Dissertation title: "Building a DAM: Parameters of differential argument marking" Doctoral field of study: Linguistics

Doctoral Dissertation Oral Defense of Milda Stanislauskas Title: "Mechanical Regulation of Focal Adhesion Kinase" Major Advisor: Dr. Yi Wu Location: Cell & Genome Sciences Building, Edmund & Arlene Grossman Auditorium, R1210, 400 Farmington Ave. Via Webex: https://uchc.webex.com/uchc/j.php?MTID=m9805b829e2d0616366ac6bee068ad042 (https://uchc.webex.com/uchc/j.php?MTID=m9805b829e2d0616366ac6bee068ad042) Meeting Number: 2870 003 0074 Meeting Password: PublicDefense

ANSC PhD Defense: Trushenkumar ShahDate: 07/9/2025Time: 11:00 AMLocation: HybridIf you require an accommodation to participate in this event, please contact Dr. Abhinav Upadhyay at 860-486-6585 orabhinav.upadhyay@uconn.edu (mailto:abhinav.upadhyay@uconn.edu)at least 5 days in advance of the seminar

Title: Patterns and Processes of Community Structure in Hummingbird Flower Mites Doctoral Field of Study: Ecology and Evolutionary Biology.

Patterns of Mercury and Methylmercury Bioaccumulation in Marine Bivalve Molluscs Oceanography

title of dissertation: Exploration of Butyrophilin Family Protein Functions in Activating γ9δ2 T Cells in Response to Phosphoantigens and Cell Stress doctoral field of study: Medicinal Chemistry, Department of Pharmaceutical Sciences, School of Pharmacy,

EEB Dissertation Defense: Insights Into Land Plant Genome Annotation, Evolution, And Conservation Through Improved Bioinformatic Approaches And Genomic Technologies Please join us for an engaging seminar that explores adaptations in land plant genomes (some of the smallest and largest will be featured!)

Shawn Cummings, in the Language & Cognition division of the Department of Psychological Sciences, will be defending his dissertation: "Linking Lexically Guided Perceptual Learning to Statistical Patterns in Speech Input". ABSTRACT: Listeners use lexical information to modify the mapping between speech acoustics and speech sound categories. Despite convention to consider lexically guided perceptual learning as a binary outcome, the magnitude of the learning effect varies in the extant literature. We hypothesize that graded learning outcomes can be linked, in part, to statistical characteristics of the to-be-learned input, consistent with the ideal adapter theory of speech adaptation. We begin with creation and analysis of a lexically guided perceptual learning corpus including stimulus sets for the /ʃ/-/s/ contrast for each of 16 talkers following standard methods (i.e., waveform averaging to create ambiguous variants), yielding variability in the statistical cues specifying this contrast across talkers (Experiment 2). We then analyze the perceptual consequences of this variability on perception prior to experimentally induced bias (Experiment 2). Finally, we (a) measure lexically guided perceptual learning for each talker, (b) identify input characteristics that are associated with learning magnitude, and (c) examine whether a computational instantiation of the ideal adapter theory can model the input-learning link (Experiment 3). Robust learning is observed for 13 of 16 talkers, with magnitudes of learning strongly convergent between behavior and model simulations. These results provide a critical and successful test of the ideal adapter framework for speech adaptation, thus informing an understanding of the mechanisms that allow listeners to solve the lack of invariance problem for speech perception.

U n i v e r s i t y of C o n n e c t i c u t DEPARTMENT OF PATHOBIOLOGY & VETERINARY SCIENCE Doctoral Dissertation Defense of Elizabeth Ramirez-Medina Major Advisors: Dr. Guillermo Risatti Associate Advisors: Dr. Paulo Verardi, Dr. Antonio Garmendia and Dr. Manuel Borca Title: "Assessment of the biological function of African Swine Fever Virus genes" Wednesday, July 16, 2025, 11am ESB 121 To attend virtually via WebEx please use the following link: https://uconn-cmr.webex.com/uconncmr/j.php?MTID=me1071857f38dad6d504c2b27e7f0b96d

Who: Sachin Tripathi, PhD Candidate in Civil Engineering (Structural Engineering). What: PhD Dissertation Defense for the dissertation titled: "Three-Dimensional Temperature Distribution and Thermal Induced Responses of a Lunar Habitat Structure Considering Regolith Cover and Self Shadow Effect." Why: As lunar exploration advances, this study addresses critical challenges in designing safe, sustainable lunar habitats. The findings provide vital insights for robust structural designs, contributing significantly to structural engineering for extraterrestrial environments. This defense is an opportunity for the UConn community, including students, faculty, and researchers, to engage with cutting-edge research for future lunar missions.

Covariate-Assisted Stochastic Block Models: Feature Selection and Latent Factors

Rachel V. Tucker (Ph.D. Candidate, Department of Communication, University of Connecticut) will defend her doctoral dissertation titled, "Communicative Disenfranchisement During Childbirth: Examining the Outcomes Associated with and Role of Race/Ethnicity and Health Insurance Status in Shaping Black or African American, Hispanic or Latina, and White Women's Interactions with Providers During Childbirth" on Friday, July 18, 2025 at 10:00 a.m. EDT on WebEx. This study used the post-positivist articulation of the theory of communicative disenfranchisement to examine the outcomes associated with adverse interactions between healthcare providers and Black or African American, Hispanic or Latina, and White cisgender women (N = 230) receiving care for childbirth in the U.S., and the intersecting role of race/ethnicity and health insurance status in shaping such interactions. The findings from this study offer theoretical insights that extend our understanding of communicative disenfranchisement as a contextually dependent process, as well as practical insights regarding how to improve maternal healthcare interactions. A copy of the dissertation is available by emailing Michael J, Melnik at michael.melnik@uconn.edu.

Title: Advancing Marginal Likelihoods for Longitudinal Data Analysis Field of Study: Statistics This dissertation proposes a novel Bayesian method that decomposes the marginal likelihood into two distinct components, enabling better assessment of covariance structures conditional on the mean structure in repeated measures data.

Elena is a PhD candidate in the Department of Anthropology at the University of Connecticut and PhD researcher with the ICArEHB center at the University of Algarve Portugal. Join us on zoom as she defends her dissertation - "Ancient Futures - Paleoclimate Changes and Social Resilience in late Quaternary Mozambique"

Dissertation Title: Multi-compartment proteomic signatures of sex-specific responses to exercise-heat stress and acclimation. Department of Kinesiology, CAHNR.

This event will be in person, but there will also be an online viewing option. Email Jessica Bourget at jessica.bourget@uconn.edu for the zoom link.