In this talk, I will summarize recent advances in computational design of new macromolecular materials that make use of nanoscale topologies, such as brushes, networks, and folded loops, that result in exceptional mechanical properties. I will first present physics-based and machine learning approaches that were developed to describe molecular and mesoscale mechanics of polymers and polymer-grafted nanoparticle systems. Following this, I will present strategies for achieving higher strength, toughness, and impact tolerance in soft materials. These strategies involve the use of star polymers and polymer grafted nanoparticles to improve diametrically opposed mechanical properties such as modulus and toughness, while also controlling the time-dependent characteristics of the response. The effect of nanoconfinement (and its release) on the modulus and toughness of polymer-grafted nanoparticles will be discussed. I will conclude with some thoughts on how to translate these findings to new material concepts that could be explored further with synergistic experiments and simulations.

Start Of Term: Welcome To New Faculty Members

Roommates of nosocomial methicillin-resistant Staphylococcus aureus (MRSA) cases have a high risk of MRSA acquisition. Guidelines recommend that these individuals be isolated and undergo surveillance testing however, the optimal surveillance testing and isolation strategies are unknown.

Child welfare agencies responsible for facilitating regular visits between foster children and their biological parents face challenges due to fixed workforce levels and varying caseloads. We introduce the Foster Care Visitation Scheduling Problem to effectively assign, schedule, and route workers for these visits. Our solution uses a two-phased, network-based optimization approach that (1) preprocesses and constructs a time-space network structure, and (2) solves a novel, large-scale integer optimization problem over this network. This approach, integrated into an accessible interface, enables foster care organizations to optimize resource allocation, enhancing visit consistency and ultimately improving foster childrens quality of life. Computational experiments on instances inspired by real data from New York State demonstrate promising performance.

Universal bounds for the potential energy of weighted spherical codes are obtained by linear programming. The universality is in the sense of Cohn-Kumar every attaining code is optimal with respect to a large class of potential functions (absolutely monotone), in the sense of Levenshtein there is a bound for every weighted code, and in the sense of parameters (nodes and weights) they are independent of the potential function. We derive a necessary condition for optimality (in the linear programming framework) of our lower bounds which is also shown to be su cient when the potential is strictly absolutely monotone. Bounds are also obtained for the weighted energy of weighted spherical designs. We explore our bounds for several previously studied weighted spherical codes.

Intelligent assistants and search engines are commonly being used to complete complex tasks. Hence, systems that can provide users with contextual task completion assistance with as little input from the user as possible are needed. In this talk, I will present our research in devising general purpose task completion agents. I will first start with the research we have done in incorporating information about users tasks and needs to search engine design. I will then describe how we have built upon this work to design conversational systems that can provide general purpose task completion assistance.

In recent years, differential privacy has emerged as the de facto standard for sharing statistics of datasets while limiting the disclosure of private information about the involved individuals. This is achieved by randomly perturbing the statistics to be published, which in turn leads to a privacy-accuracy trade-off: larger perturbations provide stronger privacy guarantees, but they result in less accurate statistics that offer lower utility to the recipients. Of particular interest are therefore optimal mechanisms that provide the highest accuracy for a pre-selected level of privacy. To date, work in this area has focused on specifying families of perturbations a priori and subsequently proving their asymptotic and/or best-in-class optimality.

Fixed Wireless Access (FWA) is one of the fastest growing use cases in 5G deployments. Global projections indicate that FWA subscriber base will exponentially increase, anticipated to reach approximately 265 million subscribers by 2029. While traditional mobile-centric cellular network infrastructures can theoretically accommodate FWA users, the distinctive characteristics of fixed wireless connectivitycharacterized by high data consumption and minimal tolerance for service interruptions creates unique challenges to be addressed. Conversely, the inherent fixed nature of FWA technology offers strategic opportunities for network optimization and potential infrastructure cost reductions within cellular network ecosystems.

Our societys ever-increasing dependence on electronic devices is driving an exponential rise in energy demand. To ensure a sustainable future, the development of energy-efficient materials for electronics is critical. Ferroelectric materials, with their intrinsic spontaneous electric polarization, hold promising potential to address this challenge. However, their large-scale integration into everyday electronics is hindered by issues such as stability concerns and limited control over their properties. In this talk, I will introduce ferroelectric thin films and how we can control their properties, with a focus on the layered perovskite Aurivillius ferroelectrics.

The tradition of marketing only aesthetically agreeable produce by retailers contributes to a major source of food waste and loss through