Master Calendar

NEURODIVERSIFYING STEM THROUGH AI-EMPOWERED SELF-REGULATED LEARNING ABSTRACT To fully realize the innovative potential of neurodiverse graduate students in science, technology, engineering, and mathematics (STEM), novel approaches are needed to bring student assets, academic environments, and mentoring relationships into alignment. This three-phase qualitative study investigated the experiences of neurodiverse graduate students and explored the potential of artificial intelligence (AI) tools to support their academic success and well-being. Early research phases highlighted self-awareness, self-regulation, self-efficacy, and self-advocacy as core processes for navigating graduate education, informing the development of a strengths-based Neurodiversity-Informed Self-Regulated Learning (ND-SRL) framework. The final phase explored the use of an AI Virtual Mentor to offer timely, affirming support that scaffolds self-regulated learning and empowers students to craft learning environments in which they may thrive. This study also investigated how students experienced the tool as a relational supplement to advising, including how prior expectations shaped engagement and perceptions of its role and limits. Findings show that students used the tool to leverage strengths and support challenges, building disciplinary knowledge and skills while enhancing productivity and well-being. Many experienced it as responsive and emotionally supportive for general guidance but set clear boundaries about its role and ethical use. This research contributes to the understanding of neurodiverse graduate experiences, offering insights into mechanisms of AI support and how neurodiverse students understand the role of AI tools alongside traditional advising.

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/)

Title: "Binding of Fusobacterium nucleatum autotransporter adhesin CbpF to human CEACAM1 and CEACAM5: A Velcro model for bacterium adhesion" Information Link: https://www.pnas.org/doi/10.1073/pnas.2516574122 (https://www.pnas.org/doi/10.1073/pnas.2516574122)

Stop by South Campus to connect with Student Health and Wellness's Sexperts & chat about sex and relationships! Sexpert Peer Health Educator Peer Support Drop-In Hours are a free service offered on the UConn Storrs campus. Peer Support Drop-In Hours are a great option for students who have questions about sex and sexual health, are looking for a non-judgmental, laid-back environment to discuss a sex related concern or issue, or are interested in improving their sexual health and personal well-being. The Sexperts are trained to provide education, support, and connection to resources on and off-campus on a wide variety of topics pertaining to sex, sexual health, and relationships.  Fall 2025 Drop In Hours: September 15th – December 5thMonday: 12pm-4pm Tuesday: 9am-6:30pm Wednesday: 11:15am-6pm Thursday: 11am-5:30pm Friday: 10:30am-5:30pm   Sexperts (and supervising staff) are designated confidential employees under UConn's Title IX Reporting Obligations. Peer support sessions are for educational and support purposes only. Peer support visits are not on-call or emergency services, and are not for individualized medical advice, nor are they counseling or therapy. If you can't make the times listed, or would prefer to schedule an appointment with a staff sex educator, please reach out to Program Manager for Sexual Health and Peer Education Initiatives, Cassy Setzler, at cassy@uconn.edu (mailto:cassy@uconn.edu) For more information, visit: studenthealth.uconn.edu/sexperts (https://studenthealth.uconn.edu/sexperts) or email cassy@uconn.edu (mailto:cassy@uconn.edu)

In recognition of Veterans Day, join us for a virtual presentation featuring Emily Lugo, Outreach Coordinator, and Rebekah Mizener, Veteran Services Coordinator, from Veterans and Military Programs at UConn. This session will highlight the university's ongoing commitment to supporting veterans, service members, and military-affiliated students.The Office of Veterans Affairs and Military Programs fosters a seamless, inclusive experience for military-affiliated students, their families, and alumni across all UConn campuses, uniting comprehensive services and support under one flagship university to ensure every student can thrive. Through community engagement, interdepartmental collaboration, and external partnerships, the office provides an individualized approach that strengthens connection and belonging within the UConn community.This event is part of a virtual workshop series highlighting programs and initiatives that foster collaboration and engagement across the university.

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

AbstractA unified framework of data-efficient and generalizable computer vision models is presented for scientific and industrial imaging, connecting various domains such as additive manufacturing, operando microscopy, and biomedical analysis. Extracting meaningful information from complex visual data is challenging due to limited annotations, heterogeneous imaging domains, fine-scale ambiguities, and inherently noisy conditions. To tackle these challenges, we introduce the concept of using paired images as a foundational approach for learning, reframing a wide range of imaging problems, including segmentation, defect detection, and dynamic analysis, as unified change detection tasks. This approach enables robust, data-efficient solutions that can be adapted to new challenges across diverse industrial and scientific imaging fields.The first problem focuses on domain adaptation for additive manufacturing vision tasks. In binder-jet 3D printing, traditional defect detection methods rely on rigid camera setups and handcrafted thresholds. We develop a Semi-Siamese neural network that directly compares a reference schematic to a camera image, allowing for pixel-level defect localization that remains effective under varying illumination and viewpoints without the need to predefine defect types.The second problem we address is the automated analysis of dynamic microscopy data. In operando environmental transmission electron microscopy, accurately segmenting nanoscale features is crucial for quantifying reaction kinetics. However, this process is often complicated by limited annotations and visually ambiguous structures. We present a change-detection-based framework that compares paired frames to capture subtle temporal and structural variations while simultaneously segmenting related regions of interest. This method transforms traditional in-situ imaging into spatially resolved operando characterization, allowing for the automated tracking of nanoscale material evolution and creating new opportunities for data-driven studies of reaction mechanisms and catalyst regeneration.The third problem considers label-efficient learning for biomedical imaging. Medical datasets tend to be small and heterogeneous, which limits the performance of conventional deep learning models. We propose a multi-fidelity framework that automatically generates low-fidelity versions of high-resolution images and trains a Semi-Siamese network to learn from the comparisons between different fidelity levels. This approach enhances the delineation of morphological boundaries while reducing reliance on extensive annotations.Together, these contributions create a cohesive framework that integrates change detection, domain adaptation, and multi-fidelity modeling for small-data, cross-domain scientific imaging. The resulting methods advance the objective of achieving autonomous, data-efficient visual understanding in complex experimental systems, paving the way toward self-driving laboratories and intelligent imaging platforms.

Celebrate the first day of International Education Week by bringing a dish to share that represents a global culture! Enjoy lunch with the UConn Law community and try new food.

Tittle - Structure-Guided Design Of Polymer Dielectrics For Sustainable Electrical And Electronic Equipment. Polymer Program, Institute of Materials Science. "Sustainable high-performance polymers by designing polycannabinoid dielectrics, showing how molecular design can deliver low-loss, high-temperature materials for next-generation electronics"

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

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/)

Differential cross sections for forward-angle photoproduction of π⁰, η, and η′ pseudoscalar mesons were measured using data from the RadPhi experiment conducted in Hall B at Jefferson Lab. RadPhi utilized a tagged bremsstrahlung photon beam incident on a stationary ⁹Be target, with a detector system configured to trigger on a recoil proton in coincidence with multiple neutral showers in the calorimeter. Events were reconstructed through kinematic fitting, with background suppressed via sideband subtraction and Monte Carlo modeling of nucleon resonance contributions. Cross sections were extracted over the photon energy range 4.4 - 5.4 GeV and binned in momentum transfer |t|, providing measurements from one of the first high-statistics experiments of forward η and η′ production from a nuclear target at these energies. Acceptance corrections were applied using a detailed GEANT-based simulation of the detector geometry and response. The resulting cross sections are consistent with 2020 CLAS results when scaled by the number of protons in beryllium, and show broad agreement with other data and theoretical models. In parallel, a high-resolution photon tagger detector, the Tagger Microscope (TAGM), was designed, constructed, and commissioned for the GlueX experiment in Hall D at Jefferson Lab. The TAGM was developed to improve energy resolution near the coherent bremsstrahlung peak by detecting post-bremsstrahlung electrons across a one GeV range along the focal plane of the tagging spectrometer. The detector consists of a 5x102 array of 2x2 mm² square BCF-20 plastic scintillating fibers thermally fused to BCF-98 light guide fibers optically coupled to silicon photomultipliers. These fibers are mounted in a precision-machined framework enabling fine positional adjustments to maintain precise alignment with post-bremsstrahlung electron trajectories, while ensuring mechanical rigidity, thermal stability, optical isolation, minimal inactive area, and radiation shielding for electronics. The construction effort involved extensive testing of fiber quality, light transmission, thermal fusing, radiation hardness, and defect analysis using SEM and EDX techniques. Following its installation and commissioning, the TAGM became a critical component of the GlueX beamline, enabling high-precision tagging essential for studies of hybrid mesons and gluonic excitations.

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

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

Barbershop The "Barbershop Group" is a therapy group for men who have had past traumatic experiences and would like to learn new ways of coping and managing the effects of that trauma. The group recognizes the need for men to develop different skills that will effectively improve how we express our emotions and build relationships in a safe space. What better place to do that than the barbershop? The Barbershop model is derived from the "Men's Trauma Recovery and Empowerment Model"- (M-TREM.) The Barbershop Group uses a Psycho-educational and skills-oriented approach that is person-centered. Over the years The Barbershop has provided men with a safe, non-judgmental setting where they can speak freely and be themselves. Unfortunately, we will not be providing haircuts, but we will be providing open discussion where honest responses in group discussions are encouraged, and we will engage in several exercises that are used to introduce you to and try new coping skills. I look forward to seeing you all at "The Shop".To join this group therapy session, please call SHaW at 860-486-4700 (tel:860-486-4705). This session is held by Greg Davis, LPC (https://studenthealth.uconn.edu/person/greg-davis/) For many concerns that students face – like overwhelming stress, anxiety, difficult relationships, depression, academic difficulties, and more – group therapy is the best option for support and healing. Facilitated by Student Health and Wellness (SHaW) counselors, our therapy groups encourage peer support, promote emotional wellbeing, and increase a felt sense of connection. Participants often find that they feel less alone in their struggles, and walk away with newfound support and ideas for coping.

Huskies on Track: Navigating ADHD at UConn This group provides a space to discuss the ways ADHD can show up in your life. We'll focus on understanding how ADHD functions in the brain, navigating common challenges people with ADHD face, and talking about the emotional experience of living in a world designed for neurotypical people. The goal of this group is to help create a sense of community and understanding for students with ADHD at UConn. Example topics that the group will cover include time management, task prioritizing, and emotional regulation. All individuals who feel the group would be beneficial to them are welcome, regardless of whether they have a formal ADHD diagnosis.To join this group therapy session, please call SHaW at 860-486-4700 (tel:860-486-4705). This session is held by Alyson Faires, Psy.D. (https://studenthealth.uconn.edu/person/alyson-faires/)For many concerns that students face – like overwhelming stress, anxiety, difficult relationships, depression, academic difficulties, and more – group therapy is the best option for support and healing. Facilitated by Student Health and Wellness (SHaW) counselors, our therapy groups encourage peer support, promote emotional wellbeing, and increase a felt sense of connection. Participants often find that they feel less alone in their struggles, and walk away with newfound support and ideas for coping.

Our in-person information sessions at UConn Storrs are held in the Charles B. Gentry Building, Room 144. UConn Storrs offers visitor parking (https://park.uconn.edu/visitors/storrs/) at:North Parking Garage or South Parking Garage for a nominal fee. Payment must be made upon entry for the expected duration of the visit. Payment is required to park in the garages 24 hours a day, seven days a week.