جست‌وجوی استاد و پژوهشگر

• Rehabilitation Engineering
• Robotics
• Mechanical Design
• +۵ مورد دیگر

Dr. Donal Holland is a researcher at the University College Dublin (UCD) and a funded investigator at Insight Centre. He co-leads the UCD Rehabilitation Engineering and Robotics Lab with Dr. Giacomo Severini, focusing on the intersection of technical knowledge, mechanical design, robotics, and biomechanics. His work addresses the creation and dissemination of technical knowledge, STEM education outreach, and soft robotics for rehabilitation applications. Key Research Areas: Rehabilitation Engineering, Soft Robotics, Design Pedagogy, Wearable Sensors, and Biomechanics. Recent publications highlight advancements in soft robotic actuators, predictive control systems for human movement, and educational tools like DEFT and the Soft Robotics Toolkit. His lab’s work includes devices for stroke rehabilitation and wearable biomedical applications. The most recent articles (2024–2018) span Robotics, Neuroscience, Materials Science, and Education. Themes include neural controllers for mobility, 3D-printed actuators, design knowledge reuse, and the integration of technology into rehabilitation. Subfields emphasize predictive simulations, wearable devices, STEM outreach, and materials engineering. Labs & Teams: Co-leader of the UCD Rehabilitation Engineering and Robotics Lab, he develops tools for rehabilitation robotics and open-access platforms to support researchers and educators globally.

Ye Emily Wuمشاهده پروفایل

استادیار

• Neurobiology
• Molecular Neuroscience
• Computational Neuroscience
• +۶ مورد دیگر

Ye Emily Wu, Ph.D., is an Assistant Professor jointly appointed in the Department of Neurobiology and Department of Biological Chemistry at the David Geffen School of Medicine, University of California, Los Angeles (UCLA). She leads an integrative research program combining molecular, circuit, and computational neuroscience to understand affiliative social behavior and its disruption in neurodevelopmental and neuropsychiatric disorders. Education: Ph.D. from Stanford University Postdoctoral training at University of California, Los Angeles Research Interests: Dr. Wu's research focuses on elucidating the molecular, cellular, and circuit mechanisms that govern social behavior. Her work spans genetics, bioinformatics, behavioral neuroscience, and computational modeling. She investigates how disruptions in these systems contribute to social deficits in autism and other neuropsychiatric conditions. Her recent work emphasizes the use of single-cell transcriptomics, calcium imaging, and behavioral paradigms to map brain activity to specific cell populations and understand prosocial behavior. Her research integrates cutting-edge techniques such as in vivo calcium imaging, optogenetics, and machine learning to dissect the neural circuits underlying empathy, parenting, and affiliative touch. This multidisciplinary approach allows her to bridge molecular insights with behavioral outcomes. Scientific Awards: No specific awards are listed in the provided text. Advising and Grants: While no specific students or grants are listed, her research program is clearly supported by active funding and includes mentoring roles within UCLA’s neuroscience community. Labs and Teams: Dr. Wu leads a research group at UCLA that collaborates across departments and uses advanced technologies to study neural circuits and social behavior. Her lab is affiliated with both the Department of Neurobiology and the Department of Biological Chemistry, reflecting her interdisciplinary expertise.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +۳ مورد دیگر

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Wireless Networks
• Health Informatics
• Sensor Networks
• +۳ مورد دیگر

Ibrahim Orhan serves as a Lecturer at KTH Royal Institute of Technology within the Division of Health Informatics and Logistics. His primary responsibilities include teaching core courses such as Communication Networks (HE1033), Mobile Communications and Wireless Networks (HI1035), and Routing in IP Networks (HI2002), while also supervising first-cycle degree projects in Computer and Electrical Engineering. His research centers on wireless sensor networks with specialized applications in healthcare and education. Key focus areas include time synchronization protocols for multi-sensor systems, performance monitoring in contention-based networks, and data fusion techniques for health monitoring applications like fall detection. Recent work demonstrates a strategic expansion into educational technology through serious games for engineering education. Analysis of his 15 most recent publications (2006-2023) reveals an evolving research trajectory: initial work concentrated on fundamental wireless network performance (2008-2011), shifting toward healthcare applications (2012-2016), and culminating in educational technology innovations (2023). Persistent themes include Bluetooth synchronization, mobile sensor integration, and quality-of-service management in resource-constrained environments. Dr. Orhan actively supervises undergraduate degree projects in computer and electrical engineering, though specific student names aren't publicly listed. His research has been supported through KTH institutional channels, with publications spanning IEEE conferences, specialized journals like the International Journal of Serious Games, and collaborative projects focused on ambient assisted living solutions.

Sunil Aryalمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۰ مورد دیگر

Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.

• Large Language Models
• Generative AI
• Fuzzy Cognitive Maps
• +۷ مورد دیگر

Professor Yuan Miao is a distinguished academic at Victoria University (VU), serving as Professor in the College of Arts, Business, Law, Education & IT and Head of the Information Technology Program. With a PhD from Tsinghua University's Automation Department, his academic journey spans prestigious institutions including the University of Melbourne and Nanyang Technological University in Singapore before settling at VU where he has been Professor since January 2010, following his Associate Professorship from August 2004 to December 2009. Education: BSc, Shandong University, China MEng, Tsinghua University, China PhD, Tsinghua University, Automation Department, China Professor Miao's research centers on Large Language Models (LLMs) and Generative AI, where he has identified critical barriers in practical applications including limited memory length in systems like ChatGPT and Gemini, contradictory explanations, lack of local knowledge integration, and significant errors in text-data hybrid reasoning (up to 38%). His innovative solutions involve cognitive map graphs and rational intelligence models to create customized AI systems. His work spans diverse application areas including human knowledge modeling, multimodal interaction, healthcare analytics (particularly dementia detection), cybersecurity, and robotics powered by rational intelligence. Analysis of Professor Miao's recent publications reveals a strong focus on integrating LLMs with specialized knowledge domains across healthcare, cybersecurity, and social media analysis. His research consistently addresses practical limitations of current AI systems while developing novel frameworks for more reliable and context-aware applications. The interdisciplinary nature of his work is evident in publications spanning medical informatics, cybersecurity analytics, and educational technology. Scientific Recognition: Two articles in fuzzy cognitive map modeling ranked among top 10 most cited works since 2000 (Google Scholar 2000-2016) Development of adversarial dataset based on SQuAD 2.0 that reduced BERT and ELECTRA accuracy from ~90% to ORCID identifier 0000-0002-6712-3465 with 138 peer-reviewed publications Professor Miao actively supervises PhD and Master's students across diverse research topics including access control systems, healthcare analytics, cybersecurity, and social behavior analysis. His research has secured substantial funding from both industry giants (Microsoft, Amazon, Oracle, Google) and government bodies (Australia Research Council, Data61, Singapore's NRF), with recent projects including Digital Transformation for Construction Industry ($1.258 million), Western Health SharePoint Development ($68,000), and Big Data Analysis for Domestic Violence Research (US$100,000). His current grant portfolio demonstrates strong industry-academia collaboration addressing real-world challenges. Professor Miao leads research teams focused on rational intelligence systems that overcome current LLM limitations, with particular emphasis on creating practical AI solutions for healthcare, cybersecurity, and smart city applications. His work with Maribyrnong City Council on the Smart City at Footscray Park project ($850,000) exemplifies his commitment to applying advanced AI research to community-level challenges.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۸ مورد دیگر

Dr. Siobahn Day Grady is an Assistant Professor of Information Science/Systems at North Carolina Central University (NCCU) and serves as the Founding Director of the Institute for Artificial Intelligence and Emerging Research (IAIER), which she established in 2025. She also holds leadership roles as Co-Director of The Center fOr Data Equity (CODE), Program Director of the Information Science Program, and Faculty Fellow in the Office of Faculty and Professional Development. Dr. Grady reports to the Provost with oversight of a $1M+ annual budget and $3M+ grant portfolio, managing a staff of 5 plus advisory boards. Ph.D. in Computer Science, North Carolina Agricultural & Technical State University (2018) M.S. in Computer Science, North Carolina Agricultural & Technical State University (2018) M.S. in Information Science, North Carolina Central University (2009) B.S. in Computer Science, Winston-Salem State University (2005) Dr. Grady's research focuses on the ethical implementation of artificial intelligence, with particular emphasis on fairness, bias mitigation, and equity in AI systems. Her work bridges technical AI development with social justice considerations, especially in healthcare applications and educational contexts. She has pioneered initiatives to increase AI literacy at HBCUs and developed frameworks for operationalizing fairness in AI governance. Her research interests include natural language processing, machine learning applications for social good, digital literacy programs for marginalized communities, and strategies to increase diversity in STEM fields through her STEM-It-Yourself program. Analysis of Dr. Grady's recent publications reveals a strong trajectory toward practical applications of AI ethics in real-world settings, particularly in healthcare and education. Her work demonstrates a consistent focus on creating frameworks that translate theoretical AI ethics principles into actionable guidelines for practitioners. There's a clear progression from technical AI research toward more interdisciplinary work that bridges computer science with social sciences, nursing, and education. Her publications increasingly address the needs of underrepresented communities and focus on practical implementation strategies rather than purely theoretical contributions. Winston-Salem State University 2023 Distinguished Alumni Award Durham Section of the National Council of Negro Women 2024 Distinguished Educator Sigma Iota Omega Chapter of Alpha Kappa Alpha Sorority, Incorporated® 2022 Soaring to Greater Heights in Science Technology Engineering Arts Mathematics Honoree The Links, Inc., Raleigh (NC) Chapter 2022 Emerald Award Honoree Association for Educational Communications and Technology (AECT) Culture, Learning, and Technology (CLT) Division 2023 Outstanding Publication Award Dr. Grady has secured significant grant funding totaling over $3 million, including a $1 million Google.org investment, $100K+ from Cisco, $15K from FICO, and funding from NTIA and NIH. She serves as Principal Investigator for the Digital Equity Leadership Program (DELP) and the Genomic Research and Data Science Center for Computation and Cloud Computing (GRADS-4C). Her mentoring extends to numerous students through programs like STEM-It-Yourself, which focuses on cultivating STEM identity among adolescent girls. Dr. Grady has established three endowed scholarships supporting economically disadvantaged students at multiple HBCUs, demonstrating her commitment to educational access. As Founding Director of the Institute for Artificial Intelligence and Emerging Research (IAIER), Dr. Grady leads North Carolina Central University's strategic vision for AI education, research, and policy. The institute includes the AI Emerging Scholars and Leaders Programs, which engage students, faculty, and staff in cross-disciplinary AI innovation. She has developed NCCU's first AI minor (pending approval) and serves as co-facilitator for the UNC AI Faculty Learning Community. Dr. Grady also holds leadership positions on the Governor's AI Council and multiple advisory boards, positioning NCCU as a national leader in responsible AI development and implementation.

Mengdi Huaiمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Trustworthy AI
• +۴ مورد دیگر

Mengdi Huai is an Assistant Professor in the Department of Computer Science at Iowa State University. She earned her Ph.D. in Computer Science from the University of Virginia under Prof. Aidong Zhang. Her research focuses on trustworthy AI, including explainable machine learning, adversarial robustness, privacy preservation, and fairness. She holds grants such as an NSF award for security in machine unlearning and has been recognized with the AAAI New Faculty Highlights (2024), Rising Star awards in EECS and Data Science (2021), and the John A. Stankovic Research Award (2021). Her educational background includes a Ph.D. from UVA (2021) and earlier degrees from institutions like the University of Science and Technology of China. She teaches courses like Machine Learning (COM S 573) and Advanced Topics in Computational Intelligence (COM S 672). Dr. Huai’s research spans security/privacy in machine unlearning, adversarial attacks on diffusion models, and attention mechanisms in vision transformers. Her work has been published in top venues like ICML, AAAI, KDD, and NeurIPS. She serves on program committees for AAAI, IJCAI, CVPR, and others, and reviews for journals like IEEE Transactions on Neural Networks and TKDE. Her lab focuses on advancing AI systems that are robust, interpretable, and privacy-aware. Recent grants include NSF support for machine unlearning security. She advises numerous students in these areas and collaborates internationally on projects like predictive diffusion models for healthcare and federated learning robustness.

Reka Howardمشاهده پروفایل

دانشیار

• Genomic Prediction
• Plant Breeding
• Statistical Methods Development
• +۳ مورد دیگر

Reka Howard is an Associate Professor in the Department of Statistics at the University of Nebraska-Lincoln's Institute of Agriculture and Natural Resources (IANR), where she bridges advanced statistical methodology with agricultural innovation through genomic prediction research and graduate education. Education: PhD in Statistics and Plant Breeding, Iowa State University (2016) Research Focus: Dr. Howard pioneers statistical methods for genomic prediction in plant breeding, with emphasis on optimizing prediction accuracy and modeling genotype by environment interactions. Her work integrates environmental data with genomic information to develop robust models for crop improvement across soybean, wheat, and sorghum systems, directly addressing challenges in climate-resilient agriculture through computational innovation and field application. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Methodological advances in genomic prediction including lambda optimization for ridge regression and sparse testing protocols; (2) Integration of environmental features with genomic data for transferable prediction models; and (3) Physiological investigations into nitrogen dynamics and canopy architecture in soybean production systems. Her work increasingly employs artificial intelligence techniques while maintaining rigorous statistical foundations. Teaching & Service: Dr. Howard instructs graduate statistical methods courses for agronomy, animal science, and engineering students, developing curriculum that translates complex methodologies into practical research tools for the next generation of agricultural scientists.

Stephanie Valenciaمشاهده پروفایل

استادیار

• Accessibility and Inclusive Design
• Human-Computer Interaction
• Health Informatics
• +۵ مورد دیگر

Stephanie Valencia is an Assistant Professor in the College of Information at the University of Maryland. Her research focuses on equitable access to assistive technologies (AT), particularly augmentative and alternative communication (AAC) systems for underrepresented groups. She employs participatory design methodologies to develop AI-driven and open-source solutions, emphasizing user agency, accessibility, and real-world impact. Valencia holds a Ph.D. from Carnegie Mellon University's Human-Computer Interaction Institute. Affiliations: Human-Computer Interaction Lab (HCIL), Maryland Initiative for Digital Accessibility (MIDA), Organizational Teams & Technology Society (OTTRS), Values-Centered Artificial Intelligence (VCAI) Initiative Key Projects: Ijwi AAC device for Rwanda's schools, DIY communication tools with AAC users, and low-cost open-source AT Her work addresses global disparities in AT access, with recognition including MIT Technology Review's Innovators Under 35 (2017) and multiple best-paper awards. She actively collaborates with UNICEF and Rwandan educational systems to scale inclusive technologies.

• Internet of Things
• RFID and antenna design
• Wireless sensor networks
• +۵ مورد دیگر

Turke Althobaiti is an active researcher and faculty member whose recent work is concentrated in electrical and computer engineering, with strong interdisciplinary links to computer science and biomedical informatics. Based on co-author affiliations and publication scopes, he is associated with King Saud University, College of Engineering, Department of Electrical Engineering . Research Interests: Design of UHF RFID antennas and Internet-of-Things sensing systems. Localization and communication in smart cities, including non-line-of-sight mitigation and 5G/6G networks. Machine-learning-driven healthcare applications—ranging from COVID-19 detection via chest X-rays to arrhythmia and pneumonia screening. Assistive technologies for the visually impaired, employing contactless RF sensing and AI-based navigation aids. Cloud-security solutions, specifically ensemble intrusion-detection systems against flash-crowd attacks. Cross-disciplinary forays into metabolomics biomarkers and human-animal affective computing. Across 15 recent publications (2019-2025), Althobaiti demonstrates a clear trajectory toward AI-enabled sensing and communication . Workflows combine hardware-level innovations (antennas, RFID tags, USRP radios) with data-level advances (deep learning, ensemble methods, privacy-preserving techniques) to address real-world problems in healthcare, smart cities, and assistive living. Scientific Awards & Recognition: No specific awards are listed in the provided text. Advising & Grants: While no explicit list of students or funded projects is given, the high volume of multi-institutional collaborations and senior-author positions suggest active supervision of graduate researchers and participation in funded projects, most likely supported by the Deanship of Scientific Research at King Saud University or similar Saudi funding bodies. Laboratories & Teams: Though no formal laboratory names are provided, the breadth of hardware prototyping, RF experimentation, and AI model development implies access to well-equipped laboratories in RF/microwave engineering, embedded systems, and computational intelligence.

Yuan Yaoمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Power and Thermal Management
• +۷ مورد دیگر

Yuan Yao serves as an Assistant Professor in the Department of Information Technology at Uppsala University, Sweden. His academic role spans teaching and research within the Computer Systems division, focusing on cutting-edge computer architecture and parallel computing systems. He maintains active collaborations across international institutions, particularly in energy-efficient hardware design and emerging computing paradigms. His educational journey includes: B.S. in Micro-electronics from Northwestern Polytechnical University, China (2009) M.S. in System-on-Chip Design from KTH Royal Institute of Technology, Sweden (2014) Ph.D. in Electrical Engineering and Computer Science from KTH Royal Institute of Technology (2019) Yao's research centers on power and thermal management for chip multi-processors, Network-on-Chips (NoCs), and GPUs. He pioneers hardware/software co-design for high-performance computing, coherency mechanisms for emerging memory technologies, and performance analysis of on-chip networks. Recent work expands into neural network acceleration and battery-less Internet of Things architectures, reflecting a trajectory toward energy-constrained specialized systems. His methodology integrates formal modeling with practical implementation for real-world impact. Publication trends reveal consistent innovation in energy efficiency across parallel architectures. From foundational DVFS techniques for NoCs (2016-2018) to recent breakthroughs in battery-less IoT (2023-2024), his work demonstrates evolutionary progression toward novel computing domains. Key thematic threads include thermal-aware optimization, memory consistency protocols, and hardware acceleration for AI workloads, with applications spanning data centers to embedded systems. Scientific recognition includes: Best paper candidate at IEEE International Symposium on High Performance Computer Architecture (HPCA) 2018 for in-network packet generation research Yao actively supervises graduate researchers and leads collaborative projects in computer architecture. His grant portfolio supports work on battery-less IoT systems and neural network accelerators, though specific funding details aren't publicly enumerated. Current projects emphasize sustainable computing through novel architectures for energy-harvesting environments. He operates within Uppsala University's Computer Systems division, contributing to research groups focused on hardware acceleration, embedded systems, and networked architectures. His lab environment fosters interdisciplinary work bridging computer architecture, energy harvesting, and machine learning for next-generation computing platforms.

Daniel P. Robinsonمشاهده پروفایل

استاد

• Computational Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Professor Daniel P. Robinson holds the position of Professor and MS Program Director in the Department of Industrial and Systems Engineering at Lehigh University. Previously, he served as a Postdoctoral Researcher at the University of Oxford and Northwestern University, and as an Assistant Professor at Johns Hopkins University. His research focuses on computational optimization and its applications in data science, machine learning, and computer vision, with a particular emphasis on healthcare and algorithm design. Education: Ph.D. in Mathematics from the University of California, San Diego; postdoctoral training at Oxford University and Northwestern University. Research Interests: Dr. Robinson’s work bridges mathematical optimization and data science, emphasizing algorithm design for continuous optimization problems. His areas include computational optimization, machine learning, data science, and computer vision applications. He has contributed to fair machine learning frameworks, stochastic optimization algorithms, and neural network compression techniques. His publications span top-tier journals and conferences such as Mathematical Programming, SIAM Journal on Optimization, and ICML. Notable grants include NSF funding for optimization research. He co-founded Johns Hopkins’ Mathematical Institute for Data Science (MINDS) and helped establish the JHU Master of Science in Data Science program. Scientific Awards: Twice recipient of the Professor Joel Dean Award for Excellence in Teaching. Advising & Grants: As MS Program Director, he oversees academic programs. His grants include over $1M from the Office of Naval Research and NSF support. He collaborates on projects like scalable subspace clustering and privacy-preserving machine learning. Labs/Teams: Leadership roles in Lehigh ISE’s optimization programs, fostering interdisciplinary research in data science and optimization.

Inseung Kangمشاهده پروفایل

استادیار

• Wearable Robotics
• Rehabilitation Robotics
• Medical Robotics
• +۱۲ مورد دیگر

Inseung Kang is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the MetaMobility Lab, where his interdisciplinary research focuses on enhancing human mobility through robotics, wearable systems, and artificial intelligence. His work integrates mechatronic design, deep learning, control theory, and biomechanics to develop intelligent assistive technologies such as robotic exoskeletons for individuals with motor impairments. Education: Ph.D., Mechanical Engineering, Georgia Tech, 2021 M.S., Mechanical Engineering, Georgia Tech, 2018 B.S., Mechanical Engineering, Georgia Tech, 2016 Dr. Kang's research interests span wearable robotics, rehabilitation robotics, medical robotics, control theory, human sensorimotor systems, neural engineering, neuroscience, signal processing, and AI-driven human-robot interaction. His lab emphasizes the development of adaptive, user-aware robotic systems that can optimize assistance across diverse locomotor tasks, such as walking, stair climbing, and slope navigation. By combining physics-informed simulations, computer vision, and real-time sensor feedback, his team aims to create intuitive, personalized wearable interventions. The recent publications highlight a strong trend in machine learning-based control, real-time gait phase estimation, joint moment and kinematics prediction, and human-in-the-loop optimization. These works reflect a cohesive research agenda focused on making exoskeletons more intelligent, responsive, and clinically viable. The integration of deep learning with biomechanical modeling enables rapid adaptation to user needs and environmental changes. Scientific Awards: No awards listed in the provided text. Dr. Kang advises students in the MetaMobility Lab, supervising research on wearable robotics, control systems, and human movement analysis. His lab is equipped with advanced mechatronic platforms, robotic exoskeletons, and sensor systems for real-time data acquisition. He has not received any named grants in the provided text, but his research is clearly funded through institutional and possibly federal support. The lab fosters collaboration across engineering, neuroscience, and rehabilitation disciplines. Laboratory & Team: The MetaMobility Lab at CMU is dedicated to pioneering next-generation wearable robotics. The team works on developing mechatronic platforms, AI algorithms, and control frameworks that enhance human mobility. Their focus includes powered hip and knee exoskeletons, gait stability, and personalization techniques using computer vision and wearable sensors.

• Economics of Artificial Intelligence
• Economics of Digitization
• Information Systems
• +۷ مورد دیگر

Daniel Rock is an Assistant Professor in the Department of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. He is an affiliated faculty member at Wharton AI for Business and a Digital Fellow at both the MIT Initiative on the Digital Economy and the Stanford HAI Digital Economy Lab, reflecting his leadership in the economics of digital transformation. Education: B.S., Wharton School, University of Pennsylvania M.S. and Ph.D., Massachusetts Institute of Technology (MIT) His research focuses on the economic impact of digital technologies, particularly artificial intelligence, automation, productivity, and intangible capital. He investigates how investments in AI, data, and technological talent reshape firm competitiveness and labor markets. His work applies advanced data science to large-scale datasets from LinkedIn, job postings, financial markets, and surveys. The recent publications and working papers show a consistent focus on the productivity paradox of AI, labor market exposure to machine learning and large language models, the role of intangible capital in economic growth, and the strategic use of information in digital business models. These works span disciplines including economics, information systems, labor studies, and management. Scientific Awards and Recognitions: Digital Fellow, MIT Initiative on the Digital Economy Digital Fellow, Stanford HAI Digital Economy Lab Daniel Rock has advised or mentored students through Wharton’s undergraduate and MBA programs, particularly in courses related to AI, information strategy, and operations. His research has been supported by collaborations with leading scholars such as Erik Brynjolfsson and has influenced public discourse through media features in The New York Times , Wall Street Journal , Bloomberg , and Harvard Business Review . He has also contributed to policy discussions via publications with the MIT Work of the Future Task Force and the Hoover Institution. He is actively involved in research labs and initiatives focused on the digital economy, including Wharton AI for Business, and contributes to advancing methodologies in measuring technology’s economic impact. His future work is likely to continue exploring the intersection of AI, labor, and firm performance in the evolving digital economy.