Talk: AI and the Metabolism of Knowledge, 2/12 12-1pm ET Wednesday, February 12, 2025, online Vince Kellen (chief information officer for the University of California, San Diego), will apply a metaphor taken from microbiology, namely metabolism, and examine how knowledge management and AI are combining in new ways. He will discuss the practical implications for managing knowledge in this new world, including managing intellectual property protection, privacy, safety, quality control, agents, regulation, and most importantly, linkages to measurable outcomes and productivity gains. 

Video of the talk

This talk is part of the AI in Practice webinar series, a collaboration between the University of Baltimore, Johns Hopkins, and UMBC. Discover how generative AI is transforming higher education and workforce preparation in this engaging three-part series, scheduled for Feb. 12, March 12, and April 9. Each session explores innovative ways to enhance teaching and learning, equips students with essential AI skills, and provides opportunities for interactive discussions with leading AI experts.

Register

  • HERE for an online Zoom link
  • HERE for to join an on-campus viewing with lunch in Engineering 102 at UMBC

UMBC Center for AI
]]> Vince Kellen (chief information officer for the University of California, San Diego), will apply a metaphor taken from microbiology, namely metabolism, and examine how knowledge management and AI... https://my3.my.umbc.edu/groups/instructional-technology/events/137594 https://dev.my.umbc.edu/api/v0/pixel/news/146965/guest@my.umbc.edu/7e27948be14be533ba3f6217669175b1/api/pixel ai seminar UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC AI https://assets2-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/xxlarge.jpg?1738552092 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/xlarge.jpg?1738552092 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/large.jpg?1738552092 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/medium.jpg?1738552092 https://assets1-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/small.jpg?1738552092 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/xsmall.jpg?1738552092 https://assets1-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/965/73809292a63da700ee70a84095c0b0f6/xxsmall.jpg?1738552092 0 0 true Mon, 03 Feb 2025 09:18:04 -0500 Thu, 13 Feb 2025 17:54:22 -0500 Talk: Machine learning for scientific computing, Felix Ye, 2/3 12-1pm EST Mon., Feb. 3, 2025, 409 Sondheim Hall, UMBC
UMBC Joint Statistics and Applied Mathematics Colloquium

Machine Learning for Scientific Computing 

Felix Ye, SUNY Albany

12-1pm EST Monday, Feb. 3, 2025
409 Sondheim Hall, UMBC

The emerging use of data-driven and machine learning methods is revolutionizing problem-solving in science and engineering, addressing complex and high-dimensional challenges that traditional methods often struggle to tackle. Scientific machine learning is rapidly evolving into a major field within scientific computing. In this talk, I will present two examples where machine learning methods have been extensively developed as numerical tools to solve real-world problems.

 In the first part, I will introduce a nonlinear stochastic model reduction technique for high-dimensional stochastic dynamical systems that have a low-dimensional invariant effective manifold with slow dynamics and high-dimensional, large fast modes. Given only access to a black-box simulator from which short bursts of simulation can be obtained, we design an algorithm that outputs an estimate of the invariant manifold, a process of the effective stochastic dynamics on it, which has averaged out the fast modes, and a simulator thereof. This simulator is efficient in that it exploits of the low dimension of the invariant manifold, and takes time-steps of size dependent on the regularity of the effective process, and therefore typically much larger than that of the original simulator, which had to resolve the fast modes. The algorithm and the estimation can be performed on the fly, leading to efficient exploration of the effective state space, without losing consistency with the underlying dynamics. 

The second part focuses on optimal transport (OT), a powerful framework for comparing probability distributions. Applications such as shuffled regression can be approached by optimizing regularized optimal transport (OT) distances, such as the entropic OT and Sinkhorn distances. A common approach for this optimization is to use a first-order optimizer, which requires the gradient of the OT distance. For faster convergence, one might also resort to a second-order optimizer, which additionally requires the Hessian. The computations of these derivatives are crucial for efficient and accurate optimization. However, they present significant challenges in terms of memory consumption and numerical instability, especially for large datasets and small regularization strengths. We circumvent these issues by analytically computing the gradients for OT distances and the Hessian for the entropic OT distance, which was not previously used due to intricate tensor-wise calculations and the complex dependency on parameters within the bi-level loss function. Through analytical derivation and spectral analysis, we identify and resolve the numerical instability caused by the singularity and ill-posedness of a key linear system. Consequently, we achieve scalable and stable computation of the Hessian, enabling the implementation of the stochastic gradient descent (SGD)-Newton methods. 

Felix Ye is a Assistant Professor in the Department of Mathematics and Statistics at SUNY Albany. His research interest is the intersection of machine learning and dynamical systems and is directed toward data-driven model reduction methods in the context of stochastic dynamical systems. He was a Postdoctoral Fellow at Johns Hopkins University and received a PhD in applied math from the University of Washington, advised by Hong Qian.

UMBC Center for AI ]]> UMBC Joint Statistics and Applied Mathematics Colloquium Machine Learning for Scientific Computing  Felix Ye, SUNY Albany 12-1pm EST Monday, Feb. 3, 2025 409 Sondheim Hall, UMBC The... https://my3.my.umbc.edu/groups/mathweb/events/137447 https://dev.my.umbc.edu/api/v0/pixel/news/146922/guest@my.umbc.edu/5847b1faa79cb58fb20d205ea5a73b29/api/pixel ai machine-learning ml talk UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC Department of Mathematics and Statistics 0 0 true Thu, 30 Jan 2025 17:50:19 -0500 Thu, 30 Jan 2025 18:12:41 -0500 Talk: Do LLMs Exhibit Cybersecurity Misconceptions? 1/31 online Evaluation of LLMs on Cybersecurity Concept Inventories

Do LLMs Show Cybersecurity Misconceptions?

Evaluation of LLMs Performance on Cybersecurity Concept Inventories
Shan Huang, UIUC
Joint work with Jeffrey Herman and Alan Sherman, et al.
12:00–1pm ET Friday, Jan. 31, 2025, online 

We evaluated the performance of five LLMs (Llama a, GPT-3.5-turbo, GPT-4, GPT-4O, and GPT-O1) on two cybersecurity concept inventories: Cybersecurity Concept Inventory (CCI) and Cybersecurity Curriculum Assessment (CCA). Using a zero-shot setting to minimize external influencing factors, we compared the performance of these LLMs with that of students previously studied, and we conducted a qualitative analysis of GPT-O1's output to examine if it exhibits misconceptions. Quantitative analysis reveals that, for the CCI and CCA, GPT-O1 significantly outperformed other models and students, correctly answering 92% of CCI and 72% of CCA test items. These results indicate GPT-O1’s strong proficiency in foundational topics (CCI) but reveal its limitations in addressing these concepts in more technically advanced scenarios (CCA). Qualitative analysis of GPT-O1’s reasoning patterns uncovered instances of insightful reasoning but also highlighted ways in which GPT-O1's answers reflect persistent student mistakes, such as biases, overgeneralizations, and logical inconsistencies. This work highlights the significant potential of GPT-O1 as a tool for introductory cybersecurity education in its ability to provide detailed explanations and structured reasoning for novice learners.

Shan Huang is a Ph.D. candidate in Computer Science at the University of Illinois Urbana-Champaign. She is broadly interested in how educational games can improve student learning. Current work includes improving student learning in cybersecurity with educational games and accessing student knowledge of cybersecurity concepts. Shan is also involved in various educational data mining projects.

UMBC Center for AI ]]> Do LLMs Show Cybersecurity Misconceptions? Evaluation of LLMs Performance on Cybersecurity Concept Inventories Shan Huang, UIUC Joint work with Jeffrey Herman and Alan Sherman, et al.... https://dev.my.umbc.edu/api/v0/pixel/news/146793/guest@my.umbc.edu/1ca42e886baeb4a9edbebb1da8e736fc/api/pixel ai cca cci cybersecurity llm UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC Cyber Defense Lab https://assets1-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/xxlarge.jpg?1738081136 https://assets2-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/xlarge.jpg?1738081136 https://assets2-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/large.jpg?1738081136 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/medium.jpg?1738081136 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/small.jpg?1738081136 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/xsmall.jpg?1738081136 https://assets2-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/793/f8af6870dd1080f5b47500ad019d548b/xxsmall.jpg?1738081136 0 0 true Tue, 28 Jan 2025 11:53:43 -0500 Naghmeh Karimi funded to study computing-in-memory AI accelerators

Naghmeh Karimi, an associate professor in the Department of Computer Science and Electrical Engineering, was recently granted more than $300,000 in funding from the Semiconductor Research Corporation (SRC) to study the security of promising hardware components that speed up the computing process. 

SRC brings together technology companies, academics, and government agencies to tackle large scientific and technical challenges, and Karimi’s research will be funded by three leading technology companies: IBM-Research, AMD, and Siemens. 

Karimi and her team, including graduate students and a collaborator from Arizona State University, will study computer chips whose design and structure allows computing-in-memory (CiM), where data processing happens directly within the computer’s memory. CiM architectures are promising for speeding up the use of machine learning algorithms because they consume less energy.

Different types of CiM devices (such as RRAM, MRAM, and SRAM) each have their own strengths and weaknesses in terms of performance, power use, and size, and to get the best results, engineers need to combine different CiM devices into one system. Building these systems in 3D layers can further improve their efficiency and performance. However, the security of these 3D architectures has received little attention to date. 

Schematic shows layers of computing elements.Karimi and her team will study the security of computing-in-memory architectures, as shown in this project overview. (Image courtesy of Karimi)


Karimi’s team will study the security of 3D CiM technologies used in AI applications. In particular, the research will focus on evaluating the security vulnerabilities of the technologies and developing mitigation strategies. 

“I’m excited about this project because the topic is very timely,” says Karimi. “The support from three leading companies in the AI field shows the importance of the problem and the promise of the solutions we are working on.”

The team aims to enhance the security of CiM-based AI accelerators against physical attacks that adversaries might launch to leak sensitive data or induce malfunctions. The researchers will work closely with the funding companies over the next three years in this area.

This post was written by Catherine Meyers and originally published online here.

UMBC Center for AI ]]> Naghmeh Karimi, an associate professor in the Department of Computer Science and Electrical Engineering, was recently granted more than $300,000 in funding from the Semiconductor Research... https://umbc.edu/quick-posts/naghmeh-karimi-ai-accelerators/ https://dev.my.umbc.edu/api/v0/pixel/news/146738/guest@my.umbc.edu/97c10bda2cac5558508bba82692d4854/api/pixel ai cim computing-in-memory funding research UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC AI 0 0 true Sun, 26 Jan 2025 18:33:37 -0500 Talk: Securing Networks with Reinforcement Learning & Game Theory 10-11am Thursday, Jan. 30, 2025; ITE459 and online

Securing Distributed Networks: Leveraging Reinforcement Learning and Game Theory for Attack Detection and Mitigation

Dr. Md Tariqul Islam, Syracuse University

10-11am January 30, 2025;  ITE 459, UMBC and online


Reinforcement learning (RL) has demonstrated remarkable success across diverse domains, from mastering complex games to optimizing real-time feedback systems in robotics and industrial control. However, its potential in cybersecurity, particularly for autonomous attack detection and mitigation in distributed systems, remains largely underexplored. Traditional single-agent RL approaches struggle in decentralized environments where multiple entities make independent decisions, necessitating multi-agent reinforcement learning (MARL). Our research explores blockchain networks as an ideal test case due to their decentralized architecture and trustless consensus mechanisms. We developed a novel MARL-based consensus mechanism for Proof-of-Stake blockchains, enabling nodes to collaboratively identify and penalize malicious behavior while preserving decentralization. This approach effectively mitigated six major blockchain attack types with minimal computational overhead. Building on these results, we propose integrating game-theoretic principles into the MARL framework to model adversarial strategies and enhance system resilience. The synergy between reinforcement learning and game theory establishes a robust foundation for dynamic and adaptive security in distributed systems, effectively addressing current vulnerabilities while anticipating and countering future threats. This integrated approach enables the design of resilient, scalable defense mechanisms tailored to the complex dynamics of decentralized architectures.


Dr. Md Tariqul Islam is an Assistant Professor of Trustworthy Cyberspace in the School of Information Studies (iSchool) at Syracuse University. His research focuses on advancing the security, efficiency, and fault tolerance of networks and distributed systems, particularly in the domains of cloud and blockchain technologies.

UMBC Center for AI
]]> Securing Distributed Networks: Leveraging Reinforcement Learning and Game Theory for Attack Detection and Mitigation Dr. Md Tariqul Islam, Syracuse University 10-11am January 30, 2025;  ITE 459,... https://informationsystems.umbc.edu/home/calendar/events/ https://dev.my.umbc.edu/api/v0/pixel/news/146680/guest@my.umbc.edu/bf62a2f97b078a9fbd21ce8220294317/api/pixel agent ai cybersecurity game-theory machine-learning multiagent reinforcement-learning UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC AI 0 0 true Thu, 23 Jan 2025 10:32:43 -0500 2024 Gartner Hype Cycle for AI

In November 2024, the Gartner company published instances of its popular hype cycles on AI in general and the narrower topic of generative AI. While generative AI is still a dominating technology, Gartner identifies AI engineering and knowledge graphs as the two biggest movers in their 2024 AI Hype Cycle. AI Engineering was placed at the “peak of inflated expectations”, and knowledge graphs on the cycle’s “slope of enlightenment”.
Gartner defines AI engineering as

"AI engineering unites various disciplines from across the organization to tame the AI hype while providing a clearer path to value when operationalizing the combination of multiple AI techniques (including GenAI models). It also includes responsible AI, dealing with risk, trust, transparency, ethics, fairness, and accountability." 

and knowledge graphs as

"Knowledge graphs are machine-readable representations of the physical and digital worlds. They capture information in a visually intuitive format, yet are still able to represent complex relationships. More importantly, they provide dependable logic and explainable reasoning (as opposed to GenAI’s fallible but powerful predictive capabilities)."

UMBC Center for AI ]]> In November 2024, the Gartner company published instances of its popular hype cycles on AI in general and the narrower topic of generative AI. While generative AI is still a dominating technology,... https://www.gartner.com/en/articles/hype-cycle-for-artificial-intelligence https://dev.my.umbc.edu/api/v0/pixel/news/146565/guest@my.umbc.edu/54937961f9343d0717ecf7e816a15f99/api/pixel ai ai-engineering hype-cycle knowledge-graph UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC AI 0 0 true Tue, 14 Jan 2025 18:59:40 -0500 Wed, 15 Jan 2025 06:21:22 -0500 Ramana Vinjamuri co-authors book on embedded systems and robotics A practical guide for undergraduate and graduate students

Introduction to Embedded Systems and Robotics, A Practical Guide

Nayan Kakoty, Rupam Goswami and Ramana Vinjamuri


UMBC CSEE professor Ramana Vinjamuri is one of the authors of a new book, Introduction to Embedded Systems and Robotics, published by Springer in December 2024. It is a technical guide to the fundamentals of embedded systems and robotics and their application to practical problems.

The book is designed to be appropriate for both undergraduate and graduate students and serve as a practical guide for doing robotics projects in their final semesters. It is written to cover the “ABC of embedded systems and robotics: A for acquiring the concepts, B for building robotic systems, and C for creating solutions.” It:

  • Includes solved problems at the end of each chapter
  • Features projects, such as developing a robotic hand and developing a biomimetic prosthetic finger
  • Introduces robotics to undergraduate and graduate scholars using plain language and with the aid of practical problems
UMBC Center for AI ]]> Introduction to Embedded Systems and Robotics, A Practical Guide Nayan Kakoty, Rupam Goswami and Ramana Vinjamuri UMBC CSEE professor Ramana Vinjamuri is one of the authors of a new book,... https://link.springer.com/book/10.1007/978-3-031-73098-6 https://dev.my.umbc.edu/api/v0/pixel/news/146529/guest@my.umbc.edu/10e557a09afdfe7ed78aa5a16db38887/api/pixel ai book robotics UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC AI https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/xxlarge.jpg?1736722004 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/xlarge.jpg?1736722004 https://assets2-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/large.jpg?1736722004 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/medium.jpg?1736722004 https://assets2-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/small.jpg?1736722004 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/xsmall.jpg?1736722004 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/529/a21fd7ab3caad45d0d3f30841bbb757a/xxsmall.jpg?1736722004 0 0 true Sun, 12 Jan 2025 18:09:52 -0500 AI in the UMBC Classroom, 12-1 Jan. 15 UMBC Winter Session Beyond the Classroom online series
There's a lot of talk about AI in the classroom, but what do we mean by that? What is the "AI" we are talking about? How and when might we use AI to enhance our teaching and learning while keeping it from replacing us as teachers and students? Join Dr. Tanya Olson for a discussion and a look towards the future. 

The session will be online from 12-1pm ET, on Wednesday, January 15, 2025. Use this link to join the online session. 

Tanya Olson lives in Silver Spring, Maryland and is a Senior Lecturer in English at UMBC. Olson holds an M.A. in Anglo-Irish Literature from University College, Dublin and the Ph.D. in 20th-Century British Literature from the University of North Carolina-Greensboro. She has taught first-year composition, technical communication, and creative nonfiction classes in the North Carolina Community College system and University System of Maryland and currently serves as the director of WARD (Writing and Rhetoric Division) at UMBC.

UMBC Center for AI ]]> There's a lot of talk about AI in the classroom, but what do we mean by that? What is the "AI" we are talking about? How and when might we use AI to enhance our teaching and learning while keeping... https://my3.my.umbc.edu/groups/summerspecialprograms/events/137013 https://dev.my.umbc.edu/api/v0/pixel/news/146520/guest@my.umbc.edu/f8d740cb379c2565c298953590b6b7ea/api/pixel ai classroon learning teaching umbc UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 Winter Session: Beyond the Classroom https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/xxlarge.jpg?1736546734 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/xlarge.jpg?1736546734 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/large.jpg?1736546734 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/medium.jpg?1736546734 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/small.jpg?1736546734 https://assets1-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/xsmall.jpg?1736546734 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/520/d56b596870b749fc123f8c0d759753ba/xxsmall.jpg?1736546734 0 0 true Fri, 10 Jan 2025 17:17:31 -0500 Fri, 10 Jan 2025 17:52:46 -0500 Benchmarks that have been killed by LLM based systems

Killed by LLM is a project that documents public AI benchmarks that LLM-based AI systems have largely solved since 2018.  Getting killed means that a benchmark no longer measures the frontier of AI technology as a challenge asking "Can AI do X?", but might still be a useful tool. Links to papers documenting fallen benchmarks are provided.

The project is on GitHub, and other people are invited to contribute new benchmarks that have been overcome.

UMBC Center for AI ]]> Killed by LLM is a project that documents public AI benchmarks that LLM-based AI systems have largely solved since 2018.  Getting killed means that a benchmark no longer measures the frontier of... https://r0bk.github.io/killedbyllm/ https://dev.my.umbc.edu/api/v0/pixel/news/146462/guest@my.umbc.edu/ca6832dc7212f2aa8d1e3e650c26f51c/api/pixel ai benchmark llm UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC AI 0 0 true Tue, 07 Jan 2025 09:18:50 -0500 Tue, 07 Jan 2025 09:20:46 -0500 Blackboard Ultra's AI Course Design Assistant Tool, 12-1pm January 8 AI-inspired assessment, question banks, journal, rubrics

Have you wondered how AI technology could transform your course preparation? Blackboard AI Course Design Assistant Tools are available for instructors in Ultra courses. In a 30-minute online quick start session, discover how you can leverage integrated AI tools to create engaging learning experiences in Ultra courses. Instructional Technology staff invites faculty to preview a suite of new AI Course Design Assistant features designed to inspire and assist faculty in streamlining the course development process. Stay after for Q&A.

This session will be facilitated by instructional technology specialist Ben Amudzi and has the following objectives.

Register here for this online event.


UMBC Center for AI ]]> Have you wondered how AI technology could transform your course preparation? Blackboard AI Course Design Assistant Tools are available for instructors in Ultra courses. In a 30-minute online quick... https://my3.my.umbc.edu/groups/instructional-technology/events/136590 https://dev.my.umbc.edu/api/v0/pixel/news/146429/guest@my.umbc.edu/a76851726df7c7a2b1799e0bd3fed867/api/pixel ai blackboard teaching UMBC AI https://dev.my.umbc.edu/groups/umbc-ai https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets2-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/original.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xlarge.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/large.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/medium.png?1691095779 https://assets3-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/small.png?1691095779 https://assets4-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xsmall.png?1691095779 https://assets1-dev.my.umbc.edu/system/shared/avatars/groups/000/002/081/cfb27ebe008c2636486089a759ea5c36/xxsmall.png?1691095779 UMBC Instructional Technology https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/xxlarge.jpg?1735685655 https://assets4-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/xlarge.jpg?1735685655 https://assets1-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/large.jpg?1735685655 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/medium.jpg?1735685655 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/small.jpg?1735685655 https://assets1-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/xsmall.jpg?1735685655 https://assets3-dev.my.umbc.edu/system/shared/thumbnails/news/000/146/429/683840ce799a6dc579d001382ffc30b1/xxsmall.jpg?1735685655 0 0 true Tue, 31 Dec 2024 18:04:32 -0500 Wed, 01 Jan 2025 08:13:20 -0500