More info about SHaW's Group Sessions. (https://studenthealth.uconn.edu/mental-health/group-therapy/)Mind and BodySupport Group: support and empowerment for students with chronic health conditions. This weekly group is for students adjusting to coping with chronic health conditions in a university setting. This is a safe space for students to share their experiences and support each other in managing their health while also managing the challenges of college life. This group is a joint effort between medical and mental health services to ensure that students are cared for in a complete and holistic manner.' This session is held by Jennifer Fox, PhD (https://studenthealth.uconn.edu/person/jennifer-fox/) & Ryan Grade BSN, RN (https://studenthealth.uconn.edu/person/ryan-grade/) 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 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.

The Office of Outreach and Engagement in collaboration with faculty from all Schools and Colleges created guidelines and recommendations on how to incorporate Community Engagement in the PTR package. At this stage of the process, we would like to get your feedback on the content and process suggested. You can find the file at PTR_Community Engagement_1April2025.pdf (https://uconn-my.sharepoint.com/:b:/g/personal/rosa_raudales_uconn_edu/EWAFp4oN9RlIo77jK5sG2uABy0qIGTaBRhGnD_6PVgb7VA?e=kfWt8c&xsdata=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%3D%3D&sdata=WGpxTFNqazVHVTdNSEwxUFgySzV5emt1RUdJY1dKNkJrTWFCRHdGaEI1az0%3D&ovuser=17f1a87e-2a25-4eaa-b9df-9d439034b080%2Cmerly.downey%40uconn.edu) Please feel free to share your comments in writing and send them to rosa.raudales@uconn.edu (mailto:rosa.raudales@uconn.edu) or join one of the meetings below to share your comments: Monday, April 14 at 1:00 pm Tuesday, April 15 at 11:00 am Wednesday, April 16 at 1:00 pm Thursday, April 17 at 2:00 pm Tuesday, April 22 at 2:30 pm   Meeting link: https://uconn-cmr.webex.com/meet/rosaraudales (https://uconn-cmr.webex.com/meet/rosaraudales) Meeting number: 319 588 423 Join by phone: +1-415-655-0002, Access Code: 319 588 423

How should we engage students on the matter of generative AI and learning in our courses? Examples from Yee, Uttich, Main & Giltner's AI Hacks for Educators will be shared, with an opportunity to practice with different generative AI tools. Session Two will go over activities to promote student critical thinking about the output of large language models, with opportunities for hands-on practice. This session is suitable for participants with minimal experience using generative AI but will be most useful if you know how to access either Microsoft CoPilot through your UConn Microsoft 365 login, or ChatGPT (both the free "mini" and paid versions will be demonstrated). Detection of AI use will not be the focus of these sessions, but we may discuss AI-resistant activities and assignments. You are encouraged to contribute your questions, ideas, and experiences. Register - https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3433 (https://fins.uconn.edu/secure_inst/workshops/workshop_view.php?ser=3433)

Toppings include:Sauerkraut Coney Island chili Coleslaw Onion Strings Hot pepper relish Cheese Sauce Bacon Pickles

Abstract: Machine learning (ML) has shown great promise in a variety of applications. One particular space where ML applications are desired is the analysis of scientific data. Many of these applications differ from the more discussed applications of ML in that data are more difficult to acquire. A wealth of written works, photographs, and data on topics such as browsing habits exist and have resulted in many of the most powerful ML models ever created. In many scientific applications, data collected from a particular technique are scarcer. Analyzing these data may require years of expertise and hours of work to analyze a single datum. ML solutions are therefore highly desirable, but difficult to achieve. Here I present several works to create domain specific ML applications under these limitations. The first application is a hierarchical classifier for small angle scattering curves to identify the structures of nanoparticles. To do this, a hierarchical model creates an interpretable series of decision boundaries that align with physical differences. Creating this hierarchical model involves both selecting the optimal tree structure and tuning each decision along the hierarchical model. Each decision in this hierarchical model is independently trained as a standalone binary classifier. We propose and justify a rebalancing of k-fold cross validation to tune models which generalize better and minimize the risk of overfitting model selection, by using more data for validation than training at each fold. A second project is symbolic regression for implicit equations. In many scientific applications, it can be more informative to learn an actual description of a relationship between two quantities, rather than a tool for mapping input to output. Symbolic regression is an ML technique that learns an actual symbolic expression of relationships between data. Implicit expressions, however, are a uniquely challenging problem, due to only possessing a single label, and often non-invertible relationships between variables. We create a probabilistic featurization of implicit equations utilizing importance sampling. This allows many tools for symbolic regression to generalize to implicit equations and discovering conserved quantities in series of differential equations. Symbolic regression is, in essence, a special case of model selection. We utilize other tools including weighted bootstrap sampling and complexity-focused checkpoints to find the symbolic expression with the fewest operators, while carefully avoiding false answers such as trivial models. By utilizing careful model selection and careful construction of features and models we can create ML tools using small amounts of data.