Children’s lives are now inextricably linked with AI-driven digital systems that shape learning, social interaction, and development. This has elevated child online safety to a central concern for families, policymakers, […]
Artificial neural networks can now learn to play games, control robots, generate language, and solve complicated reasoning tasks, yet we still lack a clear understanding of how to directly guide […]
End-to-end neural generation models have largely displaced the modular architectures that once gave dialogue system designers explicit control over what is said and how it is said. While these models […]
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes, but building reliable machine learning models is […]
Current generative vision models struggle with image customization that requires multi-step reasoning or real-world knowledge. This proposal introduces generalized image customization, enabling systems to execute complex, inferential modifications rather than […]
As multimodal artificial intelligence systems become increasingly embedded in everyday technology, there is a growing need to design human-centered AI agents that support and amplify human capabilities rather than replace […]
Advanced driver-assistance systems (ADAS) are commonly organized as modular pipelines that transform raw sensor measurements into low-level actuation commands through perception, planning, and control. While learning-based methods have achieved state-of-the-art […]
Register transfer level (RTL) simulation is an invaluable tool for developing, debugging, verifying, and validating hardware designs. However, the performance of RTL simulation has long been a limiting factor in […]
Game analysts face a significant challenge in understanding problem-solving and decision-making processes from the vast and complex sequential data generated by modern video games. Existing visualization tools often fail to […]
Modeling environmental extremes often involves large collections of spatial or temporal records where both clustering similar series and modeling dependence among extremes are challenging tasks. This Ph.D. proposal addresses several […]
The Right Livelihood International Conference is a five-week global conference exploring how education can strengthen democracy, collective intelligence, and just futures. Bringing together Right Livelihood Laureates, students, faculty, and community partners […]
Robotic space missions often use discrete event simulation to reduce risk in operation. A simulation applies a set of planned activities to a model of mission resources, and the model’s observed behavior is used to predict real-world outcomes. However, logically concurrent activities are typically simulated under one possible linearization of their events – an order […]
Please join the April 8th Archaeology and Biological Anthropology Lunch Talk, “The Emergence of Maritime Archaeology in the Republic of Benin: Research, Challenges, and Ongoing Initiatives,” presented by Affolabi Angelo Ayedoun (Anthropology, UC Santa Cruz).
Sequencing technologies have transformed population and evolutionary genetics, making it possible to ask questions at scales that were intractable a decade ago. Realizing that potential depends on tailored computational approaches, and on the tools and infrastructure those approaches are built on. My dissertation works across this spectrum. Using an in vitro Drosophila cell culture system, […]
Flow cytometry is a valuable technique in microbial research used to measure the optical properties of single-celled organisms at high throughput. Oceanographers often deploy flow cytometers on research cruises in order to study the characteristics of phytosynthetic microbes—called phytoplankton—in regions and times with diverse environmental conditions. Because cytometers cannot distinguish between subpopulations, researchers typically cluster […]
Oceanographic data, generated by modern technologies that measure biological systems across time, space, and cell populations, are often rich, high-dimensional, and highly heterogeneous. Such data provide valuable opportunities to study subcellular organization, cellular heterogeneity, and dynamic biological processes in marine environments. However, because marine plankton systems remain relatively understudied and less well characterized than many […]
Modern scientific studies increasingly rely on complex datasets exhibiting spatial and temporal dependence, particularly in social, environmental, and climate applications. This dissertation develops statistical models and computational methods for analyzing such data, with an emphasis on capturing dependence structures, nonlinear dynamics, and uncertainty quantification. A spatial deep learning framework is developed to extend classical geostatistical […]
Complex societal problems (e.g. wicked problems) such as those brought on by climate change can be addressed through a combination of Research through Design (RtD), co-design, and Serious Games (SG) by inviting affected communities to take part in developing iterative, experimental solutions and exploring their potential impact. In the course of my research, I have […]
Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However, in the modern era, computational environments have changed drastically, and we […]
Advances in microscopy have transformed our understanding of biological systems, yet the high cost and limited accessibility of commercial imaging platforms continue to re- strict their use in many research settings. This thesis presents the design and development of open hardware microscopy tools for neuroscience research, with a focus on integrating user- centered design principles […]