More info about SHaW's Group Sessions. (https://studenthealth.uconn.edu/mental-health/group-therapy/)Interpersonal Group for Undergraduate Students Undergraduate Students Interpersonal Groups focuses on promoting emotional wellbeing as you balance academics, relationships, family, and personal responsibilities. Groups offer a supportive confidential space to share your concerns, practice skills and get feedback. To join this group therapy session, please call SHaW at 860-486-4700 (tel:860-486-4705). This session is held by Maritza Lugo-Stalker, (https://studenthealth.uconn.edu/person/maritza-lugo-stalker/) 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.

Assessing Spatial Disparities in Disease Maps  Sudipto Banerjee, Senior Associate Dean for Academic Programs, UCLA Fielding School of Public Health, University of California, Los AngelesRecipient of the 2024 UConn Statistics Department Distinguished Alumnus AwardDate: Wednesday, April 23, 2025, 4:00 PM, AUST 202Meeting Link: Link (http://%20https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=me54b26f2e27287b9961d1d3402b7be37)Coffee will be served at3:30in theNoetherLounge (AUST 326)Abstract:Epidemiologists commonly use regional aggregates of health outcomes to map mortality or incidence rates andidentifygeographic disparities. However, to detect health disparities across regions, it is necessary toidentify"difference boundaries" that separate neighboring regions with significantly different spatial effects. This can be particularly challenging when dealing with multiple outcomes for each unit and accounting for dependence among diseases and across areal units. In this talk, we address the issue of multivariate difference boundary detection for correlated diseases by formulating the problem in terms of Bayesian pairwise multiple comparisons by extending it through the introduction of adjacency modeling and disease graph dependencies. Specifically, we seek the posterior probabilities of neighboring spatial effects being different. Toaccomplishthis, we adopt a class of multivariateareallyreferenced Dirichlet process models that accommodate spatial andinterdiseasedependence by endowing the spatial random effects with a discrete probability law. Our method is evaluated through simulation studies and applied to detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute. Weidentifystatistical challenges to appropriately define what constitutes a spatial disparity and to construct robust probabilistic inference for spatial disparities. As an alternative approach, we enrich the familiar Bayesian linear regression framework to introduce spatial autoregression and offer model-based detection of spatial disparities. We derive exploitable analytical tractability thatconsiderably acceleratescomputation and enables us to scale statistical detection of disparities to the counties from the contiguous United States.Short Bio: Sudipto Banerjee, Ph.D., is a Professor in the Department of Biostatistics at the Fielding School of Public Health and in the Department of Statistics at the College of Physical Sciences with an affiliate appointment in the UCLA Institute of the Environment and Sustainability. His research expertise includes Bayesian hierarchical modeling and inference for complex systems involving massive datasets ("BIG DATA"); environmental processes and their impact on public health; spatial data science; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis. His theoretical and methodological contributions in statistics are available in the form of several scholarly publications in highly regarded peer-reviewed journals in statistics, biostatistics and health sciences; an authoritative textbook on spatial statistics; a textbook on linear algebra for statisticians; an edited comprehensive handbook on spatial epidemiology; and two committee reports for the National Research Council of the National Academies. He has served as Principal Investigator of over 14 major federally funded research projects from NIH and NSF primarily devoted to the advancement of statistical theories and methods for space-time processes and their substantive impact. Methods developed by Professor Banerjee are being widely employed in epidemiological and environmental health research to enhance scientific understanding of how environmental factors affect human health over space and time. He has made pioneering contributions in several important areas at the interface of spatial data science and health research including spatial survival analysis; space-time mapping and analysis of multiple diseases; and analyzing massive volumes of space-time data (BIG DATA analytics). He has played prominent roles in substantive public health projects such as "The GuLF Study" (Deepwater Horizon disaster) and is currently overseeing data analysis efforts to evaluate the health effects from the massive natural gas leak disaster in Aliso Canyon in California. Professor Banerjee's research and scholarship has been recognized with honors such as the Abdel El-Shaarawi Award from The International Environmetric Society (TIES), the Mortimer Spiegelman Award from the American Public Health Association and the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS), elected membership of the International Statistical Institute, elected fellowships in the Institute of Mathematical Statistics (IMS), the American Statistical Association (ASA), the International Society for Bayesian Analysis (ISBA) and the American Association for the Advancement of Science (AAAS), a Distinguished Achievement Medal from the ASA's Section on Statistics and the Environment, and the ASA's Outstanding Statistical Application Award. He was elected to serve as President of the International Society for Bayesian Analysis (ISBA) in 2022 (President-Elect in 2021; Past-President in 2023). 

These sessions will provide both prospective students and undergraduate students with the opportunity to speak to a School of Pharmacy representative to ask questions about the program. This offers an easy way to connect with members from our school without having to come to the Storrs campus.

Please RSVP for this event by Tuesday, April 22 on this website (https://writingcenter.uconn.edu/graduate-writing-workshop-series/). Registrants will receive a Webex invite prior to the event. In this workshop, we will be discussing how you can develop personalized strategies that leverage your ADHD strengths while supporting areas of challenge with academic writing. We'll talk about writing under pressure, creative visualization, productivity and progress, routine and variety, collaborative writing strategies, and the importance of maintaining momentum. We'll also touch on ethical and responsible use of AI tools in relation to academic writing.This workshop is open to everyone and designed to be beneficial whether you have ADHD or not. Come and get to know your own writing process and learn in a community of other writers.