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Alan FernView profile

Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +5 more

Alan Fern is a Professor of Computer Science and Robotics in the School of Electrical Engineering and Computer Science at Oregon State University. He leads research in artificial intelligence, focusing on reinforcement learning, planning, and robotics applications like humanoid robotics and agricultural AI. His work includes co-directing the Dynamic Robotics Lab and leading the AgAID National AI Institute for agricultural solutions. Fern holds a Ph.D. from Purdue University and has contributed to over 100 publications. His recognitions include the NSF CAREER Award and multiple best paper awards. Education: B.S., Electrical Engineering, University of Maine (1997) M.S. & Ph.D., Computer Engineering, Purdue University (2000 & 2004) Research Interests: His research spans machine learning, planning, and robotics. Key areas include: AI for humanoid robotics (e.g., bipedal locomotion on Cassie) Reinforcement learning algorithms and applications Agricultural AI for specialty crops Explainable AI and anomaly detection Awards: 2017 College of Engineering Research Collaboration Award 2013 AAAI Outstanding Paper Award 2006 NSF CAREER Award Advising & Labs: Supervised over 50 students. Key collaborations include the Dynamic Robotics Lab (with Jonathan Hurst) and AgAID. His teams address challenges like robot navigation, policy learning, and AI ethics. Labs/Teams: Dynamic Robotics Lab, AgAID National AI Institute, and contributions to computational sustainability initiatives.

Jack BarthView profile

Professor

• Oceanography
• Coastal Dynamics
• Marine Ecosystems
• +6 more

Jack Barth is a Professor in the College of Earth, Ocean, and Atmospheric Sciences (CEOAS) at Oregon State University (OSU) and Executive Director of the Marine Studies Initiative (MSI). He holds a Ph.D. in Oceanography from MIT and Woods Hole Oceanographic Institution (1987). His research focuses on coastal ocean dynamics, hypoxia, and marine ecosystem interactions. Barth has led projects on ocean observing systems, including glider technology and the Ocean Observatories Initiative (OOI). He serves on the Oregon Ocean Policy Advisory Council’s Scientific Committee and co-chairs the Ocean Acidification and Hypoxia Council. Education: B.A., University of Colorado (1982); Ph.D., MIT/WHOI Joint Program (1987). Experience includes roles as Postdoctoral Research Associate (1987–1989), Assistant Professor (1989–2001), and Professor (2001–present). He also served as Associate Dean for Research in CEOAS (2016–present) and Executive Director of MSI (2016–present). Research Interests: Coastal ocean circulation, hypoxia, inner continental shelf dynamics, eastern boundary currents, and ocean observing systems. He has published over 100 papers and mentored 20+ students/postdocs. Awards include Fellowships from The Oceanography Society (2013) and the American Meteorological Society (2017). Awards: Carl-Gustav Rossby Award (1988), Pattullo Teaching Award (2010), and multiple keynote lectures. His work addresses climate impacts, marine conservation, and interdisciplinary collaboration for sustainable ocean management.

• Networking and Communications
• Blockchain Technology
• Smart Grids and Energy Systems
• +5 more

Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.

• Mechatronics
• Autonomous Systems
• Biomedical Engineering
• +7 more

Dr. Milan Simic is a Senior Lecturer in the School of Engineering at RMIT University, serving as Program Manager for the Master of Engineering (Management) degree. He holds editorial roles for the Knowledge Engineering Systems and Intelligent Decision Technologies journals and is Associate Director of the Australia–India Research Centre for Automation Software Engineering. With a PhD in Electronic Engineering from the University of Niš and a Graduate Diploma in Education from RMIT, Dr. Simic has extensive industry and academic experience in Australia and internationally. His research focuses on mechatronics, autonomous systems, biomedical engineering, robotics, intelligent transportation systems, and green energy. Notable projects include AI-driven railway system strategies, gait analysis for biomedical applications, and smart traffic control systems. He actively supervises PhD and master’s students in areas like autonomous vehicles and energy recovery systems. Dr. Simic’s work bridges engineering innovation with societal impact, emphasizing sustainable transportation solutions and smart city technologies. His contributions span journal editing, international collaborations, and curriculum development in engineering management.

John ClarkView profile

Professor

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +5 more

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.

Idar DyrdalView profile

Lecturer

• Autonomous Systems
• Sensor Technologies
• Robotics
• +3 more

Idar Dyrdal is a Lecturer at the University of Oslo's Institute of Transport Economics (ITS), holding a 20% part-time position. He is affiliated with the Section for Autonomous Systems and Sensor Technologies, focusing on research in autonomous systems and robotics. His work spans scene understanding for autonomous steering, leveraging sensor technologies and advanced algorithms. Research Interests: Idar's expertise centers on autonomous systems, sensor technologies, and their applications in vehicle control. His studies emphasize real-time data processing for safe and efficient autonomous navigation. Labs/Teams: Active within the Section for Autonomous Systems and Sensor Technologies at ITS, contributing to interdisciplinary projects in robotics and transportation systems.

Matt GarrattView profile

Professor

• Robotics
• Swarm Robotics
• Unmanned Aerial Vehicles
• +5 more

Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +4 more

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

Andrea GarulliView profile

Professor

• Control Systems
• Aerospace Engineering
• Robotics
• +5 more

Andrea Garulli is a Professor in Control Systems at the Department of Information Engineering and Mathematics (DIISM), University of Siena. He served as Dean of the Faculty of Engineering (2011-2012) and Director of DIISM (2015-2021). His research focuses on control systems, including system identification, state estimation, robust control, multi-agent systems, and aerospace applications. He co-authored the book *Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems* and developed the LFR_RAI toolbox for robustness analysis of uncertain models. Garulli has led projects like ACANTO (social robot navigation) and DALi (autonomous navigation for elderly care). His teaching includes courses on system dynamics, state estimation, and filtering. He is an Associate Editor for *Automatica* and has contributed to initiatives like the Automatic Control Telelab for remote experiments with mobile robots.

• Robotics and Autonomous Systems
• Computer Vision
• Machine Learning
• +7 more

Dr. Sen Wang is an Associate Professor in Robotics and Autonomous Systems at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with I-X (Imperial's AI initiative), the Grantham Institute, and the Robotics Forum. He directs the Sense Robotics Lab and founded the MSc in Artificial Intelligence Applications and Innovation. His research focuses on advancing robotic autonomy through probabilistic and machine learning methods, addressing challenges in unstructured environments such as underwater infrastructure inspection and climate change solutions. Key projects include leading the £18M UKRI ORCA Hub, developing underwater robotics for offshore energy infrastructure inspection, and achieving the first autonomous wind farm foundation inspection at EDF's Blyth site. He holds editorial roles at IEEE Transactions on Robotics and other journals. Research interests span robotics, computer vision, SLAM, and AI applications, with recent work emphasizing underwater systems, sensor fusion, and safety-critical autonomy. His publications bridge theoretical advancements with real-world deployments in marine robotics and environmental monitoring. Awards: 2024 AI Most Influential Scholar Award Honourable Mention Grants: £18M ORCA Hub funding (UKRI) Labs: Sense Robotics Lab, I-X AI Initiative

• Robotics
• Computer Vision
• Machine Learning
• +4 more

Nicolas RAGOT is an Associate Professor at CESI, affiliated with the Engineering and Numerical Tools department. He teaches Digital and embedded electronics, Microcontroller programming, System control, and Sensors at the Bachelor and Master levels. His research focuses on Environment perception for robotics and Computer vision, particularly in unconventional applications. He leads or collaborates on major research programs including ROJUNACO (2023–2025), FUSION (2023–2027), OASIS (2022–2024), and COLIBRY (2021–2024), all addressing robotics, digital twins, and industrial automation. He co-supervises PhD students Y. Feddoul, S. Ouarab, and S. Choudhary. His work spans smart mobility, assistive technologies, and energy-efficient systems. He is a member of the Secure Electronic Transactions (TES) competitiveness cluster's expert committee. Education: PhD in Computer Vision (University of Rouen, 2009), Master in Electrical Engineering (University of Paris XI, 2003), Engineering diploma (ESIGELEC, 1999). Research interests emphasize robot perception, human-robot collaboration, and extended reality integration. His publications span object detection, SLAM algorithms, and smart wheelchair systems. Current projects emphasize industrial robotics, digital twins, and real-time 3D reconstruction. Advising and grants include leadership roles in multiple research programs and co-supervision of three PhDs. His lab work focuses on Engineering and Numerical Tools, with contributions to CESI LINEACT's collaborative robotics initiatives.

• Telecommunications
• Wireless Sensor Networks
• Air Quality Monitoring
• +7 more

Alexandre Manuel de Castro Passos de Almeida is an Assistant Professor in the Department of Information Science and Technology at the University Institute of Lisbon (ISCTE-IUL), where he also contributes to the School of Technology and Architecture. He is an Associate Researcher at the Institute of Telecommunications - IUL, actively involved in the Radio Systems Group, focusing on telecommunications and sensor-based environmental monitoring. PhD in Telecommunications, ISCTE-IUL, 2012 His research interests span telecommunications, wireless sensor networks, air quality monitoring, computer architecture, and robotics. He has led and contributed to innovative projects such as ExpoLis, which uses mobile sensor networks on public buses to map urban air pollution. His work bridges engineering and environmental science, aiming to influence urban policy and public health through real-time data systems. The most recent publications highlight a strong trend toward deploying low-cost, mobile sensor networks for environmental monitoring, particularly in urban settings. His work integrates computer systems, signal processing, and sustainable technology, with applications in smart cities, public health, and robotics. Topics like air quality mapping, energy harvesting, and noise-aware robot navigation reflect a multidisciplinary approach to solving real-world problems. He has advised seven Master’s students at ISCTE-IUL, guiding research in sensor networks, air pollution, database performance, robotic navigation, and personal photography assistants. While no specific scientific awards are listed, his contributions to funded research projects and consistent scholarly output indicate recognition in his field. He has held leadership roles in academic governance, including Vice President of the Pedagogical Council, underscoring his institutional engagement. His teaching portfolio includes core courses such as Fundamentals of Computer Architecture, Operating Systems, and Big Data Processing, delivered across multiple undergraduate and postgraduate programs. He is involved in research projects that combine academic innovation with practical urban applications, particularly through the deployment of scalable, open-source environmental sensing systems.

• Fault-Tolerant Computing
• Storage Systems
• Erasure Coding
• +3 more

James S. Plank is a Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee. He holds a PhD from Princeton University (1993) and has been at UT since 1993. His research focuses on fault-tolerant computing, erasure coding, distributed systems, and neuromorphic computing. He teaches programming courses from introductory to graduate levels and has won multiple teaching awards, including seven departmental awards, the College of Arts and Sciences Senior Faculty Teaching Award, and the Chancellor’s Citation for Excellence in Teaching. Plank is a member of the IEEE Computer Society and has contributed to open-source software like JGraph and Jerasure. His recent research emphasizes neuromorphic computing systems, including projects like NeuroPong and RISP Neuroprocessor. He collaborates with industry and academia on storage systems, checkpointing, and hardware-software co-design. Plank advises numerous graduate and undergraduate students, evident in his annual summer student gallery. He has secured grants such as the NSF-funded "Ground-roaming autonomous neuromorphic targeter" (2020). His lab, Neuromorphic UT, explores applications in control systems, vision, and robotics. Plank’s contributions to erasure coding and storage reliability include seminal works like the RAID-6 Liberation Code and SD codes for mixed failure modes.

Hanan SametView profile

Professor

• Spatial Databases
• Data Structures
• Geographic Information Systems (GIS)
• +5 more

Hanan Samet is a Distinguished University Professor at the University of Maryland's Computer Science Department, affiliated with the Institute for Advanced Computer Studies (UMIACS) and the Center for Automation Research. He holds a Ph.D. from Stanford University (1975) and specializes in spatial databases, data structures, and geographic information systems. His research bridges computer science and geospatial analytics, with applications in image databases, computer vision, and spatio-temporal data management. Education: Ph.D., Computer Science, Stanford University, 1975 Research Interests: Focuses on spatial data structures, GIS, spatio-textual systems like NewsStand and CoronaViz, trajectory analysis, and metric indexing. His work emphasizes scalable algorithms for spatial networks and multimedia databases. Notable Projects: CoronaViz : Tracks disease spread via spatio-temporal data visualization NewsStand : Maps news articles geospatially SAND: Spatial browser for digital government Awards: ACM Paris Kanellakis Award (2014), IEEE McDowell Award (2015), UCGIS Research Award, and Fellowships in ACM/IEEE/AAAS. Recognized for advancing spatial database theory and practice. Grants/Advising: Leads NSF-funded projects on spatio-textual extraction and similarity search. Advises graduate students (e.g., Nicole Schneider, Montana Hoover) and undergraduate researchers. Labs/Teams: Active in UMIACS and the Center for Automation Research, collaborating on projects like VASCO (spatial visualization tools) and MARCO (image database systems).

Janarthanan RajendranView profile

Assistant Professor

• Reinforcement Learning
• Deep Learning
• Artificial Intelligence
• +12 more

Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.