Find Academic Supervisors

• Harmonic Analysis
• Applied Mathematics
• Machine Learning
• +4 more

Christina Frederick is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology. Her research spans computational mathematics, harmonic analysis, and machine learning applications in robotics and ocean acoustics. Active in time-frequency analysis and multiscale inverse problems Principal Investigator on NSF and Navy-funded projects Collaborates on seabed classification, intermittent diffusion algorithms, and sensor deployment Current work focuses on computational methods for autonomous systems and mathematical foundations of Riesz bases. She has contributed to robotics path planning and sonar imaging techniques. Her recent publications (2022-2024) demonstrate interdisciplinary integration of harmonic analysis with machine learning for applications in seabed classification, mobile sensor deployment, and robotics coordination. Key trends include time-frequency analysis, computational methods for autonomous systems, and Gaussian processes in acoustics. NSF Grant: Applied and computational time-frequency analysis (2023-2026) Navy Grant: Sensor deployment algorithms (2021-present) NSF Grant: Multiscale inverse problems in sonar imaging (2017-2020) Frederick's work bridges theoretical mathematics with practical engineering challenges, particularly in sensor network optimization and acoustic signal processing.

Abhijit PatwardhanView profile

Professor

• Cardiovascular Control
• Signal Processing
• Neuroscience
• +3 more

Abhijit Patwardhan, Ph.D., is a Professor and Department Chair in the F. Joseph Halcomb III, M.D. Department of Biomedical Engineering at the University of Kentucky. He holds a Ph.D. in Biomedical Engineering from the University of Kentucky (1992) and a B.E. in Mechanical Engineering from the College of Engineering, Pune, India (1984). His research focuses on cardiovascular control mechanisms, signal processing in medical diagnostics, and the physiological impacts of music and hypoxemia on neonatal and pediatric populations. He has held leadership roles, including Director of Graduate Studies (2004–2019) and Interim Chair (2015–2017), and has contributed to interdisciplinary projects in artificial gravity training and cardiovascular autonomic responses to particulate exposures. Dr. Patwardhan’s work spans clinical and computational domains, with notable contributions to understanding intermittent hypoxemia in preterm infants, neural-cardiovascular interactions during music listening, and the dynamics of Toxoplasma gondii infection. His Cardiac Rhythm Lab employs advanced signal processing techniques and machine learning for medical imaging analysis and disease modeling. He has collaborated on studies involving centrifuge training for orthostatic tolerance and the development of non-invasive monitoring systems using video-derived physiological signals. His professional activities include service to the Stanley and Karen Pigman College of Engineering and editorial roles in biomedical journals. Though no specific awards are listed, his extensive publication record reflects sustained excellence in cardiovascular and biomedical engineering research. Ongoing projects likely include translational studies on neonatal cardiorespiratory physiology and computational tools for pathogen lifecycle analysis.

Leonid RubchinskyView profile

Associate Professor

• Computational Neuroscience
• Mathematical Biology
• Mathematical & Computational Neuroscience
• +3 more

Leonid Rubchinsky is an Associate Professor in the Department of Mathematical Sciences at Indiana University–Purdue University Indianapolis (IUPUI), affiliated with the Stark Neurosciences Research Institute, Indiana University School of Medicine. His research focuses on computational neuroscience, mathematical biology, and dynamical systems applied to understanding basal ganglia function in health and Parkinson's disease. He holds academic appointments since 2004, including postdoctoral research at UC Davis (2001–2004). Education: Ph.D. in Physics (2000, Russian Academy of Sciences), M.S. in Physics (1997, UC San Diego), B.S. in Physics (1995, University of Nizhny Novgorod). Research interests include neural oscillations, synchronization mechanisms, and pathophysiological dynamics in Parkinson's disease. His work bridges mathematical modeling and experimental neuroscience to elucidate basal ganglia operations and therapeutic strategies. Publications (2008–2013) emphasize beta-band synchronization, deep brain stimulation effects, and theoretical frameworks for tremor and neural network dysfunction. Awards include IUPUI Favorite Professor recognition (2006, 2010) and international grants. Professional roles include panelist for NSF/NIH computational neuroscience grants and membership in the Organization for Computational Neuroscience Program Committee. He has organized student competitions at annual neuroscience meetings.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +4 more

Ning Zhang is a researcher affiliated with Tsinghua University , Department of Electronic Engineering, focusing on interdisciplinary applications of computer science and artificial intelligence in domains such as medical imaging, fault diagnosis, and multimodal systems. Their work bridges theoretical advancements with practical implementations in engineering, agriculture, and finance. Research Interests : Machine learning, signal processing, graph neural networks, and neuromorphic computing. Key Contributions : Recent publications highlight innovations in event-based tracking, digital twin systems, and reinforcement learning integration with vision-language models. Technological Focus : Applications include fault diagnosis in rotating machinery, medical image segmentation (CT-Net), and AI-driven urban risk assessment. The 2024-2025 publication trends reveal a strong emphasis on deep learning for remote sensing, reinforcement learning security, and stochastic control systems. Collaborations span institutions like the University of Manchester, Peking University, and Washington University in St. Louis.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Complexity Science
• Non-Equilibrium Systems
• Theoretical Physics
• +3 more

Professor Kim Christensen is a Professor of Theoretical Physics at the Department of Physics within the Faculty of Natural Sciences at Imperial College London. His research focuses on theoretical and numerical aspects of complex systems, particularly the spontaneous emergence of patterns and organization in non-equilibrium systems. He holds affiliations with the Centre for Cardiac Engineering, Centre for Complexity Science, Condensed Matter Theory Group, and other interdisciplinary initiatives. Christensen earned a Ph.D. in Science (1993) and a Master of Science (1990) from the University of Århus, Denmark. His work integrates theoretical, computational, and experimental studies across disciplines including geology, atmospheric physics, biology, sociology, medicine, and fire safety. A key contribution is the textbook Complexity and Criticality (2005), co-authored with Nicholas Moloney, which is widely used globally. His research interests emphasize interdisciplinary complexity science, with a focus on applying transferable tools to real-world systems. Recent work includes studies on atmospheric rivers, cardiac electrophysiology modeling, quantum networks, and venture capital network dynamics. He has over 8,150 citations (Google Scholar, June 2025), reflecting his impactful contributions to identifying universal research questions and knowledge transfer. Labs/Teams: Condensed Matter Theory Group, Physics of Life, ElectroCardioMaths Programme Grants/Awards: Not explicitly listed but implied by citation impact and textbook影响力

• Hardware Security
• Machine Learning Acceleration
• Hardware-Software Co-design
• +3 more

Sai Manoj P D is Assistant Professor in the Electrical and Computer Engineering Department at George Mason University, where he leads the Hardware and AI Research Team (HArt Lab). His research focuses on hardware security, machine learning acceleration, and hardware-software co-design for emerging computing platforms. Current projects investigate federated learning approaches for malware detection on heterogeneous devices, energy-efficient computing architectures for intermittent power sources, and hardware acceleration of machine learning algorithms. The HArt Lab develops novel architectures for secure and efficient computing across edge devices, embedded systems, and data centers. Research Sponsors: National Science Foundation (NSF) Virginia Commonwealth Cyber Initiative (CCI) DARPA

Oliver SchneiderView profile

Associate Professor

• Haptic Technology
• User Experience (UX) Design
• Digital Fabrication
• +5 more

Oliver Schneider is an Associate Professor at the University of Waterloo's Department of Information Systems, specializing in haptic technology, human-computer interaction, and sustainable digital fabrication. His research focuses on advancing haptic experience design, accessibility in technology, and innovative approaches to 3D printing sustainability. Key contributions include the Haptic Experience Inventory (HXI), the Proteus Card Deck for remote play, and the Substiports system for sustainable 3D printing. His work bridges engineering and psychology, addressing challenges in user perception, inclusive design, and multimodal interaction. Notable projects include the Haptic Playground (empowering accessible haptic prototyping) and TactJam (collaborative vibrotactile design). He explores intersections between haptics and robotics, such as multi-robot supervision systems and blind-user virtual world interfaces. Research interests span Human perception in haptic systems Design methodologies for tangible interfaces Sustainable manufacturing techniques Accessibility in virtual and augmented reality Recent publications analyze intermittent interaction in fabrication workflows, unified haptic experience models, and user-defined gestures in virtual environments. Articles emphasize practical applications like assistive devices and eco-friendly fabrication.

Theodosis DimitrakosView profile

Associate Professor

• Applied Probability
• Stochastic Operations Research
• Optimal Control
• +5 more

Theodosis Dimitrakos is an Associate Professor at the Department of Mathematics, University of the Aegean, specializing in Applied Probability and Stochastic Operations Research . His work focuses on optimal control of stochastic systems across diverse domains. University of the Aegean School of Sciences Department of Mathematics His research spans epidemic processes , biological populations , preventive maintenance , vehicle routing , and medical emergency systems . Publications emphasize semi-Markov decision processes and dynamic programming . Recent articles analyze stochastic vehicle routing, maintenance optimization, and predator-prey dynamics. Key trends include stochastic demand modeling , penalty systems , and multi-compartment logistics . Teaching courses: Undergraduate and Postgraduate Statistics , Probability I , and Stochastic Modeling . Author of the textbook Probability, Statistics and Stochastic Models (Eudoxos code: 143557808).

Maria KennedyView profile

Professor

• Membrane Technology
• Water Treatment
• Desalination
• +5 more

Professor Maria Kennedy is an academic specializing in membrane technology and water treatment with 11 years of research experience. Her work focuses on membrane fouling, scaling, and cleaning, particularly in desalination applications. She leads international projects in Israel (West Bank) and Jordan regarding brackish/sea water desalination. Her research innovates in hydrothermal carbonization of biomass and optimization of reverse osmosis systems. Key research areas include: Membrane-based water treatment technologies Desalination plant optimization Water-Energy Nexus solutions Environmental sustainability of water infrastructure Recent work emphasizes techno-economic analysis of PV-integrated desalination systems for low-income regions, and development of advanced pretreatment methods using hydrothermal carbonization. Her studies on hydraulic systems and water distribution equity address global water scarcity challenges through both technical and social equity lenses. Publications highlight contributions to: Algal bloom mitigation in SWRO systems Bacterial growth potential measurement Smart dosing algorithms for antiscalants Nonrevenue water assessment methodologies No academic awards are explicitly listed in the provided texts. She collaborates on international projects but no specific grants/teams are detailed.

Metin HünerView profile

Assistant Professor

• Neuroscience
• High Power Ultrasonic Applications
• Electromagnetic Compatibility
• +3 more

Metin Hüner is an Assistant Professor at the Department of Electronics and Communication Engineering, Istanbul Technical University (ITU). He holds a PhD in Physical Engineering from ITU (1998), MSc in Physics Engineering (1993), and BSc in Electronics and Communications Engineering (1991). His research focuses on Neuroscience (neuronal dynamics, ion channel conductivity) and High Power Ultrasonic Applications (ultrasonic cleaning systems, emulsion systems design). He has authored/co-authored over 10 publications and supervised 5 theses. Teaching includes graduate courses like Mathematical Methods in Electrical Engineering (ELE 519) and undergraduate courses like Basics of Electrical Circuits (EHB 211E). Professional experience includes roles as Quality Manager at 2M Cable (2003-2006) and Quality/Test Engineer at Teleco Cable (2000-2003). Research highlights include investigations into ultrasonic pressure distribution, automotive EMC simulation, and neuronal bifurcation analysis. Lab involvement includes projects on ultrasonic emulsion systems design and acceleration logger development. His work bridges theoretical physics (superconductivity, nonlinear dynamics) with applied engineering (acoustic systems, biomedical instrumentation).

• Low carbon energy systems
• System flexibility
• Demand side flexibility
• +5 more

Dr. Phil Grunewald is a Supernumerary Research Fellow at Oriel College and Research Director of the Energy Demand Observatory and Laboratory (EDOL) at the University of Oxford's Department of Engineering Science. He also serves as Oxford Principal Investigator for the JED-AI project. Previously, he led the Oxford Energy Network and the Flexibility theme at the Environmental Change Institute (2013-2020). Education PhD from Imperial College London on grid storage economics, supported by a UKERC scholarship. Research Focus Grunewald employs interdisciplinary approaches to analyze energy demand flexibility using smart meter data, machine learning, and socio-technical variables. Core research themes include: Low-carbon energy transitions Demand response and system flexibility Machine learning applications for household energy clustering Longitudinal analysis of crisis impacts on consumption Equity in energy system redesign Publications Recent articles (2019-2024) demonstrate strong focus on: 1) Data-driven household energy pattern analysis using machine learning, 2) Crisis resilience and emissions impacts of demand flexibility, 3) Socio-technical barriers to energy transitions, and 4) Methodological innovations in activity-based energy modeling. Recurring themes include temporal consumption variability, policy equity, and decarbonization trade-offs. Awards EPSRC Fellowship (2015-2021) Marie Curie Fellowship Fellow of the Institution of Civil Engineers (FICE) Projects & Grants Principal Investigator for: EDOL (EPSRC): Longitudinal household energy-observatory ReNEW (Oxford Martin): Energy equity and wellbeing Shift-0 (MCS Foundation): Heat-pump adoption impacts METER study : Machine learning analysis of UK household diaries/meter data Teams & Infrastructure Leads energy demand research group developing specialized tools for activity recording and meter data analysis. Maintains UK Household Energy Use database.

David CampbellView profile

Professor

• Condensed Matter Physics
• Nonlinear Dynamics
• Quantum Many-Body Systems
• +3 more

David Campbell is a Professor of Physics, Electrical and Computer Engineering (ECE), and Materials Science and Engineering (MSE) at Boston University, and serves as Director of Graduate Studies. He holds a Ph.D. from Cambridge University (1970). His research focuses on correlated electronic systems, nonlinear excitations, and non-equilibrium phenomena, including studies of solitons in solids, Bose-Einstein Condensates, and the Fermi-Pasta-Ulam-Tsingou (FPUT) problem. He is renowned for developing functional Renormalization Group (fRG) methods and investigating metastable states in FPUT systems. Key awards include the Julius Edgar Lilienfeld Prize (2010), Gauss Professorship (2014-15), and SigmaPhi Prize (2020). His work spans theoretical physics, quantum many-body systems, and materials science, with contributions to superconductivity, nonlinear dynamics, and precision metrology. Recent articles address metastable FPUT states, Casimir-effect metrology, and functional RG studies of Hubbard models. He collaborates globally on interdisciplinary projects, including machine learning in materials design and MEMS device engineering.

Marco ZimmerlingView profile

Professor

• Cyber-Physical Systems
• Wireless Embedded Systems
• Battery-Free IoT
• +1 more

Marco Zimmerling is a Full Professor in the Department of Computer Science at TU Darmstadt, specializing in cyber-physical systems with focus on wireless embedded systems. His research aims to design dependable, adaptive, and sustainable systems for the battery-free Internet of Things. Education: Ph.D. in Computer Engineering from ETH Zurich (2015), Diploma in Computer Science from TU Dresden. Research interests include battery-free IoT, wireless networked systems, concurrent transmissions, and dependable cyber-physical systems. His work bridges theoretical foundations with practical implementations in sustainable computing. Recent publications focus on energy-harvesting testbeds (Shepherd Nova), open-source platforms for battery-free devices (Riotee), and fault-tolerant networking. Key themes include energy efficiency, wireless protocols, and system reliability under constrained conditions. Honors include ACM SIGBED Early Career Researcher Award (2022), Test-of-Time Award (SenSys 2022), Best Paper Awards (EWSN 2022, ICCPS 2019), and DFG Emmy Noether Grant (2018). Leads research on sustainable IoT systems and wireless control networks, with projects spanning testbed development, protocol design, and real-world deployments.

Frank R. Rice, IIIView profile

Lecturer

• Physics

Frank R. Rice, III is a Lecturer in Physics at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics and Astronomy. He has held this position intermittently since 2001, with continuous tenure from 2019–2025. His research and teaching focus on physics, leveraging his academic background and professional experience. Education: B.A., Northwestern University, 1977 M.S., Naval Postgraduate School, 1986 M.S., Caltech, 1999 Ph.D., Caltech, 2020 His professional activities include managing course materials and research projects, as evidenced by directories like Adv_Lab, CsoMove, CurveFit, and others. These likely relate to experimental physics, computational tools, and educational resources. No specific awards or articles are listed in the provided texts, but his roles suggest contributions to teaching and research infrastructure.