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July 2024
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Tuesday, July 30, 2024
- All dayArt Exhibit: Origins & Forgotten in TimeExhibits featuring painter Catie Lewis and photographer Mallorie Ostrowitz
- All dayOpen Air 2024 Sculpture ExhibitionThe 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.
- All dayUConn ECE Alumni Survey - HS class of 20231-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 dayUConn Summer Horseback Riding
- All dayUConn Summer Horseback Riding
- All dayUConn Summer Horseback Riding
- All dayUrology Grand Rounds
- 8:00 AM8h 30m2024 Summer OrientationAll 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.
- 8:00 AM8h 30m2024 Summer OrientationAll 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.
- 10:00 AM1hDoctoral Dissertation Oral Defense of Cynthia BooDissertation Title: Dyadic Speech Between Friends During Game Play: Insights into Friendship via Language Use Field of Study: Psychological Sciences - Developmental Division
- 11:00 AM30mGroup Fitness Class – Equipment OrientationsFor the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).
- 11:00 AM1hBusiness Career Development Office | Drop-In HoursVirtual 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)
- 11:00 AM1hBusiness Career Development Office | Drop-In HoursVirtual 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)
- 11:00 AM1hBusiness Career Development Office | Drop-In HoursVirtual 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)
- 11:00 AM1hDoctoral Dissertation Oral Defense of Ashkan NovinDoctoral Dissertation Oral Defense of Ashkan Novin Title: Why does cancer metastasize in humans and not cows? Department: Biomedical Engineering Department
- 11:00 AM1hDoctoral Dissertation Oral Defense of Ashkan NovinDoctoral Dissertation Oral Defense of Ashkan Novin Title: Why does cancer metastasize in humans and not cows? Department: Biomedical Engineering Department
- 11:00 AM1hDoctoral Dissertation Oral Defense of Elise TavernaTitle: "Autism-Relevant Skills and Behaviors in Neurogenetic Syndromes" Field of study: Clinical Psychology
- 11:00 AM1hDoctoral Dissertation Oral Defense of Elise TavernaTitle: "Autism-Relevant Skills and Behaviors in Neurogenetic Syndromes" Field of study: Clinical Psychology
- 11:00 AM1hDoctoral Dissertation Oral Defense of Elise TavernaAutism-Relevant Skills and Behaviors in Neurogenetic Syndromes
- 12:00 PM1hAdmissions Fall 2024 Drop-In HoursLooking to learn more about a PharmD degree? Join one of our virtual drop-in sessions to chat with a member of our admissions team.
- 12:00 PM1h 30mDoctoral Dissertation Oral Defense of Alia PughThe 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.
- 12:00 PM1h 30mDoctoral Dissertation Oral Defense of Alia PughThe 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.
- 12:05 PM45mGroup Fitness Class – Yoga Flow (45)For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).
- 1:00 PM1hANSC MS Defense: Mr. Nicholas BarnelloANSC 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
- 1:00 PM1hANSC MS Defense: Mr. Nicholas BarnelloANSC 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
- 1:30 PM2hDoctoral Dissertation Oral Defense of Zihan WangDesign, 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.
- 1:30 PM2hDoctoral Dissertation Oral Defense of Zihan WangDesign, 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.
- 4:30 PM1hGroup Fitness Class – Gentle YogaFor the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).
- 4:30 PM1hGroup Fitness Class – Total Body StrengthFor the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).