All events

Fall fee bills due for all undergraduate students not in a payment plan.

The global job market is being shaped by AI, and early literacy helps students make informed academic and career choices as AI becomes central to many industries. This event will demystify AI, make AI feel approachable, and build a foundation of confidence in preparation for STEM fields. The summer camp will also build awareness of AI's impact at the societal, local, and personal scales, and promote thoughtful digital citizenship. We are committed to sustaining an inclusive environment and making accommodations necessary for students to have a positive learning experience. Additional details follow:Who: The summer camp is designed for rising 8th - 10th graders. Due to lab capacity and instructor support, the summer camp will accept at most 30 students.Registration Deadline: July 18th, 2025Event Dates: July 28th - August 1st, 2025Time:8:30 - 9AM - sign-in9AM - 12noon - morning sessions12pm (noon) - 1PM - lunch1PM - 4PM - afternoon sessions Location: Most of the program will consist of classes and activities in the Information Technology and Engineering Building at the UConn Storrs Campus.Personnel: The summer camp is organized by a group of volunteers: Prof. Jinbo Bi, Prof. Derek Aguiar, and Blake Gaines. Instructors will include the organizers and their UConn undergraduate and graduate students.Pricing: The camp is offered in a pay-what-you-want modality. Practically, this means that you can pay any amount from $0 to $250 total. We do not want financial cost to present a barrier to attendance, so if paying any amount would cause hardship, please do not pay. Any amount of money paid will help make the program sustainable in the long run.Administrative Details: All camp staff will be approved by UConn office of Protection for Minors with training so they are authorized adults to interact with students. A few legal forms (e.g., UConn Liability Waiver, Emergency Contact) will need to be signed by parent(s) or legal guardian(s) of the students. Students need to bring their own lunch or funds to purchase lunch from UConn Student Union. Lunches will be chaperoned.

Deadline to accept Work-Study awards is August 1st.  Any award not accepted by the established deadline will be canceled.

Art exhibit highlighting creative the creative talent of UConn Health Employees from across the organization.

For more information visit https://4-h.extension.uconn.edu/litchfield-county/ (https://4-h.extension.uconn.edu/litchfield-county/) 

For more information visit https://4-h.extension.uconn.edu/litchfield-county/ (https://4-h.extension.uconn.edu/litchfield-county/) 

"Expressions in Multimedia" by Martha G. Trask "Secondary Effects" by Jeff Ostergren Join us for a reception Thursday, May 22, from 4 to 6 p.m. in the Celeste LeWitt Gallery. (north side of the food court)Martha G. Trask is an expressive mixed media artist who happens to work in our library.Jeff Ostergren infuses his paint with actual medications to tell stories about the intertwined histories of pharmaceuticals and color.

For more information visit https://www.4-hfair.org/index.html (https://www.4-hfair.org/index.html) 

For more information visit https://www.4-hfair.org/index.html (https://www.4-hfair.org/index.html) 

The exhibiting artists are Marsha Borden, Helena Chastel, Kathryn Frund, Phoebe Godfrey, Hugh MacDonald, Bob Pavlik, Dan Potter, and R. Douglass Rice. Open Air 2025 is open daily and will remain on view through October 6, 2025. June 19, 2025 iis the last day to visit indoor art exhibitions. Exhibitions inside the AVS Gallery will resume on September 11, 2025

The UConn Claude D. Pepper Older Americans Independence Center (i.e., UConn Pepper Center), plans to submit an application for competitive renewal to the National Institute on Aging at NIH. At this time, the UConn Pepper Center Pilot/Exploratory Studies Core (PESC) seeks letters of intent (LOIs) for studies to be included in the application. Studies selected for inclusion in the application will be funded contingent upon continued funding of the UConn Pepper Center. We are seeking Letters of Intent (LOIs) for 1-year pilot and exploratory studies that focus on enhancing function and independence in older adults while also advancing knowledge in the UConn Pepper Center theme of Precision Gerontology, and that will support future grant applications. We welcome a variety of research approaches, ranging from biological to clinical/behavioral to health services/community-based research. Projects focusing on cognition and behavior, host defense and immunity, voiding and continence, and mobility and falls are of particular interest. For proposed studies involving human subjects research, secondary data analysis-based projects are encouraged. Projects involving primary data collection are allowed but must be sufficiently feasible to complete within 1 year. LOIs are due by September 10, 2025 Full-time UConn and JAX faculty are eligible to apply for PESC funding. Priority will be given to junior faculty/early-stage investigators as well as established investigators pursuing aging-related research (relevant to Precision Gerontology) as a new area of research. Investigators may request up to $50,000 (direct costs) for a 1-year pilot project. Those interested in submitting an LOI are strongly encouraged to contact the PESC Co-Leaders Dr. Lisa Barry (libarry@uchc.edu) Dr. Blanka Rogina (rogina@uchc.edu) and to visit the UConn Pepper Center website to learn more about the theme of Precision Gerontology and the UConn Pepper Center Research Resource Cores. https://health.uconn.edu/pepper-center/ Those seeking to apply for UConn PESC funding must submit the following by September 10, 2025: 1. A 1-page Letter of Intent (LOI) that includes:Brief description of the Specific Aims, Significance, and Approach; Statement as to how the proposed study aligns with the UConn Pepper Center theme of Precision Gerontology; Statement of how the proposed study will utilize applicable UConn Pepper Center Research Resource Cores. 2. On a separate page, a brief budget and timeline 3. NIH Biosketch for Principal Investigator. Please send LOIs to Ms. Laura Masi (masi@uchc.edu) Individuals selected to submit full proposals will be notified by the PESC Co-Leaders.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

Abstract:Ensuring machine learning robustness against adversarial attacks is critical for real-world applications. This dissertation presents novel frameworks for certified robustness and certifiable black-box attacks: UniCR, a universal certification framework for arbitrary classifiers and perturbations; UCAN, which extends randomized smoothing with anisotropic noise; and Certifiable Black-Box Attacks, which guarantee attack success probability prior to model queries. These methods are theoretically grounded and extensively validated, revealing fundamental weaknesses in current defenses. To address emerging risks in large language models (LLMs), I further systematize the field of LLM jailbreak attacks and defenses through a comprehensive taxonomy, formalized threat models, a large annotated dataset, and an open evaluation toolkit. This work unifies fragmented efforts, enables reproducible benchmarking, and provides actionable insights for robust, real-world LLM security.

Join us on Friday, August 1, 2025 from 9:30 A.M. to 12:00 P.M. for our annual fall open house! Come enjoy a fun and free morning learning about all that OLLI at UConn has to offer. A welcome message will be given and OLLI basics will be discussed. A fall semester overview and question-and-answer segment will follow. All are welcome! You can find out more information here: https://olli.uconn.edu/open-house/. This event will be held at the UConn Waterbury campus main building (99 East Main Street, Waterbury, CT 06702), and there will be free parking on the day of the event in UConn's attached parking garage (entrance on 55 North Elm Street). YOU MUST REGISTER FOR FREE PARKING. To get your parking permit, click this link: 08012025 OLLI at UConn Fall 2025 Open House The passcode is: 0801OLLI (do NOT copy and paste, it won't work!)

In April 2024, the Department of Justice issued an update to Title II of the Americans with Disabilities Act (https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ada.gov%2Fresources%2F2024-03-08-web-rule%2F&data=05%7C02%7Citaccessibility%40uconn.edu%7C20c771e5a88d478a8e9308dd66f63f7d%7C17f1a87e2a254eaab9df9d439034b080%7C0%7C0%7C638779929987281013%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=L%2Bz6mRTbBflpXOJe9W3QB7cZ9xNsKm4I%2B5%2FLQclhc5c%3D&reserved=0) which requires state and local governments – including public universities – to ensure that their digital content is accessible to people with disabilities. These changes will impact many in our community, and we want to share information and resources that will help everyone prepare and comply with the new standards.What is Impacted? Any digital content that we provide or make available on behalf of the University of Connecticut must meet the standards set forth by this ruling, whether this content is provided directly by us or via third-party organization as part of a contract, licensing, or other agreement. This includes websites, digital files, social media content, and mobile applications.DeadlinesWebpages & ApplicationsCreated on or after April 2024 must be accessible at launch. Created before April 2024 need to be accessible by April 2026. Digital FilesCreated on or after April 2024 need to be accessible at launch. Created before April 2024 and still in use need to be accessible by April 2026. Created before April 2024 and not in use can be marked as archived and do not need to be remediated unless someone requests it. Technical Standard The Web Content Accessibility Guidelines (WCAG) Version 2.1, Level AA (https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.w3.org%2FWAI%2Fstandards-guidelines%2Fwcag%2F&data=05%7C02%7Citaccessibility%40uconn.edu%7C20c771e5a88d478a8e9308dd66f63f7d%7C17f1a87e2a254eaab9df9d439034b080%7C0%7C0%7C638779929987301355%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=GtBfaBbRmzVFT4Jwcx%2B5gcH9Ej1haYILGDt9MSwHiCk%3D&reserved=0) are the current technical standard for web content and mobile apps. Please note that UConn also has web standards (https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbrand.uconn.edu%2Fweb&data=05%7C02%7Citaccessibility%40uconn.edu%7C20c771e5a88d478a8e9308dd66f63f7d%7C17f1a87e2a254eaab9df9d439034b080%7C0%7C0%7C638779929987315699%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=IA0Va6a06XjzRJ8LZXG5PMuWZiM0%2FeMIREYo4qO3Mhs%3D&reserved=0) which must be adhered to. We require websites built on behalf of the University and/or using University funds to use a UConn domain and be hosted by UConn. While we know that our faculty and staff support efforts to provide accessible content and equitable experiences, performing this work and maintaining the standards going forward will be an ongoing, shared responsibility.

This session is for F-1 students who will do an internship or work off-campus before graduating, or for students who have internships and clinical placements as part of your academic curriculum. All internships, work and placements off-campus must be authorized through Curricular Practical Training (CPT) or Pre-Completion Optional Practical Training (OPT), even if unpaid and required for your class or program. If you are considering a future off-campus work opportunity or placement, you are required to attend this workshop before you apply for CPT or Pre-Completion OPT with ISSS.

Speaker: Michael Blinov Title: TBD Via Webex: https://uconnhealth.webex.com/uconnhealth/j.php?MTID=mdacf393cf6a2ee5314a3b76525ea5e57 (https://uconnhealth.webex.com/uconnhealth/j.php?MTID=mdacf393cf6a2ee5314a3b76525ea5e57)

Title: Towards Robustness in Medical Imaging Classification Abstract: Deep neural networks are increasingly used in medical imaging to assist in detecting and diagnosing a wide range of conditions. Yet current image classifiers remain highly vulnerable to adversarial attacks and natural distribution shifts, which can undermine diagnostic reliability and erode clinicians' trust. They are also susceptible to underconfidence attacks, perturbations that sharply lower model confidence while leaving predicted labels unchanged, creating an additional, often overlooked threat to safe deployment. To address these challenges, this thesis introduces three complementary contributions that improve the robustness and trustworthiness of medical-image classifiers: 1. Robust training with data augmentation (RTDA) - a training algorithm that mixes adversarial training with data augmentation, simultaneously increasing resilience to crafted perturbations and to natural distribution shifts. 2. ConfSmooth - a novel underconfidence attack that reduces a model's confidence while preserving its predicted label, exposing vulnerabilities that bypass existing defense strategies. 3. Underconfidence Adversarial Training (UAT) - an extension of adversarial training that equips classifiers to withstand both underconfidence and misclassification attacks, achieving state-of-the-art robustness.

Title: Towards Robustness in Medical Imaging Classification Abstract: Deep neural networks are increasingly used in medical imaging to assist in detecting and diagnosing a wide range of conditions. Yet current image classifiers remain highly vulnerable to adversarial attacks and natural distribution shifts, which can undermine diagnostic reliability and erode clinicians' trust. They are also susceptible to underconfidence attacks, perturbations that sharply lower model confidence while leaving predicted labels unchanged, creating an additional, often overlooked threat to safe deployment. To address these challenges, this thesis introduces three complementary contributions that improve the robustness and trustworthiness of medical-image classifiers: 1. Robust training with data augmentation (RTDA) - a training algorithm that mixes adversarial training with data augmentation, simultaneously increasing resilience to crafted perturbations and to natural distribution shifts. 2. ConfSmooth - a novel underconfidence attack that reduces a model's confidence while preserving its predicted label, exposing vulnerabilities that bypass existing defense strategies. 3. Underconfidence Adversarial Training (UAT) - an extension of adversarial training that equips classifiers to withstand both underconfidence and misclassification attacks, achieving state-of-the-art robustness.

Abstract: Deep neural networks are increasingly used in medical imaging to assist in detecting and diagnosing a wide range of conditions. Yet current image classifiers remain highly vulnerable to adversarial attacks and natural distribution shifts, which can undermine diagnostic reliability and erode clinicians' trust. They are also susceptible to underconfidence attacks, perturbations that sharply lower model confidence while leaving predicted labels unchanged, creating an additional, often overlooked threat to safe deployment. To address these challenges, this thesis introduces three complementary contributions that improve the robustness and trustworthiness of medical-image classifiers:Robust training with data augmentation (RTDA) - a training algorithm that mixes adversarial training with data augmentation, simultaneously increasing resilience to crafted perturbations and to natural distribution shifts. ConfSmooth - a novel underconfidence attack that reduces a model's confidence while preserving its predicted label, exposing vulnerabilities that bypass existing defense strategies. Underconfidence Adversarial Training (UAT) - an extension of adversarial training that equips classifiers to withstand both underconfidence and misclassification attacks, achieving state-of-the-art robustness.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

Department of Molecular and Cell Biology University of Connecticut   Announces the  Master's Thesis DefenseLee Dollar B.S. Animal Science, University of Vermont, 2022Investigating the Role of an Essential Dynamin-Related Protein in Toxoplasma gondii     Friday, August 1, 2025 1:00 PM BPB 201   Major Advisor: Dr. Aoife Heaslip Committee Member: Dr. Kenneth Campellone Committee Member: Dr. Victoria Robinson

Committee Dr. Sara Harkness Dr. Charlie Super Dr. Maria LaRusso

Dana is a DNP student. The title of their Final Defense project is "Understanding Primary Caregiver Satisfaction Using the EMPATHIC-30 for NICU Families."

Dana is a DNP student. The title of their Final Defense project is "Understanding Primary Caregiver Satisfaction Using the EMPATHIC-30 for NICU Families."

Join us every Friday this summer from 4-6 PM near the Rome Commons courts for fun games, new friends, and enjoy an afternoon of friendly competition All skill levels are welcome, so bring your energy and excitement — we'll see you on the court!