جست‌وجوی استاد و پژوهشگر

Arvind Karunakaranمشاهده پروفایل

استادیار

• Sociology of Work and Occupations/Professions
• Organization Theory
• Technological/Organizational Change
• +۴ مورد دیگر

Arvind Karunakaran is an Assistant Professor at Stanford University in the Department of Management Science and Engineering. He holds affiliations with the Center for Work, Technology, and Organization (WTO) , Stanford Technology Ventures Program (STVP) , Stanford Institute for Human-centered Artificial Intelligence (HAI) , and the Digital Economy Lab (DEL) . His research focuses on authority and accountability in the workplace , particularly under technological change and human-AI augmentation. Education: Ph.D. from MIT Sloan School of Management (2018) His work employs ethnographic and field-based methods to study empirical puzzles in workplace dynamics, complemented by comparative-historical analysis and quantitative/textual data analysis . Key research areas include social/algorithmic evaluation of workers , human-AI reskilling , and conflicts in symmetrical relations . Recent publications analyze AI's impact on workplace inequality , platform governance , and crowd-based accountability . His findings are published in top journals like Administrative Science Quarterly and Organization Science . Scientific Awards & Recognitions: Best Published Paper Award (ASA, 2025) Gerard J. Tellis Best Junior Faculty Paper Award (AIM Conference, 2025) W. Richard Scott Article Award (ASA, 2024) Responsible Research in Management Award (RRBM, 2024) Future of Organizations Fellowship (2024) Professor Karunakaran advises doctoral and master's students in areas related to technology and organizations , and teaches courses on organizational behavior , organizational theory , and innovation management .

Pamela Hindsمشاهده پروفایل

استاد

• Organizational Behavior
• Human-Computer Interaction
• Cross-Cultural Collaboration
• +۳ مورد دیگر

Pamela J. Hinds is the Rodney H. Adams Professor and Fortinet Founders Chair in Stanford University's School of Engineering, holding a joint appointment in the Department of Management Science and Engineering. She co-directs the Center on Work, Technology, and Organization and serves on the Director's Council for the Hasso Plattner Institute of Design. Her research focuses on technology's impact on teams, collaboration, and innovation in global and cross-cultural contexts. She earned her Ph.D. in Organizational Science and Management from Carnegie Mellon University (1997). Research Interests: Dr. Hinds explores cross-boundary work teams, particularly those spanning national borders, examining cultural dynamics, language barriers, and site visits' role in knowledge sharing. She investigates human-robot interaction in workplaces and emerging technologies' effects on work practices, including AI, 3D printing, and open innovation systems. Her work bridges organizational behavior, human-computer interaction, and global work dynamics. Awards & Recognition: Lasting Impact Award (2018) Senior Editor of Organization Science Co-editor of Distributed Work (MIT Press) Advising & Leadership: As department chair, Hinds shapes educational and research agendas. Her grants include NSF-funded studies on digital technologies in agriculture and crowd-based innovation. She advises initiatives at the intersection of technology, work design, and global collaboration. Labs & Teams: Co-leads the Center on Work, Technology, and Organization, fostering interdisciplinary research on modern work environments. Engages in Stanford's Design School (d.school) to integrate human-centered design principles into workplace innovation.

Alexander Mathisمشاهده پروفایل

استادیار

• Computational Neuroscience
• Machine Learning
• Sensorimotor Control
• +۶ مورد دیگر

Alexander Mathis is an Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Brain Mind Institute (School of Life Sciences). His research bridges computational neuroscience and machine learning to decode sensorimotor behaviors and develop AI tools for behavioral analysis. Pure Mathematics MSc, Ludwig Maximilians University Munich PhD in Computational Neuroscience, Ludwig Maximilians University Munich His work focuses on understanding how the brain generates behavior through computational models and algorithms. Key contributions include the DeepLabCut toolbox, hBehaveMAE , and other frameworks for pose estimation, action segmentation, and brain-inspired AI. His group also explores proprioception, motor control, and neural coding theories. Recent publications include unsupervised hierarchical behavior modeling via masked autoencoders (ECCV 2024), synthetic basketball benchmarks (Shot7M2), and extensions of BABEL into hBABEL. These works highlight his interest in temporal hierarchies in behavior and scalable AI solutions for neuroscience. Scientific Honors Robert Bing Prize (2024) Eric Kandel Young Neuroscientists Prize (2023) Frontiers of Science Award (2023) Marie Skłodowska-Curie Postdoctoral Fellowship Studienstiftung des deutschen Volkes scholarship He advises PhD candidates in neuroscience and life sciences, leads courses on brain-like computation and software engineering for life sciences , and collaborates across AI4Science initiatives. His group actively participates in competitions, such as NeurIPS' MyoChallenge, where brain-inspired reinforcement learning algorithms have won awards.

Joydeep Biswasمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Long-term autonomy
• +۵ مورد دیگر

Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.

Nico Van de Wegheمشاهده پروفایل

استاد

• GeoAI
• GIScience
• Spatiotemporal Analysis
• +۸ مورد دیگر

Prof. dr. Nico Van de Weghe is a full Professor of GIScience at the University of Ghent (UGent), affiliated with the CartoGIS research unit. His work bridges computer science, social science, and natural science through geospatial information studies, focusing on enabling machines to reason spatially (GeoAI). Since 2004, he has specialized in knowledge-based AI, particularly spatiotemporal reasoning and moving object analysis, with applications in animal behavior, criminology, healthcare, mobility, and sports. Van de Weghe's research emphasizes hybrid GeoAI systems combining knowledge-driven and data-driven approaches. Keywords include GeoAI, GIScience, Spatiotemporal Analysis, Moving Objects, and Data Mining. Recent publications highlight urban road network analysis, hybrid trajectory modeling, BIM semantic enrichment, and cycling safety studies using virtual reality.

Marina von Keyserlingkمشاهده پروفایل

استاد

• Animal Welfare Science
• Dairy Cattle Welfare
• Farm Animal Behavior
• +۳ مورد دیگر

Marina von Keyserlingk is a Professor in Applied Biology within the Faculty of Land and Food Systems at the University of British Columbia (UBC). She serves as Director of the UBC Dairy Education and Research Centre and leads the renowned UBC Animal Welfare Program, one of the largest and most respected animal welfare science programs globally. Her work has significantly influenced dairy farming practices worldwide, particularly in the areas of dairy cow and calf welfare. Dr. von Keyserlingk's research focuses on animal behavior, housing, and management practices and how these contribute to the health and welfare of dairy cattle. Her work spans multiple dimensions of animal welfare science, including: Dairy cattle behavior and cognition Cow-calf separation practices Pain assessment and management in farm animals Public perceptions of farm animal welfare Alternative dairy farming systems Welfare assessment methodologies Her most recent research examines pain responses in calves, lameness assessment techniques, effects of environmental enrichment on calf cognition, and public attitudes toward dairy farming practices. This work demonstrates a strong trend toward integrating scientific assessment of animal welfare with public values and perceptions, recognizing that sustainable animal agriculture must address both scientific and societal concerns. Dr. von Keyserlingk has received numerous prestigious awards for her contributions to dairy science and animal welfare: Elanco Award for Excellence in Dairy Science (2013) Metacam Bovine Welfare Award (2013) Canadian Animal Industries Award in Extension & Public Service (2012) 26th Annual World Buiatrics Congress Keynote Speaker (2010) She has supervised numerous graduate students, including Dr. Lexis Ly who recently completed her PhD in 2025. Dr. von Keyserlingk also teaches undergraduate courses including "Animals and Society" and "Research Methods in Applied Animal Biology." Her research program has secured significant funding that supports multiple postdoctoral fellows, graduate students, and research staff working within the UBC Animal Welfare Program. The UBC Animal Welfare Program, which she helps lead, is currently recruiting PhD students interested in improving the lives of dairy cattle and the people who care for them. The program continues to be at the forefront of animal welfare science, conducting research that has practical applications for improving animal care worldwide.

Yasamin Mostofiمشاهده پروفایل

استاد

• Wireless Systems
• Robotics
• Control Systems
• +۱۰ مورد دیگر

Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .

Manuel Kaufmannمشاهده پروفایل

مدرس

• 3D Human Motion Capture
• Computer Vision
• Sensor Data Processing
• +۳ مورد دیگر

Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.

Heather Knightمشاهده پروفایل

استادیار

• Social Robotics
• Human-Robot Interaction
• Non-Verbal Machine Communication
• +۳ مورد دیگر

Heather Knight is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Oregon State University, part of the College of Engineering. Her research focuses on social robotics and human-robot interaction, emphasizing non-verbal communication and the design of non-anthropomorphic robots. She directs the CHARISMA Research Lab, which applies principles from performing arts to enhance robot charisma through expressive motion and interaction design. Dr. Knight holds a Ph.D. in Robotics from Carnegie Mellon University (2016), and dual M.S. degrees in Robotics (Carnegie Mellon) and Electrical Engineering & Computer Science (MIT, 2008), alongside a B.S. in Electrical Engineering from MIT (2006). She also leads Marilyn Monrobot, a robot theater company producing comedic performances and an annual Robot Film Festival. Her work bridges technical innovation with societal impact, exploring how robots can better communicate intent through motion and interaction. Notable contributions include the 'Sanitizerbot' project for playful public health support and the development of Laban-based motion frameworks for expressive robotics. Awards include being named to Forbes' 30 Under 30 in Science (2011) and her TED talk on robot comedy. Key Research Themes: Multi-robot formations, expressive motion design, human-robot social dynamics, and ethical integration of AI in robotics. Recent Projects: VR storytelling with social robots, service robots in workplace environments, and motion-based affective communication. Her articles span topics from robot humor generation to industrial light communication systems, reflecting a commitment to both foundational research and applied solutions for human-robot coexistence.

• Perceptual information processing
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Hiroshi Ishikawa is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering. He also serves as a Visiting Professor at the National Institute of Informatics since 2016. Previously, he held positions at Nagoya City University from 2004-2010 as Assistant Professor, Associate Professor, and Professor. His academic journey includes being a JST PRESTO Researcher from 2009-2013 and an Associate Research Scientist at New York University's Courant Institute of Mathematical Sciences from 2000-2001. Ph.D. in Computer Science, New York University (2000) Master of Science, Kyoto University Bachelor's degree in Mathematics, Kyoto University Faculty of Science (1991) Hiroshi Ishikawa's research spans perceptual information processing, computer vision, artificial intelligence, deep learning, and discrete optimization. His work focuses on developing algorithms for image restoration, segmentation, and understanding, with significant contributions to energy minimization techniques in computer vision. He has pioneered approaches in sketch simplification, medical image segmentation, and higher-order graph cuts. His research bridges theoretical advances in mathematical optimization with practical applications in medical imaging, computer graphics, and consumer electronics. Ishikawa's recent publications demonstrate a strong focus on leveraging deep learning for image enhancement and understanding. His work spans super-resolution techniques, colorization methods, human avatar generation, and medical image analysis. A notable trend is the increasing integration of attention mechanisms and generative models to solve complex vision problems, with growing emphasis on real-world applications in medical imaging and computer graphics. His research group consistently produces high-impact work that appears in top-tier computer vision conferences. 75th Annual IEICE Best Paper Award (2019) Innovative Technologies 2016 Special Prize for Culture (Ministry of Economy, Trade and Industry) MIRU Nagao Award (Best Paper Award) (2009) Young Author Award (IEEE Computer Society Japan Chapter, 2006) MIRU2006 Excellent Paper Award (2006) Harold Grad Memorial Prize (Courant Institute of Mathematical Sciences, NYU, 2000) As a professor at Waseda University, Ishikawa has mentored numerous students who have become active researchers in computer vision, including Yuya Masuda, Edgar Simo-Serra, and Satoshi Iizuka. His research has been supported by various grants, including JST PRESTO funding from 2009-2013. He has served on editorial boards for prestigious journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and has held leadership roles in major computer vision conferences such as ICCV, CVPR, and ACCV. Ishikawa leads a vibrant research group at Waseda University focused on computer vision and image processing. His laboratory collaborates extensively with researchers at Nagoya City University, National Institute of Informatics, and international institutions. The group maintains strong connections with industry partners, particularly in medical imaging and consumer electronics sectors, translating theoretical advances into practical applications.

Sebastian Kubeمشاهده پروفایل

استاد

• Behavioral Economics
• Experimental Economics
• Social Preferences
• +۵ مورد دیگر

Sebastian Kube is a Professor of Behavioral and Experimental Economics at the University of Bonn, Germany. He holds concurrent positions as Senior Researcher at the Max Planck Institute for the Study of Public Goods, Research Fellow at the Institute for the Future of Labor (IZA), and Vice Director of the BonnEconLab. He also coordinates the European Doctoral Program in Quantitative Economics and serves on multiple editorial/research boards. University of Bonn Max Planck Institute for Research on Collective Goods Institute for the Study of Labor (IZA) BonnEconLab European Doctoral Program in Quantitative Economics His experimental research examines social preferences , cooperation mechanisms , incentive design , and norm enforcement . Key areas include: how remuneration schemes affect workplace cooperation, the effectiveness of social sanctions in social dilemmas, and the psychological foundations of environmental behavior. Recent work explores trust communication strategies for maintaining cooperation, information asymmetry effects in group dynamics, and incentive reversal phenomena where higher rewards paradoxically reduce effort. His 2025 publications particularly focus on sanction removal strategies and environmental behavior. Scientific Awards: Excellence in Teaching Award (2017, 2016, 2015, 2012) IZA Young Labor Economist Award (2011) Etienne-Laspeyres Prize (2011) Heinz Sauermann Prize (2004) As Vice Director of BonnEconLab, he oversees experimental infrastructure for economic research. His methodological approach combines field experiments with laboratory studies , often using behavioral game theory frameworks. He has published extensively in top journals including American Economic Review , Journal of Public Economics , and Management Science .

Shang-Tse Chenمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Cybersecurity
• +۵ مورد دیگر

Shang-Tse Chen is an Associate Professor at the Department of Computer Science and Information Engineering and Graduate Institute of Networking and Multimedia , National Taiwan University . He leads the NTU AI Security Lab , focusing on applied and theoretical machine learning with emphasis on cybersecurity, adversarial ML, and ML privacy/fairness. Education: PhD in Computer Science (Georgia Tech, 2019), BSc in CSIE (NTU, 2010) Awards: K. T. Li Young Researcher Award (2025), IBM PhD Fellowship (2018), KDD Best Student Paper Runner-Up (2016), NSF SaTC Grant (2017-2021) His research spans adversarial ML, certified defenses, model inversion attacks, and intersection with differential privacy/fairness. Recent work includes physical adversarial attacks on object detectors and practical defenses using JPEG compression. He teaches courses like Security and Privacy of Machine Learning and Introduction to Medical Informatics . Key publication trends show focus on adversarial robustness (ICML/NeurIPS/ICLR), cybersecurity applications (ACSAC), and ML fairness (ACL/EMNLP). Collaborations include industry partnerships with Intel Labs and Symantec. Scientific Awards: K. T. Li Young Researcher Award (2025) ACM TiiS Best Paper Honorable Mention (2020) IBM PhD Fellowship (2018) KDD Audience Appreciation Award Runner-Up (2018) Symantec Fellowship Runner-Up (2016) KDD Best Student Paper Runner-Up (2016) NSF Grant (2017) He advises 13 current students (PhD/MS/Undergrad) and has mentored alumni now at CMU/UC Berkeley. The lab actively recruits postdocs and students across levels.

Anna Bernardمشاهده پروفایل

استادیار

• Economics
• Policy
• Behavioral Economics
• +۳ مورد دیگر

Anna Bernard is an Assistant Professor at the Department of Microeconomics , Universidade Católica Portuguesa (CATÓLICA-LISBON). Her work bridges Behavioral Economics , Experimental Economics , and Cultural Economics , focusing on crowdfunding dynamics, charitable giving, and diversity in cultural industries. Education: PhD in Economics, Paris School of Economics - Université Panthéon-Sorbonne Masters in Economics, Paris School of Economics - Université Panthéon-Sorbonne Her research combines empirical methods with experimental designs to explore public goods crowding effects, risk perception in crowdfunding, and socioeconomic impacts of policies like R&D tax credits. She has collaborated with the French Ministry of Culture and startups. Recent publications highlight trends in digital economy and cultural consumption , with a focus on teenager behavior , crowdfunding incentives , and redistribution dynamics . Despite no explicit awards listed, her work has been published in journals such as Revue Économique and Journal of Cultural Economics .

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.

Jonathan P. Wongمشاهده پروفایل

استاد

• Military Strategy
• Military Acquisition and Procurement
• Military Budgets and Defense Spending
• +۱۳ مورد دیگر

Jonathan P. Wong is a Senior Policy Researcher at the RAND Corporation and Professor of Policy Analysis at the RAND School of Public Policy. His work bridges academic research and defense policy, focusing on military strategy, acquisition, force planning, and the integration of emerging technologies into military operations. Education: Ph.D. and M.Phil. in Policy Analysis (Pardee RAND Graduate School), M.A. in Security Studies (Georgetown University), B.A. in Political Science (UC San Diego) Professional Experience: U.S. Marine Corps infantry officer (2001–2011), Consultant at Boston Consulting Group, current RAND researcher and professor Wong’s research interests center on defense innovation, military logistics, artificial intelligence in warfare, space capabilities, and the human dimension of military operations. He has led studies on human-machine teaming, non-lethal weapons, defense acquisition reform, and U.S. military posture in the Indo-Pacific. His work emphasizes practical, policy-relevant insights grounded in operational experience. The trends in his recent publications reveal a deep engagement with modern defense challenges: integrating commercial technologies into military systems, improving acquisition agility, enhancing battlefield awareness, and ensuring that defense innovation serves warfighter needs. His research spans strategic, operational, and tactical levels, often focusing on the U.S. Army and Marine Corps. Wong has not received public recognition in the form of listed awards in the provided texts, but his leadership in congressionally mandated studies and high-impact policy research suggests significant influence in defense circles. Advising & Grants: Co-led major projects such as the Long Range Precision Fires study and research on integrating women into Marine Corps infantry. His work is frequently funded by the Department of Defense and other federal entities, though specific grant details are not listed. Wong is affiliated with RAND’s defense and national security research teams, contributing to strategic analysis and policy development. He is not associated with a formal lab but works within RAND’s collaborative research environment, often partnering with other veterans and defense experts to produce actionable insights for military leaders and policymakers.