جست‌وجوی استاد و پژوهشگر

Dhruv Shahمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Dhruv Shah is an Incoming Assistant Professor of Electrical and Computer Engineering at Princeton University starting January 2026 and currently serves as a Senior Research Scientist at Google DeepMind. He is also an Associated Faculty member in Princeton's Center for Statistics and Machine Learning, focusing on the convergence of machine learning and robotics for real-world deployment. His academic credentials include a Ph.D. and M.S. in Electrical Engineering and Computer Sciences from the University of California, Berkeley (2024) and a B.Tech. (Honors) in Computer Science and Engineering from the Indian Institute of Technology, Bombay (2019). Shah's research pioneers foundation models for robotics, emphasizing large-scale robot learning, out-of-distribution generalization, and long-horizon reasoning. His group adopts a full-stack methodology spanning algorithmic innovation to system design, drawing from cognitive science to develop physical AI systems at the perception-learning-control interface. Key focus areas include reinforcement learning, human-robot interaction, and continual learning for challenging environments. Analysis of his 15 most recent publications reveals dominant trends in foundation models for visual navigation, language-conditioned policies, and multi-agent systems. His work increasingly integrates multimodal inputs (vision, language) while addressing generalization gaps in real-world settings, with strong emphasis on efficient data curation and scalable robot learning frameworks. His accolades feature the Microsoft Future Leaders in Robotics & AI Fellowship (2024), two IEEE ICRA Best Conference Paper Awards (2024), multiple ICRA finalist awards across cognitive robotics and manipulation categories, and the Berkeley Fellowship (2019-2024). Shah will recruit PhD students for Princeton's upcoming admissions cycle, establishing a research group dedicated to full-stack robotics development. While specific grant details aren't provided, his trajectory indicates significant funding for AI-robotics convergence projects, particularly in foundation model development and real-world deployment challenges. His laboratory at Princeton will integrate algorithmic innovation with system design, focusing on physical AI systems that bridge perception, learning, and control while maintaining strong ties to cognitive science principles for human-aligned robotic intelligence.

Zsolt Kiraمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.

• Computer Vision
• Machine Learning
• Robotics
• +۶ مورد دیگر

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

Amir-massoud Farahmandمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Machine Learning
• Computer Science
• +۴ مورد دیگر

Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +۷ مورد دیگر

Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.

• System Informatics
• Big Data Analytics
• Data Fusion
• +۹ مورد دیگر

Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.

Andrew McCallumمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

Dawn A. Lottمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Decision-Making Processes
• Operations Research
• +۵ مورد دیگر

Professor Dawn A. Lott holds the position of Professor of Applied Mathematics at Delaware State University. She obtained her Ph.D. in Engineering Sciences & Applied Mathematics from Northwestern University (1994), M.Sc. from Michigan State University (1989), and B.Sc. from Bucknell University (1987). Her postdoctoral training was at the University of Maryland (1997). Her research focuses on numerical and analytical studies of nonlinear partial differential equations modeling solid/fluid mechanics, biomechanics, and physiology. She also investigates decision-making processes using operations research and machine learning techniques. Key areas of expertise include computational methods, artificial intelligence, and algorithm design. Recent work emphasizes decision-making under uncertainty in IoT-enabled battlefield scenarios. Her publications explore MATLAB/Java comparisons for decision algorithms, graph-based reasoning systems, and SAGE-inspired optimization frameworks. Collaborations with researchers like Raglin and Metu highlight interdisciplinary approaches to military and operational challenges. No specific grants, awards, or student advisories are noted in the provided materials. Her contributions bridge applied mathematics with real-world applications in defense, healthcare, and computational systems.

Ioannis Athanasiadisمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Agricultural Modeling
• +۷ مورد دیگر

Ioannis Athanasiadis is a Full Professor and Chair of Artificial Intelligence at Wageningen University & Research (The Netherlands). He leads the Artificial Intelligence (AIN) group, focusing on advancing AI methods for global challenges in agriculture, ecology, and sustainability. Previously, he was faculty at the Dalle Molle Institute for Artificial Intelligence (IDSIA, Switzerland) and the Democritus University of Thrace (Greece). He holds a PhD (2005, cum laude) in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research integrates machine learning, knowledge engineering, and environmental modeling to address food security, climate adaptation, and ecosystem services. He leads initiatives like AgML (AgMIP's machine learning benchmarking effort) and coordinates European grants such as LTER-LIFE and CYBELE . Prof. Athanasiadis has supervised over 40 PhD/postdoc researchers and serves as Editor of Environmental Modelling and Software . He collaborates internationally on projects involving AI for crop modeling, digital twins, and sustainable agriculture. His team develops frameworks like Crop2ML and PyCrop2ML to enhance interoperability between process-based models and machine learning systems.

Xuemin (Sherman) Shenمشاهده پروفایل

استاد

• Wireless communications and networking
• Resource allocation and mobility management
• Wireless network security and privacy preservation
• +۶ مورد دیگر

Xuemin (Sherman) Shen is a University Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Royal Society of Canada, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Professor Shen serves as Editor-in-Chief of multiple prestigious journals including the IEEE Internet of Things Journal and Springer Peer-to-Peer Networking and Applications. Dr. Shen received his Doctorate in Electrical Engineering from Rutgers University in 1990, following a Master of Applied Science from the same institution in 1987. His undergraduate degree is a Bachelor of Applied Science in Electrical Engineering from Dalian Marine University, China (1982). Professor Shen's research spans wireless communications and networking, with particular expertise in resource allocation, mobility management, wireless network security, and privacy preservation. His work extends to IoT applications, connected and automated vehicles, network digital twins, and satellite-terrestrial networks. His research has been applied to vehicular networks, wireless body area networks, remote e-healthcare systems, and smart grid technologies, demonstrating both theoretical depth and practical impact across multiple domains. His recent publications reveal strong trends in AI-assisted networking, security and privacy preservation for IoT applications, and energy management in vehicular and smart grid systems. The research shows an increasing focus on integrating AI techniques with traditional networking approaches, addressing critical challenges in security, privacy, and resource management for next-generation wireless systems. R.A. Fessenden Award (2019) from IEEE Canada James Evans Avant Garde Award (2018) from the IEEE Vehicular Technology Society Joseph LoCicero Award (2015) from the IEEE Communications Society Education Award (2017) from the IEEE Communications Society West Lake Friendship Award from Zhejiang Province (2023) President's Excellence in Research from University of Waterloo (2022) Canadian Award for Telecommunications Research (2021) Professor Shen has mentored over 100 graduate students and postdoctoral fellows throughout his career, with many now holding prominent academic positions at top universities worldwide. His supervision has been recognized with multiple awards including the Award of Excellence in Graduate Supervision (2006) from the University of Waterloo. He has served in numerous leadership roles including Past President of the IEEE Communications Society and has chaired major international conferences including IEEE Globecom 2024.

Christophe Viavatteneمشاهده پروفایل

دانشیار

• Flood Risk Management
• Water and Environmental Sustainability
• Geographic Information Systems (GIS)
• +۴ مورد دیگر

Christophe Viavattene is an Associate Professor of Environmental Sustainability at Middlesex University , specializing in flood risk management and urban resilience. He has spent 17 years at the Flood Hazard Research Centre , leading interdisciplinary research across economic and physical sciences in water and flood risk. Research Focus: Flood vulnerability (coastal, urban, fluvial), GIS tools (INDRA model), Multi-Coloured Manual (MCM) methodology, mental health impacts of flooding, groundwater flood risk in arid regions, and air quality risk assessment. Teaching: Programme Leader for the MSc Sustainability and Environmental Management , teaching problem-based sustainability, GIS, and environmental monitoring. Key Collaborations: EU FP7 RISC-KIT, EU CONHAZ, Environment Agency UK projects. Recent Publications: Bayesian flood models, coastal hotspots analysis, and groundwater flood damage methodologies. His work bridges academic research with practical applications, including flood storage compensation frameworks and vulnerability indicator libraries.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

• Artificial Intelligence
• Digital Economy
• E-Commerce
• +۷ مورد دیگر

Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business and a Professor of Marketing at The Wharton School, University of Pennsylvania. He is also the Co-Director of the Wharton Human-AI Initiative. His work spans the Department of Operations, Information and Decisions, focusing on the intersection of technology, business, and society. Education: PhD in Management Science and Information Systems, Carnegie Mellon University MPhil in Management Science, Carnegie Mellon University Masters in Information Systems, Birla Institute of Technology and Sciences (BITS, Pilani), India Bachelors in Electronics Engineering, Birla Institute of Technology and Sciences (BITS, Pilani), India Research Interests: Kartik’s research focuses on the digital economy, particularly the impact of AI, algorithms, and analytics on consumers, businesses, and society. His work explores internet marketing, e-commerce, digital media, information diffusion, platform economics, and the ethical implications of AI. He investigates how technology transforms business models and consumer behavior in online environments. Publication Trends: His recent research combines machine learning, causal inference, and behavioral modeling to study digital platforms, user behavior, and AI applications in marketing and operations. The articles reflect a strong focus on empirical analysis of social media, search engines, and platform design, with applications in advertising, content sharing, and product adoption. Scientific Awards: Best Information Systems paper published in Management Science, 2013-2016 Finalist for Best Information Systems paper in Management Science (2012-2015) Recognized as one of the world’s top 40 business professors under 40 Eleven-time recipient of teaching excellence awards at Wharton MBA Excellence in Teaching Award, 2007 “Goes above and beyond the call of duty” award (multiple years) Advising and Grants: While no formal PhD students are listed, Kartik has supervised independent studies and mentored numerous students through research projects. His entrepreneurial ventures, such as Yodle and Jumpcut Media, reflect real-world applications of his research. He has also secured significant industry engagement through consulting and executive education with major firms like Google, American Express, and Citi. His work is supported by academic recognition, editorial roles, and media outreach. Labs and Teams: Kartik co-directs the Wharton Human-AI Initiative, a research hub exploring the integration of human and artificial intelligence in business. He also leads research groups focused on digital platforms and AI ethics, collaborating with scholars across disciplines to advance understanding of algorithmic decision-making and its societal impact.

Aylin Caliskanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Aylin Caliskan is an Assistant Professor at The Information School at the University of Washington, with a courtesy appointment at the Paul G. Allen School of Computer Science & Engineering. She co-directs the Tech Policy Lab and is a faculty affiliate at the UW NLP RAISE and VSD Lab. Her research focuses on AI ethics, bias detection, and human-centered AI, with a particular emphasis on how biases from human society propagate into machine learning models. University of Washington – Assistant Professor Tech Policy Lab – Co-Director UW NLP RAISE – Faculty Affiliate VSD Lab – Faculty Affiliate Brookings Institution – Nonresident Fellow in Governance Her research explores the mechanisms by which human society’s biases are encoded into AI systems, particularly in language and vision-language models. She develops evaluation techniques, transparency methods, and bias mitigation strategies to address these issues. Her work has been published in top-tier venues such as Science, PNAS, ACL, EMNLP, AAAI, and ACM FAccT. She has also advised and collaborated with numerous students and researchers, including Kyra Wilson, Mattea Sim, Gandalf Nicolas, and Kshitish Ghate. Her recent and accepted publications focus on generative AI’s societal impacts, including bias amplification in AI models, gender and race bias in resume screening, and the propagation of stereotypes through multimodal systems. These studies often analyze how biases encoded in AI can influence human decision-making and societal norms. Scientific Awards: NSF CAREER Award (2024) 100 Brilliant Women in AI Ethics (2023) IJCAI Early Career Spotlight (2023) She teaches courses on Generative AI and has delivered talks at institutions such as Stanford, NYU, and Howard University. Her work also intersects with law and policy, as seen in her Brookings Institution publications and service.

Johan Driesenمشاهده پروفایل

استاد

• Renewable Energy
• Power Electronics
• Electrical Drives
• +۷ مورد دیگر

Johan Driesen is a full Professor at KU Leuven's Faculty of Engineering Sciences, where he serves as Department Head of Electrical Energy Systems and Applications (ELECTA) and Director of the KU Leuven Institute for Energy and Society (KIEM). He also holds leadership positions at EnergyVille as Division Head and Subdivision Head. His work spans both academic and applied research in electrical energy systems. His research interests focus on renewable energy integration, power electronics, electrical drives, electric vehicles, and smart grids. Driesen's work particularly emphasizes distributed generation of electricity, with significant contributions to floating photovoltaics, offshore wind integration, and low-voltage DC systems. His research group actively investigates the optimization of grid integration for renewable energy sources and the development of advanced power electronic converters. Driesen's recent publications reveal a strong emphasis on practical applications of renewable energy systems, with particular focus on reliability analysis of low-voltage DC systems, optimization of PV-battery systems, and integration of electric vehicles with renewable energy sources. His work bridges theoretical analysis with real-world implementation challenges. Laureate prize of the Belgian Royal Society of Electrotechnics (KBVE/SRBE) 'Research and Development' for master theses 1996 2nd place in final round IEEE Region 8 Student Paper Contest 1997 in München, Germany Laureate biannual prize 'R.Sinave' of the Belgian Royal Society of Electrotechnics (KBVE/SRBE) for best PhDs 2002 Driesen has supervised numerous students and researchers, including J. Despeghel who completed work on residential PV-battery system optimization. His current research portfolio includes substantial projects such as Flux50 (working on the future energy system), solar and wind energy in the Belgian marine zone, and smart charging solutions that integrate e-mobility with renewable energy. He serves as promoter or co-promoter on multiple major research initiatives with funding extending through 2028. At EnergyVille, Driesen leads research teams working on cutting-edge energy solutions, with particular focus on DC systems, grid integration challenges, and the development of innovative power electronic solutions for renewable energy applications. His work has significant industrial relevance and practical implementation potential.