Find Academic Supervisors

• Condensed Matter Physics
• Materials Science
• Nanotechnology
• +5 more

John B. Ketterson is a Professor of Physics and Astronomy at Northwestern University , with a courtesy appointment in Electrical and Computer Engineering. His research focuses on condensed matter physics, materials science, and nanotechnology, particularly exploring superconductivity, spin dynamics, and magnetic nanostructures. He holds the Fayreweather Professorship, reflecting his academic leadership. Key research interests include vortex dynamics in superconductors, spin-wave propagation in ferromagnetic materials, and the design of novel superconducting devices. His work bridges fundamental physics and applied technologies, such as Josephson junctions, magnetic sensors, and magnonic crystals. Recent studies emphasize nanopatterning effects on material properties and the development of heteromorphic superlattices with minimal interface defects. Publications demonstrate a strong emphasis on experimental and theoretical investigations of magnetic and superconducting systems, including studies of Suhl instabilities, ferromagnetic resonance, and the interplay between spintronics and antiferromagnetic materials. His contributions span from nanoscale device physics to broader questions in condensed matter theory. Ketterson’s work has been supported through collaborations in advanced nanofabrication and characterization techniques, with applications in quantum devices, energy-efficient electronics, and sensing technologies. He actively explores the intersection of material synthesis and device functionality, targeting innovations in superconducting electronics and spin-based technologies.

Mingzhou DingView profile

Professor

• Cognitive Neuroscience
• Neural Signal Processing
• Multimodal Neuroimaging
• +5 more

Mingzhou Ding is a Distinguished Professor and J. Crayton Pruitt Family Professor in the Department of Biomedical Engineering at the University of Florida. He holds a B.S. in Astrophysics from Peking University (1982) and a Ph.D. in Physics from the University of Maryland (1990). His research focuses on applying engineering methods to understand neural mechanisms of cognitive functions, with expertise in multimodal neuroimaging, Granger causality analysis, and large-scale brain networks. Education: B.S., Astrophysics, Peking University, 1982 Ph.D., Physics, University of Maryland, 1990 Research interests span cognitive aging, pain processing, and neural oscillations, with a focus on disorders like Parkinson’s disease. He has pioneered methods like Granger causality for analyzing brain signals and has contributed to decoding visual attention and emotional processing mechanisms. His work integrates EEG, fMRI, and MEG modalities to study brain network dynamics. Key awards include the Pruitt Family Professorship (since 2008) and the UF Research Foundation Professorship (2013-2016). He has been recognized for mentoring excellence (2018-2019) and has served on NIH study sections and editorial boards. His recent studies explore transcranial stimulation effects on attention, AI-generated affective datasets, and brain network biomarkers for pain and cognitive decline. Current projects include developing AI tools for neuroimaging, studying alpha oscillations’ role in default mode network modulation, and investigating neural correlates of fear conditioning in primary visual cortex.

Mark SheplakView profile

Professor

• MEMS
• Aeroacoustics
• Fluid Mechanics
• +6 more

Mark Sheplak is a Professor at the University of Florida, holding joint appointments in the Department of Electrical & Computer Engineering and the Department of Mechanical & Aerospace Engineering within the Herbert Wertheim College of Engineering. He leads the Interdisciplinary Microsystems Group (IMG), focusing on MEMS-based sensor and actuator development for aerospace and fluid dynamics applications. His research emphasizes high-performance instrumentation in harsh environments, including hypersonic flow measurement, aeroacoustic noise localization, and wall shear stress sensing. Education: He earned his BS, MS, and PhD in Mechanical Engineering from Syracuse University (1989, 1992, 1995). Postdoctoral work at MIT (1995–1998) preceded his role at UF since 1998. He is affiliated with centers like the Multi-functional Integrated System Technology (MIST) and Florida Center for Advanced Aero-Propulsion (FCAAP). Research Highlights: His group develops MEMS sensors for pressure, shear stress, and dynamic flow characterization, with applications in aerospace, energy, and automotive sectors. Notable projects include the High Operational Temperature Sensors (HOTS) program (DARPA-funded) and the AEROMORPH Center of Excellence for morphing aerospace systems. He holds awards such as AIAA Associate Fellow and Acoustical Society of America Fellow. Grants & Collaborations: IMG collaborates with institutions like Sandia National Labs and Florida State University. Recent projects include sapphire-based pressure sensors and flush-mount microphones for aircraft arrays. His work spans academic-industry partnerships, including the start-up IC² for commercializing wall shear stress sensors. Students & Advising: He has advised numerous PhD students, including Austin Vera (NDSEG Fellow), Brett Freidkes (NSF GRFP), and Haocheng Zhou. IMG also fosters undergraduate research, exemplified by Alexander Reilly’s AIAA Region II award. Labs & Teams: IMG operates advanced fabrication and testing facilities, emphasizing interdisciplinary research. Key labs include the MEMS fabrication lab and wind tunnel facilities for sensor validation.

Kristoffer McKeeView profile

Senior Lecturer

• Renewable Energy
• Condition Monitoring
• Predicting Remaining Useful Life
• +2 more

Dr. Kristoffer McKee is a Senior Lecturer at Curtin University's School of Civil and Mechanical Engineering, part of the Faculty of Science and Engineering. With expertise in renewable energy systems, condition monitoring, and vibration analysis, his research focuses on developing advanced electromechanical systems and MEMS sensors. His work spans electromagnetic energy harvesting, MEMS gyroscope design for harsh environments, and offshore infrastructure integrity. Research interests include: Innovations in renewable energy harvesting through magnetic spring-based oscillators Design optimization of MEMS sensors for reliability in extreme conditions Vibration analysis and control in mechanical systems Structural integrity assessment for offshore and energy infrastructure Engineering education methodologies Publications demonstrate strong focus on energy conversion technologies (52%) and microsystem design (48%). Recent articles show increasing emphasis on space-grade MEMS applications and sustainable energy solutions.

Aasifa RounakView profile

Assistant Professor

• Nonlinear Dynamics
• Vibro-Impact Systems
• Bifurcation Theory
• +4 more

Dr. Aasifa Rounak is an Assistant Professor in the School of Mechanical and Materials Engineering at University College Dublin (UCD). She holds dual roles as a BE Automotive Engineering Programme Staff and Chair of the School’s EDI Committee. Her research focuses on nonlinear dynamical systems, particularly discontinuity-induced bifurcations in mechanical systems undergoing impacts and friction, with applications in automotive engineering, biomedical tools, and structural health monitoring. Education: BTech from Veer Surendra Sai University of Technology (VSSUT), Burla MSc in Applied Mechanics from Indian Institute of Technology (IIT) Madras PhD in Applied Mechanics from IIT Madras Research Themes: Nonlinear dynamics, stochastic bifurcations, vibro-impact systems, energy harvesting, neuromorphic computing for control systems. Her work bridges theoretical analysis (e.g., bifurcation maps, Lyapunov exponents) and experimental validation (e.g., modal testing of micro-drills, electronic circuit models). Teaching: Coordinates modules like Solid Mechanics 1/2 and Vehicle Structural Analysis. Her pedagogy emphasizes hands-on learning and geometric intuition, as seen in her 2024 Teaching Excellence Award. Awards & Grants: Recipient of 2024 Teaching Excellence Award (UCD) Principal Investigator on grants like "Stochastic Dynamics and Chatter in Metal Cutting Applications" (2022–2026) Labs & Teams: Leads the Dynamical Systems and Risk Laboratory, overseeing projects on energy harvesting sensors and neuromorphic control systems. Collaborates with industry partners like Salt Medical on biomedical device dynamics. Professional Activities: Peer reviewer for journals like Nonlinear Dynamics and Journal of Vibration and Control . Active in COST Action ODIN for inspection-optimization frameworks.

Stephanie R JonesView profile

Professor

• computational neuroscience
• electroencephalography
• magnetoencephalography
• +5 more

Stephanie R Jones is a Professor of Neuroscience at Brown University, affiliated with the Carney Institute for Brain Science. Her research integrates human brain imaging and computational neuroscience methods to study brain dynamics in health and disease. She works closely with animal neurophysiologists and clinicians to develop data-constrained neural models that are functionally and translationally relevant. Dr. Jones' primary research interests include: Computational neuroscience approaches to understanding brain function Electroencephalography (EEG) and magnetoencephalography (MEG) analysis Neural rhythms and their role in cognitive processes Transcranial magnetic stimulation (TMS) and other brain stimulation techniques Biophysically principled models of neural circuits Her work focuses on developing models that bridge the gap between human brain imaging signals (MEG/EEG) and their underlying cellular and network level generators. Current projects apply interdisciplinary techniques to study neural dynamics in healthy functions such as perception, attention, and decision making, as well as in neural pathologies including depression, autism spectrum disorders, aging, Alzheimer's, and schizophrenia. She also investigates the impact of brain stimulation and mind-body practices on brain dynamics with the goal of improving treatments for neuropathology. Dr. Jones has received numerous awards and honors throughout her career, including: Claflin Distinguished Scholar Award, Massachusetts General Hospital (2008-2010) Harvard Catalyst Advanced Imaging Award, Harvard Medical School (2012-2013) New Frontiers Fund Awards from the Brown Institute for Brain Science Brown University DEAN's Emerging Areas of New Science Award Invited Panel Advisor for NIH and NSF sponsored "BRAIN" Initiative Workshops As an advisor and collaborator, Dr. Jones works with a diverse team of researchers across multiple disciplines. Her lab, the Jones Laboratory for Human Electrophysiology and Computational Neuroscience, develops the Human Neocortical Neurosolver (HNN), a software tool for interpreting the cellular and network origin of human MEG/EEG data. She serves as an Associate Editor for the MIT Press Journal Neural Computation and as an NSF/NIH Panel Grant Reviewer for Collaborative Efforts in Computational Neuroscience. The Jones Lab bridges experimental and theoretical techniques to study human brain dynamics, with a mission to develop biophysically principled models of neural circuits that connect electrophysiological measures of brain function to underlying cellular and network dynamics. The lab collects MEG/EEG data non-invasively in humans and collaborates closely with animal electrophysiologists and clinicians to develop translationally relevant models.

• Combustion Engineering
• Thermoacoustics
• Fluid Dynamics
• +4 more

Artur K. Pozarlik is an Associate Professor in the Department of Thermal Engineering at the University of Twente. His research focuses on combustion engineering, thermoacoustics, and renewable energy systems, contributing to UN Sustainable Development Goals related to clean energy. He has extensive experience in experimental and computational studies of combustion dynamics, fluid dynamics, and energy conversion processes. Key research interests include optimizing hydrogen-based combustion systems, analyzing thermoacoustic instabilities in pressurized combustors, and developing smart control strategies for energy systems. His work integrates experimental setups with computational fluid dynamics (CFD) models to enhance efficiency and stability in energy applications. Recent studies involve supercritical oxy-combustion in gas turbines and limit cycle oscillations in high-pressure combustors. Collaborations emphasize interdisciplinary approaches to sustainable energy solutions. His publications (n=90) highlight contributions to combustion science, with a focus on hydrogen energy systems, acoustic design modifications, and predictive control strategies. Supervised 18 works, fostering next-generation expertise in thermal engineering. Labs/Teams: Active in combustion dynamics laboratories and energy systems research groups at the University of Twente.

Adnan TahirovicView profile

Professor

• Nonlinear control theory
• Multi-agent systems
• Mobile robotics
• +6 more

Prof. Adnan Tahirovic is a Professor in the Department of Automatic Control at the University of Sarajevo's Faculty of Electrical Engineering. He leads the Lab for Cooperative Artificial Intelligence and Advanced Control Systems. His academic background includes a PhD from Politecnico di Milano and research stints at Imperial College London, NASA-JPL, and Caltech. Education: M.Sc. (Control Theory, 2006) and Ph.D. (Information Technology, 2011). His research focuses on nonlinear control, multi-agent systems, mobile robotics, computational neuroscience, and AI. Key projects include MORUS (NATO-funded maritime security), AeroSTREAM (EU-funded autonomous aerial systems), and MARBLE (blue economy robotics). Research Interests: Nonlinear systems, optimal control, multi-agent reinforcement learning, motion planning in robotics, and AI applications in medicine. His work addresses the curse-of-dimensionality in optimal control, yielding breakthroughs in nonlinear systems and multi-agent coordination. Publications: Over 30 journal/conference papers, including IEEE Transactions and high-impact robotics conferences. Books include works on MPC-based mobile vehicle navigation and motion planning algorithms. Awards: Silver Plaque award, Microsoft Azure Research Award, DAAD Fellowship, and Golden Plaque for academic excellence. He has supervised over 40 master’s/PhD students, many advancing to prestigious institutions. Labs/Teams: Directs the Cooperative AI & Control Lab, collaborating with institutions like Imperial College London and University of Zagreb. Current projects span maritime robotics, blue economy applications, and computational neuroscience modeling.

Shantia YarahmadianView profile

Associate Professor

• Mathematical Biology
• Partial Differential Equations
• Alzheimer's Disease Modeling
• +2 more

Shantia Yarahmadian is an Associate Professor of Mathematics at Mississippi State University. He holds a Ph.D. in Applied Mathematics from Indiana University and conducts interdisciplinary research combining mathematics with biology and engineering. His primary research focuses on: Mathematical modeling of Alzheimer's disease progression Cancer treatment optimization for radiotherapy Stochastic processes in biological systems Wavelet-based signal processing methods Recent work includes computational models for amyloid-beta aggregation in Alzheimer's disease, optimization frameworks for radiation therapy, and novel wavelet algorithms for signal reconstruction. His laboratory develops mathematical tools to address complex biological problems and engineering challenges. Yarahmadian maintains active collaborations across engineering, computer science, and physics departments. His research portfolio includes stochastic modeling of neurodegenerative processes, quantum-resistant cryptography, and computational methods for partial differential equations.

Yuriy V. PershinView profile

Professor

• Condensed Matter Physics
• Emerging Memory Devices
• Unconventional Computing
• +3 more

Yuriy V. Pershin is a Professor in the Department of Physics and Astronomy at the McCausland College of Arts and Sciences, University of South Carolina. His research focuses on emerging memory devices (e.g., memristors, memcapacitors), unconventional computing paradigms, and 2D materials like graphene. He leads experimental and theoretical investigations into device fabrication, nonlinear dynamics, and nanoscale phenomena. Research Interests: Emerging Memory Devices: Designing memristive systems with memory retention capabilities, modeling their electrical behavior, and exploring their applications in low-power circuits. Unconventional Computing: Developing computing architectures that integrate memory and logic (e.g., neuromorphic networks), demonstrated through FPGA implementations and memristive neural networks. 2D Materials: Studying graphene kinks and antikinks as nanoscale motion carriers, with applications in nanoelectromechanical systems (NEMS). Key contributions include defining rigorous tests for ideal memristors, optimizing Joule-loss reduction in memristive systems, and proposing hardware implementations of memcomputing. His work bridges fundamental physics with applied engineering, leveraging tools like SPICE modeling and molecular dynamics simulations. Publications emphasize theoretical rigor and experimental validation, often addressing controversies in memristor characterization. Notable themes include noise-induced chaos in memcomputing, synchronization in memristive networks, and graphene-based electromechanical systems. Awards and Grants: No specific awards listed, though his work reflects sustained research funding in nanotechnology and device physics. Collaborations span academia and industry, focusing on practical applications of novel materials and circuits. Labs/Teams: His laboratory focuses on interdisciplinary projects at the intersection of physics, electronics, and materials science, with ongoing efforts in device fabrication, circuit emulation, and theoretical modeling.

Maria Herrojo RuizView profile

Researcher

• Computational Neuroscience
• Motor Learning
• Anxiety
• +1 more

Maria Herrojo Ruiz is a Reader in Psychology at Goldsmiths, University of London and co-director of the MSc in Computational Cognitive Neuroscience. Her research employs computational approaches to study motor learning, decision-making, and performance anxiety, with applications in neurological and psychiatric conditions. She uses EEG, MEG, and deep brain stimulation methodologies. Research focuses include: Neural oscillations in reward-based learning Effects of anxiety on predictive processing Interoceptive contributions to motor skill acquisition Awards include the Susanne Klein-Vogelbach Prize for human movement research (2014).

Arti AhluwaliaView profile

Professor

• In-vitro models
• Biomaterials
• Tissue Engineering
• +3 more

Arti Ahluwalia is Professor of Bioengineering at the University of Pisa's School of Engineering, affiliated with the Research Center 'E. Piaggio' and serving as Director of the Interuniversity Center for the Promotion of the 3Rs Principles in Teaching and Research (Centro 3R). With a multidisciplinary background spanning physics, instrumentation science, and bioengineering, her research integrates biomaterials, microfabrication, and computational modeling. Research focuses on human-based organ and system models using soft materials, cell derivatives, and intelligent bioreactor design. Current projects include: in-vitro models of metabolic disease/aging, nanotoxicology, advanced imaging, and open biomedical engineering education. Her work has produced commercialized multicompartment bioreactors through spin-off companies Kirkstall Ltd and IVTech srl. Recent publications demonstrate strong emphasis on open-source medical devices, 3Rs principles implementation, and computational platforms for nanotoxicology and cellular construct design. Key themes include regulatory frameworks for open-source medical technology, diagnostic tools for resource-limited settings, and ethical alternatives to animal testing. Teaching encompasses Biomechanics of Tissues, Micro & Nanobioscopy, and Design Principles for Bionics Engineering across undergraduate and graduate biomedical engineering programs.

• Computer Vision
• Biological Vision
• Machine Learning
• +5 more

Heiko Neumann is a Professor at the University of Ulm heading the Vision and Perception Science Group, where his research investigates visual information processing mechanisms in biological and technical systems with emphasis on adaptation to dynamic environments. His work bridges neuroscience and computational approaches to advance understanding of biological vision while developing novel computational vision systems for real-world applications. Neumann's primary research spans computer vision, biological vision, machine learning, and neuromorphic engineering. He focuses on how visual systems process information and learn from changing environments, with applications in perceptual human-computer interfaces, biometric systems, automotive technologies, medical image analysis, and visually-guided robotics. His neural modeling provides insights into brain functions for visual sensory processing and behaviorally relevant tasks, driving innovations in computational vision mechanisms. Analysis of his 2023-2025 publications reveals a strong trend toward thalamo-cortical models for visual binding, deep learning for shape/motion understanding, and multimodal integration (visual-auditory). His research increasingly applies neural modeling to robotics and human-computer interaction, with growing emphasis on neuromorphic computing architectures and event-based vision systems. The work spans theoretical neuroscience and practical applications in medical imaging and autonomous systems, showing consistent integration of biological principles into artificial vision solutions. Neumann's research receives significant funding from the European Union, Bundesministerium für Bildung und Forschung (BMBF), and Deutsche Forschungsgemeinschaft (DFG). While specific advisees aren't listed in available materials, he leads an active research group with current and previous members as indicated on his institutional webpage (currently inaccessible due to 404/401 errors). He directs the Vision and Perception Science Group at the University of Ulm, conducting interdisciplinary research at the neuroscience-computer vision intersection. The group develops neural models to understand biological vision mechanisms and applies these insights to create advanced computational vision systems for applications including medical image analysis, automotive technologies, and robotics, with recent work emphasizing neuromorphic architectures and real-time visual processing.

• Renewable Energy Systems
• Nanomaterials
• MEMS Technology
• +5 more

Diana Borca Tasciuc is a Professor in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). She holds a B.S. in Physics from Bucharest University (1996), and M.S. and Ph.D. in Mechanical Engineering from UCLA (2001, 2005). She joined RPI as an Assistant Professor in 2006 and transitioned to her current position. Her research spans renewable energy systems, nanomaterials, MEMS technology, and biomedical engineering. Education: B.S. Physics, University of Bucharest (1996) M.S. Mechanical Engineering, UCLA (2001) Ph.D. Mechanical Engineering, UCLA (2005) Her work focuses on thermal energy harvesting, luminescent solar concentrators, and nanoscale thermal transport. Recent research includes phase change material-based ocean thermal energy systems and vibration-based energy harvesting for wireless sensors. She has pioneered advancements in asymmetric light transmission interfaces and nanostructured surfaces for energy efficiency. Her publications highlight interdisciplinary efforts in solar technology, microsystems, and biomedical applications. She has contributed to standardizing annual energy production metrics for building-integrated photovoltaics and developed open-source simulation tools for luminescent solar concentrators.

Gizem AcarView profile

Assistant Professor

• Nonlinear Dynamics
• Vibrations
• Fluid-Structure Interactions
• +7 more

Gizem Acar is an Assistant Professor in the Department of Mechanical Engineering at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She earned her B.S. and M.S. from Bogazici University (2010-2012) and a Ph.D. from Michigan State University (2017), followed by postdoctoral work at the University of Maryland (2017-2020). Her research focuses on nonlinear dynamics and vibrations in systems like metamaterials, wind turbines, granular media, and fluid-structure interactions, with applications in energy forecasting and robotic locomotion. Education: PhD (2017), Mechanical Engineering, Michigan State University MS (2012), Mechanical Engineering, Bogazici University BS (2010), Mechanical Engineering, Bogazici University Research Interests: Her work explores programmable metastructures, vortex-induced vibrations in beams, noise-driven dynamics in oscillator arrays, and machine learning for energy output predictions. She investigates wave propagation in solids, granular media dynamics, and multi-stable systems influenced by noise. Publications: Recent work includes studies on cylindrical beams under combined excitations, AI-based renewable energy modeling, and noise-induced transitions in coupled oscillators. Her research bridges theoretical analysis (e.g., Floquet theory) with experimental methods (e.g., discrete element modeling). Awards: Early Career Mentor Award (2025), Stevens Institute of Technology Consistent recognition for excellence in teaching (2021-2024) Service & Grants: Co-PI on a U.S. Army contract about energy supply chain resilience. She chairs institutional committees on vibrations and dynamics education and serves on NSF review panels. Active in organizing ASME conferences and reviewing for journals like Nonlinear Dynamics and Journal of Sound and Vibration . Labs & Teams: Leads the Nonlinear Dynamics and Vibrations Research team at Carnegie Lab 213, focusing on experimental and computational studies of complex mechanical systems.