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Join us for an engaging, hands-on experience that brings genetics education to life through the power of virtual reality and community engagement. This unique event combines cutting-edge technology with student-driven learning and local professional interaction to deepen understanding of how genetics solves real-world human problems. Students will explore key concepts in genetics and gene editing through an immersive VR experience and showcase their takeaways in a reverse career fair-style poster presentation. Attendees—including professionals from the Greater Hartford area—are invited to circulate, connect with students, and provide real-time feedback and career insights. Whether you're a student curious about science careers or a local professional passionate about mentoring the next generation, this event offers a collaborative space for exploration, reflection, and dialogue.

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.

Get your Graduation and Mother's Day cards and gifts here! 20% off cards, a large assortment of jewelry, museum t-shirts, art umbrellas and other art themed items, scarves, games, accessories, UConn cards and postcards, and other gifts. (30% for Paid Museum Members) Become a museum supporter and save! CT Art Trail Passports are here. Get your passport here and visit 30 world-class museums, nature centers and historic sites, created to promote Connecticut's rich cultural assets as part of a travel experience, for just $35.

UConn Sexpert Peer Support Drop-In Hours are a free service offered by peer health educators, the UConn Sexperts, 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. Our UConn 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.Mondays: 10am-1pm Tuesdays: 10am-12pm Wednesdays: 9am-6pm Thursdays: 9am-6pm Fridays: 2pm-5pm UConn 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. For more information, visit: studenthealth.uconn.edu/sexperts (https://studenthealth.uconn.edu/sexperts/)