Exhibits featuring painter Catie Lewis and photographer Mallorie Ostrowitz

The exhibiting artists are Jenny Carpenter, the CoyWolf Collective, Dan Devine, Walter Early, Johanna Jackson, Cara Lynch, R. Douglass Rice, Sebastian Shames, Margaret Roleke and Martha Willette Lewis. Plan your visit to explore these exciting sculptures and their relationship to the coastal landscape.

1-year out alumni surveys have been emailed to the class of 2023. Surveys were sent to the email address used for registration. Please contact carissa.rutkauskas@uconn.edu (mailto:carissa.rutkauskas@uconn.edu) if you are a UConn ECE alumni from the high school class of 2023 and did not receive an email with the survey link.

All incoming UConn students (both transfer and first-year) are required to attend one of our On-campus New Student Orientation sessions. These sessions give incoming students the opportunity to learn more about our campus resources, to meet other incoming students, to consult with an Academic Advisor, and to create their upcoming semester class schedule. 

Dissertation Title: Dyadic Speech Between Friends During Game Play: Insights into Friendship via Language Use Field of Study: Psychological Sciences - Developmental Division

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

Virtual drop in hours (https://career.business.uconn.edu/undergraduate/appointment/) are Monday-Friday via Nexus. You can also make an appointment (https://career.business.uconn.edu/undergraduate/appointment/) with one of our career counselors or email your career questions to recruit@business.uconn.edu. (mailto:recruit@business.uconn.edu)

Virtual drop in hours (https://career.business.uconn.edu/undergraduate/appointment/) are Monday-Friday via Nexus. You can also make an appointment (https://career.business.uconn.edu/undergraduate/appointment/) with one of our career counselors or email your career questions to recruit@business.uconn.edu. (mailto:recruit@business.uconn.edu)

Doctoral Dissertation Oral Defense of Ashkan Novin Title: ‎ Why does cancer metastasize in humans and not cows?‎ Department: Biomedical Engineering Department

Doctoral Dissertation Oral Defense of Ashkan Novin Title: ‎ Why does cancer metastasize in humans and not cows?‎ Department: Biomedical Engineering Department

Title: "Autism-Relevant Skills and Behaviors in Neurogenetic Syndromes" Field of study: Clinical Psychology

Autism-Relevant Skills and Behaviors in Neurogenetic Syndromes

Title: "Autism-Relevant Skills and Behaviors in Neurogenetic Syndromes" Field of study: Clinical Psychology

Looking to learn more about a PharmD degree? Join one of our virtual drop-in sessions to chat with a member of our admissions team.  

The Effects of a Running Glossary and Morphological Family Size on Academic Word Learning in Middle School Students, a doctoral dissertation from the Department of Educational Psychology.

The Effects of a Running Glossary and Morphological Family Size on Academic Word Learning in Middle School Students, a doctoral dissertation from the Department of Educational Psychology.

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

ANSC MS Defense: Mr. Nicholas BarnelloDate: 07/30/2024Time: 1:00 PM - 2:00 PMLocation: George White Building, Room 115 (York)If you require an accommodation to participate in this event, please contact Dr. Kristen Govoni at 860-486-2919 (tel:+18604862919) orkristen.govoni@uconn.edu (mailto:kristen.govoni@uconn.edu)at least 5 days in advance of the seminar

ANSC MS Defense: Mr. Nicholas BarnelloDate: 07/30/2024Time: 1:00 PM - 2:00 PMLocation: George White Building, Room 115 (York)If you require an accommodation to participate in this event, please contact Dr. Kristen Govoni at 860-486-2919 (tel:+18604862919) orkristen.govoni@uconn.edu (mailto:kristen.govoni@uconn.edu)at least 5 days in advance of the seminar

Design, Performance Evaluation and Uncertainty Analysis of Metamaterials School of Mechanical, Aerospace, and Manufacturing Engineering Abstract: Mechanical metamaterials are artificial structures that possess exceptional mechanical properties that are not naturally occurring. The complex geometrical and topological features of these metamaterials pose significant challenges to metamaterial-related research questions. Therefore, methodologies are proposed for solving three key research questions regarding metamaterial: design of metamaterial units, metamaterial units' performance evaluation, and uncertainty analysis of the complex topological structures. In the area of design metamaterial unit, deep generative model-based design frameworks are proposed for the design of metamaterial units, where deep learning models are used as feature extractor to extract the complex topological features from metamaterial units to a low-dimensional latent feature space. The latent feature space is linked by supervised learning models to the metamaterial units' performance metrics (mechanical properties, manufacturability metrics, etc.). Inverse design optimization is then performed for obtaining optimal metamaterial units with desired performance. In this dissertation, four kinds of metamaterials are designed: 2D phononic metamaterials, 3D metamaterials considering manufacturability, 3D connectivity-guaranteed porous metamaterials, and metamaterials designed with uncertainty considerations. For predicting the properties of metamaterials, deep learning models are developed to solve the structure-property relationship, leveraging their capability to handle high-dimensional, nonlinear mappings. However, real-world engineering applications often face the challenge of limited data, where traditional data-driven deep learning models may fall short. To overcome this, physics-embedded deep learning models are proposed, addressing the small data issue by incorporating physical principles. These models demonstrate superior performance compared to conventional deep learning approaches. In terms of manufacturing uncertainty analysis, the dissertation first introduces methods for quantitatively representing aleatoric uncertainty in network-like topological structures. This representation method is then extended to any topological structures using the shortest-path distance. Machine learning techniques are employed to mitigate input uncertainties in complex topologies, enhancing the efficiency of uncertainty quantification. Additionally, uncertainty propagation methods are proposed to trace aleatoric uncertainties through metamaterial systems. The proposed methodologies are validated through examples, highlighting their benefits. Overall, this dissertation provides insights into the design and manufacturing of metamaterial units.

Design, Performance Evaluation and Uncertainty Analysis of Metamaterials School of Mechanical, Aerospace, and Manufacturing Engineering Abstract: Mechanical metamaterials are artificial structures that possess exceptional mechanical properties that are not naturally occurring. The complex geometrical and topological features of these metamaterials pose significant challenges to metamaterial-related research questions. Therefore, methodologies are proposed for solving three key research questions regarding metamaterial: design of metamaterial units, metamaterial units' performance evaluation, and uncertainty analysis of the complex topological structures. In the area of design metamaterial unit, deep generative model-based design frameworks are proposed for the design of metamaterial units, where deep learning models are used as feature extractor to extract the complex topological features from metamaterial units to a low-dimensional latent feature space. The latent feature space is linked by supervised learning models to the metamaterial units' performance metrics (mechanical properties, manufacturability metrics, etc.). Inverse design optimization is then performed for obtaining optimal metamaterial units with desired performance. In this dissertation, four kinds of metamaterials are designed: 2D phononic metamaterials, 3D metamaterials considering manufacturability, 3D connectivity-guaranteed porous metamaterials, and metamaterials designed with uncertainty considerations. For predicting the properties of metamaterials, deep learning models are developed to solve the structure-property relationship, leveraging their capability to handle high-dimensional, nonlinear mappings. However, real-world engineering applications often face the challenge of limited data, where traditional data-driven deep learning models may fall short. To overcome this, physics-embedded deep learning models are proposed, addressing the small data issue by incorporating physical principles. These models demonstrate superior performance compared to conventional deep learning approaches. In terms of manufacturing uncertainty analysis, the dissertation first introduces methods for quantitatively representing aleatoric uncertainty in network-like topological structures. This representation method is then extended to any topological structures using the shortest-path distance. Machine learning techniques are employed to mitigate input uncertainties in complex topologies, enhancing the efficiency of uncertainty quantification. Additionally, uncertainty propagation methods are proposed to trace aleatoric uncertainties through metamaterial systems. The proposed methodologies are validated through examples, highlighting their benefits. Overall, this dissertation provides insights into the design and manufacturing of metamaterial units.

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

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