Find Academic Supervisors

Bochen JiaView profile

Associate Professor

• Human Factors and Ergonomics
• Wearable Sensors
• Worker Capacity and Safety
• +4 more

Bochen Jia is an Associate Professor at the University of Michigan-Dearborn's College of Engineering and Computer Science , specializing in Industrial and Manufacturing Systems Engineering . With a background in Industrial & Systems Engineering (PhD and MS) and Engineering Mechanics (MS and BS), he leads research on human factors, ergonomics, and human-exoskeleton interactions. Research Highlights: Investigates worker capacity changes under modern occupational risks Led ASTM International task group to establish exoskeleton digital modeling standards Explores ergonomic challenges in connected/autonomous vehicle design Develops wearable systems for posture and fatigue monitoring Advances pedestrian safety through AR/VR interface design Publications Trends show expertise in biomechanics, AI-driven workload assessment, autonomous vehicle-pedestrian communication, and STEAM education integration over 2018-2025. Scientific Awards include 11 grants from organizations like ASTM International, Michigan Mobility Transformation Center, and Ford Motor Company. Key projects involve exoskeleton standards, automated driving education, and pedestrian safety technologies. Teaching Innovation includes integrating ergonomic competitions, practical engineering tools, and lab development for automotive education. Established a multipurpose lab with external funding to prepare students for future mobility challenges.

Yi Lu MurpheyView profile

Professor

• Machine Learning
• Pattern Recognition
• Computer Vision
• +5 more

Dr. Yi Lu Murphey is a Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. She directs the Intelligent Systems Lab and holds editorial responsibilities at the Journal of Pattern Recognition. Ph.D. in Computer/Information/Control Engineering (1989) from University of Michigan M.S. in Computer Science (1983) from Wayne State University Her research focuses on machine learning applications for: Automotive diagnostics and prognostics Vehicle power management optimization Robotic vision systems Driver behavior analysis Medical data processing Recent publications demonstrate expertise in: Deep learning architectures Autonomous vehicle systems Biomedical signal processing Power electronics optimization Multimodal data fusion Intelligent transportation Scientific honors include: IEEE Fellow Major funding sources: National Science Foundation National Institutes of Health Toyota Research Institute Ford Motor Company Nissan USA Her lab develops technologies for: Driver workload estimation Pedestrian detection Personalized route prediction Vehicle context detection Electric vehicle energy management

Karina Bakkeløkken HjelmervikView profile

Professor

• Fluid mechanics
• Physical oceanography
• Numerical modelling of water currents and waves
• +5 more

Professor Karina Bakkeløkken Hjelmervik is a Professor in the Department of Maritime Operations , Faculty of Technology, Natural Sciences and Maritime Studies at the University of South-Eastern Norway , Campus Vestfold. Since 2009 she has combined mathematics and oceanography to advance numerical modelling of coastal waters, in particular the Oslofjord system. Education: PhD in Fluid Mechanics, University of Oslo, 2009 – thesis on wave–current interactions in coastal tidal currents. Cand. Scient. in Oceanography, University of Bergen – thesis on cold CO₂ droplets in seawater. Cand. Mag. in Science, University of Bergen. Research interests revolve around fluid mechanics and physical oceanography . She develops and validates high-resolution numerical models that simulate water currents, tides, waves, and dispersion processes. Her work supports operational ocean forecasting, tidal-energy assessment, oil-spill contingency planning, and maritime training simulators. Project leadership spans more than 15 funded initiatives totalling over 30 MNOK. Flagship projects include the FjordOs series (2013–2021) that produced an operational forecast model for the Oslofjord, and collaborative grants on tidal power, clean propulsion, and micro-technology for pathogen detection. Publications and dissemination include 20+ peer-reviewed journal articles since 2012, keynote talks at IEEE OCEANS and JONSMOD, and popular lectures such as Mathematics in the Oslofjord . Student supervision is extensive: she mentors master’s and PhD candidates across mathematics, physics, and maritime programmes, though specific student names are not listed. Contact: karina.hjelmervik@usn.no | +47 31 00 93 25

• Machine Learning
• Neuroscience
• Biomedical Engineering
• +3 more

Julien Audiffren is a Lecturer and Senior Researcher in the Department of Informatics at the University of Fribourg. His research focuses on machine learning applications in biomedical engineering, human perception, and data analysis. He holds roles in both education and research, contributing to interdisciplinary projects in informatics and health sciences. His research interests span machine learning methodologies, including multi-label classification, adaptive algorithms for perception quantification, and applications in sports science and healthcare. He has published extensively on topics such as postural control, fall prediction, and virtual reality training systems. Recent work emphasizes the development of robust adaptive procedures for threshold estimation in neuroscience experiments and the use of large language models for semi-structured data cleaning. His contributions bridge computational methods with practical applications in health monitoring and sports performance analysis. No scientific awards or grants are explicitly mentioned in the provided information. He is affiliated with the Interfaculty Informatics Department and collaborates on projects involving wearable sensors and AI-driven health solutions.

Babiloni FabioView profile

Professor

• Neuroscience
• Biomedical Engineering
• Electroencephalography (EEG)
• +5 more

Fabio Babiloni is a Full Professor of Physiology at Sapienza University of Rome, Italy, and held a concurrent Full Professorship at Hangzhou Dianzi University (2015–2018). His research focuses on neuroscience, biomedical engineering, EEG signal processing, brain-computer interfaces, and mental state estimation through neurophysiological signals. He has held significant editorial roles at IEEE journals and international societies. Notable awards include the 2021–2024 recognition as one of the top 2% cited neuroscientists globally and Italy’s research innovation award (2019). His work emphasizes practical applications like assessing mental workload in aviation controllers, drivers, and virtual reality environments. Research interests span EEG technology advancements, stress and workload biomarkers, and interdisciplinary neuroscientific methodologies. Over 4000 citations highlight his contributions to neurophysiological signal analysis in high-stakes operational contexts. Editorial leadership includes associate editorships at top journals and advisory roles in IEEE EMBS. His lab (Lab NSI) integrates cutting-edge tools for real-world neurophysiological monitoring.

Brent HechtView profile

Associate Professor

• Human-Computer Interaction
• Social Computing
• Spatial Computing
• +6 more

Brent Hecht is an Associate Professor of Computer Science and Communication Studies at Northwestern University and Director of Applied Science at Microsoft. His research focuses on human-centered AI, spatial computing, and algorithmic fairness. He holds a PhD in Computer Science from Northwestern and has dual BS degrees in Computer Science and Geography. His work bridges HCI, social computing, and geography, addressing algorithmic bias and ethical AI. He has led studies on remote work impacts, data labor, and AI ethics, publishing at top venues like CHI, CSCW, and SIGKDD. His lab, People, Space, and Algorithms Research Group, explores sustainable AI ecosystems. He received the NSF CAREER Award and multiple best paper awards. He advises students in computer science and technology/social behavior programs. Hecht collaborates with Microsoft Research, Xerox PARC, and Google Research. His work has been featured in major media outlets. His current roles include co-leading Microsoft's future-of-work initiatives and promoting equitable data practices in AI.

• Cognitive Neuroscience
• Cognitive Psychology
• Neuroimaging
• +6 more

Dr. Daniel C. Krawczyk is a Professor of Cognitive Neuroscience and Cognitive Psychology at the University of Texas at Dallas, holding the Debbie and Jim Francis Chair in Behavioral and Brain Sciences. His research focuses on higher cognitive functions, including decision-making, memory application, and social cognition. He employs methods like functional neuroimaging and virtual reality to study healthy individuals and those with neurological impairments, aiming to improve diagnostics and treatments for cognitive disorders. Krawczyk holds a BSc from SUNY Fredonia (1998), and MSc/PhD from UCLA (2000/2003), followed by a postdoc at UC Berkeley. He joined UT Dallas in 2006 as a joint faculty member with UT Southwestern Medical Center, advancing to Associate Professor (2012) and Full Professor (2018). Education: Bachelor’s in Psychology, State University of New York at Fredonia (1998) Master’s in Psychology, University of California, Los Angeles (2000) Doctorate in Psychology, University of California, Los Angeles (2003) Research Interests: Dr. Krawczyk’s work bridges cognitive neuroscience and clinical applications. He investigates how expertise (e.g., chess) influences cognitive processes, the neural basis of social cognition deficits in disorders like anorexia nervosa, and cognitive rehabilitation strategies for traumatic brain injury (TBI). His lab develops virtual reality tools to assess executive function and employs neuroimaging to map brain networks underlying reasoning and decision-making. Key Contributions: His research has advanced understanding of neuroplasticity in TBI patients through cognitive training, revealed neural correlates of self-perception in eating disorders, and demonstrated how hormones like oxytocin modulate social judgments. He has pioneered interventions using strategic reasoning training to improve outcomes in chronic TBI. Labs/Teams: Leads the Krawczyk Lab at UT Dallas, focusing on cognitive neuroscience and translational research in neurorehabilitation and mental health.

Vassilis PapakostopoulosView profile

Assistant Professor

• Ergonomics
• Cognitive Science
• Human Factors Engineering
• +4 more

Vassilis Papakostopoulos is an Assistant Professor at the Department of Product and Systems Design Engineering, University of the Aegean, specializing in Ergonomics. His academic background includes a PhD in Cognitive Psychology (Panteion University, 2008), MSc in Ergonomics (Loughborough University, 1998), and BSc in Psychology (University of Crete, 1996). He has extensive research experience in driver behavior analysis, human-robot interaction, and workplace design. His research focuses on visual perception, motor coordination, and ergonomics applications in transportation systems. Key areas include advanced driver assistance systems (ADAS), human factors in autonomous vehicles, and safety in urban environments. He has contributed to major EU-funded projects such as interACT, ASK-IT, and PReVENT, addressing challenges in traffic safety and human-machine interaction. Teaching responsibilities include undergraduate and graduate courses on ergonomics, user-centered design, and product development. He has published over 20 peer-reviewed articles, emphasizing holistic approaches to driver behavior analysis and interdisciplinary ergonomics applications. As Greece’s representative in the Centre for Registration of European Ergonomists (CREE), he promotes professional standards in ergonomics practice. Recent research trends include autonomous vehicle interactions, motorcycle safety, and organizational policies affecting risky driving behaviors. His work bridges historical ergonomic principles (e.g., ancient Athenian court design) with modern challenges in smart city infrastructure and human-robot collaboration.

• Cognitive Psychology
• Psychophysiology
• Computational Modeling
• +3 more

Dr. Victor Mittelstädt is a Post-doc Researcher at the Chair of Biological Psychology, University of Tübingen, where he investigates cognitive control mechanisms using behavioral and computational approaches. Affiliated with the Faculty of Science, his research bridges cognitive psychology, psychophysiology, and decision neuroscience. Education: Ph.D. in Psychology, University of Freiburg (2018) M.Sc. in Psychology, RWTH Aachen University (2015) B.Sc. in Psychology, University of Ulm (2012) Research Focus: His work centers on cognitive architecture in conflict tasks, including: Dynamic adjustments in distractor suppression and proactive control Neurocognitive mechanisms of task switching and voluntary action Computational modeling of aging effects on conflict processing Cross-modal transfer of cognitive control Awards & Fellowships: Feodor Lynen Research Fellowship (Humboldt Foundation) DAAD Research Grant Scholarly Contributions: He serves as a reviewer for 30+ journals including Journal of Experimental Psychology , Cognition , and Psychological Research , and evaluates grants for the German Research Foundation (DFG). His publications employ advanced methodologies like diffusion modeling and EEG to dissect cognitive processes.

Megan BartlettView profile

Researcher

• Human Factors
• Human-Automation Interaction
• Cognitive Psychology
• +3 more

Megan Bartlett is a Researcher at Flinders University, affiliated with the Brain and Cognition Laboratory within the College of Education, Psychology and Social Work. Her research focuses on Human Factors, particularly human-automation interaction, cognitive psychology, and the design of effective automation interfaces. Her PhD explored automation interface design, user differences, and training protocols, using methods like mathematical modeling and Bayesian parameter estimation. Key research interests include attention, perception, and expertise development, with contributions to understanding how automation aids impact decision-making efficiency in diverse domains like forensic analysis and security screening. Her work addresses UN Sustainable Development Goals related to innovation and responsible consumption. Mrs. Bartlett’s recent studies investigate strategies for optimizing human-automation collaboration, including the inefficiencies in diagnostic aid usage by experts versus novices and the role of anthropomorphic design in automation trust. She has collaborated on projects assessing baggage screening and forensic face-matching tasks, emphasizing real-world applications of cognitive science principles. No scientific awards are explicitly listed in the provided texts. Her advising role is not detailed, though her PhD supervisors were Professors Jason McCarley and Mike Nicholls. She contributes to the Brain and Cognition Laboratory, focusing on interdisciplinary research at the intersection of human cognition and technology.

Ruohan WangView profile

Research Fellow

• User-centric machine learning
• Meta-learning
• Continual learning
• +2 more

Ruohan Wang is a Senior Research Scientist at the Institute for Infocomm Research, A*STAR. Previously, he was a postdoctoral researcher at University College London (UCL), supervised by Prof. Massimiliano Pontil, and an applied scientist intern at AWS Berlin under Dr. Cedric Archambeau. He holds a Ph.D. in Machine Learning from Imperial College London, supported by the Singapore National Science Scholarship, under Prof. Yiannis Demiris and Prof. Carlo Ciliberto. His research focuses on machine learning, including representation learning, meta-learning, continual learning, and imitation learning. He aims to develop robust ML systems that leverage existing knowledge for novel tasks and enable continuous improvement. Notable contributions include work on expert distillation, adversarial imitation learning, and real-time workload classification during driving. Ruohan's articles span foundational topics like meta-representation learning, decision anticipation in autonomous systems, and collaborative human-robot storytelling. His work bridges theory and application, emphasizing human-centric AI systems.

Tulga ErsalView profile

Associate Professor

• System Dynamics and Control
• Autonomous and Semi-Autonomous Vehicles
• Human-Autonomy Interaction
• +6 more

Tulga Ersal is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan's College of Engineering. His work focuses on system dynamics and control, with applications in autonomous vehicles, energy systems, and human-autonomy interaction. He leads research at the 2019 Auto Lab in Ann Arbor, Michigan, and mentors PhD and Master’s students in fields like robotics, powertrain optimization, and biofeedback systems. B.S.E. from Istanbul Technical University (2001) M.S. and Ph.D. from University of Michigan (2003, 2007) Dr. Ersal’s research spans three core areas: Vehicle Systems: Autonomous/semi-autonomous navigation, trajectory planning on deformable terrains, and powertrain management Energy Systems: Battery modeling, fuel cells, microgrid optimization using extremum seeking Human Factors: Driver distraction modeling, cognitive workload estimation via eye tracking, and haptic shared control frameworks His publications often integrate control theory with real-world applications, emphasizing computational efficiency and robustness. Recent work includes terrain-adaptive trajectory planning (2025), Bayesian workload estimation (2024), and 3D terrain navigation algorithms (2023). He advocates for model fidelity and practical implementation in hardware-in-the-loop simulations. Scientific awards include: Kenneth M. Reese Outstanding Research Scientist Award (2018-19) UMOR Research Faculty Recognition Award (2016) Dr. Ersal's advising philosophy prioritizes individualized guidance, weekly one-on-one meetings, and collaborative group discussions. His students frequently present at conferences like the American Control Conference and ASME Dynamic Systems and Control Conference. Funding for student travel comes through Rackham Travel Grants, conference support programs, and research projects.

Fred FengView profile

Associate Professor

• Human Factors and Ergonomics
• Human-Machine Interaction
• Data Analytics and Visualization
• +6 more

Fred Feng is an Associate Professor in the Industrial and Manufacturing Systems Engineering department at the University of Michigan-Dearborn's College of Engineering and Computer Science. His work bridges human factors, data science, and transportation safety. Ph.D. in Industrial and Operations Engineering from University of Michigan, Ann Arbor (2015) M.S. in Mechanical Engineering from Tsinghua University (2009) B.E. in Automotive Engineering from Tsinghua University (2006) Research Focus: Human factors in automated vehicles, road safety analysis, human-machine interaction design, and data-driven modeling of mobility systems. His work combines naturalistic driving data with statistical and computational methods to improve transportation safety for vulnerable road users. Publication Trends: Recent work (2022-2023) explores micro-mobility sustainability frameworks and safety-critical scenarios for autonomous vehicles. Earlier studies (2017-2019) focused on driver distraction detection, overtaking behavior analysis, and vehicle dynamics modeling. Grants: Recipient of multiple competitive grants including NSF CAREER funding (2022-2027) and multiple industry-sponsored research projects with Toyota and Ford.

• Forensic Science
• Criminology
• Cybersecurity and Privacy
• +7 more

Professor Ruth Morgan is a leading interdisciplinary researcher at University College London (UCL) , holding the position of Professor of Crime and Forensic Sciences in the Department of Security and Crime Science . She serves as Director of the UCL Centre for the Forensic Sciences (since 2010) and Co-Director of the UCL Arista Institute (since 2022), focusing on creative interdisciplinary solutions to global challenges. Interdisciplinary research across Engineering, Science, Arts, and Humanities Specialist Adviser to the House of Lords Science and Technology Select Committee (2018-19) World Economic Forum Young Scientist (2019) Her research integrates forensic science, criminology, cybersecurity, and science policy , with a focus on trace evidence dynamics, AI, digital forensics, and decision-making under stress . Recent publications emphasize 3D forensic modeling, cognitive biases in anthropology, and ethical frameworks for emerging technologies . Awards include the Chartered Society for Forensic Sciences' PW Allen Award (2006, 2016, 2018) and the Provost's Teaching Award (2015) . She advocates for problem-based research and interdisciplinary collaboration to address complex societal issues.

Sebastian BosseView profile

Researcher

• Computational Perception
• Machine Learning
• Computer Vision
• +6 more

Dr.-Ing. Sebastian Bosse heads the Interactive & Cognitive Systems research group at Fraunhofer HHI in Berlin, Germany. His work integrates computational perception and cognition with machine learning and human-machine interaction , addressing applications in multimedia , augmented reality , medicine , agriculture , and industrial production . Research Interests include: Computational Perception & Cognition Machine Learning for Computer Vision Human-Machine Interaction in immersive environments AI-Driven Quality Assessment Neurophysiological Response Analysis Multi-View Gesture Recognition Recent Articles focus on multi-view coding , VR/AR applications , deepfake detection , agricultural AI , and neural quality metrics . Key collaborations span surgical robotics , explosive ordnance exploration , and agricultural phenotyping . Labs & Teams : Leads the Interactive & Cognitive Systems group at Fraunhofer HHI, specializing in pose/gesture analysis , contact-free interaction , and image quality estimation using subjective testing and neurophysiological data .