With the increasing popularity of AI and machine learning, human-AI teaming has a wide range of applications in transportation, healthcare, the military, manufacturing, and people’s everyday life. Measurement of human-AI team effectiveness is essential for guiding the design of AI and evaluating human-AI teams. To develop suitable measures of human-AI teamwork effectiveness, we created a search and rescue task environment in Minecraft, in which Artificial Social Intelligence (ASI) agents inferred human teams’ mental states, predicted their actions, and intervened to improve their teamwork (Huang et al., 2022). As a comparison, we also collected data from teams with a human advisor and with no advisor. We investigated the effects of human advisor interventions on team performance. In this study, we examined intervention data and compliance in a human-AI teaming experiment to gain insights into the efficacy of advisor interventions. The analysis categorized the types of interventions provided by a human advisor and the corresponding compliance. The finding of this paper is a preliminary step towards a comprehensive study on ASI agents, in which results from the human advisor study can provide valuable comparisons and insights. Future research will focus on analyzing ASI agents’ interventions to determine their effectiveness, identify the best measurements for human-AI teamwork effectiveness, and facilitate the development of ASI agents.

The self-assembly of strongly-coupled nanocrystal superlattices, as a convenient bottom-up synthesis technique featuring a wide parameter space, is at the forefront of next-generation material design. To realize the full potential of such tunable, functional materials, a more complete understanding of the self-assembly process and the artificial crystals it produces is required. In this work, we discuss the results of a hard coherent X-ray scattering experiment at the Linac Coherent Light Source, observing superlattices long after their initial nucleation. The resulting scattering intensity correlation functions have dispersion suggestive of a disordered crystalline structure and indicate the occurrence of rapid, strain-relieving events therein. We also present real space reconstructions of individual superlattices obtained via coherent diffractive imaging. Through this analysis we thus obtain high-resolution structural and dynamical information of self-assembled superlattices in their native liquid environment.

Studying the so-called ”hidden” phases of quantum materials—phases that do not exist under equilibrium conditions, but can be accessed with light—reveals new insights into the broader field of structural phase transitions. Using terahertz irradiation as well as hard x-ray probes made available by x-ray free electron lasers (XFELs) provides unique capabilities to study phonon dispersion in these materials. Here, we study the cubic peak of the quantum paraelectric strontium titanate (SrTiO3, STO) below the 110 K cubic-to-tetragonal tran- sition. Our results reveal a temperature and field strength dependence of the transverse acoustic mode in agreement with previous work on the avoided crossing occurring at finite wavevector, as well as evidence of anharmonic coupling between transverse optical phonons and a fully symmetric A1g phonon. These results elucidate previous optical studies on STO and hold promise for future studies on the hidden metastable phases of quantum materials.

This thesis focuses on how domain formation and local disorder mediate non-equilibrium order in the context of condensed matter physics. More specifically, the data supports c-axis CDW ordering in the context of the rare-earth Tritellurides. Experimental studies were performed on Pd:ErTe3 by ultra-fast pump-probe and x-ray free electron laser (XFEL). Ginzburg Landau models were used to simulate domain formation. Universal scaling analysis on the data reveals that topological defects govern the relaxation of domain walls in Pd:ErTe3. This thesis presents information on progress towards using light to control material domains.

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and time scales and enabling large-scale MD simulations with DFT-level accuracy. In this work, we investigate the feasibility of GNNs to learn the HK map from the external potential approximated as Gaussians to the electron density 𝑛(𝑟), and the mapping from 𝑛(𝑟) to the energy density 𝑒(𝑟) using Pytorch Geometric. We develop a graph representation for densities on radial grid points and determine that a k-nearest neighbor algorithm for determining node connections is an effective approach compared to a distance cutoff model, having an average graph size of 6.31 MB and 32.0 MB for datasets with 𝑘 = 10 and 𝑘 = 50 respectively. Furthermore, we develop two GNNs in Pytorch Geometric, and demonstrate a decrease in training losses for a 𝑛(𝑟) to 𝑒(𝑟) of 8.52 · 10^14 and 3.10 · 10^14 for 𝑘 = 10 and 𝑘 = 20 datasets respectively, suggesting the model could be further trained and optimized to learn the electron density to energy functional.

On January 5, 2020, the World Health Organization (WHO) reported on the outbreak of pneumonia of unknown cause in Wuhan, China. Two weeks later, a 35-year-old Washington resident checked into a local urgent care clinic with a 4-day cough and fever. Laboratory testing would confirm this individual as the first case of the novel coronavirus in the U.S., and on January 20, 2020, the Center for Disease Control (CDC) reported this case to the public. In the days and weeks to follow, Twitter, a social media platform with 450 million active monthly users as of 2020, provided many American residents the opportunity to share their thoughts on the developing pandemic online. Social media sites like Twitter are a prominent source of discourse surrounding contemporary political issues, allowing for direct communication between users in real-time. As more population centers around the world gain access to the internet, most democratic discussion, both nationally and internationally, will take place in online spaces. The activity of elected officials as private citizens in these online spaces is often overlooked. I find the ability of publics—which philosopher John Dewey defines as groups of people with shared needs—to communicate effectively and monitor the interests of political elites online to be lacking. To best align the interests of officials and citizens, and achieve transparency between publics and elected officials, we need an efficient way to measure and record these interests. Through this thesis, I found that natural language processing methods like sentiment analyses can provide an effective means of gauging the attitudes of politicians towards contemporary issues.

This paper serves as an analysis of the current operational conditions of a real-world company – referred to as “Company X” – with respect to the IC substrate industry. The cost of substrates, a crucial component in the production of Company X’s product, has recently diverged from Company X’s predictions and is contributing to declining profitability. This analysis aims to discover the underlying cause for price divergence and recommend potential resolutions to improve the forecast of substrate costs and profitability. The paper is organized as follows: Chapter 1 is an introduction to IC substrates and the industry as a whole, Chapter 2 is a breakdown of the specific factors responsible for substrate prices, and Chapter 3 delivers a final recommendation to Company X and concludes the paper.