Quantum computing is an emerging field with the potential advantages in many fields. In this talk, I will provide an introduction to quantum optimization algorithms, focusing on Quantum Annealing, Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE). We will explore the real-world applications of these algorithms, highlighting their advantages, limitations, and the current state of research. The discussion will also cover the challenges of implementing these algorithms on near-term quantum hardware and their potential to address complex optimization problems.

We introduce a new technique to construct rank-metric codes using the arithmetic theory of Drinfeld modules over global fields, and Dirichlet Theorem on polynomial arithmetic progressions. Using our methods, we obtain a new infinite family of optimal rank-metric codes with rank-locality, i.e. every code in our family achieves the information theoretical bound for rank-metric codes with rank-locality. This is a joint work with Giacomo Micheli and Luca Bastioni.

Abstract: Tuning protein expression by targeting RNA structure using small molecules is an unexplored avenue for cancer treatment. To understand whether this vulnerability could be therapeutically targeted in the most lethal form of prostate cancer, castration-resistant prostate cancer (CRPC), we use a clinical small molecule, zotatifin, that targets the RNA helicase and translation factor eIF4A. Zotatifin represses tumorigenesis in patient-derived and xenograft models and prolonged survival in vivo alongside hormone therapy. Genome-wide transcriptome, translatome, and proteomic analysis reveals two important translational targets: Androgen Receptor (AR), a key oncogene in CRPC, and hypoxia-inducible factor 1A (HIF1A), an essential cancer modulator in hypoxia. We solve the structure of the 5UTRs of these oncogenic mRNAs and strikingly observe complex structural remodeling of these select mRNAs by this small molecule. Remarkably, tumors treated with zotatifin become more sensitive to radiotherapy, usually blunted by HIF1A. Therefore, translatome therapy opens new avenues to treat the deadliest cancers.

Abstract: Lithium-ion batteries (LiBs) have become a critical component of countless devices we use in our daily life, and they are predicted to play a pivotal role in electrification of transport (and grid energy storage) in the near future. In order to enable a widespread energy transition, the development of advanced battery technologies is crucial. Ideally, electrode materials should have high specific charge capacities, enable high cell voltages and allow fast charge and discharge kinetics. In order to maximize the cell voltage, the cathode (positive electrode) should ideally operate at the highest and the anode (negative electrode) at the lowest possible potential. In this sense, the most promising anode material would be lithium metal since it has a specific charge capacity of 3860 mAh g1 and operates at the lowest electrode potential possible (i.e., 0 V vs. Li+/Li, or 3.04 V vs. the standard hydrogen electrode). All-solid-state batteries (ASSBs) are quite promising to enable the use of lithium metal anodes due to their projected safety characteristics (e.g., non-flammable components, mechanical rigidity, very high transference numbers). Unfortunately, most solid electrolytes have a narrow electrochemical stability window (ESW) and thus are subject to parasitic side reactions with lithium metal and high voltage cathode active materials, resulting in accelerated cell failure. It is essential to understand the chemical and quantitative nature of such reactions (and their kinetics) to design better performing battery systems. In this talk, it will be presented how the use of electrochemical methods (such as the novel CTTA method) and advanced analytical characterization methods (e.g., XPS, ToF-SIMS, FIB-SEM, operando HAXPES, etc.) can be used to obtain fundamental understanding of degradation reactions occurring at the electrodeelectrolyte interface in all-solid state batteries (with a particular focus on argyrodite-type sulfide-based solid electrolytes).

Abstract: Companies use different criteria such as lead time, cost, and quality to evaluate suppliers often using multiple suppliers with the aim of reducing stockout risk. However in many industries, there may be significant differences between the quality levels of different suppliers. This leads to a more difficult variant of the classical dual sourcing problem in the literature. In general, dual sourcing problems are difficult to solve as the conventional solution method, dynamic programming, suffers from the curse of dimensionality assuming Markovian property is satisfied. Recent developments in machine learning, data-centered optimization, artificial intelligence made it possible to tackle difficult dynamic control problems more algorithmically. Significant achievements are reported in academic and practical applications of these techniques to various supply chain problems. One common feature of these applications is that the solution technique is computationally expensive and, unlike theoretical results, it may suffer from severe deviations from the optimal (high variance).This seminar will review of dual sourcing problems, and their solutions with conventional methods (theory-induced heuristics) and novel, data-centered techniques (reinforcement learning).

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.