algorithm. Variational inference is a method used to approximate complex probability distributions when true inference is not possible, making it a powerful method for modeling complex spatial datasets. The proposed approach significantly improves the computational efficiency of spatial data analysis, achieving speeds up to three orders of magnitude faster than existing methods while maintaining comparable accuracy.

Lee's method can be used to predict weather patterns in remote and rapidly changing environments like the Gulf Coast, southern California, and polar regions like Alaska or Antarctica. The ability to process high-dimensional data quickly and accurately is crucial for making timely and informed decisions in these regions, especially when they are at risk of geophysical hazards such as hurricanes, wildfires, or sea ice loss. Additionally, the methodology has potential applications in infectious disease modeling and public health data analysis, where rapid and accurate predictions are essential for effective intervention and policymaking.

Lee's research is part of his broader dissertation work, which focuses on developing and applying variational inference techniques to various statistical models. His goal is to simplify these methods, making them more accessible to non-experts and applicable to a wide range of data types. By doing so, Lee aims to bridge the gap between advanced statistical methodologies and practical applications, enabling more efficient and effective data analysis across different fields.

At the JSM 2025, Lee will have the opportunity to present his research to a large audience of statisticians from around the world. The conference, which attracts over 5,000 participants annually, provides a platform for sharing cutting-edge research, networking with peers, and exploring new developments in the field of statistics. Lee’s presentation will be part of a session hosted by KISS, where he and other award recipients will showcase their work and receive their awards.

Receiving the KISS Outstanding Student Paper Award is a significant milestone in Lee’s academic journey. It not only recognizes his contributions to the field but also provides him with valuable exposure and opportunities for future collaboration. As Lee continues to advance his research, his innovative methodologies and their applications are poised to make a lasting impact on the field of statistics and beyond.

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Computational statistics

Keeping data safe by keeping it separate

Teresa Donnellan — Tue, 25 Feb 2025 15:35:36 +0000

Keeping data safe by keeping it separate Teresa Donnellan Tue, 02/25/2025 - 10:35

Developing a robust cybersecurity system demands a lot of data, but keeping all that data in a single location introduces the risk of a data breach. Luckily, George Mason �鶹�� researcher Zhuangdi Zhu in the Department of Cyber Security Engineering specializes in federated learning, a method of decentralized machine learning that allows multiple entities to collaboratively train a machine learning model without sharing their raw data.

Zhu is using this expertise to assist Wajih Ul Hassan of the �鶹�� of Virginia develop scalable host-based intrusion detection systems (HIDS). Together, Zhu and Hassan are leveraging artificial intelligence (AI) to enhance a given host’s cybersecurity while ensuring data privacy.

HIDS monitor and analyze the activities within a single host or device. These systems use data, such as system logs, web browsing logs, and process logs, to detect irregularities that may indicate a security threat. For example, Zhu noted, a HIDS might detect malicious software that prompts a browser to download a harmful PDF file. By identifying such irregularities, the HIDS can prevent potential security breaches. Central management of these logs through managed security service providers (MSSPs) on cloud servers, however, could potentially breach user privacy. Enter: federated learning.

Zhuangdi Zhu, photo provided

In a federated learning system, each entity trains a local model on its own data and only shares the model updates with a central server. Local models are trained on individual datasets, and their findings are sent to a central server, Zhu explained. The server then aggregates these parameters to create a global model, which is subsequently sent back to the local models. This iterative process continues until a robust and accurate model is developed. Federated learning thus ensures sensitive data remains within the local models, addressing privacy concerns and complying with data regulations.

The project, funded by the Commonwealth Cyber Initiative, is set to span one year, starting in the summer of 2025. In addition to developing a federated learning framework for HIDS, the team plans to create an open-source dataset for intrusion detection, which will serve as a valuable resource for future research and development in this field.

Zhu's work on decentralized machine learning not only addresses critical privacy concerns but also paves the way for more secure and efficient threat detection systems. Her work on this project represents a significant step forward in the ongoing effort to protect digital environments from evolving cyber threats.

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Zhuangdi Zhu

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Using AI to uncover human smuggling networks

Teresa Donnellan — Wed, 15 Jan 2025 22:39:59 +0000

Using AI to uncover human smuggling networks Teresa Donnellan Wed, 01/15/2025 - 17:39

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Carlotta Domeniconi

Sean Luke

Guadalupe Correa-Cabrera

As vulnerable members of society, migrants are subject to exploitation. Professor Carlotta Domeniconi in the Department of Computer Science at George Mason �鶹�� is using artificial intelligence (AI) to combat that abuse. She is the principal investigator (PI) on a groundbreaking project aimed at understanding and modeling human smuggling networks.

Carlotta Domeniconi

In collaboration with the Department of Homeland Security’s Criminal Investigations and Network Analysis (CINA) Center for Excellence, Domeniconi and co-PIs Guadalupe Correa-Cabrera and Sean Luke are developing advanced machine learning techniques to analyze publicly available data and uncover the intricate workings of human smuggling networks between Mexico and the United States. While Domeniconi and Luke, both computer science faculty, bring expertise in machine learning, Correa-Cabrera is a professor in George Mason's Schar School of Policy and Government specializing in border studies, U.S.-Mexico relations, international security, migration studies, and illicit networks.

A significant challenge in this project is the sparsity and complexity of the data, said Domeniconi. Criminal networks are large, dynamic, and constantly evolving.

"The very nature of criminal networks—their secret nature—makes it practically impossible to gather useful data," she explained. This challenge led to the team’s innovative approach of using publicly available text data—past cases of prosecuted smugglers at both the state and federal levels in the United States as well as social media posts and news articles by and about smugglers. They are collecting data from the 1980s to 2024 to analyze the temporal dynamics of these networks.

“There are videos of smugglers actually showcasing people traveling across very difficult areas from Central America,” Domeniconi noted as an example. “You can extract a lot of information about the routes they take, where they cross the borders, and the means they use."

The team is using natural language processing (NLP) and deep learning models that are pre-trained on vast amounts of text data, enabling them to understand and extract meaningful information from the data they are collecting. Two doctoral students will support the co-PIs, one from computer science and the other from the Schar School. The team is working on automating the process of building and analyzing these criminal networks using large language models and graph mining techniques. By visualizing these networks as knowledge graphs, the team aims to mine behavioral patterns and understand how these networks adapt to changes in policy enforcement. Ultimately, this project could provide a methodology applicable to various criminal networks beyond human smuggling.

Guadalupe Correa-Cabrera

“The current project bridges the gap between machine learning and social science,” explained Correa-Cabrera. “We can replicate this same model and methodology to analyze legal cases of drug smuggling, money laundering, and arms trafficking, thereby modeling and explaining the evolution of these activities over the past few decades.”

Domeniconi's pioneering work in modeling human smuggling networks is a testament to the power of AI in addressing complex social issues. By merging domain expertise with state-of-the-art machine learning techniques, this project not only promises to unravel the clandestine operations of human smuggling but also to create a robust methodology applicable to various criminal networks. The insights gained from this research could significantly enhance national security measures, offering a strategic advantage in the fight against human smuggling and those who perpetrate this crime.

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Human Trafficking

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Aisha Behr leverages dual roles in industry and academia to advance deep learning

Teresa Donnellan — Wed, 08 Jan 2025 15:17:30 +0000

Aisha Behr leverages dual roles in industry and academia to advance deep learning Teresa Donnellan Wed, 01/08/2025 - 10:17

With degrees from George Mason �鶹�� in civil engineering, systems engineering, and earth systems and geoinformation sciences, alumna Aisha Behr’s path to becoming a leader in artificial intelligence (AI) and machine learning is as diverse as it is inspiring. Now a director for the AI and machine learning business units at CGI, Behr also serves as an adjunct professor in George Mason’s Department of Information Sciences and Technology (IST), teaching applied information technology (AIT) classes in machine learning and deep learning.

Behr started her career in government contracting, where she first encountered the burgeoning field of data science.

Her work required her to apply data science techniques to geospatial analysis, sparking a deep interest in the subject. While working full-time, Behr pursued a PhD in earth systems and geoinformation sciences at George Mason. Her dissertation, which aimed to improve the SVD algorithm with spatial statistics, highlighted her innovative approach to integrating machine learning with spatial data.

Aisha Behr. Photo provided

While balancing her professional career with her academic pursuits, Behr found a new calling in teaching, starting as an adjunct after completing her PhD in 2020. Her industry experience proves invaluable in the classroom, allowing her to offer students a unique perspective on the practical applications of their studies.

"I wanted to make sure that students had a really good process and experience through the whole course," said Behr. By providing code templates and practical assignments, she ensures that students can confidently apply what they learn in the AI and machine learning field.

Behr's teaching philosophy is centered around making complex concepts accessible and engaging. She developed the courses AIT 636 Interpretable Machine Learning and AIT 746 Applied Deep Learning to emphasize hands-on learning.

“Dr. Behr has been an exemplary adjunct in the IST department, playing a critical role in developing our new applied AI technologies concentration for the master’s in AIT program, as well as several graduate courses,” said Ioulia Rytikova, an IST professor and the director of the master’s program in AIT.

Behr is excited about the potential of generative AI and other emerging technologies. She will continue integrating such advancements into her teaching, possibly through guest lectures and hands-on labs.

"I want our students to be able to have exposure to these very, very new technologies," she emphasized, noting that she likes to demonstrate to students how keeping up with technology can give them a competitive edge in their careers.

"I enjoy learning new concepts,” said Behr. “Being able to show a student that you can learn and then accelerate your career is why I like it so much."

Behr’s journey is a powerful example of how passion, adaptability, and a commitment to education can lead to a fulfilling and impactful career. Her dedication to bridging industry and academia ensures that her students are well-prepared to navigate and excel in the ever-evolving field of AI and machine learning.

Learn more about Information Sciences and Technology at George Mason

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Student team wins first place, $60k from cyber warfare challenge

Teresa Donnellan — Fri, 01 Nov 2024 15:44:34 +0000

Student team wins first place, $60k from cyber warfare challenge Teresa Donnellan Fri, 11/01/2024 - 11:44

A student team from a research group in George Mason �鶹��'s Center for Secure Information Systems, led by Professor Kun Sun, won first place in the Naval Surface Warfare Center Dahlgren Division (NSWCDD) Cyber Resiliency and Measurement Challenge. The students were selected to compete in person from October 22–23 at the �鶹�� of Mary Washington’s Dahlgren Campus.

The team consisted of two undergraduates, Sam Best and Noah Korzak, majoring in computer science and cyber security engineering, respectively, and doctoral student Tommy Chin.

Samuel Best, Noah Korzak, and Tommy Chin at the NSWCDD Cyber Resilience and Measurement Challenge

“I thought the competition was a great opportunity to learn from,” said Best, adding, “Although the prize is a great plus, the skills I learned are far more valuable to my career.”

Sun reflected on the experience: "Over the past two months, it has been a valuable opportunity for our students to apply their knowledge from the classroom to tackle real-world cyber warfare challenges.”

“Eleven teams from colleges and universities across the nation competed in the challenge, aiming to address real-world cybersecurity problems faced by the Navy while gaining valuable knowledge and skills essential for tackling complex cyber threats in the future,” wrote Morgan Tabor of NSWCDD Corporate Communications.

“One of NSWCDD’s priorities is to develop innovative solutions that strengthen our nation’s cyber defenses and enhance system resiliency against cyber threats. By participating in this challenge, teams are helping us explore new ways to measure and improve tools that assess and improve cyber defensive capabilities,” said another NSWCDD representative.

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Military 5G gets a security boost with anti-jamming research

Teresa Donnellan — Mon, 21 Oct 2024 19:01:12 +0000

Military 5G gets a security boost with anti-jamming research Teresa Donnellan Mon, 10/21/2024 - 15:01

As communication networks evolve, they present both opportunities and security challenges to military technicians. Over the past year, Assistant Professor Moinul Hossain from George Mason �鶹��’s Department of Cyber Security Engineering has contributed to a research initiative aimed at ensuring the safety and integrity of 5G networks used by the United States military. Sponsored by the Army Research Laboratory (ARL) and led by the �鶹�� of Maryland (UMD), the project focused on safeguarding military communications against potential threats such as jamming attacks.

Hossain’s colleagues will present their work, a research paper titled “Phantom Guard: An MDP-Based Anti-Jamming Defense Strategy for Military 5G Networks,” at the end of this month at the Institute of Electrical and Electronics Engineers (IEEE) Military Communications Conference (MILCOM). As Hossain will be on leave during the conference, George Mason doctoral student Mahmudul Hassan Ashik, who contributed to the project, will join ARL’s Jeffrey Twigg and Fikadu Dagefu and UMD colleagues for the presentation.

Moinul Hossain. Photo provided

"As 5G technology becomes a cornerstone of global communications, safeguarding its security within military applications is crucial,” said Ashik, who is working on a PhD in cyber security engineering. “�鶹�� research tackles these complex challenges of protecting sensitive data transmitted over 5G networks in high-risk, adversarial environments."

While commercial 5G is already widely used for communication between devices, the military’s adoption of this technology necessitates additional measures to safeguard sensitive data and ensure secure communication, even in contested environments. Hossain’s project, which began in May 2023 and concluded in August 2024, centered on addressing this critical security concern.

"5G is a major pillar of national security, alongside emerging technologies like artificial intelligence and quantum computing,” Hossain explained. “Its high throughput and low latency make it ideal for various applications, including the military. However, military use of 5G requires certain modifications to meet security standards, particularly to defend against adversarial threats like jamming."

Jamming occurs when an adversary sends a disruptive signal to interfere with communication channels, making it difficult for a receiver to decode the intended message. While this threat is not normally a concern for commercial 5G users, it is a major risk in military settings, where communication must be maintained even in hostile environments.

The research team focused on developing anti-jamming strategies to ensure military communications networks could withstand such attacks. Hossain likened this issue to bad actors compromising a secure office building (say, a headquarters) by infiltrating a less-protected satellite office. In military applications, particularly those involving unmanned vehicles or robots, compromising such resource-constrained devices could result in the loss of critical information at headquarters or even control over sensitive devices.

As the military continues to integrate emerging technologies like 5G into its operations, safeguarding these networks from adversarial threats becomes increasingly important. The collaboration between George Mason, UMD, and ARL demonstrates the critical role academic research plays in fortifying national security.

At MILCOM, the team’s work will be shared with a wider audience of defense and communication experts, contributing to the ongoing efforts to secure military networks in a connected world.

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PhD student knows how to (neural) network

Teresa Donnellan — Tue, 15 Oct 2024 18:54:28 +0000

PhD student knows how to (neural) network Teresa Donnellan Tue, 10/15/2024 - 14:54

Electrical and computer engineering PhD student Shay Snyder is not only “a very capable student,” as his George Mason �鶹�� advisor Maryam Parsa said, but he is also a tenacious networker. Passionate for his subject and tireless in his pursuit of research opportunities, Snyder joined George Mason's Department of Electrical and Computer Engineering (ECE) and assisted in securing at least two years of funding for a project in Parsa’s lab.

Parsa’s research focuses on neuromorphic, or "brain-inspired," computing across various levels of the computing stack. The question is "how can neuroscience and biology help us redesign computers to be more capable, energy-efficient, and robust?" said Parsa, who is an assistant professor in ECE.

Although Snyder began his PhD in January 2023, he had been part of the Mason Nation for the prior year, working as an undergraduate research assistant for Parsa as he finished up a bachelor’s degree at East Tennessee State �鶹��.

Snyder was an effective networker even as a teenager. He attended a two-week summer camp at Oak Ridge National Laboratory as a junior in high school and was eager to return.

Shay Snyder. Photo provided by Maryam Parsa.

“I found out that they have an internship program for high schoolers, so my entire senior year of high school, I emailed and emailed, saying like, ‘Hey, I like this about your work, and I want to work with you," Snyder said.

He recalled, “I sent so many emails, I got two e-mail accounts blocked because they thought I was spamming them, but luckily I heard back … so a week after I graduated from high school, I got to go to work at Oak Ridge for a summer, and that kind of started everything.”

He added, “It was a miracle.”

The internship was an eye-opening experience for Snyder. “Where I grew up in East Tennessee was much more remote," he said. "There are not a lot of technology jobs, and coming from my high school, I think over 50% of the kids were below the poverty line. So getting to see that was a whole new world for me, and I was determined that I wanted to continue doing work like they do at Oak Ridge.”

After that initial internship, Snyder continued reaching out to scientists he had met or learned about at Oak Ridge. He attended Lunch and Learn events for undergraduate students, at one of which he met Catherine Schuman, who introduced Snyder to the concept of neuromorphic computing.

“I was super inspired,” he said. He followed up with Schuman and was offered a yearlong position at Oak Ridge doing autonomous vehicle research for General Motors. He took his undergraduate classes part-time and worked remotely to leverage this opportunity.

Snyder first encountered Parsa when she gave a presentation at the International Conference on Neuromorphic Systems. She was then a post-doc also working at Oak Ridge National Laboratory with Schuman. Snyder contacted her, and the two stayed in touch. After joining George Mason, Parsa hired Snyder remotely as an undergraduate research assistant while he finished his undergraduate degree. Meanwhile, Snyder also started a part-time internship at the Pacific Northwest National Laboratory.)

“[Snyder] was still an undergraduate, and he worked on an area that was completely different from his background,” said Parsa. “The project was on neuromorphic computing, but it involved learning multiple new areas including Bayesian learning and Physics-Informed Neural Networks (PINN). He quickly learned the necessary background and developed Lava Bayesian Optimization that later merged with the Intel’s neuromorphic optimization framework, Lava-Optimization.”

The work has been published in multiple conference papers at the International Conference on Neuromorphic Computing Systems (ICONS), the Great Lakes Symposium on Very Large Scale Integration (GLSVLSI), and the International Conference on Machine Learning and Applications (ICMLA), and Snyder’s contribution is publicly available in Intel’s open-source Lava framework.

Two years ago, after Parsa circulated a call for proposals from the Army Ground Vehicle Research Center with her lab, Snyder approached her with an idea involving hyperdimensional computing. She encouraged him to write a draft. The two refined the idea and submitted a white paper on the topic. They were then invited to submit a full proposal, which was accepted for funding. So, the same semester that Snyder joined Parsa’s lab as a PhD student, the lab also began to receive funding for a project based on Snyder’s idea.

Maryam Parsa and lab members. Photo provided by Maryam Parsa.

Since Spring 2023, Snyder has become a full PhD student at George Mason and remains an important part of Parsa’s lab. Snyder has taken lead on multiple projects, academic, industry and government collaborations, and published multiple papers. He has also been mentoring not only high school interns and undergraduate students in Parsa’s lab, but also other PhD students. Since Snyder joined Parsa’s lab, he has been the first author of five published conference papers in top artificial intelligence and neuromorphic venues and coauthor in one published journal paper and six published conference papers.

Parsa deeply appreciates having Snyder in her lab, not only for his technical and professional skills but also for the positive energy he brings to the team. She highlights how Snyder's can-do attitude, curiosity, and collaborative nature have been instrumental to the success of numerous projects. His peers frequently rely on him, valuing his contributions and leadership in driving projects forward. Parsa regards working with Snyder as a true pleasure, given the impact he has made inside and outside in the lab.

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