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Vibrant paintings by Andrea Sanchez and Jaii Marc Renee on display in the Celeste LeWitt Gallery — Join us for a meet and greet from 1:30 to 2:30 p.m. Friday, Aug. 29.

The event is taking place on the Goosechase app and is available for all on-campus students to participate. Challenges encourage students to familiarize themselves with the Storrs campus and their residence community. All participants will receive prizes!This is an Honors Event. See tags below for categories. #UHLevent11176

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.

UConn Health Day 2 new employee orientation is conducted on Saba. It focuses on comprehensive training for our newest workforce, covering diversity awareness, sexual harassment prevention, and compliance to ensure a respectful and compliant work environment.AgendaDay 2Format: Saba Self-Guided Learning Time: 8 am - 4:30 pm Location: RemoteActivities: Dive deeper into your compliance and role-specific training and explore resources at your own pace.

Beat the crowds and head straight to the food at the Block Party on Wednesday! Plan ahead and stop by the HTB Atrium, with your UConn ID, to pick up your Block Party bracelet: Monday, 8/25: 9am - 2pm Tuesday, 8/26: 10am - 3pm Wednesday, 8/27: 9am - 1pm

Please join UConn Leadership, faculty, and staff in welcoming new and returning students for the 2025-2026 academic year. Convocation is a fresh start to university life. Join us and listen to motivational speakers and have a great time to kickoff the new year.

Demo and discussion of how to employ a discussion norms exercise during the first week or two of class that helps students identify the key ideas in a subject, practice thoughtful discussion protocols, and develop discussion norms. Register - https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3618

Students who may benefit from attending a Let's Talk: Mental Health Office Hours session include:Students who want help connecting to resources but are unsure where to begin Students who are looking for advice on a non-clinical issue Students who are unsure about therapy and are curious about what it is like to talk to a therapist Students who may have concerns about the mental health of a friend and seek advice on how to support their friend If a student is not an imminent risk, and is refusing your support in contacting our office, you may also consider contacting the UConn Student CARE Team (https://studentcareteam.uconn.edu/). This session is held by Sarah Hallwood, LCSW, LICSW (https://studenthealth.uconn.edu/person/sarah-hallwood/)

Students who may benefit from attending a Let's Talk: Mental Health Office Hours session include:Students who want help connecting to resources but are unsure where to begin Students who are looking for advice on a non-clinical issue Students who are unsure about therapy and are curious about what it is like to talk to a therapist Students who may have concerns about the mental health of a friend and seek advice on how to support their friend If a student is not an imminent risk, and is refusing your support in contacting our office, you may also consider contacting the UConn Student CARE Team (https://studentcareteam.uconn.edu/). This session is held by Chelsea Morales, Psy.D (https://studenthealth.uconn.edu/person/chelsea-morales/)

Abstract:Machine learning (ML) is a powerful tool for data analysis, although it struggles in many applications where training data are expensive or scarce.Here I present a set of works designed to use available data efficiently. First, I introduce a method for symbolic regression of implicit equations. Symbolic regression is useful for uncovering knowledge of the underlying mathematics of a system. Implicit equations are difficult to discover due to the challenge of distinguishing between meaningful implicit relationships and trivial solutions. We enable symbolic regression for implicit equations with a simple probabilistic representation, that enables effective comparison between equations, even with few and noisy training data.The second work focuses on enhancing ensemble methods based on bootstrap aggregation, such as Random Forests (RF) by using a sampling scheme that reduces the number of out-of-bag samples, while maintaining variability among bootstrap datasets. We achieve this by implementing a weighting vector drawn from a multinomial distribution to apply random weights to training data. This is mathematically equivalent to duplicating the data multiple times prior to drawing a bootstrap sample. Our approach works particularly well to improve the performance of RF on heavily imbalanced datasets.The final project is the development of an automated ML pipeline for the analysis of experimental small angle scattering (SAS) data.SAS curves are useful in studying the structure of nanoparticles (NPs), but are difficult and time-consuming to interpret. We have developed a novel hierarchical approach for classifying NP morphology, and a suite of regression models for predicting corresponding structural parameters.The hierarchical classification is designed to learn decision boundaries at interpretable junctions along underlying manifolds. Selectively sampling data from parameter ranges near these junctions emphasizes them, allowing the model to learn the desired decision boundaries, and uses biased sampling near these junctions to more effectively learned the desired decision boundaries. Our method independently tunes the hyperparameters for binary classifiers at each node of the hierarchical trees, achieving performance comparable to neural networks with an order of magnitude fewer data.This automated method for rapid analysis of SAS data enables new experimental design such as time-series analysis, real-time visualization and automated feedback for active learning.In summary, by carefully considering how data are represented and model selection is implemented, the performance of ML can be significantly enhanced in small data scenarios.

Graham Roberts School of Computing Rethinking Sampling and Model Selection Strategies in Small Data Machine Learning By sampling and utilizing data in different format we can improve machine learning capabilities in small data environments, such as ML for science.

Graham Roberts School of Computing Rethinking Sampling and Model Selection Strategies in Small Data Machine Learning By sampling and utilizing data in different format we can improve machine learning capabilities in small data environments, such as ML for science.

Students who may benefit from attending a Let's Talk: Mental Health Office Hours session include:Students who want help connecting to resources but are unsure where to begin Students who are looking for advice on a non-clinical issue Students who are unsure about therapy and are curious about what it is like to talk to a therapist Students who may have concerns about the mental health of a friend and seek advice on how to support their friend If a student is not an imminent risk, and is refusing your support in contacting our office, you may also consider contacting the UConn Student CARE Team (https://studentcareteam.uconn.edu/). This session is held by Fumi Sowah, LCSW (https://studenthealth.uconn.edu/person/olufumilayo-sowah/)

In this introductory workshop, we will provide an overview of instructional design, a systematic process for planning a course. You will begin to build a course design framework and walk away with tools and resources to customize your course plan. Please come prepared with a course or instructional topic to work on during the session. Objectives: As we discuss five key elements of course design, you will begin to: *Recognize how ID can help you and your students.​ *Identify situational factors impacting your course.​ *Write your learning objectives.​ *Plan your assessments.​ *Plan your learning activities/instructional materials.​ *Check your design plan for alignment.​August 25, 2:00 – 3:00 Presenters: Betsy Guala and Tim Stubbs Register – https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3543 (https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3543)

Registration is required https://cetl.uconn.edu/programs-and-events/new-ta-orientation-programs-and-services/

Speakers: Adam Lindsay, MD and Matthew Partan, MD Date: Monday, August 25, 2025 Time: 5:30PM-7:00PM Location: Live Virtual https://us06web.zoom.us/j/83709698368?pwd=WepSda20un4BJmOXE87DHDONHZhXbU.1 Target Audience: Faculty, Staff, and trainees at the UConn Health Center Title: • Adam Lindsay, MD: IlluminOss Photodynamic Bone Stabilization System for Humeral Metastatic Disease: Results from the LightFix Trial • Matthew Partan, MD: Enhanced Detection of Orthopaedic Imaging Findings Using Deep Learning Technology Learning Objectives: Participants will (be able to): 1. Evaluate the clinical outcomes, advantages, limitations, and patient selection criteria of the IlluminOss system compared with traditional fixation methods, and apply this evidence to surgical decision-making in humeral metastatic disease. 2. Review the data presented to integrate deep learning technology into musculoskeletal imaging by understanding its diagnostic performance, limitations, and applications to improve accuracy and patient outcomes.