Name: Ziyuan Li (https://www.business.uconn.edu/person/ziyuan-li/) and Lukas Schnabel (https://www.business.uconn.edu/person/lukas-schnabel/) Institution: University of Connecticut Topic: Ph.D. GE PresentationsL. Schnabel: The Impact of Ingredient Purpose Disclosures on Consumers' Purchasing BehaviorZ. Li: Group vs. Solo Consumption During a Public Health Crisis

10:00am - 11:30am | Interlibrary Services 12:30pm - 2:30pm | Wellness & Leisure Reading

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

Dr. Jakob den Brok, Center for Astrophysics | Harvard & SmithsonianUnveiling Molecular Gas Conditions and their link to Star Formation with CO Observations in Nearby Galaxies An intricate interplay exists between the molecular gas in the interstellar medium (ISM) and the onset of star formation. Gaining insight into the molecular gas's composition and its physical and chemical conditions is crucial to comprehending how star formation is regulated, and how galaxies evolve. However, the most abundant molecule, H_2, is challenging to observe directly under commonly cold ISM conditions across nearby galaxies. Carbon Monoxide (CO) emission has become the most accessible tracer of the bulk molecular ISM distribution. I will present recent findings from multiple comprehensive astrophysical surveys mapping CO and isotopologue line emission across entire nearby galaxies. With these, I explore the CO excitation conditions from kiloparsec to 100s-parsec scales and show how it is possible to also assess the chemical enrichment of C and O elements. Furthermore, I present the analysis of variations in the CO-to-H_2 conversion factor, which is needed accurately translate the observed CO brightness into a H_2 mass surface density. I link these variations to different galactic environments across the entire molecular disk of iconic star-forming galaxies such as M51 and M101. In summary, my talk illustrates the use of CO line observations as diagnostics for accurately accounting and interpreting the variation of CO-derived properties of the molecular ISM from kiloparsec to 100s-parsec scales.

Challenges in Time-Varying Causal Effect Moderation Analysis in Mobile Health   Presented by Walter Dempsey, University of Michigan Friday, 24/01/25, 11am, ET OnlineWebex link:https://tinyurl.com/rmme-Dempsey (https://tinyurl.com/rmme-Dempsey)Meeting # 2633 697 8890Password: RMMESTATAbstract: Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions in multiple domains of health sciences. Sequentially randomized experiments called micro-randomized trials (MRTs) have grown in popularity as a means to empirically evaluate the effectiveness of mHealth intervention components. MRTs have motivated a new class of causal estimands, termed "causal excursion effects", that allow health scientists to answer important scientific questions about how intervention effectiveness may change over time or be moderated by individual characteristics, time-varying context, or past responses. In this talk, we present two new tools for causal effect moderation analysis. First, we consider a meta-learner perspective, where any supervised learning algorithm can be used to assist in the estimation of the causal excursion effect. We will present theoretical results and accompanying simulation experiments to demonstrate relative efficiency gains. Practical utility of the proposed methods is demonstrated by analyzing data from a multi-institution cohort of first year medical residents in the United States. Second, we will consider effect moderation with tens or hundreds of potential moderators. In this setting, it becomes necessary to use the observed data to select a simpler model for effect moderation and then make valid statistical inference. We propose a two-stage procedure to solve this problem that leverages recent advances in post-selective inference using randomization. We will discuss asymptotic validity of the conditional selective inference procedure and the importance of randomization. Simulation studies verify the asymptotic results. We end with an analysis of an MRT for promoting physical activity in cardiac rehabilitation to demonstrate the utility of the method. .Bio:Walter Dempsey is an Assistant Professor in the Department of Biostatistics, Assistant Professor of Data Science at the Michigan Institute of Data Science (MIDAS), and an Assistant Research Professor in the d3lab located in the Institute of Social Research at the University of Michigan. His research focuses on Statistical Methods for Digital and Mobile Health. Specifically, his current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks.

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/). Providers rotate weekly on Fridays! Check out more of our mental health providers here (https://studenthealth.uconn.edu/staff-directory/?&group=MENTAL%20HEALTH%20PROVIDERS).