جست‌وجوی استاد و پژوهشگر

Masami Imaiمشاهده پروفایل

استاد

• Banking
• Financial Crises
• Japanese Economy
• +۳ مورد دیگر

Masami Imai is Professor of Economics and East Asian Studies at Wesleyan University, where he serves as Director of the Fries Center for Global Studies. His academic career spans prestigious research institutions including the Tokyo Center for Economics Research (TECR), Federal Deposit Insurance Corporation (FDIC), and University of Tokyo's CIRJE. His research expertise focuses on banking systems, financial crises, and East Asian economies, particularly Japan. Key interests include corporate innovation during crises, capital allocation efficiency, regulatory responses to banking failures, and regional economic development in post-conflict societies. His scholarly output shows strong temporal trends: early work (2000s) centered on Japanese banking history and deposit insurance reforms, mid-career research (2010-2015) analyzed global financial crisis impacts and political economy of banking, while recent publications (2020-2024) examine zombie lending, legislative malapportionment effects, and ethnic minority contributions to economic development. Most significant paper published in 2012 in Journal of Financial Intermediation Professor Imai teaches Introduction to Economic Theory, Macroeconomic Analysis, Money and Banking, Economy of Japan, and Quantitative Methods. His research fellowships demonstrate deep engagement with Japanese financial institutions and international economic policy analysis. Current work continues to bridge historical banking practices with contemporary crisis management frameworks.

Megan Careyمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cerebellar Research
• Motor Learning
• +۳ مورد دیگر

Megan Carey is a Researcher at the Champalimaud Foundation , leading the Carey Lab . Her research focuses on understanding how cellular and synaptic mechanisms in the cerebellum influence motor behavior and learning. Key projects include studies on cerebellar neural circuits, locomotor adaptation, and sensory-motor integration. Her recent work highlights cross-species comparisons (mice and flies), zebrafish visual system neurobiology, and pharmacological modulation of motor learning. The lab employs genetic perturbations, optogenetics, and behavioral analysis to dissect neural circuit dynamics. Notable collaborators include senior scientists, postdoctoral researchers, and PhD students. The lab's publications span high-impact journals like J. Neurosci. , eLife , and Neuron , with funding from HHMI and NIH.

Nikhil Srivastavaمشاهده پروفایل

دانشیار

• theoretical computer science
• algebraic graph theory
• spectral graph theory
• +۵ مورد دیگر

Nikhil Srivastava is an Associate Professor of Mathematics at the University of California, Berkeley, and a Senior Scientist at the Simons Institute. He received his PhD in Computer Science from Yale University in 2010 under the supervision of Daniel Spielman, following his undergraduate studies at Union College. After postdoctoral positions at the Institute for Advanced Study (Princeton), MSRI, and a research position at Microsoft Research India, he joined UC Berkeley in 2015. His research spans theoretical computer science and mathematics, with particular focus on spectral graph theory, random matrices, the geometry of polynomials, asymptotic convex geometry, and numerical analysis . His work often bridges the gap between pure mathematics and theoretical computer science, developing deep connections between polynomial methods, linear algebra, and combinatorial structures. Srivastava's research has been recognized with several prestigious awards including the SIAM Polya Prize , the NAS Held Prize , and the AMS Foias Prize , highlighting his significant contributions to the field. His most recent work focuses on numerical linear algebra, spectral theory, and computational mathematics, with publications spanning from algorithmic foundations to applications in mathematical physics. He actively mentors graduate students, currently advising Rikhav Shah, Zack Stier, and Isabel Detherage. His past students include Jorge Garza Vargas (co-advised with Dan Voiculescu), Theo McKenzie, Jess Banks, Satyaki Mukherjee, Archit Kulkarni, Nick Ryder, and Aaron Schild (co-advised with Satish Rao). His research has been supported by the NSF and the Sloan Foundation. At Berkeley, Srivastava has been actively involved with the Simons Institute, participating in numerous programs including Complexity and Linear Algebra (Fall 2025), Sublinear Algorithms (Summer 2024), and several others dating back to 2013. He regularly teaches courses in mathematics, including Discrete Mathematics (Math 55) and Multivariable Calculus (Math 53), and runs a seminar on Discrete Analysis in Evans Hall.

Patrick Shane Crawfordمشاهده پروفایل

استادیار

• Natural Hazards Resilience
• Geospatial Analysis
• Remote Sensing
• +۵ مورد دیگر

Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.

Peter Finoمشاهده پروفایل

استادیار

• Biomechanics
• Motor Control
• Neurorehabilitation
• +۷ مورد دیگر

Peter Fino is an Assistant Professor in the Department of Health, Kinesiology, and Recreation within the College of Health at the University of Utah. He directs the Neuromechanics and Applied Locomotion Lab, which is situated within the Cognitive and Motor Neuroscience research theme. His research focuses on understanding and improving mobility in individuals with neurological dysfunction, particularly those with brain injuries, using core concepts from biomechanics and motor control to develop better diagnostic tools and rehabilitation approaches. Dr. Fino's research explores how humans maintain stability during everyday activities that require complex motor control. His primary areas of investigation include: Foot placement control during walking and turning on uneven surfaces Balance recovery mechanisms following perturbations Effects of neurological conditions like concussion and Parkinson's disease on gait Development and application of inertial sensor technology for clinical assessment Neuroanatomical correlates of motor dysfunction after brain injury Nonlinear dynamics approaches to understanding human movement His lab employs a multidisciplinary approach, collaborating with engineers, clinicians, physical therapists, and neuroscientists to translate research findings into practical applications that improve people's lives. Current projects examine mobility in populations with traumatic brain injury, Parkinson's disease, and other neurological conditions, with particular focus on turning gait, dual-task performance, and objective measurement of balance recovery using wearable sensor technology. Dr. Fino actively mentors a diverse research team comprising PhD students, MS students, research coordinators, and undergraduate researchers. His lab has produced numerous graduates who have gone on to academic positions, clinical research roles, and healthcare professions. He maintains strong collaborative relationships across the University of Utah and with external institutions including Oregon Health & Science University, University of Nebraska Omaha, and US Army-Baylor Physical Therapy. The lab participates in outreach through National Biomechanics Day and partnerships with high school programs to introduce students to biomechanics and neuroscience.

• Quantum Engineering
• Quantum Optics
• Photonics
• +۳ مورد دیگر

Rachel N Clark is a Lecturer in the School of Physics at the University of Bristol, actively contributing to the Quantum Engineering Technologies research group with a focus on quantum photonics and quantum light source development. Education: Master of Physics (MPhys) Master of Science (MSc) Doctor of Philosophy (PhD) Her research spans Quantum Engineering , Quantum Optics , and Photonics with concentrated expertise in solid-state quantum emitters—particularly aluminum nitride color centers—and single photon source engineering. Key investigations include evanescent-field photon collection enhancement, room-temperature quantum emitter characterization, and nanofabrication of quantum photonic devices for improved efficiency and integration. Analysis of her 13 publications (2022-2025) reveals dominant trends in aluminum nitride quantum emitter development, with 60% of works addressing room-temperature quantum light sources and photon collection efficiency. Her research bridges fundamental quantum optics with practical quantum technology applications, emphasizing scalable fabrication methods and metrology standards for quantum photonic systems. No scientific awards were documented in the provided materials. Information regarding graduate students, research grants, or specific laboratory teams beyond her Quantum Engineering Technologies affiliation was not indicated in the source texts.

• Data Mining
• Machine Learning
• Relational Learning
• +۱۲ مورد دیگر

Jennifer Neville is a Senior Principal Researcher at Microsoft Research Redmond and holds the Samuel Conte Chair Professor of Computer Science and Statistics at Purdue University. With over 100 publications and 10K citations, her research spans data mining, machine learning, and AI algorithms for relational and networked domains including social networks, epidemiology, and web analytics. Education: BS in Computer Science, University of Massachusetts Amherst (2000) MS in Computer Science, University of Massachusetts Amherst (2004) PhD in Computer Science, University of Massachusetts Amherst (2006) Her work focuses on relational learning techniques that exploit connections between entities to enhance pattern discovery. Recent research explores large language models (LLMs), emphasizing alignment with user intent through interaction at scale, while addressing statistical biases from graph structures. Selected scientific awards include the NSF Career Award (2012), ICDM Best Paper (2009), and IEEE’s 10 to Watch in AI (2008). She served on the AAAI Executive Council (2015-2018) and chaired multiple conferences including SIAM Data Mining (2019) and ACM Web Search (2016). Contact: neville@cs.purdue.edu jenneville@microsoft.com

• Pediatric Critical Care
• Pediatric Endocrinology
• Diabetic Ketoacidosis
• +۴ مورد دیگر

Michael S.D. Agus, M.D. is the Constantine Anast, MD Professor of Pediatrics at Boston Children's Hospital, which is affiliated with Harvard Medical School. He serves in the Department of Pediatrics with a focus on critical care and endocrinology. Dr. Agus is a prominent researcher in pediatric critical care medicine, with particular expertise in glycemic control, diabetic ketoacidosis, and sepsis management in critically ill children. Dr. Agus's research interests center around optimizing care for critically ill children, with a strong focus on glucose management in pediatric intensive care settings. His work has significantly contributed to understanding the optimal blood glucose ranges for critically ill children, the management of diabetic ketoacidosis in pediatric patients, and the role of corticosteroids in pediatric septic shock. He has led several major NIH-funded clinical trials including the HALF-PINT (Heart and Lung Failure-Pediatric INsulin Titration) trial and the SHIPSS (Stress Hydrocortisone In Pediatric Septic Shock) study. Dr. Agus has been instrumental in developing clinical guidelines for glycemic control in critically ill children, as evidenced by his contributions to the Society of Critical Care Medicine Guidelines. His research employs advanced methodologies including transcriptomics to identify subclasses of pediatric septic shock, potentially enabling more personalized treatment approaches. His work bridges the gap between critical care medicine and endocrinology, focusing on how metabolic management affects outcomes in critically ill children. Dr. Agus has received significant recognition for his research, with numerous high-impact publications in leading medical journals including Critical Care Medicine , Pediatric Critical Care Medicine , and The Lancet Child & Adolescent Health . His work on glycemic control has been particularly influential in shaping clinical practice for critically ill children with diabetes or hyperglycemia. As a principal investigator on multiple NIH grants, Dr. Agus leads a robust research program focused on improving outcomes for critically ill children. His work extends beyond direct patient care to include healthcare systems research, examining optimal staffing models in pediatric intensive care units and building global collaborative research networks in pediatric critical care. He is actively involved in translating research findings into clinical practice through guideline development and educational initiatives.

• Verification and Validation
• Search-Based Software Engineering
• Autonomous Driving
• +۳ مورد دیگر

Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.

Nils Wildeمشاهده پروفایل

استادیار

• Robotics
• Autonomous Systems
• Human-Robot Interaction
• +۱۶ مورد دیگر

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

• Machine Learning
• Fairness in Artificial Intelligence
• Federated Learning
• +۷ مورد دیگر

Yang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at the Baskin School of Engineering, University of California, Santa Cruz. Previously, they were affiliated with Harvard University and earned their PhD in 2015 from the Department of EECS at the University of Michigan, Ann Arbor. Their research lies at the intersection of machine learning, fairness, and trustworthy AI, with a strong focus on large language models, federated learning, and causal reasoning. Their research interests include: Machine Learning and Fairness Federated and Privacy-Preserving Learning Large Language Model Safety and Unlearning Causal Inference and Counterfactual Reasoning Anomaly Detection and Robust Forecasting Human-AI Interaction and Ethical AI Recent publications (2024–2025) demonstrate a strong trend in developing methods for machine unlearning, fairness in LLMs, and robustness under label noise and distribution shifts. Their work frequently appears in top-tier venues such as NeurIPS, ICLR, ICML, AAAI, and KDD, often in collaboration with researchers like Zhaowei Zhu, Mingyan Liu, Jiaheng Wei, and Kun Zhang. Themes include algorithmic fairness, model accountability, and human-aligned AI systems. Scientific contributions include: Frameworks for LLM unlearning and model editing Methods for fair classification and recourse Robust time series forecasting under anomalies Test-time adaptation in multimodal models Causal approaches to debiasing and policy learning While no formal advising list is provided, the depth and volume of collaborative work suggest active mentorship of graduate students and postdocs. Their research program is highly active, with numerous ongoing projects in trustworthy and socially responsible AI.

Anna Shustermanمشاهده پروفایل

استاد

• Conceptual Development
• Language and Cognition
• Numerical Cognition
• +۲ مورد دیگر

Anna Shusterman is a Professor in the Psychology Department at Wesleyan University, where she has taught and directed research in the Cognitive Development Laboratories since 2007. She co-founded and serves as co-chair of Wesleyan's College of Education Studies, established in 2020. Her educational background includes: Bachelor's degree in Neuroscience from Brown University Graduate studies and post-doctoral fellowship at Harvard University's Laboratory for Developmental Studies under Elizabeth Spelke and Susan Carey Dr. Shusterman's research centers on conceptual development in children, with a focus on the interplay between language and cognition in spatial and numerical domains. She investigates how language shapes children's understanding of numbers and space, and develops methodological approaches for translating developmental science into practical preschool classroom applications. Her work explores how young children perceive and learn about the world through simple, engaging games. Analysis of her recent publications (2019-2025) reveals consistent emphasis on numerical cognition, language acquisition, and spatial reasoning. Key themes include language's role in math development, early numeracy in diverse populations (particularly deaf and hard of hearing children), and implementation of research-based math activities in preschool settings. Her work demonstrates how language exposure in any modality supports numerical concept development and how grammatical structures influence number word acquisition. Dr. Shusterman has secured significant research funding including NSF CAREER grants and collaborative research awards. She mentors undergraduate researchers in her laboratory and actively bridges academic research with practical educational applications through her work with the College of Education Studies. She directs the Blue Lab within Wesleyan's Cognitive Development Laboratories, located in Judd Hall. The NSF-funded lab conducts studies with children under 12 from central Connecticut preschools, schools, and daycare centers, using fun games to investigate how children think about numbers, space, language, and social concepts.

• Hyperdimensional Computing
• Neuromorphic Hardware
• In-Memory Computing
• +۳ مورد دیگر

Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.

• Applied Mathematics
• Mathematical Modelling
• Computational Modelling
• +۷ مورد دیگر

Professor Matthew Simpson is a leading figure in applied mathematics at the School of Mathematical Sciences, Faculty of Science, Queensland University of Technology (QUT). He holds the position of Professor of Applied Mathematics and is an Australian Research Council (ARC) Future Fellow, reflecting his sustained research excellence. His work bridges mathematical theory and biological applications, particularly in cell migration, tissue invasion, and multiscale modeling. BE (Environmental) Honours 1, University of Newcastle (1995–1998) PhD (with Distinction), Environmental Engineering, University of Western Australia (2000–2003) Research Fellow, Department of Mathematics and Statistics, University of Melbourne (2003–2006) ARC Postdoctoral Fellow, University of Melbourne (2006–2009) Lecturer (2010–2011) and Senior Lecturer (2011–2013), QUT Associate Professor (2013–2014), QUT Professor and ARC Future Fellow (2014–present), QUT Matthew Simpson’s research focuses on mathematical and computational modeling of biological systems , particularly collective cell motion, diffusion processes, and reaction-diffusion dynamics. His interests span multiscale modeling , random walk processes , cell biology , and numerical and computational mathematics . He develops and analyzes models to understand phenomena such as wound healing, cancer progression, and tissue engineering. His recent publications (2023–2025) demonstrate a strong trend toward integrating data-driven modeling , likelihood-based inference , and equation learning with traditional mechanistic models. These works emphasize parameter identifiability , uncertainty quantification , and prediction robustness in biological contexts. Themes include sharp-fronted wave propagation, mechanical cell interactions, tumor spheroid formation, and generalized diffusivity in food drying, showcasing the breadth and depth of his modeling expertise. Among his key accolades are: J.H. Michell Medal (2012) – Awarded by ANZIAM for distinguished research by an early-career applied mathematician in Australia and New Zealand. ARC Future Fellowship (2013–2017) – For the project 'New data-driven mathematical models of collective cell motion' (FT130100148). Professor Simpson has also played significant editorial and leadership roles, including: Executive Associate Editor, Journal of Engineering Mathematics Academic Editor, PLoS ONE Editorial Board Member, ANZIAM Journal Co-chair of the 2015 ANZIAM meeting He has supervised PhD students on topics such as moving boundary problems, first-passage times, stochastic simulations, and curvature-dependent growth in biological systems. His research projects have been funded by competitive Australian grants (ARC DP and FT schemes), including studies on 3D cell migration, ghrelin’s role in cell invasion, and epithelial-to-mesenchymal transition in cancer and wound healing. He is actively involved in developing computational tools for biological modeling and promoting best practices in scientific publishing.

Roderich Grossمشاهده پروفایل

مدرس ارشد

• Swarm Robotics
• Modular Robotics
• Computational Intelligence
• +۵ مورد دیگر

Roderich Gross is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. He is also a Visiting Scientist at CSAIL, MIT, and leads the Enabling Technologies theme at Sheffield Robotics. His academic journey includes a Ph.D. in engineering science from Université libre de Bruxelles (2007), followed by postdoctoral fellowships as a JSPS Fellow (Tokyo Institute of Technology), Research Associate (University of Bristol), and Marie Curie Fellow (EPFL & Unilever). Research Interests : Swarm robotics, self-reconfigurable robots, multi-robot coordination, robotics software/tools (human-robot interaction interfaces, formal design tools), machine learning for behavior inference (Turing Learning, GANs), autonomous systems, natural computing (swarm intelligence, evolutionary algorithms). Scientific Contributions : Inventor of Turing Learning, a machine learning method for behavior inference. Key work includes swarm coordination, self-assembly, fault-tolerant quadcopters, energy-efficient drone delivery, and infrared-based swarm communication. Scientific Recognition : Held prestigious fellowships including JSPS and Marie Curie, and served as Associate Editor for leading robotics journals (IEEE Robotics and Automation Letters, Swarm Intelligence) and conference roles (General Chair DARS 2016, Program Co-Chair GECCO 2018). Grants : Principal Investigator on Horizon Europe OpenSwarm (£463,699), EPSRC Core Capital (£104,196), DSTL Multi Robot Systems (£98,781), and industry-funded modular robotics projects. Labs : Affiliated with the Natural Robotics Lab at the University of Sheffield.