This training is not only for people who are thinking about starting a practice, but also for those who have an existing practice. It's never too late to make some adjustments to your practice that will help it grow more effectively. This training relates to direct practice as it aims to help the clinician build a practice that will benefit the clients it serves and ensure their practice adheres to the strictest of ethical principles. Participants will learn how to build a private practice that meet the needs of the clients they serve and strengthens both the client's treatment experience and the profession as a whole. This training is split into 3 consecutive Thursday webinars. Participants must attend all 3 sessions to earn CECs. DAY 1 covers the top mistakes people make in private practice, and begins to layout the framework for building a better one. We will explore the nuts and bolts of who, why, where, and when to open a private practice. Then we will dive into how to set up your practice with your own policies and procedures.

Learn more about the Master's in Early Childhood for Infants and Children with Autism here. (https://uconnucedd.org/masters-degree-early-childhood-for-infants-and-children-with-autism/)

Stop by DTWN 219 from April 28 to May 2nd, 9AM-3PM to grab your de-stress finals care packages filled with hot chocolate packets, fidget toys, playdough, and snacks! An Honors GPS member or staff will also be there and available to answer any questions about your honors journey. 

Join us for virtual office hours! Drop in with your questions—we're here to support you in the most convenient way possible.Register today! (https://connect.grad.uconn.edu/register/stem-virtual-250429)

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.