The Graduate School

The role of temperature and thermal adaptation on plankton ecology

Oceanography

Title: Levy Processes Governed by Telegraph Signal Process: Inferences and Applications
Field of Study: Statistics

Title: Some Approaches to Feature Extraction Applied to Sparse High Dimensional Spatio-Temporal Data.
Field of Study: Statistics

Towards High Performance Model Inference and Training: From Algorithm to Hardware
Doctoral Field of Study: Computer Engineering

Title: Posttraumatic Stress Symptoms and Physical Activity among Young Adults with Chronic Pain
Doctoral Field of Study: Clinical Psychology

Title: “My Trauma Motivates Me.” Adopted Students’ Success in Postsecondary Education: A Descriptive Study

Field of Study: Educational Psychology (Counselor Education/Counseling Psychology)

Title: On Large-Scale Transfer Learning with Heterogeneous Data
Doctoral field of study: Statistics

Human Development and Family Sciences

Influence of Health Information Saliency and Sociodemographic Moderators in Surgical Decision-Making and Regret among Early-Stage Breast Cancer Survivors

Doctoral Field of Study: Philosophy
Dissertation Title: Expressivist Semantics, Metaethical Taxonomy and Functional Explanation

Ph.D. candidate in Philosophy

“Expressivist Semantics, Metaethical Taxonomy and Functional Explanation”

Please attend:

Doctoral Dissertation Oral Defense

Title: Securing BGP ASAP: ASPA and other post-ROV policies

Ph.D. Candidate: Justin Furuness

Major Advisor: Dr. Amir Herzberg
Associate Advisors: Dr. Bing Wang, Dr. Ghada Almashaqbeh

Date/Time: Friday July 12th 2PM 2024

Location: ITE201

WebEx

Meeting LineL https://meetingsamer41.webex.com/meetingsamer41/j.php?MTID=me9cba4aa1f391c40b1225841520e5313
Meeting number: 2553 067 2425
Password: pgMGEhKr968 (74643457 when dialing from a video system)

Abstract:
Before the adoption of Route Origin Validation (ROV), prefix and subprefix hijacks were the most effective and common attacks on BGP routing. Recent studies indicate that ROV adoption is increasing; with sufficient adoption, prefix and subprefix attacks become ineffective. We study this changing landscape and, in particular, the Autonomous System Provider Authorization (ASPA) proposal, which focuses on route leakage but also thwarts some BGP attacks.

Using recent measurements of real-world ROV adoption, we evaluate its security impact. Our simulations show a substantial impact; already today, prefix hijacks are less effective than origin hijacks. Therefore, we expect attackers to shift to origin hijacks and other post-ROV attacks.

We present extensive evaluations of the impact of ASPA, comparing it to alternatives such as BGPsec, Path-End, OTC, and EdgeFilter. We assess the defense against multiple post-ROV attacks, including a novel attack, neighbor spoofing, which is extremely powerful if not blocked. We show that ASPA significantly protects against post-ROV attacks, even with partial adoption—contrasting with BGPsec. However, interestingly, we find that ASPA is not better than OTC at preventing unintentional route leaks.

The ASPA proposal presents a scenario where ASPA fails, involving a provider AS attacking an AS in its customer cone. We show that ASPA can similarly fail against an edge attacking AS. We present a possible fix; however, we also demonstrate that the fix does not significantly improve ASPA’s defenses against a random attacker.

ANSC PhD Defense: Ms. Amanda Reiter
Date: 07/15/2024
Time: 9:00 AM - 10:00 AM
Location: George White Building, Room 115 (York)

If you require an accommodation to participate in this event, please contact Dr. Sarah Reed at 860-486-8452 or sarah.reed@uconn.edu at least 5 days in advance of the seminar

Doctoral Dissertation

Dissertation Title: Approach-Avoidance Behaviors Toward Primary and Secondary Reinforcers Associated with Nicotine and Cannabis
Doctoral Field of Study: Psychological Sciences - Behavioral Neuroscience

Title: Investigations in Irregularly-spaced High Frequency Financial Time Series
Field of study: Statistics

Volatility modeling is crucial in finance, particularly for intra-day transaction-level asset returns. The irregular and high-frequency nature of this data, characterized by random gaps between successive transactions, presents unique challenges. This dissertation introduces a new set of time series models designed to accommodate these random gaps, effectively modeling intra-day asset returns in both univariate and multivariate setups.

Impact of Freezing Conditions on the Porous Structure of Freeze-Dried Cakes and Primary Drying Efficiency
In pharmaceutical sciences

Reframing The Supplemental Nutrition Assistance Program (SNAP): Applying a Human Rights Framework to Federal Food Assistance in the United States - UConn School of Social Work.

Deutschland postmigrantisch: Zugehörigkeitspraktiken im digitalen Raum
Department of Literatures, Cultures, and Languages
German Studies

Dissertation titled Haddock: A Language and Platform for MDD-Based Constraint Programming. Field: Computer Science.

Characterization of a resistance gene for white pine blister rust through the annotation of a complex conifer genome

Generalization of Phonetic Recalibration Across Talkers is Constrained by Acoustic Similarity, a dissertation by David Saltzman in the Department of Psychological Sciences, Perception, Action, and Cognition - Language & Cognition program.

Doctoral Dissertation Defense in Chemical Engineering.
Title: “In-situ Laser Diagnostics for the Characterization of Flame-Synthesized Nanoparticles”

Essays on Digital Innovations in Online Platforms
Field of study: Operations and Information Management

Title: Assessing in situ Forming Implant Formulations Using Real Time Imaging

Dissertation Title: Socio-demographic, behavioral and state safety policy factors associated with multiple concussions in early adolescence (11–14-year-olds) in the 2017 - 2021 Youth Risk Behavior Surveillance Study

Biomimetic Supramolecular Materials Assembled from Synthetic Polypeptides
Field of study: Polymer Science, Ph.D.

Anion Exchange Membrane Cells: Fundamentals and Applications for Production of Hydrogen and Electricity

Dissertation Title: Devolution and Human Rights in Welfare Policy: A Comprehensive Analysis of Temporary Assistance for Needy Families (TANF). Department: School of Social Work

Field of Study: English
Dissertation Title: Spacetimes of Hope: Feminist and Queer Utopias in Postcolonial Anglophone Literatures

The Multi-Modal Measurement of Child Chronic Irritability Across Timescales

The Two-Photon Exchange Contribution to Electron-Neutron Elastic Scattering (nTPE) and Extraction of GMn at Q^2 = 4.5 GeV^2 in Hall A at Jefferson National Lab

Field of study: Physics

Bioaccumulation and chronic toxicity of diet borne Cu and Zn into freshwater macroinvertebrates from urban stream sources.

PhD Candidate in Philosophy

“Hacking Reality: Propaganda and Epistemology in Online Environments”

Title: Presenting a Motif-Based Model for Clavanin Activity
Doctoral Field of Study: Chemistry
Synopsis: This presentation will focus on findings concerning the antimicrobial activity of metal-binding peptides from the tunicate, Styela clava. Antimicrobial peptides (AMPs) are one of the present lines of inquiry for combatting the growing problem of antibiotic resistance.

Interfacing Information Technology and Molecular Biology for New Applications and Discoveries of CRISPR-Cas12a (Field of Study: Biomedical Engineering)

Enhancing Attention-Based Models by Learning Dynamics with Static Content

Title: Multistage Minimum Risk Fixed-Width and Fixed-Size Confidence Set for a Univariate Normal Mean and for a Multivariate Normal Mean Vector with Data Analysis

Doctoral Field of Study: Statistics

This dissertation explores the development and application of multistage minimum risk methods for constructing fixed-width and fixed-size confidence sets. These methods are applied to both univariate normal means and multivariate normal mean vectors, accompanied by data analysis to demonstrate their efficacy.

Nagham Farah, from the Biomedical Science PhD Program in the Genetics and Developmental Biology area of concentration will be having her public PhD Thesis Defense titled, “Cerebellar Hemisphere Formation: Purkinje Cell Diversity and the FOXP Code”. Please join her on Thursday, July 25, 2024 at 10:00AM in the Edmund and Arlene Grossman Auditorium at the Cell & Genome Sciences Building (400 Farmington Ave, Farmington, CT) or virtually via the Webex link below.
Webex:
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m80f6c2b71a8ff45557d918a62cbfa5ca

Nagham Farah, from the Biomedical Science PhD Program in the Genetics and Developmental Biology area of concentration will be having her public PhD Thesis Defense titled, “Cerebellar Hemisphere Formation: Purkinje Cell Diversity and the FOXP Code”. Please join her on Thursday, July 25, 2024 at 10:00AM in the Edmund and Arlene Grossman Auditorium at the Cell & Genome Sciences Building (400 Farmington Ave, Farmington, CT) or virtually via the Webex link below.
Webex:
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m80f6c2b71a8ff45557d918a62cbfa5ca

Dissertation entitled, “Creating Diverse, Equitable, and Inclusive Organizational Climates: A Social Dominance Perspective,” presented as partial fulfillment of a Ph.D. in Industrial and Organizational Psychology from the Department of Psychological Sciences.

Title: “A Bayesian Framework for Social Network Analysis and Associated Models”

Department of Statistics

Development of Advanced Microneedle Arrays for the Treatment of Chronic Wounds
Biomedical Engineering

Doctoral Defense for the Biomedical Sciences PhD, Cell Biology Area of Concentration.

Doctoral Defense for the Biomedical Sciences PhD, Cell Biology Area of Concentration.

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When There are No Colleges Nearby: The Pursuit of Economic Mobility in an Education Desert, Department of Sociology

Title: Bayesian Model Assessment for Integrating Response Times with Cognitive Diagnosis Models
Field of Study: Statistics

This dissertation constructs joint models integrating response accuracy and response time within a Bayesian Markov chain Monte Carlo framework and introduces a new model selection criterion, enhancing educational insights into cognitive processes, test-taking strategies, and latent abilities through real-world data analysis.

ANSC MS Defense: Mr. Gopi Yalavarthi
Date: 07/29/2024
Time: 2:00 PM - 3:00 PM
Location: George White Building, Room 115 (York)

If you require an accommodation to participate in this event, please contact Dr. Jenifer Nadeau at 860-486-4471 or jenifer.nadeau@uconn.edu at least 5 days in advance of the seminar

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

ANSC MS Defense: Mr. Nicholas Barnello
Date: 07/30/2024
Time: 1:00 PM - 2:00 PM
Location: 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 or 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.