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Cindy H. WhiteView profile

Associate Professor

• Interpersonal Communication
• Relational Communication
• Nonverbal Communication
• +3 more

Cindy H. White is an Associate Professor and current Department Chair in the Department of Communication at the University of Colorado Boulder's College of Media, Communication and Information. She holds a Ph.D. in Communication from the University of Arizona (1996), an M.A. from Texas Tech University (1989), and a B.A. from Texas Tech University (1987). Her office hours are held via Zoom on Mondays (2:30-3:45 PM) and Thursdays (12:30-1:15 PM). Dr. White's research program examines interpersonal communication dynamics with focus areas including: Patterns of interaction in personal relationships Nonverbal behavior and relational communication Family communication and relational transitions College student-parent communication about finances Communication challenges in health contexts (e.g., bystander intervention in drinking situations) Socialization processes in communication learning Her publications demonstrate consistent focus on interpersonal adaptation, deception studies, family/relational communication, and health communication interventions. Recent works (2014-2021) show increased attention to theoretical frameworks in adaptation and practical applications in educational settings, while earlier foundational work (1990s-2000s) established expertise in deception detection and conversational analysis. She teaches undergraduate and graduate courses including: COMM 3510: Family Communication COMM 4510: Senior Seminar in Interpersonal Communication COMM 4950: Senior Thesis (Honors) COMM 6780: Roles, Relationships, and Identities in Interaction and mentors students through the honors thesis program.

Kai EssigView profile

Professor

• Human Factors
• Interactive Systems
• Eye Tracking
• +15 more

Prof. Dr. Kai Essig is a Professor of Human Factors and Interactive Systems at the Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Kamp-Lintfort, Germany. He has a strong interdisciplinary background combining computer science, cognitive science, and human-computer interaction, with a focus on eye tracking, visual perception, and assistive technologies. Master of Science in Computer Science and Chemistry, Bielefeld University (1998) Ph.D. in Computer Science, Bielefeld University (2007) His research centers on eye tracking, human-computer interaction, usability engineering, visual attention, and cognitive interaction technology . He investigates how movement expertise influences visual perception and how multimodal software can support real-time human actions. His work integrates computer vision, machine learning, and neuroscience to develop intelligent systems that adapt to user behavior. The 15 most recent publications reflect a consistent trend in eye movement analysis, mental representations, brain-machine interfaces, and assistive technologies . These works span domains such as sports psychology, robotics, augmented reality, and cognitive neuroscience, demonstrating a strong interdisciplinary approach. Key themes include gaze-based interaction, automated annotation of visual behavior, and the implementation of smart systems for daily living assistance. Scientific recognition includes: Landmark in the Land of Ideas (2018) – for the ADAMAAS project, awarded by 'Land of Ideas', a joint initiative of the German government and the Federation of German Industries Prof. Essig has been actively involved in research projects such as ADAMAAS (Adaptive and Mobile Action Assistance in Daily Living Activities), which received national recognition. He has collaborated extensively with the Neurocognition and Action-Biomechanics Research Group at Bielefeld University and the Excellence Cluster CITEC. While no formal advising of students is listed, his publications suggest mentorship and collaboration with junior researchers. His lab work is centered on eye-tracking systems, multimodal interaction, and cognitive modeling , particularly within applied environments like smart glasses and assistive technologies.

Brian ZiebartView profile

Professor

• Machine Learning
• Robotics
• Assistive Technologies
• +3 more

Brian Ziebart is a Professor in the Department of Computer Science at the University of Illinois at Chicago. He earned his Ph.D. in Machine Learning from Carnegie Mellon University in 2010. Research Interests: Machine Learning, Robotics, Assistive Technologies, Human-Computer Interaction, Adversarial Prediction, Inverse Optimal Control, Structured Prediction. Key Grants: NSF CAREER (RI)-1652530, NSF EAGER (SCH)-1650900, NSF IIS-1526379, NSF III-1514126, Future of Life Institute grant, NSF NRI-1227495. Notable Awards: Best Paper Runner-Up (ECCV, 2012), Best Paper Award (ICML, 2011), CMU School of Computer Science Dissertation Honorable Mention (2011). Teaching & Leadership: Senior Lecturer at CMU, actively involved in mentoring students and leading research teams.

Steven O. KimbroughView profile

Professor

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +6 more

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

• Network Security and Privacy
• Open-RAN Security and Privacy
• Green Networking
• +2 more

Mohammad Shojafar (M'17-SM'19) is an Associate Professor at the Institute for Communication Systems within the Faculty of Engineering and Physical Sciences at the University of Surrey , UK. He has secured over £1.9M in research funding as Principal Investigator for projects like ORAN-TWIN (EPSRC), PRISENODE (MSCA-IF), TRACE-V2X (MSCA-SE), and D-XPERT (Innovate UK), among others. Previously held positions include Senior Researcher at University of Toronto and Toronto Metropolitan University, Senior Researcher at Italian universities (Telecom Italia Mobile), and Postdoc at University of Padua Key affiliations: Associate Editor for IEEE Transactions on Network and Service Management, Intelligent Transportation Systems, Green Communications and Networking, and Consumer Electronics Magazine Research Specialism: 5G/6G Security and Privacy Open-RAN Security Green Networking Adversarial Machine Learning Applied Cryptography Publication Trends: Focus on Open RAN security challenges (bearer context migration poisoning, KPI poisoning attacks), IoT/Fog security (GAN-based attacks, distributed intrusion detection), Lightweight Cryptography (multi-signature protocols, authentication schemes), and AI-driven Network Optimization (federated learning, reinforcement learning applications). Recent work addresses security in vehicular networks, smart grids, and video streaming frameworks. Scientific Recognition: Marie Curie Individual Fellowship (MSCA-GF-IF) Intel Innovator ACM Professional Member Sustainability Fellow at Institute for Sustainability IEEE Senior Member Supervision: Currently supervising 6 PhD students and has graduated 5 PhD/MSc students since 2021. Active in 5G/Open RAN security research with over 20 related publications since 2022.

Stavros VologiannidisView profile

Assistant Professor

• Control Theory
• Robotics
• Machine Learning
• +3 more

Stavros Vologiannidis serves as an Assistant Professor in the Department of Informatics, Computer and Telecommunications Engineering at the International University of Greece. His academic career spans both teaching and research in control theory, robotics, and machine learning applications. Previously, he was associated with the Mathematics Department at Aristotle University of Thessaloniki where he completed his education and conducted postdoctoral research. Education: B.Sc. in Mathematics from Aristotle University of Thessaloniki (1997) Ph.D. in Control Theory from Aristotle University of Thessaloniki (2005) with dissertation titled 'ALGEBRAIC-POLYONYMICAL COMPUTING METHODS IN CONTROL THEORY' Dr. Vologiannidis' research focuses on Classical and Intelligent Control Theory, Robotics, and Machine Learning, with particular expertise in polynomial matrices and automatic control systems. His work bridges theoretical mathematics with practical engineering applications, especially in educational robotics and industrial control systems. He has developed several educational platforms including EUROPA, a ROS-based educational robot for teaching sensor integration and data acquisition. His publication record shows a clear evolution from theoretical control systems research toward applied machine learning and educational technology. Recent work demonstrates increasing focus on practical applications of AI in education, urban feature recognition, industrial monitoring, and robotics education across multiple educational levels from middle school through university. His research combines mathematical rigor with real-world implementation. Scientific Recognition: Excellence Scholarship in the 'Excellence Scholarships 2010' program of the Research Committee Total citations exceeding 250 with Scopus H-index of 9 Dr. Vologiannidis has secured numerous research grants and led multiple projects including 'Rapid Earthquake Damage Assessment Consortium – REDACt', 'Predictive Maintenance 4.0', and 'Development of computational methods for optimization of eigenvalue assignment problems'. He has collaborated extensively with institutions across Europe including UTIA Foundation in Prague and has participated in EU-funded projects like GALENOS and GN4-1 GÉANT Research and Education Networking. His laboratory work centers around the EUROPA educational robotics platform and the StreetScouting urban feature detection system, both of which integrate hardware, software, and educational applications. These projects demonstrate his commitment to translating theoretical research into practical educational and industrial tools.

Luis Miguel BergasaView profile

Professor

• Perception Systems
• Autonomous Vehicles
• Deep Learning
• +4 more

Luis Miguel Bergasa is a Full Professor at the Department of Electronics, University of Alcalá (UAH), with a career spanning over 25 years. He leads the RobeSafe Lab (since 2010) and serves as Director of Digital Transformation at UAH (since 2022). His academic roles include heading the Department of Electronics (2004–2010) and coordinating multiple educational programs. He teaches Perception Systems (Master in Industrial Engineering) Intelligent Control Systems (Computer Science) Computer Vision (Computer Science) His research focuses on Perception Systems for Intelligent Vehicles , emphasizing driver behavior analysis, scene understanding, and sensor fusion via deep learning. He has authored over 300 papers and holds 9 patents. Notable recognitions include being ranked #81 in Computer Science (Spain) by Research.com (2025) and receiving 30+ awards in Robotics/Automotive fields. Recent publications highlight advancements in Transformer-based driver action recognition (2025) Infrastructure-vehicle cooperative frameworks (2025) 3D semantic segmentation for autonomous perception (2024) Simulation-to-reality gap bridging (2024) Scientific Leadership Senior Editor, IEEE Transactions on ITS (2025) International Program Committee roles in 15+ conferences (2024–2025) Co-founder of Vision Safety Technologies Ltd (2009–2016) He supervises 9 active PhD students and has advised 14 former PhD candidates. His group collaborates with institutions in Germany, USA, China, and Spain, including KIT, UC Berkeley, and Northwestern Polytechnic University.

Saibal MukhopadhyayView profile

Professor

• VLSI Systems
• Nanotechnology
• Low-Power Electronics
• +5 more

Dr. Saibal Mukhopadhyay is a Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, where he joined in 2007. He holds the Joseph M. Pettit Professorship and is recognized as an IEEE Fellow for his contributions to low-power and reliable VLSI systems. Education: BEng (Jadavpur University, India), Ph.D. (Purdue University) Labs: Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab His research focuses on VLSI Systems , Nanotechnology , and Low-Power Electronics , with emphasis on technology-circuit co-design for energy-efficient computing. Recent work explores Compute-in-Memory (CIM) architectures and Spiking Neural Networks for edge AI. Key article themes include Transformer Model Acceleration , Quantum Computing Calibration , 3D Object Detection , and Device Aging Analysis , reflecting his interdisciplinary approach bridging hardware design and machine learning. Scientific Awards IEEE Fellow (2018) ONR Young Investigator (2012) NSF CAREER Award (2011) IBM Faculty Awards (2009, 2010) Best Paper Awards (IEEE-Nano 2003, ICCD 2004)

• CO2 Sequestration
• Rock Mechanics
• Geotechnical Engineering
• +3 more

Xiaoshu Lu serves as an Adjunct Professor in the Department of Civil Engineering at Aalto University's School of Engineering, Finland. His primary contact is xiaoshu.lu@aalto.fi with phone number +358503503021, and his research profile is accessible via Aalto University's research portal. His research spans CO2 sequestration, rock mechanics, and sustainable construction, with emphasis on enhanced rock weathering for carbon capture, diamond bit fragmentation mechanisms, and biomass-to-energy systems. Interdisciplinary work includes agricultural engineering applications in plant disease detection and transportation engineering for traffic flow forecasting using graph neural networks. Recent publications (2024-2025) reveal dominant themes in geological CO2 storage and rock mechanics, featuring experimental studies and comprehensive reviews. While most work focuses on environmental/geotechnical civil engineering, select publications demonstrate cross-disciplinary reach into computer vision and transportation systems. Key accolades include: Napier Shaw Bronze medal (2006) Best 2012 Research Paper Competition from Aalto University School of Engineering (2013) Second prize in China's Innovation and Entrepreneurship Competition for Overseas Talents (2017) Honourable Tang Aoqing Professorship at Jilin University, China (2013-present) No details regarding graduate student supervision or research grants appear in the source material. He contributes to the 'Performance in Building Design and Construction' research group at Aalto University.

• Design Computing
• Spatial Modeling
• Machine Learning
• +7 more

Sean Hanna is a Professor of Design Computing at The Bartlett School of Architecture , University College London , and a member of the UCL Space Syntax Laboratory . His interdisciplinary work bridges architecture, computational modeling, and machine learning.

• Visual Perception
• Object Recognition
• Visual Crowding
• +7 more

Dr. Ramakrishna Chakravarthi is a Senior Lecturer at the School of Psychology, University of Aberdeen, where he leads the Consciousness, Attention, and Perception (CAP) Lab. His research focuses on the neural processes underlying visual perception, particularly object recognition, attention, short-term memory, and visual crowding. He employs psychophysics, EEG, and computational techniques to investigate fundamental questions about how we perceive and process visual information. Chakravarthi's research interests center on visual crowding as a window into object recognition mechanisms. His work examines how visual features are detected and integrated to form coherent object representations, with particular attention to spatial attention, numerosity processing, and time perception. His lab investigates neural oscillations in attention and awareness, the neural basis of crowding, subitization processes, and time perception using advanced methodologies including Steady State Visual Evoked Potentials, Multivariate Classifiers, and Representational Similarity Analysis. Analysis of his recent publications reveals a consistent focus on visual crowding as a fundamental phenomenon for understanding object recognition. His work spans multiple levels of analysis from behavioral psychophysics to neural mechanisms, with increasing integration of computational approaches. Recent research has expanded into social aspects of perception, particularly social agency and temporal binding in social interactions, while maintaining his core focus on visual processing mechanisms. Dr. Chakravarthi has successfully secured significant research funding, including a £344,000 BBSRC grant as co-PI on 'Neural mechanisms of long-range spatial vision' and multiple smaller grants totaling over £20,000. He has supervised numerous PhD students to completion and currently mentors several doctoral candidates across diverse projects related to visual perception, attention, and consciousness. The CAP Lab, which he directs, is part of the Perception and Attention theme at the University of Aberdeen's School of Psychology. The lab maintains active research programs in visual crowding, attentional mechanisms, object recognition, and time perception, utilizing state-of-the-art psychophysics and EEG facilities. Chakravarthi also coordinates the Perception and Attention group meetings, fostering collaboration among researchers with related interests.

• Machine Learning
• Network Performance
• IoT Security
• +3 more

Andreas Johnsson is an Adjunct Senior Lecturer at the Department of Information Technology , Uppsala University, Sweden. His research spans Machine Learning , Network Performance , and IoT Security in the context of 5G/6G Networks and Edge Computing . Research interests include federated learning, transfer learning, and network optimization techniques. His recent work (2024-2021) focuses on self-regulated learning models for 6G, multi-objective neural architecture search, IoT intrusion detection generalizability, and delay prediction in heterogeneous networks. He has co-authored over 15 publications in high-impact venues like IEEE Transactions on Machine Learning in Communications and Networking and IEEE NOMS . Andreas actively collaborates with researchers such as Jalil Taghia, Farnaz Moradi, and Hannes Larsson. His contributions extend to change detection algorithms, policy adaptation frameworks, and feature selection methodologies in dynamic network environments. No formal scientific awards or student advisement details are currently documented.

Jan SalmenView profile

Researcher

• Machine Learning
• Computer Vision
• Neural Networks
• +3 more

Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.

Mathis RichterView profile

Researcher

• Higher cognition
• Concept representation
• Dynamic Field Theory
• +9 more

Dr. Mathis Richter is a Postdoctoral Researcher at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr University Bochum, Germany. He has been affiliated with the INI since 2008, progressing from Research Assistant to Research Associate, and currently serves as a Postdoctoral Researcher since July 2018. At the INI, he contributes to both the Embodied Cognition group and the Autonomous Robotics group, led by Prof. Dr. Gregor Schöner. Dr. Richter earned his Dr.-Ing. (Ph.D. equivalent) in Engineering from Ruhr-Universität Bochum between 2011 and 2018, following an M.Sc. and B.Sc. in Applied Computer Science from the same institution. His academic journey includes an exchange year at the University of Birmingham, UK. His research centers on higher cognition, specifically concept representation, how concepts combine to form complex mental scenes, and the neural mechanisms organizing cognitive operations in time. Using Dynamic Field Theory as his primary framework, he develops mathematical models explaining how neural populations represent objects and concepts. His work demonstrates how these cognitive models connect to sensory-motor systems, often implemented on robotic platforms to validate their autonomy and functionality. Analysis of Dr. Richter's publications reveals a consistent focus on neural dynamic modeling of cognitive processes, with particular emphasis on spatial relations, language grounding, and embodied cognition. His research trajectory shows increasing sophistication in modeling complex cognitive phenomena while maintaining strong connections to robotic implementations. As an educator, Dr. Richter has taught Lab courses in Autonomous Robotics across multiple terms since Winter 2015/2016 and has delivered Lectures in Computational Neuroscience: Neural Dynamics since Winter 2018/2019. His teaching directly reflects his research expertise in neural dynamics and cognitive systems. Dr. Richter actively participates in interdisciplinary research that bridges cognitive science, neuroscience, computer science, and robotics, contributing to the INI's mission of understanding how organisms generate behavior and cognition through interaction with their environments.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.