Find Academic Supervisors

• Rehabilitation Robotics
• Neural Technologies
• Brain-Computer Interfaces
• +10 more

Dingguo Zhang is a Reader in Robotics Engineering at the Department of Electronic & Electrical Engineering, Faculty of Engineering & Design, University of Bath. He is affiliated with several interdisciplinary research centers including the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI, the Centre for Bioengineering & Biomedical Technologies (CBio), the Bath Institute for the Augmented Human, and IAAPS. His work bridges robotics, neuroscience, and biomedical engineering to develop advanced assistive and rehabilitative technologies. His research focuses on Rehabilitation Robotics, Neural Technologies, Brain-Computer Interfaces (BCIs), and Robotic Exoskeletons . He investigates how neural signals such as EEG and EMG can be used to control prosthetic devices, exoskeletons, and communication systems, particularly for individuals with neurological impairments. His work also involves signal processing, machine learning, and adaptive control strategies to improve human-machine interaction. Key themes include decoding motor and speech intentions, fatigue assessment, and closed-loop neuromodulation. The recent publications reflect a strong trend toward invasive and non-invasive neural interface development , especially in decoding speech from intracranial EEG and enhancing BCI performance through advanced preprocessing and deep learning. There is also significant focus on wearable robotics for stroke and spinal cord injury rehabilitation , with integration of soft actuators, hybrid control, and real-time adaptation. Multimodal sensing (e.g., EEG-fNIRS, EMG-force) and patient-centered design are recurring elements across his work. Dr. Zhang has received recognition through his status as an IEEE Senior Member (EMBS, RAS, SMC) and IFESS Lifetime Member . He serves on the editorial boards of five journals including IEEE Transactions on Neural Systems & Rehabilitation Engineering and Frontiers in Neurorobotics , and has held leadership roles such as co-chair of IFESS 2024. He is actively involved in research funding and supervision. He is Principal Investigator (PI) on the EPSRC-funded dSPEECH project, which aims to decode speech using invasive BCIs, and Co-Investigator on an NIHR-funded project developing an ear-switch-controlled exoskeleton for stroke rehabilitation . He welcomes PhD students and is committed to training the next generation of researchers in neurotechnology and robotics. Dr. Zhang leads or contributes to research within collaborative labs and teams focused on bioengineering, augmented human systems, and accountable AI . These include the Centre for Bioengineering & Biomedical Technologies and the Bath Institute for the Augmented Human, where interdisciplinary teams develop next-generation assistive technologies grounded in ethical and transparent AI principles.

Mrinal K. SenView profile

Professor

• Seismic Wave Propagation
• Full Waveform Inversion (FWI)
• Uncertainty Quantification
• +5 more

Mrinal K. Sen is a Professor and the Jackson Chair in Applied Seismology at the University of Texas at Austin. He is affiliated with the University of Texas Institute for Geophysics (UTIG), where he has served as Interim Director (2017–2018) and Associate Director (2016–2020). His research focuses on seismic wave propagation, full waveform inversion (FWI), uncertainty quantification, and physics-assisted machine learning for geophysical applications. Ph.D., University of Hawaii at Manoa M.S., Indian Institute of Technology B.S., Indian Institute of Technology Sen's work spans seismic inversion algorithms, finite difference methods, and trans-dimensional MCMC for FWI. He applies machine learning to unconventional reservoir characterization and seismic data analysis, integrating high-performance computing and physics-based constraints. His recent publications highlight hybrid quantum neural networks, Bayesian frameworks, and deep learning for FWI and noise reduction. Scientific awards include the Virgil Kauffman Gold Medal and Honorary Membership in SEG . His research is supported by collaborations with the Bureau of Economic Geology, Department of Earth and Planetary Sciences, and Center for Planetary Systems Habitability.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +6 more

Anis Yazidi is an Associate Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, where he leads research in the Research Group for Digital Signal Processing and Image Analysis. His academic profile demonstrates significant contributions to artificial intelligence, machine learning, and signal processing with over 50 publications between 2021-2025 in high-impact venues including IEEE Transactions, Frontiers journals, and AAAI proceedings. Professor Yazidi's research spans multiple interconnected domains with particular emphasis on Tsetlin Machines, deep learning for medical applications, and signal processing theory. His work bridges theoretical foundations with practical implementations, developing novel frameworks like DREAMS for EEG analysis with model card reporting and interpretable methods for ECG classification. His contributions to learning automata theory, particularly in convergence analysis of Tsetlin-based algorithms, represent significant theoretical advances. The research portfolio also extends to cybersecurity applications of AI for IoT protection, agricultural technology for plant disease detection, and renewable energy modeling for wind-speed statistics. Yazidi's publication record reveals a strong interdisciplinary approach, with collaborations across computer science, neuroscience, medical diagnostics, and engineering disciplines. His recent work shows increasing focus on trustworthy AI systems, ethical considerations in medical applications, and specialized neural architectures tailored for specific data modalities including EEG, ECG, and eye-tracking data. The research demonstrates both theoretical rigor in algorithm development and practical implementation for real-world problems. Professor Yazidi maintains an active collaboration network with researchers across the Department of Informatics at UiO, particularly with Pedro Lind, Hugo Lewi Hammer, and Paal Engelstad, while also engaging in international collaborations. His work contributes significantly to both the theoretical foundations of learning systems and their practical implementation in diverse application domains from healthcare to renewable energy.

Jeffrey TingView profile

Senior Lecturer

• Machine Learning
• Computer Vision
• Plenoptic Lightfield Imaging
• +4 more

Jeffrey Ting is the Interim Head of Computing and Mathematical Sciences and Senior Lecturer at the University of Wolverhampton's School of Mathematics and Computer Science (Faculty of Science and Engineering). His academic qualifications include an MSc in Control Systems from the University of Sheffield, a Postgraduate Diploma in Business Administration from the University of Birmingham, and a BSc Joint Honours in Computer Science and Electronic Engineering from the University of Birmingham. His research focuses on Machine Learning , Computer Vision , and Plenoptic Lightfield Imaging , with broader expertise spanning Mobile Technologies, Software Engineering, Cyber Security, and Web Technologies. Current investigations include developing a hybrid framework for light field scene reconstruction as part of his PhD research. Analysis of his 15 most recent publications reveals predominant themes in educational technology (mobile learning systems, interactive tools) and control systems (industrial automation, multivariable control), with later work expanding into medical informatics and imaging technologies. Publications demonstrate consistent interdisciplinary collaboration and practical applications in industrial and educational contexts. Professional affiliations include Chartered Membership in the British Computer Society and Fellowship in the Higher Education Academy. He currently supervises a PhD project on light field reconstruction and has extensive industry experience in software development and management prior to his academic career.

Travis MitchellView profile

Lecturer

• Computational fluid dynamics
• Lattice Boltzmann methods
• Multiphase fluid dynamics
• +3 more

Dr. Travis Mitchell is a Lecturer at the School of Mechanical and Mining Engineering , The University of Queensland , and an affiliate of the Centre for Multiscale Energy Systems . He holds a PhD in Multiphase Computational Fluid Dynamics and dual degrees in Mechanical Engineering (BE Hons) and Mathematics (BSc). Education: PhD in Multiphase Computational Fluid Dynamics, The University of Queensland BE (Hons) in Mechanical Engineering, The University of Queensland BSc in Mathematics, The University of Queensland Research Interests focus on numerical modeling of multiphase fluid dynamics in porous media , with applications spanning CO2 electrolysis , hydrogen production via methane pyrolysis , biomedical fluid-structure interaction , and geomechanical fracture analysis . His methodological expertise includes Lattice Boltzmann techniques and high-performance computing . Recent Work Trends encompass multiphase transport in fractured media , gas diffusion electrode optimization , fiber-based air filter design , and thermocapillary flow modeling , reflecting his interdisciplinary impact in energy, health, and resource engineering. Scientific Recognition includes the ICMMES-CSRC Award for multiphase lattice Boltzmann research and an EAIT Citation for Excellence in Student Learning (2023) . Teaching Portfolio includes coordination of MECH2700: Computational Engineering and Data Analysis and lectures in MECH3780: Computational Mechanics and MECH6480: Computational Fluid Dynamics .

Péter EklerView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +7 more

Péter Ekler is an Associate Professor at the Department of Automation and Applied Informatics , Budapest University of Technology and Economics (BME) , Hungary. He is affiliated with the Applied Mobile Research Group (AMORG) and the Applied Computer Science Group , focusing on cutting-edge research in AI, IoT, and mobile systems. Research Interests: Artificial Intelligence and Machine Learning Internet of Things (IoT) and Smart Cities Wireless Sensor Networks and Energy-Efficient Routing Network Coding and Mobile Peer-to-Peer Systems Blockchain and Distributed Systems Computer Vision and Sensor Data Analysis His recent work includes the development of AI-based thermal imaging systems, energy-aware IoT routing, and secure authentication mechanisms using JWT. He has published extensively on optimizing IoT performance, enhancing mobile streaming with network coding, and applying machine learning to real-world sensor data. Scientific Contributions: Published over 50 peer-reviewed articles from 2013 to 2023 Active on platforms like Google Scholar, Scopus, ResearchGate, and ResearcherID Research spans both technical systems and historical studies in science Contact & Affiliations: Email: Ekler.Peter@aut.bme.hu Location: Q.B226, Magyar tudósok krt. 2., Budapest 1117, Hungary Phone: +36 (1) 463-3702 LinkedIn: http://hu.linkedin.com/in/peterekler

• Human Robot Interaction
• Healthcare Robotics
• Wearable Sensors
• +6 more

Tamon Miyake is an Assistant Professor at Waseda University's School of Creative Science and Engineering, specializing in Human Robot Interaction and Healthcare Robotics. His research focuses on wearable sensors, muscle deformation analysis, gait training, and mixed reality systems for rehabilitation applications. Dr. Miyake received his Doctor of Engineering from Waseda University in March 2020. His primary research areas include muscle deformation sensing, gait analysis and training, mixed reality rehabilitation systems, and human-robot interaction for healthcare applications. His work bridges engineering and clinical applications, particularly in physiotherapy training and rehabilitation robotics. His research output demonstrates a strong focus on developing objective measurement systems for clinical applications, particularly in analyzing muscle deformation during physiotherapy and developing training systems that provide quantifiable metrics for skill acquisition. His recent work shows increasing integration of AI and machine learning techniques with traditional biomechanical analysis. His publications reveal a consistent research trajectory focused on improving rehabilitation outcomes through technology, with particular emphasis on gait training, muscle load analysis, and developing objective metrics for physiotherapy practice. His work spans both engineering development and clinical validation studies. Dr. Miyake has been actively involved in the IEEE Robotics and Automation Society as Co-Chair of the Student Activities Committee from 2017-2019, demonstrating his commitment to academic community building.

Mohamad T. ShahabView profile

Assistant Professor

• Control Theory
• Robotics
• Adaptive Control
• +4 more

Mohamad T. Shahab is an Assistant Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University (TMU), where he joined in 2025. His research focuses on control theory and autonomous systems, with particular expertise in adaptive control, robotics, and optimization. Dr. Shahab completed his PhD in Electrical and Computer Engineering at the University of Waterloo in 2020. He received his BSc in Control and Instrumentation Engineering (2007) and MSc in Systems Engineering (Automation and Control) (2015), both from King Fahd University of Petroleum and Minerals (KFUPM). Prior to joining TMU, he held postdoctoral appointments at KAUST working with Professor Eric Feron and worked as an R&D engineer with Baker Hughes. His research spans Control Theory, Robotics, Adaptive Control, Control & Optimization, and Autonomous Systems . Dr. Shahab's work particularly focuses on adaptive control strategies that yield linear-like behavior, robustness analysis of adaptive control systems, and applications to robotics and autonomous aerial vehicles. His recent work has centered on modular tetrahedral-shaped aerial robots and their control under uncertainty. Dr. Shahab has published numerous papers in top-tier journals including IEEE Transactions on Automatic Control, Automatica, and International Journal of Adaptive Control and Signal Processing, as well as in major conferences like IEEE ICRA and AIAA SciTech. His research shows a clear trajectory toward developing more robust and reliable adaptive control strategies for complex systems, particularly in robotics and autonomous vehicles. Dr. Shahab is actively recruiting graduate students (MASc, MEng, PhD) and undergraduate researchers interested in control theory, robotics, and autonomous systems. He encourages students with strong backgrounds in control, optimization, robotics, and machine learning to contact him.

Kevin CarlbergView profile

Associate Professor

• Contextual AI
• Computational Physics
• Computational Mathematics
• +3 more

Kevin Carlberg is an Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering at the University of Washington. He currently holds leadership roles in AI Research Science at Meta Reality Labs and has prior experience at Sandia National Laboratories. His research focuses on contextual AI, computational physics, and model reduction techniques. He holds a PhD in Aeronautics and Astronautics from Stanford University (2011), an MS from Stanford (2006), and a BS from Washington University in St. Louis (2005). Carlberg leads interdisciplinary teams to develop AI-driven methodologies for extreme-scale simulations and wearable technologies. His work emphasizes reducing computational complexity for high-consequence applications, such as national security and VR/MR devices. Key areas include nonlinear model reduction, uncertainty quantification, and data-driven time-parallelism. He has pioneered techniques like the network uncertainty quantification (NetUQ) method and deep neural network-based error modeling. His research has been published in top journals like Computer Methods in Applied Mechanics and Engineering and Journal of Computational Physics , focusing on ROMs, machine learning integration, and high-performance computing. His contributions address challenges in Kolmogorov widths and real-time simulation requirements for large-scale systems.

Mrinal K SenView profile

Professor

• Seismic Wave Propagation
• Geophysical Inverse Problems
• Seismic Anisotropy
• +7 more

Mrinal K Sen is a Professor and Morgan J. Davis Centennial Chair in Geosciences at the University of Texas at Austin's Jackson School of Geosciences. He leads the Sen Research Group and holds affiliations with the Department of Earth and Planetary Sciences and the Institute for Geophysics. His work focuses on seismic wave propagation, anisotropy, and geophysical inverse problems, with applications in exploration seismology, reservoir characterization, and whole-Earth seismology. Sen pioneered methods combining Bayesian statistics, machine learning, and advanced numerical techniques to address uncertainty quantification and improve seismic imaging. Education: Ph.D., University of Hawaii at Manoa M.S., Indian School of Mines (now IIT) B.S., Indian School of Mines (now IIT) Research Interests: Seismic Inversion and FWI Bayesian Methods and Uncertainty Analysis Machine Learning in Geophysics Anisotropy and Wave Propagation Reservoir and CO2 Storage Modeling Recent work emphasizes hybrid quantum-neural networks for image generation, trans-dimensional MCMC for FWI, and physics-guided deep learning. His articles highlight advancements in stochastic inversion, fluid saturation modeling, and ambient noise monitoring for groundwater studies. Labs/Teams: Director of the Sen Research Group and former Interim Director (2017-2018) and Associate Director (2016-2020) of the UT Institute for Geophysics.

Ala Al KafriView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Medical Imaging Analysis
• +5 more

Ala Al Kafri is a Senior Lecturer in Computer Science at Teesside University's Department of Computing & Games, affiliated with the Centre for Digital Innovation. With over 16 years of teaching experience across multiple institutions globally, he specializes in machine learning, artificial intelligence, and medical imaging applications. His research focuses on AI-driven diagnostics for lumbar spine diseases, with notable contributions to MRI analysis using convolutional neural networks. He has collaborated on projects involving physiotherapy education technology, autism screening, and multidisciplinary learning initiatives. Education includes a PhD in Machine Learning for Lumbar Spine Disease Diagnosis from Liverpool John Moores University (2019), an MSc from New York Institute of Technology (2005), and a bachelor’s degree. Professional development includes a Postgraduate Certificate in Academic Practice. Research interests span medical AI, data science, and education technology, with emphasis on practical applications in healthcare and learning environments. Key collaborations include developing an AI tool for lumbar spine disease prediction with University Multimedia Nusantara, and creating video-based physiotherapy assessment systems. Recent projects include AI-powered physiotherapy education systems, autism screening using ML, and smart highway vehicle detection. His work integrates technical innovation with real-world societal impact through multidisciplinary approaches.

Suzan ArslanturkView profile

Associate Professor

• Medical Informatics
• Predictive Analytics
• Machine Learning
• +4 more

Dr. Suzan Arslanturk is Associate Professor in Computer Science and Industrial & Systems Engineering at Wayne State University's College of Engineering. She directs the Machine Learning and Health Informatics Laboratory, focusing on predictive analytics for healthcare applications. Research domains include: Cancer subtyping through multi-omics data integration Biomarker discovery for prostate cancer using cross-cancer learning Drug repurposing for cancers with DNA-repair deficiencies Neonatal brain anomaly detection via MRI analysis Operational healthcare optimization during medical surges Leads the development of deep learning frameworks for medical imaging segmentation and clinical text analysis. Publications demonstrate innovations in multimodal data fusion, domain adaptation, and unsupervised abnormality detection. Advises PhD candidates in computational healthcare research and directs academic programs in data mining and intelligent systems.

Seyed Ziae Mousavi MojabView profile

Assistant Professor

• Data Mining
• Machine Learning
• Evolutionary Algorithms
• +6 more

Seyed Ziae Mousavi Mojab is an Assistant Professor (Teaching) in Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. He holds a B.S.E. in Computer Science from the University of Michigan Ann Arbor, and both M.S. and Ph.D. degrees in Computer Science from Wayne State University. His research focuses on evolutionary computation, optimization methods, workflow scheduling, medical image processing, data analytics, and business intelligence. Key investigations include cultural algorithms for big data workflows, deep learning ensembles for medical diagnostics, and neural architecture synthesis techniques. Recent publications demonstrate applications in robotic surgical segmentation (2023), COVID-19 detection via chest X-ray analysis (2020-2021), and optimization frameworks for cloud-based big data systems (2019-2022). Research consistently integrates evolutionary computation with deep learning architectures across medical and data-intensive domains.

Rani Warsi SullivanView profile

Professor

• Viscoelasticity in composites
• Composite Structures
• Composite Structural Testing

Dr. Rani Warsi Sullivan is Professor and Head of Aerospace Engineering at Mississippi State University, holding the Bill & Carolyn Cobb Endowed Chair. She earned her Ph.D. (2003), M.S. (1993), and B.S. (1989) in Engineering-related disciplines from Mississippi State University. Her research focuses on viscoelastic behavior in composites, composite structural analysis, and experimental testing methods. She has secured funding from NASA, Office of Naval Research, Boeing, and other leading aerospace organizations. Professor Sullivan teaches undergraduate and graduate courses including Engineering Mechanics, Vibrations, and Aerospace Structural Analysis. Her educational innovations received the Outstanding Educational Paper award from the Bagley College of Engineering. National Academy of Engineering Frontiers of Engineering Education Award (2012) Bagley College of Engineering Hearin Excellence Award (2012) NASA Summer Faculty Fellowship (2010) ASEE Best Paper Award, Mechanics Division (2008) She directs the Composite Structures Research Laboratory and serves as Faculty Advisor for student engineering organizations.

Ravi PrakashView profile

Professor

• 360-degree Video Streaming
• Wireless Networking
• Mobile Computing
• +5 more

Ravi Prakash is a Professor in the Department of Computer Science and the Hobson Wildenthal Honors College at the University of Texas at Dallas. He holds a Ph.D. in Computer and Information Science from The Ohio State University (1996), with research focused on 360-degree video streaming, wireless networking, mobile computing, and distributed systems. His work addresses challenges in field-of-view prediction, edge caching, and coexistence of wireless technologies in unlicensed bands. He teaches courses ranging from discrete mathematics to advanced operating systems and internet policy. Education: Ph.D. (1996) and M.S. (1991) from Ohio State University; B.Tech. (1990) from Indian Institute of Technology, Delhi. Research Interests: 360-degree video optimization (caching, editing) Wireless MAC protocols (WiFi, cognitive radio) Mobile ad hoc networks (MANETs) Distributed system algorithms Legal/ethical implications of computing Professional Service: Co-chair (ICDCS 2025), General Co-chair (MASS 2025), IEEE Transactions on Mobile Computing associate editor, and UT Dallas Senate member. Awards: NSF CAREER Award (2001), multiple teaching excellence awards, and best paper recognitions. Labs/Teams: Active in wireless networking testbeds (ASSERT), mobile computing optimization, and 360-video streaming frameworks (CooPEC, 360RAT).