The development of the Human Health Exposure Analysis Resource (HHEAR) Data Repository for environmental epidemiology research

Implementation of the exposome paradigm is a critical aspect of the next generation of environmental health research studies. To spur exposomics research, the U.S.-based Human Health Exposure Analysis Resource (HHEAR) provided scientific investigators access to both laboratory and statistical analyses aimed at incorporating and expanding the breadth of biological markers of environmental exposures within their research. To extend the benefits of this program to the broader scientific community, the HHEAR Data Center established a public data repository to facilitate pooling and sharing of data generated by the HHEAR program. All HHEAR investigators deposited epidemiologic data on study participants, to accompany the biomarkers of exposure generated by the HHEAR laboratories. The latest semantic technologies are used to efficiently conduct data standardization across studies and promote data sharing by aligning the repository with the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. This includes standardizing individual study data to a common ontology and representing data within a knowledge graph. A clear user interface enables search, construction, and download of customized datasets and maintenance of provenance through use of digital object identifiers. The repository will eventually contain information from 35,989 individuals across 55 environmental health studies, including data on biomarkers of environmental exposures, sociodemographics, health outcomes, and physical and mental assessments. All data are freely downloadable for reuse after a brief application for data access. Designed to support cutting-edge research and education, the HHEAR Data Repository provides a rich, harmonized resource of exposure biomarkers and corresponding health data from diverse study populations

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

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

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

Investigation of errors and uncertainty in low-frequency impedance tube measurements

August2025School of ArchitectureThe accuracy of impedance tube measurements can be improved by determining the acoustic conditions within the impedance tube prior to introducing a sample for test. By measuring the empty impedance tube, the influence of environmental changes on critical parameters can be quantified. Low-frequency dissipation is estimated using Bayesian inference to incorporate into a transfer function model, reducing error when measuring samples. Previous work has focused on a frequency range of 1.5 kHz - 5 kHz; the subject of this study is a lower frequency range (sub-1.5 kHz) using larger sized impedance tubes. The empty tube model is then used to validate the acoustic performance of jute and thermoplastic starch-based microslit panel absorbers. By determining conformity of alternative-material acoustic elements to modeled behavior, and including acoustic testing in the design process, the viability of sustainable material substitutions can be determined.M

Image-driven fact-checking of ai generated chest radiology reports

August2025School of EngineeringWith the developments in radiology artificial intelligence (AI), many researchers have turnedto the problem of automated reporting of imaging studies. The goal of such work is to produce a preliminary read of imaging studies in locations such as emergency rooms where a radiologist may not be readily available, or to present a preliminary structured report to radiologists to reduce their dictation workload. An automatically produced structured report could also be more consistent and easier to read, leading to improved accuracy and lower overall costs of radiology reads in clinical workflows. Among the imaging areas where this has been found most useful are chest X-rays, which are the most common imaging modality read by radiologists in hospitals and tele- radiology practices today. With the recent rise of generative AI, a number of researchers and corporations are attempting to generate preliminary reports for chest X-ray images thanks to the availability of relatively large datasets such as MIMIC and CheXpert that come with their companion reports for training large vision-language models (VLMs). These newly emerged VLMs can generate longer and more natural sentences when prompted with good radiology-specific linguistic cues. However, despite the powerful language generation capabil- ities, ensuring there are no hallucinations, incorrect mentions of findings or their descriptions, has been difficult for these models limiting their clinical applicability. While methods for hallucination removal and fact-checking exist for large language models, with strategies such as direct policy optimization (DPO) or proximal policy optimization (PPO), and reward models, they are mostly applicable during training or fine-tuning of the models. On the other hand, methods that check facts during inference time often consult external general knowledge or detect errors through analysis of produced text either by themselves or through an LLM serving as a judge. In radiology report generation, however, neither is possible since the report has to be specific to the patient and consistent with the evidence seen in the imaging. Since the automated reporting LLMs themselves have hallucinations, there are no teacher LLMs that are good enough to correct automatically generated radiology reports. Further, they may not be able to corroborate their deductions with the patient-specific im- age. Finally, any fact checking should be agnostic to the radiology report generation tool to give versatility of use during clinical deployment where different choices of vendors may be prevalent with separate evolving capabilities over time. Thus, there is a need to develop an independent fact-checking method for use during clinical inference to bootstrap radiology report generation and increase their adoption in clinical workflows. This Master’s thesis investigates a hypothesis that it is possible to develop such inde- pendent discriminative neural networks as fact-checking models for use during inference to detect and correct errors in automatically generated reports. The key idea explored in the thesis is that by creating a synthetic dataset of real and fake findings derived from ground truth reports and pairing them with the corresponding chest X-ray images, a fact-checking classifier could be trained to distinguish between real/correct description of findings and incorrect description of findings when they are paired with the corresponding images. Such an independently developed classifier can then be used to detect and correct errors in the reports generated by automated radiology reporting tools. To proceed with the verification of the hypothesis, the thesis is divided into 4 investi- gations. First, by examining several radiology reporting methods, we analyze the types of errors made by the report generators to conclude four major error types such as irrelevant predictions, polarity reversal or omissions, incorrect location predictions and other types such as incorrect severity assessments. We then simulate the errors to create a large synthetic dataset by perturbing findings and their locations in ground truth reports reflecting real and fake findings-location pairs with images. We then proceed to build a discriminative classifier to detect the errors and remove the finding errors in reports using two different methods, one that is based on the findings alone and the other that captures their spatial locations. Finally, we develop methods to correct the automated report while still ensuring language correctness by careful prompting of a large language model using information derived from the fact checking model. Throughout, we conduct experiments with multiple benchmark datasets and conduct ablation experiments to select relevant architectural configurations and document the overall improvement in the quality of the report by the use of our fact-checking model to detect and correct errors. A novel measure was developed for assessing the report correctness leveraging both clinical accuracy and phrase grounding accuracy. Explainable visualizations were generated to show the deviation of the reported findings from predicted findings and their locations generated by the fact-checking model. The overall results indicated that it was possible to develop a fact-checking model using an independently collected dataset of real and fake findings to simulate the errors made by report generators. The resulting fact-checking model was over 90% accurate as tested on multiple benchmark datasets and led to improvement in the quality of the automatically generated reports in the range of 7-29%. A high degree of concordance was found between the use of our fact-checking model and ground truth for verification of automated reports leading us to also conclude that the fact-checking model has the potential to serve as a surrogate ground truth during clinical inference. This proves further utility of our model as an additional validation checkpoint in making AI models robust and ready for clinical workflows.M

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

Reflections From Former EICs: 40 Years of IEEE Intelligent Systems.

The year 2025 marks the 40th anniversary of IEEE Intelligent Systems, a significant milestone in the magazine’s remarkable evolution. With this issue, we celebrate four decades of the magazine’s insightful contributions to the artificial intelligence (AI) community. Past issues, articles, and summaries are conveniently aggregated at the IEEE Computer Society Digital Library and IEEE Xplore. This anniversary offers a moment for reflection, recollection, and celebration. We invited five former editors-in-chief (EICs)—Daniel Edmund O’Leary, James Hendler, Daniel Zeng, V.S. Subrahmanian, and San Murugesan—to reflect on the magazine’s history, accomplishments, and future and share their memories. Here are their reflections

Efficient singularity-robust inverse kinematics and redundancy management for robotic systems

August2025School of EngineeringA robot must control the position of its hand or end effector, but this requires understanding the complex relationship between the end effector pose and the joint angles. This thesis seeks to provide a unified, computationally efficient, and singularity-robust framework for the inverse kinematics (IK) and redundancy management of robotic systems. IK is the problem of finding which joint angles correspond to an end effector pose. There is a need for an IK solver which is efficient, robust, and precise, and which finds all solutions including singular solutions. We present new IK methods using geometric subproblem decomposition which apply to 6-DOF arms, 7-DOF arms, and parallel manipulators (also solving forward kinematics for parallel manipulators). Depending on intersecting or parallel joint axes, the methods are closed-form or use 1D or 2D search. Search methods may be converted to high-order polynomials. The open-source implementation, IK-Geo, is the fastest general IK solver in our testing, with >40x faster IK for the UR5 than IKFast. 7-DOF manipulators avoid singularities and obstacles better than 6-DOF manipulators because they have an extra internal degree of freedom. However, redundancy parameterizations create new algorithmic singularities. We propose a new parameterization called the Stereographic SEW angle that reduces the presence of algorithmic singularities in the workspace. We prove algorithmic singularities are unavoidable, but the stereographic SEW angle is ideal in that the robot pose is singular only when the wrist is on a half-line from the shoulder. We apply our 7-DOF analysis to the ABB YuMi and provide the first complete and validated definition of the SEW angle used by the ABB controllers. Cuspidal robots are a surprising and increasingly common class of manipulator. Classical path planners may fail for these robots because they can travel between IK solutions without encountering a singularity. We are the first to show the ABB GoFa and some 3-parallel-axis robots are cuspidal. We also propose a graph-based planner to find optimal joint paths for a given end effector path and an optimizer to adjust the workpiece placement. For the first time, we apply cuspidality analysis to 7-DOF arms. While redundant arms can usually travel between self-motion manifolds without encountering a singularity, certain 7-DOF arms may or may not be cuspidal once the redundancy is parameterized. We find the ABB YuMi is cuspidal after parameterization, while the KUKA iiwa is not.Ph