About

Alasdair Young is a Professor and Neal Family Chair in the Sam Nunn School of International Affairs at Georgia Tech. He serves as the Interim Associate Dean for Faculty Development for the Ivan Allen College of Liberal Arts. His research focuses on trade and regulatory policies, particularly concerning the European Union and the transatlantic relationship, with a growing interest in economic security and economic statecraft. Young directs the Center for Research on International Strategy and Policy and has previously co-directed the Center for European and Transatlantic Studies. He has authored five books, including 'Supplying Compliance with Trade Rules: Explaining the EU’s Responses to Adverse WTO Rulings' (2021) and 'Policy-Making in the European Union' (9th edition, 2025), and has edited numerous volumes. His publication record includes over twenty refereed journal articles in outlets such as the Journal of European Public Policy, World Politics, and the Journal of European Integration, along with more than 40 book chapters. Young has also performed consultancy work for the US and UK governments and the European Commission. His academic background includes a DPhil from the University of Sussex, an MIA from Columbia University, and a BA from the University of Pennsylvania. Prior to Georgia Tech, he taught at the University of Glasgow for ten years and held research positions at the European University Institute in Florence and the University of Sussex.

Research topics

• Artificial Intelligence
• Computer Science
• Computer Security
• Human–computer interaction
• Embedded system
• Engineering
• Physical medicine and rehabilitation
• Medicine
• Mathematics
• Simulation

Selected publications

• Reducing Lumbar Extensor Exertion in Lifting Tasks with a Powered Back Exosuit

  IEEE Transactions on Biomedical Engineering · 2026-01-01

  articleSenior author

  OBJECTIVE: The study seeks to determine whether a powered, cable-driven exosuit has the potential to lower the lumbar muscle activity and overall metabolic expenditure of symmetric and asymmetric lifting tasks. METHODS: A lightweight, cable-driven back exosuit, using a three-state impedance controller, was developed to provide variable assistance based on user posture. Experimental electromyography (EMG), metabolic cost, and user preference data were recorded for ten participants evaluated wearing the powered back exosuit versus the backX, a commercially available passive back support exoskeleton, and a no exo baseline. RESULTS: Both exoskeletons significantly reduced (p$< $0.05) muscle activation of certain lumbar flexor and extensor muscles when compared to a no exo condition across all conditions tested, though neither significantly reduced the metabolic cost associated with lifting. Users tended to prefer lifting with the powered device as opposed to the passive or no exo condition. CONCLUSION: Despite the increased mass of powered back support exoskeletons, these devices can reduce lumbar muscle activity to a similar degree as passive exoskeletons, and are favored by users over their passive counterparts. SIGNIFICANCE: While current powered back support devices tend to incur the cost of being heavy, rigid, and inconvenient for certain lifting postures, these results show that cable-driven powered devices may minimize these factors to the point that they are favored over the currently popular passive devices on the market.

  PublisherDOI

• Paretic limb biomechanical response to hip exoskeleton limb assistance strategies during walking for individuals post-stroke

  Journal of Biomechanics · 2026-03-21

  article
  PublisherDOI

• Deep domain adaptation eliminates costly data required for task-agnostic wearable robotic control

  Science Robotics · 2025-11-19 · 1 citations

  articleSenior author

  Data-driven methods have transformed our ability to assess and respond to human movement with wearable robots, promising real-world rehabilitation and augmentation benefits. However, the proliferation of data-driven methods, with the associated demand for increased personalization and performance, requires vast quantities of high-quality, device-specific data. Procuring these data is often intractable because of resource and personnel costs. We propose a framework that overcomes data scarcity by leveraging simulated sensors from biomechanical models to form a stepping-stone domain through which easily accessible data can be translated into data-limited domains. We developed and optimized a deep domain adaptation network that replaces costly, device-specific, labeled data with open-source datasets and unlabeled exoskeleton data. Using our network, we trained a hip and knee joint moment estimator with performance comparable to a best-case model trained with a complete, device-specific dataset [incurring only an 11 to 20%, 0.019 to 0.028 newton-meters per kilogram (Nm/kg) increase in error for a semisupervised model and 20 to 44%, 0.033 to 0.062 Nm/kg for an unsupervised model]. Our network significantly outperformed counterpart networks without domain adaptation (which incurred errors of 36 to 45% semisupervised and 50 to 60% unsupervised). Deploying our models in the real-time control loop of a hip/knee exoskeleton ( N = 8) demonstrated estimator performance similar to offline results while augmenting user performance based on those estimated moments (9.5 to 14.6% metabolic cost reductions compared with no exoskeleton). Our framework enables researchers to train real-time deployable deep learning, task-agnostic models with limited or no access to labeled, device-specific data.

  PublisherDOI

• Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton

  IEEE Transactions on Medical Robotics and Bionics · 2025-04-14 · 2 citations

  articleOpen accessSenior author

  Fall incidents due to slips are some of the most common causes of injuries for industry workers and older adults, motivating research to assist balance recovery following slips. To assist balance recovery during a slip, a detection algorithm that can work with an assistive device, such as an exoskeleton, needs to be able to detect slips rapidly after onset, which remains a critical gap in the field. Here, we compared the ability of linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), and convolutional neural networks (CNN) to detect slip using only native sensors on a hip exoskeleton. We trained and evaluated user-independent models on early-stance (ES) and late-stance (LS) slips of various magnitudes collected through treadmill-based slips. All models, except LDA with LS slips, detected slips with >90% accuracy. Overall, he best model was XGBoost, with its fastest results achieving average detection times and median accuracies of 155.06 ms at 96.25% for ES slips and 228.88 ms at 93.75% for LS slips, while also achieving 100% sensitivity at 195.64 ms (ES) and 266.24 ms (LS). Our results indicate a promising direction for further research into designing a generalizable model for balance recovery during slip perturbations using robotic hip exoskeletons.

  PublisherOA PDFDOI

• Uncertainty-Aware Ankle Exoskeleton Control

  ArXiv.org · 2025-08-28

  preprintOpen access

  Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.

  PublisherOA PDFDOI

• Toward personalizing prosthesis prescription: A take‐home study of three microprocessor‐controlled prosthetic knees: A randomized crossover study

  PM&R · 2025-10-24

  articleOpen accessSenior author

  BACKGROUND: Previous studies on microprocessor-controlled prosthetic knees (MPKs) often investigate benefits of MPKs as a class of knees rather than clinically relevant differences between specific knees, despite their distinct features. OBJECTIVES: To systematically evaluate and report outcomes associated with three commercially available MPKs following a standardized real-world use period. DESIGN: Randomized crossover study. SETTING: Research laboratory and community environment. PARTICIPANTS: Ten patients with transfemoral amputation. INTERVENTIONS: Three MPKs were fitted, trained, and worn for a 1-week period including C-Leg 4.0 (Ottobock, Duderstadt, Germany), Rheo Knee-Model RM7 (Össur, Reykjavik, Iceland), and Power Knee-PKA01 (Össur, Reykjavik, Iceland). MAIN OUTCOME MEASURES: Primary outcomes were the 10-meter walk test (10-mwt), the 2-minute walk test (2-mwt), and the Prosthesis Evaluation Questionnaire (PEQ). Secondary outcomes were stance time asymmetry, physiological cost index, stair and ramp speeds, the narrowing beam walking test, and community ambulation monitoring. RESULTS: Participants walked 11% faster in Rheo than Power Knee during the 10-mwt (95% confidence interval [CI]: 0.046-0.184, p = .015). In the 2-mwt, participants walked 12% faster in C-Leg (95% CI: 0.034-0.241, p = .003) and 9% faster in Rheo (95% CI: 0.031, 0.163, p = .027) than in Power Knee. On the PEQ, participants reported greater satisfaction with C-Leg compared to Power Knee (p = .006). Ramp ascent speed was 8% faster in Rheo than Power Knee (95% CI: 0.026-0.130, p = .024). No significant differences were found for other secondary outcomes. Notably, 10 of 12 outcomes showed individuals performing their best by a defined difference on an MPK different from the cohort's best-performing MPK. CONCLUSIONS: Participants walked faster in C-Leg and Rheo than Power Knee and reported greater satisfaction with C-Leg. Consideration of patient needs and characteristics may allow more individualized MPK prescription and thereby improve rehabilitation outcomes. DATABASE REGISTRATION: NCT06399471.

  PublisherDOI

• Real-Time Adaptation of Deep Learning Walking Speed Estimators Enables Biomimetic Assistance Modulation in an Open-Source Bionic Leg

  IEEE Transactions on Medical Robotics and Bionics · 2025-03-12 · 2 citations

  articleSenior author

  This study introduces a novel continual learning algorithm that incrementally improves the performance of deep-learning-based walking speed estimators during level-ground walking with a powered knee-ankle prosthesis. While user-dependent (DEP) estimators generally outperform user-independent (IND) estimators, they require the pre-collection of DEP training data. In contrast, our real-time algorithm adapts IND estimators to self-labeled DEP data generated during walking, eliminating the need for pre-collected datasets. The algorithm also features a biomimetic scaling mechanism that adjusts prosthetic assistance based on speed estimates. We evaluated our algorithm on novel subjects (N=10) with unilateral above-knee amputations during treadmill and overground walking. For treadmill trials, when adapted with estimated and ground truth labels, estimators achieved mean absolute errors (MAEs) of 0.074 [0.023] (mean, [standard deviation]) and 0.074 [0.018] m/s, respectively, reflecting a significant 28% (p ¡ 0.05) reduction in MAE compared to non-adapted estimators. For overground trials, treadmill-adapted estimators demonstrated a significant 18% (p ¡ 0.05) reduction in MAE compared to non-adapted estimators. Our algorithm significantly reduced speed estimation errors within one minute of walking and delivered biomimetic assistance (r <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${=}0.91$ </tex-math></inline-formula>) across speeds. This approach allows off-the-shelf powered prostheses to seamlessly adapt to new users, delivering biomimetic assistance through precise, real-time walking speed estimation.

  PublisherDOI

• Ankle Exoskeleton Control via Data-Driven Gait Estimation for Walking, Running, and Inclines

  IEEE Robotics and Automation Letters · 2025-04-16 · 2 citations

  article

  Ankle exoskeletons have the potential to augment mobility, but control strategies have largely failed to seamlessly adapt to changes in the locomotion task. Here, we introduce a multi-headed network that predicts gait speed, ground incline, stance/swing transitions, and percent stance. These predictions are mapped to exoskeleton torque using typical biological torques as a guide. The model was trained on 9 subjects walking/jogging for 12 minutes across a range of speeds and inclines. The controller was validated on 4 subjects, and achieved stance phase prediction error of 3.4% across a range of speeds and inclines, both inside and outside the training set distribution. A secondary analysis showed similar accuracy could have been obtained with only 10% of the collected data, suggesting researchers may need fewer total strides of training data, provided the data is sufficiently diverse across users and tasks. Metabolic cost was improved during running compared to wearing the exoskeleton powered off, but was beneficial for only one subject during level walking and ramp ascent when compared to no exoskeleton. Overall, our controller smoothly adapted to time-varying inclines and walking/jogging speeds, and achieved high accuracy with a reduced training dataset, though larger torque magnitudes may be required to see metabolic benefit.

  PublisherDOI

• Real-Time Balancing of Stability and Plasticity in Continual Learning Enables Adaptive Speed Estimation for Lower-Limb Prostheses

  IEEE Transactions on Medical Robotics and Bionics · 2025-08-08

  articleSenior author

  A primary challenge in continual learning (CL) for wearable robotics, especially prosthetics, is balancing the need to retain learned knowledge (stability) with the necessity to adapt to new information (plasticity). This balance is crucial for online adaptation, enabling systems to transition between tasks without losing prior knowledge. In this paper, we introduce a novel online optimizer-based framework designed to manage the stability-plasticity balance through strategic datapoint replay and learning-rate adjustments of a deep neural network. We applied this framework to speed estimation systems for transfemoral prostheses (TFA users), conducting offline validation tests using data from 10 individuals with TFA, and online tests with three TFA and six able-bodied (AB) participants. Our results demonstrate statistically significant improvements: in offline settings, our method showed a 39.2% increase in stability and a 35.2% boost in plasticity over traditional CL approaches during leave-one-subject-out validation. Similarly, in real-time trials with AB participants, we observed statistically significant gains in handling both previously encountered and new walking speeds. Finally, trials with individuals with TFA showed that the system improved the plasticity of the baseline model by 67.45% and the stability of the traditional CL approach by 31.36%; reducing overall average walking speed estimation error by 19.47%.

  PublisherDOI

• Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB

  2025-06-10 · 2 citations

  article

  3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse grid of projected dots. Yet, these sparse LiDARs struggle with scene coverage on limited input views, leaving large gaps in depth information. In this work, we propose using an alternative class of "blurred" LiDAR that emits a diffuse flash, greatly improving scene coverage but introducing spatial ambiguity from mixed time-of-flight measurements across a wide field of view. To handle these ambiguities, we propose leveraging the complementary strengths of diffuse LiDAR with RGB. We introduce a Gaussian surfel-based rendering framework with a scene-adaptive loss function that dynamically balances RGB and diffuse LiDAR signals. We demonstrate that, surprisingly, diffuse LiDAR can outperform traditional sparse LiDAR, enabling robust 3D scanning with accurate color and geometry estimation in challenging environments.

  PublisherDOI

Recent grants

• A new framework for self-adaptive artificial intelligence to personalize assistance for patients using robotic exoskeletons and prostheses

  NIH · $2.4M · 2022–2027

• NRI: FND: Robotic Human Enhancement Enabled through Wearable Hip Exoskeletons Capable of Community Ambulation

  NSF · $702k · 2018–2023

• FRR: A new strategy for task-agnostic control of robotic exoskeletons by estimating underlying biological effort using deep learning

  NSF · $800k · 2023–2027

• Collaborative Research: Towards Task-Agnostic and Device-Agnostic Ankle Exoskeleton Control for Mobility Enhancement

  NSF · $300k · 2023–2027

Frequent coauthors

• Levi J. Hargrove

  Northwestern University

  53 shared

• Inseung Kang

  28 shared

• Ann M. Simon

  Shirley Ryan AbilityLab

  24 shared

• Jonathan Camargo

  Universidad de Los Andes

  21 shared

• Todd Kuiken

  20 shared

• Dean D. Molinaro

  Georgia Institute of Technology

  19 shared

• Kinsey Herrin

  Georgia Institute of Technology

  18 shared

• Lauren H. Smith

  15 shared

Awards & honors

• Ivan Allen College’s Distinguished Researcher Award (2015)
• Jean Monnet Chair (2012-15)