5th Annual Major Event
Hosted by Office for Academic and Pre-Professional Advising
The Office for Academic and Pre-Professional Advising will be hosting its 5th Annual Major Event on Wednesday, November 16 from 10 am to 3:30 pm on the 3rd floor of the University Center. The event encompasses a series of programs designed to enlighten students on the variety of academic programs, professional pathways, and co-curricular opportunities available at UMBC. Not to mention, there will be free food and T-shirts at all events!
Make sure you stop by and gain some valuable information!!
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The Office for Academic and Pre-Professional Advising will be hosting its 5th Annual Major Event on Wednesday, November 16 from 10 am to 3:30 pm on the 3rd floor of the University Center. The...
Flyer
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Commuter Connections
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Off-Campus Student Services
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Wed, 16 Nov 2022 09:57:54 -0500
We will host our SQL workshop in ITE 237 from 12-1PM. We strongly recommend that you bring a device to practice scripting with us! We hope to see you there!!
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We will host our SQL workshop in ITE 237 from 12-1PM. We strongly recommend that you bring a device to practice scripting with us! We hope to see you there!!
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Information Systems Security Association, UMBC Chapter
https://my3.my.umbc.edu/groups/issa
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Information Systems Security Association, UMBC Chapter
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Tue, 15 Nov 2022 14:32:13 -0500
Talk: Can we ever trust our chatbots?
Research towards trustable collaborative assistants
UMBC Ebiquity Research Lab presents
Can we ever trust our chatbots?
Towards trustable collaborative assistants
Professor and AAAS Leshner Fellow
Artificial Intelligence Institute, University of South Carolina
3-4pm ET Wed, 16 Nov. 2022, ITE 325B and Webex
AI services are known to have unstable behavior when subjected to changes in data, models or users, especially in sustainable and societal applications. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, limits the transparency, explainability, safety, and adoption of AI. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). In this talk, I will cover chatbots (collaborative assistants), the problem of trust in this context and how one may make them more trustable. I will cover software testing, AI robustness, randomized control trial and the idea of rating AI based on their behavior. I will highlight some of our work, present key results and discuss ongoing research
Biplav Srivastava is a Professor of Computer Science at the AI Institute at the University of South Carolina. Previously, he was at IBM for nearly two decades in the roles of a Research Scientist, Distinguished Data Scientist and Master Inventor. Biplav is an ACM Distinguished Scientist, AAAI Senior Member, IEEE Senior Member and AAAS Leshner Fellow for Public Engagement on AI (2020-2021). His focus is on promoting goal-oriented, ethical, human-machine collaboration via natural interfaces using domain and user models, learning and planning. Biplav has been working in AI trust for the last 3 years pursuing ideas in AI testing, rating, randomized control and adversarial learning. He applies these techniques in areas of social as well as commercial relevance with particular attention to issues of developing countries (e.g., transportation, water, health and governance). Biplav's work has led to many science firsts and high-impact commercial innovations ($B+), 190+ papers and 60+ US patents issued, and awards for papers, demos and hacks. He has interacted with commercial customers, universities and governments, been on multilateral bodies, and assisted business leaders on technical issues.
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UMBC Ebiquity Research Lab presents Can we ever trust our chatbots? Towards trustable collaborative assistants Dr. Biplav Srivastava Professor and AAAS Leshner Fellow Artificial...
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ai
chatbot
nlp
research
talk
Computer Science and Electrical Engineering
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Computer Science and Electrical Engineering
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Mon, 14 Nov 2022 14:31:28 -0500
Workshop Wednesday with i3b: Inclusive Hiring in DoSA
Workshop Wednesdays w/ i3b are monthly opportunities for faculty, staff, graduate students and adult learners to engage around social justice topics that can support their personal growth and professional development. All sessions are interactive, and designed for skill-building and practice.
Session Focus: Inclusive Hiring in DoSA - This session is specifically designed for search committee members and search committee chairs. We will talk through the Inclusive Hiring toolkit and expectations for Divisional searches. We will also explore the impact of bias on hiring processes and how to mitigate bias impact.
Please RSVP below.
Our i3b staff strives to create the most inclusive environment possible. All undergrads, grads, staff, faculty, and alumnx are encouraged to attend! If you need to request a disability-based accommodation or have any questions? Contact us at i3b@umbc.edu. For our uploaded event recordings, attendee lists, works cited, and other materials, please check out our Initiatives for Identity, Inclusion and Belonging (i3b) Events Drive on our Linktree. Materials will be available in this Google Drive within one business week after the event unless otherwise specified.
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Workshop Wednesdays w/ i3b are monthly opportunities for faculty, staff, graduate students and adult learners to engage around social justice topics that can support their personal growth and...
http://i3b.umbc.edu
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i3b
retrievernation
training
umbc
workshopwednesday
The Mosaic: Center for Cultural Diversity
https://my3.my.umbc.edu/groups/themosaic
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Initiatives for Identity, Inclusion & Belonging (i3b)
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Mon, 14 Nov 2022 09:06:11 -0500
PhD Defense: Backdoor Attacks in Computer Vision
Towards Adversarially Robust Machine Learning Models
PhD Dissertation Defense
Backdoor Attacks in Computer Vision: Towards Adversarially Robust Machine Learning Models
12-2 ET Friday, 18 November 2022, WebEx
Committee: Drs. Hamed Pirsiavash (Advisor/Co-Chair), Anupam
Joshi (Chair), Tim Oates, Tom Goldstein (UMCP), Dr. Pin-Yu Chen (IBM)
Deep Neural Networks (DNNs) have become the standard building block in numerous machine learning applications, including computer vision, speech recognition, machine translation, and robotic manipulation, achieving state-of-the-art performance on complex tasks. The widespread success of these networks has driven their deployment in sensitive domains like health care, finance, autonomous driving, and defense-related applications.
However, DNNs are vulnerable to adversarial attacks. An adversary is a person with malicious intent whose goal is to disrupt the normal functioning of a machine learning pipeline. Research has shown that an adversary can tamper with the training process of a model by injecting misrepresentative data (poisons) into the training set. Moreover, if provided control over the training process as a third party, they can deliver a model to a victim, which deviates from normal behavior. These are called backdoor attacks. The manipulation is done in a way that the victim's model will malfunction only when a trigger is pasted on a test input. For instance, a backdoored model in a self-driving car might work accurately for days before it suddenly fails to detect a pedestrian when the adversary decides to exploit the backdoor. Vulnerability to backdoor attacks is dangerous when deep learning models are deployed in safety-critical applications.
This dissertation studies ways in which state-of-the-art deep learning methods for computer vision are vulnerable to backdoor attacks and proposes defense methods to remedy the vulnerabilities. We push the limits of our current understanding of backdoors and address the following research questions.
Can we design practical backdoor attacks? We propose Hidden Trigger Backdoor Attack - a novel clean-label backdoor attack where the poisoned images do not contain a visible trigger. This enables the attacker to keep the trigger hidden until its use at test-time. We believe such practical attacks reveal an important vulnerability of deep learning models. These flaws need to be studied extensively before deploying models in critical real-world applications.
Is it secure to train models on large-scale public data? Self-supervised learning (SSL) methods for vision have utilized large-scale unlabeled data to learn rich visual representations. These methods use public images downloaded from the web, e.g., Instagram-1B and Flickr image datasets. We show that if a small part of the unlabeled training data is poisoned, SSL methods are vulnerable to backdoor attacks. Backdoor attacks are more practical in self-supervised learning, since the use of large unlabeled data makes data inspection to remove poisons prohibitive. Hence, using large and diverse data to remove data biases and reduce labeling costs might unknowingly set up avenues for adversarial manipulation. Practitioners must make informed choices when choosing training data for machine learning models.
Can we design efficient and generalizable backdoor detection methods? Methods to detect backdoors in trained models have relied on analysis of model behavior on carefully chosen inputs. But often, this analysis has to be repeated for a new model. We propose a backdoor detection method that optimizes for a set of images which, when forwarded through any model, indicates successfully whether the model contains a backdoor. Our "litmus" test for backdoored models improves on state-of-the-art methods without requiring access to clean data during detection. It is computationally efficient and generalizes to new triggers as well as new architectures. We effectively detect backdoor attacks on thousands of networks with different architectures trained on four benchmark datasets, namely the German Traffic Sign Recognition Benchmark (GTSRB), MNIST, CIFAR10, and Tiny-ImageNet. This will serve as a benchmark for future research.
Do architectural design choices contribute to backdoor robustness? We find an intriguing difference between Vision Transformers and Convolutional Nets; interpretation algorithms effectively highlight the backdoor trigger on test images for transformers but not for CNNs when attacked with Hidden Trigger Backdoor Attacks. Based on this observation, we find that a test-time image blocking defense reduces attack success rate by a large margin for transformers. We show that such blocking mechanisms can be incorporated during the training process to improve robustness even further. We believe our findings will encourage the community to make better design choices in developing novel architectures robust to backdoor attacks.
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PhD Dissertation Defense Backdoor Attacks in Computer Vision: Towards Adversarially Robust Machine Learning Models PhD Candidate: Aniruddha Saha (Computer Science) 12-2 ET Friday, 18...
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ai
defense
phd
Computer Science and Electrical Engineering
https://my3.my.umbc.edu/groups/csee
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Computer Science and Electrical Engineering
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0
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Fri, 11 Nov 2022 17:08:36 -0500
Talk: Myths in Cybersecurity: Pros & Cons of Analogies
Avoiding the hazards and pitfalls that derail us
The UMBC Cyber Defense Lab presents
Myths in Cybersecurity: Pros and Cons of Analogies
Dr. Josiah Dykstra
Technical Fellow, National Security Agency
12-1 pm, Friday, 18 November 2022 via WebEx
Joint work with Dr. Eugene H. Spafford and Dr. Leigh Metcalf
Cybersecurity is rich with analogies, from keys and locks to Trojan horses. We look for the "needle in the haystack" and "evict malware resident on our systems." We debate "baked-in" versus "bolted-on" security. We do not mean all these things literally, of course. The language and analogies we use in this field are borrowed from many different domains. Analogies can help explain basic cybersecurity concepts, but too often they omit or overgeneralize important details. They can mislead, sometimes deliberately, because the experience they purport to connect might be out of proportion. Despite their shortcoming and imprecision, using an analogy or an abstraction might be helpful in appropriate situations. Using analogies, abstractions, and metaphors shapes technology's development, practice, and policies. The analogies are more than simple figures of speech. They have a normative dimension; sometimes, they can be used to help the imaginary shape reality.
This talk explores the use and misuse of analogies and metaphors across cybersecurity. We consider analogies from the physical world, medicine and biology, war and military, and law before discussing tips for avoiding pitfalls in using analogies and metaphors. This talk is adapted from material in the forthcoming book
Cybersecurity Myths and Misconceptions: Avoiding the Hazards and Pitfalls That Derail Us by Drs. Spafford, Metcalf, and Dykstra. The book presents 175+ common myths and misconceptions held by users, leaders, and cybersecurity professionals, along with tips for how to avoid them.
Josiah Dykstra is a Technical Fellow in the Cybersecurity Collaboration Center at the National Security Agency (NSA) and the owner of Designer Security, LLC. In 2013, he earned his PhD in computer science from UMBC studying cloud forensics with Dr. Sherman. He is interested in cybersecurity science, especially where humans intersect with technology. He has studied stress in hacking, action bias in incident response, and economics of cyber threat intelligence. Dr. Dykstra is a frequent author and speaker, including at the Black Hat and RSA Conference. He received the CyberCorps Scholarship for Service (SFS) fellowship and is one of seven people in the SFS Hall of Fame. In 2017, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) from then President Barack Obama. Dr. Dykstra is a Fellow of the American Academy of Forensic Sciences and a Distinguished Member of the Association for Computing Machinery (ACM). He is the author of numerous research papers and two books.
Host: Alan T. Sherman,
sherman@umbc.edu. 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 12-1pm. All meetings are open to the public. Upcoming CDL meetings: Dec 2, Peter Peterson (UMN Duluth), Adversarial Thinking Dec 20, 10:30am-12:30pm, 3 In-Person presentations from the UMBC fall 2022 cybersecurity research class, location TBA SFS/CySP Research Study: January 2-6, 2023 (tentative)
Whether you’ve already committed to a major or are still exploring options, this event is for YOU! At the Major Event, we gather UMBC alumni, academic departments, and campus representatives together on one awesome day to help students gain greater confidence in their academic pathway!
Students at all academic levels are welcome to attend any or all of our four unique programs throughout the day:
FREE food and Major Event t-shirts will be provided to participants at each program!
Can't join us in person? We will have a virtual sign-in option via Webex for each program except the Majors Fair. To join virtually, simply click the green box above at the desired program start time.
Questions? Email Kelly Fanning at kfanning@umbc.edu.
For more details, visit
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Whether you’ve already committed to a major or are still exploring options, this event is for YOU! At the Major Event, we gather UMBC alumni, academic departments, and campus representatives...
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The Shriver Center
https://my3.my.umbc.edu/groups/shriver
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The Shriver Center
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true
Fri, 11 Nov 2022 10:45:04 -0500
TALK: Can We Build the Universe Using only Math?
Prof. Manil Suri defends the thesis of his new book, 11/14
Can We Build the Universe Using Only Math?
Professor Manil Suri
5:30-6:45 pm Mon. 14 Nov. 2022
Albin O. Kuhn Library Gallery, UMBC
- Dr. Philip Rous, Physics
- Dr. Michele Osherow, English
- Dr. Preminda Jacob, Visual Arts
- Dr. Kathleen Hoffman, Math & Biology
Dr. William LaCourse, Dean of the College of Natural and Mathematical Sciences, will introduce the defense. Reception and book-signing to follow All are welcome!
Manil Suri is a distinguished university professor in mathematics at the University of Maryland, Baltimore County. He is the author of three award-winning novels, including
The Death of Vishnu, which have been cumulatively translated into 27 languages. He is a former contributing opinion columnist at the New York Times, for which he has written several essays on mathematics. His new book, The Big Bang of Numbers: How to Build the Universe Using Only Math will be available for purchase and signing at the event.
Presented by the College of Natural and Mathematical Sciences, and the UMBC Honors College
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Can We Build the Universe Using Only Math? Professor Manil Suri 5:30-6:45 pm Mon. 14 Nov. 2022 Albin O. Kuhn Library Gallery, UMBC UMBC professor Manil Suri defends the thesis of his new...
https://www.manilsuri.com/
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math
talk
Computer Science and Electrical Engineering
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CNMS and UMBC Honors College
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Fri, 11 Nov 2022 10:35:42 -0500
UMBC M.E.-led Research Highlighted in APS Press Release
Prof. Carlos Romero-Talamás' research in Fusion Energy!
Every year, the American
Physical Society's Division of Plasma Physics (APS-DPP) highlights high-impact and
transformational research as part of their annual meeting on a variety of topics
in plasma physics. The research team led by Prof. Romero-Talamás is
investigating a new type of fusion energy reactor with the Centrifugal Mirror
Fusion Experiment, which aims to achieve sustained temperatures exceeding 10
million degrees Fahrenheit. The research is a collaboration between UMBC and
the University of Maryland, College Park.
The full press release can be found
here.
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Every year, the American Physical Society's Division of Plasma Physics (APS-DPP) highlights high-impact and transformational research as part of their annual meeting on a variety of topics in...
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Mechanical Engineering
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Mechanical Engineering
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Fri, 11 Nov 2022 09:15:38 -0500
Open-Rank Tenure/Tenure-Track Faculty Position in CBEE
Open to researchers in the broad field of human health
Open-Rank Tenure/Tenure-Track Faculty Position in the Department of Chemical, Biochemical, and Environmental Engineering at UMBC
The Department of Chemical, Biochemical, and Environmental Engineering (CBEE) at the University of Maryland Baltimore County (UMBC) seeks to fill an open-rank, tenure/tenure track position in the broad field of human health, including areas involving: synthetic biology, systems biology, bioinformatics, biomedical engineering, development and production of novel engineered biologics, environmental informatics, environmental microbiology, and environmental health science and engineering. We are especially interested in applicants who will conduct interdisciplinary research which will complement the current expertise in our department and college. Industrial experience is welcome.
Review of applications will begin on Nov. 15, 2022 and will continue until the position is filled.
UMBC is an Affirmative Action/Equal Opportunity Employer. Minorities, women, veterans, and individuals with disabilities are encouraged to apply.
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Open-Rank Tenure/Tenure-Track Faculty Position in the Department of Chemical, Biochemical, and Environmental Engineering at UMBC The Department of Chemical, Biochemical, and Environmental...
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faculty-search
fall-2023
Chemical, Biochemical and Environmental Engineering
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Chemical, Biochemical and Environmental Engineering
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Thu, 10 Nov 2022 12:58:34 -0500
Thu, 10 Nov 2022 13:52:49 -0500
