Exploring efficacy in digital therapeutics: serious games for theory of mind training and visual rehabilitation

May2025As digital media increasingly transforms healthcare and education, the unique affordances of serious video games present distinct challenges and opportunities, setting them apart from traditional serious games. While conventional serious games often superficially gamify clinical practices, diminishing meaningful engagement, or replicate clinical protocols so rigidly that intrinsic player motivation suffers, serious video games uniquely offer interactive affordances that could effectively reconcile clinical precision with authentic player engagement. This dissertation examines this critical tension through the iterative development and empirical evaluation of two digital therapeutic prototypes: \textit{Emotion Adventure}, designed to foster Theory of Mind, the ability to understand and interpret others’ emotional and mental states, in children with Autism Spectrum Disorder, and \textit{Eye Rehab}, a virtual reality game aimed at improving stroke-related visual impairments. These prototypes were systematically designed and evaluated using the Mechanics, Dynamics, and Aesthetics (MDA) framework, which methodically connects foundational game mechanics, emergent player dynamics, and experiential aesthetics to ensure balanced game design. In usability evaluations, \textit{Emotion Adventure} employed a narrative-driven approach to successfully promote empathic decision-making and maintain player engagement within structured gameplay interactions. However, given the complexities and resource demands involved in empirically measuring cognitive therapeutic outcomes, a second prototype, \textit{Eye Rehab}, was developed. This physiological digital therapeutic utilized virtual reality-based gaze interactions to precisely target measurable improvements in visual alignment and ocular motor functions, validated clinically through the Lancaster Red-Green test, a standardized diagnostic tool used to assess ocular alignment and muscle function. Building upon insights gained through these prototypes, this dissertation hypothesizes a replicable design approach termed \textit{Selective Simulation}, which strategically embeds essential therapeutic actions directly into core gameplay mechanics. Unlike earlier theoretical concepts such as persuasive or applied games that offer generalized guidance, \textit{Selective Simulation} provides concrete and empirically informed design principles to intentionally integrate therapeutic activities within engaging game mechanics. Ultimately, this dissertation contributes to the broader field by proposing a replicable and structured framework for serious video game design, bridging theoretical insights from media studies, cognitive psychology, and human-computer interaction with methodologies rooted in clinical practice. This interdisciplinary approach underscores the distinct potential and complexity of video games as digital therapeutics, advocating for designs that rigorously balance therapeutic efficacy and engaging gameplay.Ph

Decision support models and policy innovations to support automating store fulfillment

December 2024School of EngineeringOmnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks that used to be completed by in-store customers doing their own shopping to the responsibility of retailers. To support research on omnichannel serivces,relevant connected in-store and online customer data sets for omnichannel retail research are generated via a mapped categorization of products into product families. Using this mapping to connect previously separate in-store and online customer data sets, these data sets focus on a grocery retail environment, and collect additional data from publicly available websites. These connected data sets contain information about product family data on in-store and online customer demand values, impulse purchases, product dimensions, weight, and price. Additional data is provided on in-store and online customer arrival data. These data sets aid this work in generating numerical insights and can support future grocery retail logistical research. To support omnichannel services, many retailers have deployed a store fulfillment strategy, where online orders are picked from inventory in brick-and-mortar stores. As store fulfillment is currently a labor-intensive operation, this dissertation explores a new policy that relies on the assistance of in-store customers for item extraction from the store shelves and a fleet of Autonomous Mobile Robots (AMRs) to collect and transport them to a designated station. While a set of dedicated pickers and AMRs are manageable by the store, the arrival of in-store customers who are willing to assist an AMR at a given location in the store is out of the store's control, and therefore, uncertain. We model the stochastic order-picking problem with uncertain synchronization times of in-store customers and AMRs, first as a static approach via generating a consensus from multiple scenarios and decisions to visit picking locations are made at the beginning of the picking journey. Then we consider managing resources in a dynamic way, where the store makes new decisions as new information becomes available. We model the dynamic problem as a Markov Decision Process to determine how a retailer should dynamically assign tasks to a set of AMRs and dedicated pickers. We develop a heuristic solution framework that generates a set of initial assignments and routes for picking resources and dynamically updates them as the actual synchronization times between AMRs and in-store customers unfold. We analyze multiple strategies to generate the initial set of task assignments and routes as well as update such decisions based on the system state. We test our proposed approaches using actual online grocery data. Computational results illustrate the potential for AMRs and in-store customers augmenting the dedicated pickers to achieve equivalent pick rates compared to systems with only dedicated pickers. We further demonstrate that it is more effective for achieving higher picking performance to have in-store customers help the AMRs compared to a warehouse like environment where dedicated pickers are synchronized with AMRs. Moreover, our proposed policy improves the operating margin of the store compared to utilizing only dedicated pickers. Lastly, our solution approach is capable of generating high-quality solutions at a pace suitable for practical settings. In addition to fulfilling online customer requests, omnichannel retailers also must support in-store customers, who want to interact with products and often drive sales through impulse purchases and customer loyalty. Yet, how best to support both online and in-store customer channels efficiently and seamlessly is a current challenge for retailers. Thus, the second focus of this work is to explore whether new material handling equipment has the potential to be deployed in a retail store environment to support omnichannel services. To do so, we utilize pick performance data from a newly designed and built picker-to-stock robotic platform suitable for piece-level pick, sort, and place tasks in retail environments. Then an agent-based simulation model is created to mimic a store's logistical operations that integrates data from the robotic platform's lab demonstrations and data from online and in-store customer demand. An iterative process determines the minimum amount of manual and robotic resources needed to operate the store that satisfies a given service level for online order fulfillment and replenishment tasks. Then, to assess the economic viability of deploying such a robotic platform with currently achieved values and improved performance, these resource levels are combined with operational metrics obtained from the simulation and various cost aspects via an economic analysis model. Computational experiments show that deploying the robotic platform for picking and restocking goods in a store environment is operationally and economically viable for retail grocery stores providing omnichannel services using a store fulfillment strategy.Ph

Molecularly-induced effects on the synthesis and properties of thin film inorganic/molecular-nanolayer interfaces and their multilayers

May2025School of EngineeringInserting molecular nanolayers (MNLs) at inorganic thin film interfaces has been shown to enhance chemical and mechanical stability, and access unexpected electrical/thermal transport and mechanical responses. Stacking inorganic nanolayers and MNLs offer the potential for crafting new classes of high-interface-fraction multilayered composites with emergent responses arising from the superposition of effects from multiple MNL interfaces. This work demonstrates studies on the synthesis of metal-oxide/MNL multilayers and metal/MNL/metal sandwiches, and their mechanical and acoustic properties. Synthesis techniques used include low-temperature atomic layer deposition (ALD) or sputter deposition combined with MNL formation from vapor-phase molecular flux exposures. Results of experiments combining multiple spectroscopy, microscopy, and diffraction techniques unveil different correlations between MNL structure and chemistry on inorganic nanolayer growth kinetics, chemistry, morphology, phase stability, and oxidation, as well as provide insights into their atomistic mechanisms. Ab initio molecular dynamics simulations were used to reveal MNL-induced strain-hardening and toughening in metal/MNL/metal sandwiches, with atomistic insights on the effects of MNL molecular chain length and terminal chemistry. Pump-probe time-domain Brillouin spectroscopy unveiled unusual enhancements in optoacoustic transmission in titania/MNL multilayers at selected sub-terahertz frequencies. This is attributed to MNL-induced global optical effects and interference of acoustic trains reflected from MNL interfaces, and hence, sensitive to and tunable via MNL structure and chemistry. Such tunable MNL-induced emergent responses in inorganic/MNL multilayers could open new vistas for viscoelastic bandgap engineering and phononic laser development.Ph

Interpretable transfer learning: understanding and controlling knowledge transfer

May2025School of ScienceTransfer learning involves leveraging the knowledge gained while solving one problem and applying it to a different but related problem, thus facilitating the adaptation of learned patterns and representations. This approach is particularly beneficial when labeled data is scarce or training resources are limited. Over the past decade, transfer learning has emerged as a critical technique in the field of machine learning, revolutionizing how models are trained and deployed across various domains. Approaches such as fine-tuning pretrained models, representation transfer, and domain adaptation have enabled models to leverage knowledge learned from large-scale datasets and transfer it to new, related tasks with limited labeled data. However, the interpretability of the transferred knowledge remains a challenge in transfer learning. While pretrained models often achieve impressive performance gains, understanding how and why these models make specific predictions is often non-trivial. This thesis seeks to further our understanding of transfer learning by investigating the knowledge transferred between source and target domains. Previous research on interpretable transfer learning has focused on empirical evaluations of network architectures that lead to better transfer, as opposed to understanding what knowledge enables positive versus negative transfer of knowledge. Furthermore, transfer learning has predominantly functioned as a tool for enhancing performance in target domains, overlooking the potential harm of propagating undesirable knowledge encoded in source models to downstream tasks. To this end, we address three research questions surrounding interpretable transfer learning: Can we interpret what, where, and how the knowledge is transferred from a source domain to a target domain? Can we mitigate the transfer of undesirable knowledge to downstream tasks? Can we automatically identify and transfer common concepts or attributes that are helpful to the target task? For the first research question, we designed and implemented Auto-Transfer (AT), a framework that automatically learns to route source representations to appropriate target representations, following which they are combined in meaningful ways to produce accurate target models. We demonstrated upwards of 5% accuracy improvements compared with the state-of-the-art knowledge transfer methods on several benchmark datasets. We qualitatively analyze the goodness of our transfer scheme by showing individual examples of the essential features using visual explanation methods. We also observed that our improvement over other methods is higher for smaller target datasets, making it an effective tool for small data applications that may benefit from transfer learning. For the second research question, we proposed a novel approach for suppressing the transfer of user-determined semantic concepts (viz. color, glasses, etc.) in intermediate source representations to target tasks without retraining the source model, which can otherwise be expensive or even infeasible. Notably, we tackled a bigger challenge in the input data as a given intermediate source representation is biased towards the source task, thus further entangling the desired concepts. We evaluated our approach both qualitatively and quantitatively in the visual domain and demonstrated that our approach successfully suppresses user-determined concepts without altering other concepts. Lastly, we explored the automatic identification of beneficial concepts for the target task, using examples from the biomedical domain. We introduced Conceptual Counterfactual Explanation (CoCoX), a method that integrates conceptual and counterfactual explanations to pinpoint the most relevant medical concepts for a black-box chest X-ray classifier. Furthermore, we enhanced the joint embedding space of biomedical foundation models with textual concepts, achieving performance improvements of over 5\% across various downstream tasks from diverse biomedical domains. Overall, through this thesis, we developed methods to support the interpretability of knowledge transferred between source and target domains, mitigate the transfer of undesirable knowledge, and improve performance on resource-constrained tasks. As the field of transfer learning continues to evolve, achieving a balance between performance and interpretability remains a crucial area of focus for advancing the robustness and reliability of machine learning models across diverse real-world application domains.Ph

Regulation of microtubule bundle mechanics by prc1 in metaphase and anaphase

May2025School of ScienceThe mitotic spindle is composed of distinct networks of microtubules, including interpolar bundles that can bridge sister kinetochore fibers and bundles that organize the spindle midzone in anaphase. The crosslinking protein PRC1 can mediate such interactions between antiparallel microtubules. PRC1 is a substrate of mitotic kinases including CDK/cyclin-B, suggesting that it can be phosphorylated in metaphase and dephosphorylated in anaphase. How these biochemical changes to specific residues regulate its function and ability to organize bundles is not known. Here, we perform biophysical analyses on microtubule networks crosslinked by two PRC1 constructs, one a wild-type reflecting a dephosphorylated state, and one phosphomimetic construct with two threonine to glutamic acid substitutions near PRC1’s microtubule binding domain. We find that the wild-type construct builds longer and larger bundles that form more rapidly and are much more resistant to mechanical disruption than the phosphomimetic PRC1. Interestingly, microtubule pairs organized by both constructs behave similarly within the same assays. Our results suggest that phosphorylation of PRC1 in metaphase would tune the protein to stabilize smaller and more flexible bundles, while removal of these PTMs in anaphase would favor the assembly of larger more mechanically robust bundles to resist chromosome and pole separation forces at the spindle midzone.In addition to these findings on PRC1’s biochemical regulation during mitosis via phosphorylation, we have begun characterizing the biophysical properties of PRC1 binding using a combination of in vitro experiments and computational simulations to theoretically model protein-protein interactions in the spindle. To achieve this, we are collaborating with mathematicians and physicists to focus on creating models that predict the formation of the mitotic spindle via relevant motor and non-motor crosslinking proteins. A computational model that reflects braking and coasting behaviors exhibited by crosslinked microtubule pairs based on previously published data from our lab has been developed. We find that braking occurs with smaller microtubule separation compared to coasting; the reduced separation between microtubule pairs results in increased resistive forces exerted by PRC1 and thus a reduced sliding speed. The model also shows that higher initial sliding speeds lead to a transition to braking. The results give insight on the relationship between microtubule separation and forces in the spindle exerted by crosslinkers and other MAPs. Furthermore, our collaborative project is currently exploring the possibility of PRC1 cooperativity. Thus far, data on the rate of PRC1 recruitment to single microtubules and overlaps from experiments suggest that a simplified binding model does not sufficiently explain PRC1’s binding behavior, as occupancy effects do not account for the experimental results. We plan on pursuing these findings further, as they may give insight into why PRC1 preferentially binds to antiparallel overlaps compared to single microtubules. The works presented here characterize the behaviors and regulatory mechanisms of the essential human mitotic crosslinker PRC1 via biochemical and biophysical approaches, as well as with structural and computational modeling.Ph

Plasticity in molecular crystal cyclotetramethylene tetranitramine (β-hmx)

May2025School of EngineeringMolecular crystal cyclotetramethylene tetranitramine (β-HMX) is the active ingredient in widely used plastic bonded explosives. Plasticity is believed to be essential for its reaction initiation and detonation. To explore the energetic cost associated with the relative gliding of crystal planes, we calculate γ-surfaces for the most active glide planes in β-HMX, the (101) plane and the (011) plane, with pressure up to 15 GPa. Stable stacking faults are observed on both glide planes, suggesting dislocation disassociation into partials takes place. Furthermore, the γ-surface of the (101) plane indicates twinning on the (101) plane. With increasing pressure, the values of γ-surfaces increase drastically, however, the topography of γ-surfaces remains the same. Homogeneous dislocation nucleation was found to be a relevant mechanism of plastic deformation in β-HMX. In this work, we conduct atomistic simulations to investigate the conditions under which dislocations nucleate homogeneously in the (101) and (011) planes at pressures up to 20 GPa. Critical resolved shear stresses (CRSS) for dislocation nucleation are reported. The competition between the homogeneous nucleation and other mechanisms of plastic deformation shows that homogeneous nucleation is less likely to happen at pressures above 5 GPa, while at pressures below this threshold, homogeneous nucleation competes with shear localization. Further, molecular dynamics simulations are performed to evaluate the dislocation velocity vs. resolved shear stress relation at pressures up to 20 GPa in several slip systems, which helps defining the strain rate sensitivity of the crystal. Based on this data and data from the literature, we establish a mechanism-based constitutive model for β-HMX crystals. The model captures the thermally activated and dislocation drag regimes for dislocation motion and, more importantly, the model is strongly pressure-dependent, and rate sensitive. An isotropic version of the model based of Reuss averaging is also presented. This model has the potential to be broadly applicable in the continuum modeling of HMX. Further we study conditions under which plastic deformation in HMX becomes non-crystallographic, particularly in situations such as pore collapse under shock loading, which is considered to be a key mechanism of detonation. We observe fluidization once the applied pressure and rate are above specific thresholds, and associate this transition with the concomitant fulfillment of two conditions, one dependent on the maximum shear stress and the other dependent on the deformation rate.Ph

An adaptive and flexible framework for convergent manufacturing with robot manipulators

December 2024School of EngineeringRobots have become indispensable in industrial manufacturing, with easy reconfigurability,high repeatability, and the ability to operate in harsh environments. They play a crucial role in factory production lines, executing pre-programmed motions in tasks such as packaging, welding, and assembly. Despite their ability to perform repetitive tasks with speed and precision, robots still face limitations in handling certain payloads (e.g., flexible or bulky objects) or complex tasks which require significant setup, calibration, or programming efforts (e.g., multi-robot coordination). A significant challenge to the universal deployment of industrial robotics in advanced manufacturing is integration and lack of robust planning. Currently, robot motion involves manually teaching waypoints and actions through a teach pendant or pre-program motions through commercial offline software packages. Further, the prevailing practice often involves running robots in the open-loop mode without optimization or feedback, overlooking the potential for improved performance and shorter cycle times with sensor-guided operation. To address these challenges, this thesis proposes an innovative and systematic approach to enhance industrial robotic performance and efficiency by focusing on robot interoperability, coordination, and robustness. This is achieved by integrating various sensors across representative manufacturing processes. This thesis explores the standardization of highlevel robot control, robust motion planning and tracking algorithm, and a combination of sensors with feedback to optimize the overall flexibility and performance of the robotic system. The integration of multiple robots and sensors into a unified framework is essential for convergent manufacturing, enhancing both performance and robustness. Several milestones have been achieved in this thesis work, including successful robotic manufacturing projects involving a mock assembly line, dual arm spraying, metal additive manufacturing, and fabric handling. The ultimate goal of this work is to demonstrate a convergent manufacturing system, comprised of multiple robots and sensors that can achieve new or improved capabilities.Ph

Application of cold spray to manufacturing of solid-state batteries

May2025School of EngineeringAs current liquid electrolyte lithium-ion batteries reach their theoretical limit, exploration of solid-state batteries (SSBs) as an energy dense and safe alternative continues to expand. Due to many decades of research the stage is being approached where the main hurdle is how to manufacture high performance batteries at scale. Tantalum doped Lithium Lanthanum Zirconium oxide (LLZTO) is a solid electrolyte material of much interest, due to its high ionic conductivity and stability with lithium metal, which faces manufacturing challenges to its high sintering temperature. It is also commonly mixed with lithium cobalt oxide (LCO), a much-used cathode material, to increase ionic transport. In this study cold spray (CS), a kinetic spraying technique with a low process temperature capable of producing dense films, is investigated for its ability to create dense films of LLZTO and LCO. Single layer dense films of LLZTO with a thickness of around 12 µm were produced on an aluminium substrate. Attempts to produce a thicker film by depositing further layers of LLZTO were unsuccessful and caused large surface perturbations which allowed aluminium to come to the surface. LCO was likewise successfully deposited on aluminium and was able to form a 1µm thick film on Alumina. Both films experienced changes in crystallographic structure during deposition, and LLZTO displayed an increase in lattice strain which could affect electrochemical performance. Despite the immaturity of using this technique with solid state batteries, this study shows that with further development, cold spray could become a viable option to produce SSBs at large scale.M

Investigating the molecular mechanisms of bacterial adaptations to pressure

May2025School of ScienceAll organisms must carefully regulate stress response pathways and the rate at which they grow and divide, and hence their size. The vast majority of microbes on Earth live in the deep biosphere, which is comprised of areas with high hydrostatic or lithostatic pressure. The molecular mechanisms underlying the adaptations of these organisms to survive in these extreme environments remain elusive. Despite this, there is great biological significance in understanding how cell growth/division and stress response pathway regulation are altered in these organisms, especially given the role of pressure in food sterilization. In this work, we investigated a pressure-adapted strain of E. coli from the perspective of both stress response and cell size, two essential properties of life. First, we determined that even pressure-adapted organisms are stressed by pressure but distinctly compared to non-adapted organisms. In particular, we demonstrated that the upregulation of the molecular chaperone, GroEL, was favored over that of the DnaK chaperone in response to pressure shock in the pressure-adapted strain, whereas the opposite was true for the non-adapted strain. We interpret this differential regulation as a consequence of the distinct functions of these two proteins. Our results also suggest that the alternative sigma factor RpoE and its anti-sigma factors may work in concert as pressure sensors. Second, we showed that the small cell size phenotype of the pressure-adapted strain is the result of the slow growth of the strain rather than an increase in the accumulation of cell division machinery. Slow growth may result from mutations in GlnA, which is implicated in the activation of the nitrogen starvation response, as well as in the RpoB subunit of RNA Polymerase. We also performed the first ever live cell imaging of FtsZ under pressure, demonstrating that the division ring formed by FtsZ is disrupted under pressure in vivo. Taken together, our results expand upon our understanding of how microbes can adapt to live in high pressure environments and survive pressure shocks.Ph

A unified architecture and control framework for safe and collaborative human-robot manipulation of deformable objects

May2025School of EngineeringDeformable object manipulation (DOM) is pervasive in daily life and in industrial settings. Tasks such as cable routing, tent manufacturing, pouring granular material from bags, and laying up composite sheets all involve objects with high internal degrees of freedom, making them notoriously difficult to model and control. This dissertation focuses on enabling multiple mobile robots, in collaboration with human operators, to perform DOM in various scenarios—ranging from 1D ropes and cables to 2D fabric and composite sheets—while ensuring safety, efficiency, and ease of use. A central challenge in DOM is obtaining accurate state estimates from sensors that are frequently subject to occlusions and limited feedback. To address this, we employ position-based dynamics for real-time simulation of object motion, contact, and friction, providing critical data such as predicted object shape, stress, and proximity to obstacles. This simulation underlies a suite of controllers. First, we incorporate control barrier functions to ensure the robots adapt their motion and maintain safe distances from obstacles and prevent overstretching of the deformable objects. Second, we develop an efficient global planning pipeline to manipulate deformable linear objects in cluttered environments, approximating them as serially connected rigid links. Third, we introduce a local control framework for 2D composite layup, where robots transport and position large fabric sheets collaboratively with a human operator. The same simulation reports tensions and contact forces to avoid overstress and prevent unsticking of the material from curved surfaces. We validate the developed architecture and algorithms in simulation and with physical robots under different modes of shared autonomy: human teleoperation, human-robot collaborative manipulation, and fully autonomous control with human guidance. Demonstrations include robotic rope and stiff rod navigation through obstacles, multi-robot formation in tent manufacturing, and real-time composite layup assistance. The system features a unifying touchscreen user interface that simplifies multi-robot programming and visualization, promoting seamless scalability across tasks and facilitating broader industrial adoption. Ultimately, this work advances DOM toward a robust, user-friendly framework, paving the way for safe and versatile human–robot collaboration on a wide range of deformable objects.Ph