Justus Piater is a Professor of Computer Science at the University of Innsbruck, serving as the Head of the Digital Science Center and an ELLIS Fellow. His primary affiliation is the Department of Computer Science within the Faculty of Mathematics, Computer Science and Physics. He has held academic roles since 2002, including Assistant and Associate Professorships at Université de Liège before joining the University of Innsbruck in 2010. His research focuses on robot learning, perception, and manipulation, emphasizing how robots can learn to perceive and act with understanding. Notable projects include the EU-H2020 IMAGINE project and the Euregio International Project Network OLIVER. Education includes a Ph.D. in Computer Science from the University of Massachusetts Amherst (2001), complemented by earlier studies in Germany and Belgium. His service roles include Dean of the Faculty of Mathematics, Computer Science and Physics (2014–2017) and Vice Chair of the Department of Computer Science (2014–present). His research outputs span robotics, AI, and interdisciplinary education, with a strong emphasis on autonomous systems and cognitive development.
Dr. Kemal Akkaya is a Professor at the Department of Electrical & Computer Engineering, Florida International University (FIU), where he leads the Advanced Wireless and Security Lab (ADWISE). He holds a Ph.D. in Computer Science from the University of Maryland Baltimore County and has expertise in Network Security, IoT/CPS Security, Blockchain Applications, and 5G Security. His professional roles include serving as Research Director for FIU’s Emerging Preeminent Program in Cybersecurity and as Program Director for the first BS degree in IoT in the U.S. Education: Ph.D. in Computer Science, University of Maryland Baltimore County M.S. in Computer Engineering, Middle-East Technical University, Turkey B.S. in Computer Science, Bilkent University, Turkey Research Interests: Dr. Akkaya focuses on securing IoT and cyber-physical systems, blockchain for micro-payments, and network defense mechanisms. His work emphasizes privacy-aware protocols, secure key management, and SDN/NFV-based solutions. Awards: FIU Faculty Senate Excellence in Research Award (2020) College of Engineering and Computing Faculty Research Award (2020) Top Cited Article Award from Elsevier (2010) Advisees and Grants: While no student names are listed, his lab (ADWISE) likely supports graduate research in IoT and cybersecurity. His grants include interdisciplinary initiatives at FIU, focusing on securing emerging technologies like 5G and blockchain. Labs/Teams: Leads the ADWISE Lab, collaborating on projects like secure IoT payment systems, drone communication security, and resilient smart grid networks.
Esen Yel is an Assistant Professor in the Electrical, Computer, and Systems Engineering (ECSE) department at Rensselaer Polytechnic Institute (RPI) since January 2024. She leads the Reliable Intelligent Systems Lab (RISL), focusing on enhancing safety in autonomous systems through planning, uncertainty-aware decision-making, and runtime monitoring. Her work integrates reachability analysis, machine learning, and adaptive control to ensure safe operations in unpredictable environments. Educational background: Ph.D. in Systems Engineering, University of Virginia (2021) M.S. and B.S. in Electrical and Electronics Engineering, Bogazici University, Turkey (2016 and 2014) Postdoctoral Scholar in Aeronautics and Astronautics at Stanford University (2021–2023), contributing to the Stanford Intelligent Systems Lab (SISL) and Stanford Center for AI Safety Research interests emphasize safety-critical autonomous systems , including: Uncertainty-aware planning and decision-making Runtime monitoring and recovery mechanisms Machine learning for adaptive control Formal verification of neural networks Robotics and UAV operations under degraded conditions Her publications (2017–2024) explore themes like spatiotemporal prediction, reachability analysis, meta-learning for UAVs, and safety validation in perception systems. She directs the RISL lab, advancing interdisciplinary research in reliable AI and robotics.
Heidar A. Malki is a Professor of Engineering Technology and Senior Associate Dean of the Technology Division at the Cullen College of Engineering, University of Houston (UH). He holds a joint appointment in the Electrical and Computer Engineering Department and has over three decades of academic and research experience. He earned his Ph.D. in Electrical Engineering from the University of Wisconsin-Milwaukee (1990). His roles include Department Chair (2009–present) and Associate Dean for Research (2004–2009). He is a Senior Member of IEEE and serves as an Associate Editor for the IEEE Transactions on Fuzzy Systems. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Milwaukee (1990) M.S. in Electrical Engineering, University of Wisconsin-Milwaukee (1985) B.S. in Electrical Engineering, University of Wisconsin-Milwaukee (1983) Research Interests: Dr. Malki specializes in control systems, neural networks, fuzzy logic, and smart grid optimization. His work bridges academic research with industrial applications, particularly in the energy and telecommunications sectors. Notable areas include neuro-fuzzy controllers, power system dynamics, and cyber-security for critical infrastructure. He has collaborated with organizations like Southwestern Bell and the oil/gas industry on neural network applications. Publications & Awards: With over 100 publications, Dr. Malki’s work spans journals like IEEE Transactions on Fuzzy Systems and International Journal of Bifurcation and Chaos . His awards include the Fluor Daniel Outstanding Faculty Award (2001, 2003) and recognition in Who's Who in America . He has authored textbooks on control systems and contributed to academic volumes on fuzzy logic applications. Grants & Leadership: He secured funding for initiatives like the Houston Information Technology Workforce Certification Center and led conferences such as the 1997 IEEE International Conference on Neural Networks. His educational contributions include pioneering web-based control systems laboratories and interdisciplinary graduate programs in technology. Labs & Teams: His research teams focus on advanced wireless sensor networks, mechatronics, and energy system optimization. Collaborations extend to NASA and the U.S. Department of Energy, emphasizing applied engineering solutions for real-world challenges.
Steve Ulrich is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Carleton University and Director of the Spacecraft Robotics and Control Laboratory. He holds a B.Eng. and M.A.Sc. in Electrical Engineering from Université de Sherbrooke and a Ph.D. in Aerospace Engineering from Carleton University. His research focuses on spacecraft guidance, navigation, and control (GN&C), computer vision-based navigation systems, and space robotics. He has contributed to ESA’s PROBA-2 spacecraft systems and MIT’s space systems laboratory experiments. Education: B.Eng, Electrical Engineering, Université de Sherbrooke (2004) M.A.Sc, Electrical Engineering, Université de Sherbrooke (2006) Ph.D, Aerospace Engineering, Carleton University (2012) Research Interests: Spacecraft GN&C algorithms/software Proximity operations and formation flying Attitude dynamics and control Robotics and computer vision Solar drag and perturbation management His recent work emphasizes deep learning for autonomous capture systems, real-time vision-based navigation, and adaptive control strategies for space robotics. Articles highlight advancements in obstacle avoidance, optimal guidance, and experimental validation of control algorithms. The Spacecraft Robotics and Control Laboratory develops technologies for on-orbit servicing and debris removal. Advising focuses on graduate students in aerospace robotics and control systems. His lab collaborates with industry on projects like tether-based debris capture and CubeSat navigation systems.
Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.
Jerin John is an Assistant Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University, Canada. His research focuses on rocket propulsion concepts, combustion, rheology, and green propellants. He holds a Ph.D. (2018) and M.Sc. (2015) in Aerospace Engineering from KAIST, South Korea, and a B.E. in Aeronautical Engineering from Anna University, India. Prior to his academic role, he worked as a Propulsion Engineer at Perigee Aerospace Inc. (2019–2021) and as a post-doctoral fellow at KAIST (2018–2019). His work emphasizes innovative propulsion systems, including hybrid rockets and electromagnetic propulsion for drones and planetary landers. Research Interests: Rocket Propulsion Concepts (e.g., hybrid engines, hypergolic ignition) Combustion of gel fuels and droplet dynamics Rheology of non-Newtonian fluids in propellants Material characterization for energetic materials Soft matter physics in aerospace applications Advising & Grants: While no specific grants or advising details are provided, his academic role implies active engagement in student mentorship and research funding. His publications span experimental and numerical studies, reflecting a strong focus on interdisciplinary aerospace engineering challenges. Labs/Teams: No specific labs or teams are mentioned, but his research likely involves collaborations in propulsion and materials science laboratories.
Fei Miao is a Pratt & Whitney Associate Professor at the School of Computing, University of Connecticut, and a courtesy faculty member of the Department of Electrical & Computer Engineering. She serves as Director of the Miao Embodied AI Lab and is affiliated with the Institute for Advanced Systems Engineering. Previously, she was a postdoc researcher at the GRASP Lab and PRECISE Lab with Professors George J. Pappas and Daniel D. Lee at the University of Pennsylvania. Dr. Miao received her PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2016, where she also earned a dual Master's degree in Statistics from the Wharton School. She completed her undergraduate studies at Shanghai Jiao Tong University, earning a Bachelor's degree in Automation with a minor in Finance in 2010. Her research focuses on developing the foundations for the science of Embodied AI, with emphasis on assuring safety, efficiency, robustness, and security of cyber-physical systems through the integration of learning, optimization, and control. Her technical expertise spans multi-agent reinforcement learning, robust optimization, uncertainty quantification, control theory, and game theory. These methods are applied to connected and autonomous vehicles, intelligent transportation systems, transportation decarbonization, smart cities, and power networks. Her work involves both theoretical development and practical implementation, including system modeling, theoretical analysis, algorithmic design, and experimental validation using real urban transportation data, simulators, and small-scale autonomous vehicles. Dr. Miao's publication record reveals a strong focus on robustness in AI systems for transportation applications, with recent work emphasizing uncertainty quantification, safety guarantees, and multi-agent coordination. Her research demonstrates a clear trajectory from foundational theoretical work to practical implementations in real-world transportation systems. Her notable awards include the prestigious NSF CAREER Award (2021) for "Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles," a Best Paper Award at ICCPS'21 for "DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services," and the "Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation" during her PhD studies. Dr. Miao has secured significant research funding, including a $509,573 NSF CAREER Award (2021-2026) and a $2.3 million NSF collaborative grant as PI of UConn (2020-2023). She has also received multiple NSF grants for projects related to electric vehicle fleets, vehicular sensing, and control for smart city systems. She actively collaborates with researchers across institutions and has given talks at leading universities and industry research labs including CMU, Microsoft Research, Northeastern, Caltech, UCLA, USC, UCSD, Facebook FAIR, Lawrence Berkeley National Lab, UC Berkeley, Nvidia, Stanford, Princeton University, Columbia University, Waymo, and New York University.
Dr. Balakrishnan Prabhakaran is a Professor of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He specializes in multimedia systems, focusing on areas like video and healthcare data analytics, 3D video streaming, wireless network QoS, and collaborative virtual environments. His work integrates technical innovation with healthcare applications, including telemedicine and rehabilitation systems. Education: PhD (Computer Science & Engineering) from Indian Institute of Technology, Chennai (1995); MSc (CSE) from IIT Chennai (1990); BEng (Electronics & Communication) from Madurai-Kamaraj University (1986). Research Interests: Current projects include intelligent medical imaging platforms (e.g., IntelliCardiac), mixed reality systems for pain management (MR.MAPP), and robotic grasp synthesis. Past work involved multimedia databases, web-based presentation servers, and QoS in ad-hoc networks. Notable Achievements: Recipient of NSF CAREER Award (2003), 2007 School of Engineering Service Award, and recognition as an ACM Distinguished Scientist. He chairs the PhD Studies program at UT Dallas and has led numerous academic initiatives including graduate admissions committees. Teaching: Courses span multimedia systems, advanced operating systems, and animation programming. He has taught at institutions including National University of Singapore and University of Maryland. Labs/Teams: Active in developing immersive healthcare technologies through projects like H-TIME (haptic tele-examination) and VirTePeX (virtual tele-physical exam systems). Collaborates on cloud-based body sensor networks (BSNCloud) and mixed reality frameworks (ScanToVR).
Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.
Dean Petters is a Senior Lecturer in Psychology at Sheffield Hallam University, specializing in Cognitive Psychology and Attachment Theory. He holds a PhD in Cognitive Science (2006) from the University of Birmingham, focusing on computational modelling of infant-mother attachment using autonomous agents. His research has been recognized with the prestigious Bowlby-Ainsworth Award (2019) for contributions to attachment theory. His teaching spans cognitive psychology (emotion, decision-making, consciousness, cyberpsychology) and attachment theory (social development, caregiving, therapeutic applications). He has held academic leadership roles, including Programme Team Leader for Psychology at Arden University and Faculty Academic Lead for Technology-Enhanced Learning at Birmingham City University. Key research areas include computational models of emotion, developmental object recognition, and technology-enhanced learning. His publications explore interdisciplinary topics such as the Church-Turing thesis' implications for cognitive science, and the integration of 4E cognition with attachment theory. Notable awards include the Bowlby-Ainsworth Award, celebrating contributions to Bowlby and Ainsworth's foundational work. His work bridges artificial intelligence, developmental psychology, and human-computer interaction, with a focus on simulating attachment dynamics through computational systems.
Haoxiang Lang serves as Department Chair and Associate Professor in the Department of Automotive and Mechatronics Engineering at Ontario Tech University's Faculty of Engineering and Applied Science. His laboratory, GRASP Lab, focuses on advanced robotics and intelligent systems research. Education: PhD in Mechanical Engineering, University of British Columbia (2013) MASc in Mechanical Engineering, University of British Columbia (2008) BSc in Marine Engineering, Ningbo University (2003) Dr. Lang's research spans mechatronics, autonomous robotics, visual servoing, and machine learning. His work focuses on developing advanced control systems for robotic manipulation, autonomous vehicle navigation, and sensor fusion techniques. Recent projects include vision-based combat vehicle control, deep learning for aerial image processing, and thermal management for robotic systems. His publications demonstrate strong emphasis on robotic perception-control integration, with recurring themes in visual servoing, sensor fusion, autonomous navigation, and machine learning applications in robotics. Recent works increasingly incorporate deep neural networks for complex tasks like object manipulation and environment mapping. Dr. Lang leads the GRASP Lab, which develops experimental platforms including autonomous combat vehicles and robotic manipulators. The lab focuses on practical implementations of control theories and collaborates on industrial applications.
Edward H Adelson is the John and Dorothy Wilson Professor of Vision Science at MIT's Department of Brain and Cognitive Sciences, and an investigator at CSAIL. His research spans human and machine vision, computational photography, and tactile sensing for robotics. He is renowned for contributions to multiscale image processing (Laplacian pyramids), motion energy models, and the plenoptic function. His lab pioneers tactile sensors like GelSight, enabling robots to perceive texture, force, and 3D geometry with human-like sensitivity. Adelson holds a Ph.D. and has over 100 publications. His work on layered motion representation won the Longuet-Higgins Award (2005), and his neural motion studies received the Rank Prize (1992). He is a member of the National Academy of Sciences (2007) and American Academy of Arts & Sciences (2010). Current projects focus on soft tactile fingers for robotics using elastomeric materials, integrating vision-based sensing with compliant mechanics. His innovations have applications in medical devices, dexterous manipulation, and human-robot interaction.
Jeong Joon Park is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan, part of the College of Engineering. His research focuses on advancing 3D vision, artificial intelligence, and generative models, with applications in computer vision, machine learning, and robotics. He emphasizes collaborative research culture and student-driven innovation. Research Interests : 3D scene generation and reconstruction Generative models for multi-modal perception Language-driven vision and robotics Diffusion models and PDE-solving Recent Research Trends : His work spans 3D/4D generation (e.g., LIFT-GS , 4d-fy ), robust sensor fusion ( Cocoon ), and novel view synthesis using diffusion models. Themes include multimodal integration, physics-informed learning, and scalable generative architectures. Advising & Grants : Students are expected to lead independent projects, publish as first authors, and engage in teaching (GSI roles). Lab funding covers conference travel (e.g., CVPR, NeurIPS). Internships are encouraged for real-world alignment. Labs/Teams : Part of the CSE department’s vibrant research community, fostering interdisciplinary collaboration and innovation in AI-driven 3D technologies.
Mariusz Wzorek is an Assistant Professor and Head of Unit at the Department of Computer and Information Science (IDA) at Linköping University, Sweden. He is affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division, which focuses on advancing research and education in artificial intelligence, robotics, and collaborative systems. Dr. Wzorek holds a PhD from Linköping University (2023) and has expertise in autonomous systems, unmanned aerial vehicles (UAVs), and robotics. His research emphasizes safe navigation, collaborative robotics, and applications in emergency response and public safety scenarios. Key areas include UAV-based collision avoidance, geolocation using aerial imagery, and distributed systems for multi-agent coordination. His recent work involves developing systems like the RGS⊕ (RDF Graph Synchronization) for collaborative robotics, secure remote ID protocols for UAVs, and algorithms for autonomous search and rescue missions. These projects address challenges in sensor fusion, real-time decision-making, and robust communication in dynamic environments. Publications highlight contributions to UAV navigation, wireless mesh networks in emergencies, and 3D reconstruction using heterogeneous UAV teams. His research bridges theoretical foundations in AI with practical applications in robotics and safety-critical systems. While no specific scientific awards are listed, his active role in the AIICS division and numerous peer-reviewed publications reflect his significant contributions to the field.
