Find Academic Supervisors

Alex HarkerView profile

Senior Lecturer

• Audio Programming
• Spectral Domain Audio Processing
• Convolution and Deconvolution
• +7 more

Alex Harker is a Senior Lecturer in Music and Music Technology at the University of Huddersfield, affiliated with the Department of Media, Humanities and the Arts and the School of Arts and Humanities. He serves as Director of the Creative Coding Lab and is part of the Centre for Research in New Music and Centre for Experimental Practices (CXP). His work bridges music composition, creative programming, and audio technology, with international recognition for software tools like the HISSTools Library and the Fluid Corpus Manipulation Toolbox. University: University of Huddersfield Academic Rank: Senior Lecturer Projects: FluCoMa (2017-2023), TAAT (2025) Harker’s research focuses on the intersection of computational methods and musical expression, including spectral processing, convolution techniques, and real-time audiovisual systems. His work aligns with UN Sustainable Development Goals related to innovation and cross-domain collaboration. He supervises PhD students and has contributed to conferences like the International Computer Music Conference and Sound and Music Computing Conference. Recent publications include software tools (HISSTools Library, Fluid Corpus Manipulation Toolbox), compositions (e.g., drift shadow ), and interactive installations (e.g., Tell It To The Trees ). His software solutions are used globally in academia and industry for applications ranging from psychoacoustics to audio DJing.

• Compact ultrafast X-ray source development
• Ultrafast X-ray scattering
• High-field nonlinear X-ray matter interaction
• +3 more

Matthias Fuchs is an Adjunct Professor in the Department of Physics and Astronomy at the University of Nebraska-Lincoln , where he leads the Group for Ultrafast and High-Field X-ray Science . His work leverages partnerships with UNL's Extreme Light Laboratory and its PetaWatt (PW) Diocles laser facility, one of only two such systems globally. Research Focus : Compact ultrafast X-ray sources, ultrafast X-ray scattering, and high-field nonlinear X-ray interactions with matter. Technical Collaborations : Pioneering table-top free-electron lasers (FELs), laser-wakefield acceleration, and coherent transition radiation (CTR) applications. Publications : 15+ peer-reviewed articles in journals like Nature Physics , with emphasis on X-ray photon absorption, second harmonic generation, and terahertz pulse engineering. Scientific Impact : His 2016 Nature Physics study on K-shell two-photon absorption was highlighted in Physik Journal as a cover illustration, demonstrating novel X-ray-matter interactions under extreme laser intensities. Laboratory Collaborations : Engaged with both the Extreme Light Laboratory and Diocles laser facility for experimental validation of ultrafast X-ray techniques.

Walter BinderView profile

Professor

• Performance Analysis
• Java Virtual Machine
• Parallel Computing
• +5 more

Walter Binder is a Professor at the University of Lugano, Switzerland, specializing in performance analysis and optimization of Java-based and parallel systems. He has actively contributed to academic committees in conferences such as GPCE, CGO, and ‹Programming›, focusing on virtual machine efficiency, compiler design, and benchmarking methodologies. His research centers on performance profiling tools for the Java Virtual Machine (JVM), including the development of the P3 profiler suite for parallel applications and the Renaissance benchmark suite. Key areas of interest include concurrency, synchronization, dynamic compilation, and multi-language program analysis, with applications in big data processing and stream computing frameworks. Recent publications highlight trends in compiler-level event profiling, AST interpreter optimization, and SQL-to-stream benchmark generation. These works span subfields like SIMD vectorization, task granularity analysis, and native-image startup performance, emphasizing platform independence and low overhead. Notable tools and methodologies developed by Binder have been instrumental in advancing JVM-based parallel computing research. His contributions extend to automated large-scale analysis of public code repositories and performance coaching for fork/join applications.

Daniel FodoreanView profile

Lecturer

• Electric Machine Design
• Control Systems
• Magnetic Field Computation
• +8 more

Daniel Fodorean is an academic at the Technical University of Cluj-Napoca, serving as a Lecturer in the Department of Electrical Machines, Marketing & Management. His research focuses on electric machine design, control systems, and magnetic field computation, with a particular emphasis on synchronous and hybrid excited machines for automotive applications. Education: Electrical Engineering (Diploma, 2001), M.Sc. in Variable Speed Electrical Drives (2002), Ph.D. in Double Excited Synchronous Machines (2005). Professional Experience: Assistant Lecturer at Technical University of Cluj-Napoca (2006–present); temporary teaching/research roles at Université de Technologie de Belfort-Montbéliard (2003–2004, 2005–2006). His work involves advanced control strategies (DTC-FOC, PWM), finite element analysis, and optimization techniques (gradient, surface response) for machines like permanent magnet and transverse flux motors. Key projects include double excited synchronous machine prototyping and thermal/mechanical design for hybrid systems. Recent publications address parameter optimization, vector control, and flux weakening in high-speed applications. Scientific Awards: Scholarships at Technological University of Belfort-Montbéliard (2001–2002, 2003). He contributes to international conferences such as ICEM, IEMDC, and COMPUMAG, with a focus on electromagnetic field computation and drive system efficiency. His expertise spans analytical modeling, numerical simulation (Flux2D/3D), and experimental validation of motor performance.

• Electrical Machines
• Energy Storage Systems
• Power Electronics
• +3 more

Dr. Nan Zhao serves as Senior Lecturer in Electrical Engineering at Lancaster University's School of Engineering, UK, since 2022. Previously, he held positions as Assistant Professor at University College Dublin (2018-2022) and Sessional Lecturer at McMaster University (2017-2018), where he earned his PhD in Electrical Engineering in 2017. His research spans electrical power engineering with core focus areas including energy storage systems , renewable integration (solar, wind, wave), electric machines , and EV powertrain systems . Current projects involve AI-driven ocean energy arrays and high-performance wave energy conversion systems. His publication record demonstrates deep engagement with power system stability challenges in renewable-dense grids, particularly through virtual synchronous generators and transportable storage solutions. Key editorial activities include contributions to Electronics Letters and IET Renewable Power Generation . His research group actively recruits PhD students and postdocs, with Renqi Guo currently listed as a PhD student. Primary research collaborations occur through the Energy TALOS group. Notable scientific contributions include: Hybrid synchronous condenser-virtual generator systems for microgrid stability (2025) Statistical deadband estimation for transportable energy storage (2025) Novel wave energy conversion grid integration techniques (2024) Dr. Zhao maintains active industry engagement through research on practical grid integration challenges, with recent work addressing frequency response services, curtailment reduction, and cyber-physical grid resilience. His lab focuses on translating theoretical power electronics advances into deployable renewable energy solutions.

Yan HuangView profile

Researcher

• Distributed Optimization
• Federated Optimization
• Large-Scale Machine Learning
• +5 more

Yan Huang (黄炎) is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems. He earned his Ph.D. in Control Science and Control Engineering from Zhejiang University (2020-2024) under Prof. Jinming Xu and Prof. Jiming Chen, with prior degrees from Southeast University and Anhui University. His work focuses on distributed optimization and machine learning. Ph.D. in Control Science and Control Engineering, Zhejiang University, 2020-2024 M.S. in Control Science and Control Engineering, Southeast University, 2017-2020 B.S. in Automation, Anhui University, 2013-2017 Yan Huang specializes in distributed and federated optimization theory , applying these techniques to large-scale machine learning and networked control systems . His research addresses data heterogeneity, robustness to failures, and computational efficiency in decentralized networks. He has developed flexible frameworks for gradient tracking and personalized optimization. His recent publications emphasize stochastic gradient tracking , variance reduction , and differential privacy in distributed settings. Key topics include adaptive stepsizes , Byzantine-robust algorithms , and computation-communication trade-offs , reflecting his focus on scalable and secure optimization methods. Outstanding Graduate, Zhejiang University, 2024 Outstanding Doctoral Student, Zhejiang University, 2022 and 2023 2nd prize in China Post-graduate Mathematical Contest in Modeling, 2017 Outstanding Graduate, Anhui University, 2017 Huang has taught courses on Principles of Automatic Control , Modern Control Theory , and Distributed Control and Optimization . He has also presented at ICML 2022 and Zhejiang University's academic forum, highlighting his work on decentralized SGD and data heterogeneity.

John R. LangeView profile

Associate Professor

• High Performance Computing
• Operating Systems
• Virtualization
• +3 more

John R. Lange is an Associate Professor in the Computer Science Department at the University of Pittsburgh's School of Computing and Information. Currently on leave at Oak Ridge National Laboratory, he has established himself as a leading researcher in high-performance computing and systems software. His work bridges academic research and practical supercomputing applications, with extensive collaborations across national laboratories and research institutions. PhD in Computer Science from Northwestern University (2006) M.S. in Computer Science from Northwestern University (2010) B.S. in Computer Engineering from Northwestern University (2003) B.S. in Computer Science from Northwestern University (2003) Dr. Lange's research centers on High Performance Computing systems, with particular expertise in operating systems for supercomputing environments, virtualization technologies, and network optimization. His work explores how specialized operating systems can enhance performance in large-scale computing infrastructures, addressing challenges in resource management, fault tolerance, and system composition. He investigates the intersection of traditional HPC architectures with emerging virtualization techniques to create more flexible and efficient computing environments. His publication record demonstrates a consistent focus on advancing the state of the art in high-performance systems, with recent work emphasizing exascale computing challenges, network optimization for leadership-class supercomputers, and innovative approaches to system composition. His research shows an evolution from foundational virtualization techniques toward more sophisticated solutions for next-generation supercomputing architectures, with increasing emphasis on practical implementations in real-world leadership computing facilities. Best Paper Finalist at SC 2024 for network cost optimization research Best Paper Finalist at SC 2023 for Frontier exascale supercomputer architecture Best Paper Nominee at HPDC 2012 for VNET/P overlay networking As an educator, Dr. Lange has taught numerous systems courses including Introduction to Systems Software (CS 0449), Computer and Network Security (CS 1652), and Operating Systems (CS 2510 and CS 3510). His teaching spans both undergraduate and graduate levels, focusing on practical systems knowledge with hands-on projects. His research has been supported by collaborations with Oak Ridge National Laboratory and other national computing facilities, enabling his work to directly impact real-world supercomputing infrastructure. Dr. Lange is actively involved with The Prognostic Lab and maintains strong connections with the Oak Ridge Leadership Computing Facility (OLCF), where his current leave appointment allows him to work at the forefront of exascale computing research and development.

Jaime Renedo AngladaView profile

Research Fellow

• Electrical Engineering
• Mechatronics
• Electric Machines
• +3 more

Jaime Renedo Anglada serves as a Visiting Fellow within the Mechatronics Engineering Group at the University of Southampton's Faculty of Engineering and Physical Sciences, while concurrently holding a position at CERN's Magnets, Superconductors and Cryostats (MSC) group since March 2017, where he designs and maintains normal conducting magnets for particle accelerators. His academic credentials include: M.Sc. in Electrical Engineering from Comillas Pontifical University, Madrid, Spain (2013) Ph.D. from the University of Southampton, UK (2017) focused on electric machines for renewable energy systems, electric vehicles, and marine thrusters His research centers on permanent magnet machine modeling , electromagnetic field theory , and energy conversion , with critical applications in renewable energy integration , power systems stability , and electric transportation . His doctoral work examined large synchronous turbo-generators for power system stability analysis during his tenure at Comillas' Institute for Research in Technology. At Southampton, he has taught Electrical and Electronics Systems (FEEG1004), Advanced Electrical Systems (SESM6034), and Mechanical Science (GENG0003), supported by EPSRC and TSL Technology Ltd funding. His professional engagement includes active membership in Cigre UK and the IEEE Power and Energy Society. He contributes to the Mechatronics Engineering Group's research on magnetic gear systems while advancing accelerator magnet technology at CERN's Technology Department.

Marcel HeertjesView profile

Professor

• Nonlinear Control
• High-Precision Mechatronic Systems
• Industrial Control
• +4 more

Marcel Heertjes is a Full Professor (Part-time) in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e). His primary professional affiliation is with ASML where he serves as principal engineer and control competence leader. He is affiliated with multiple research groups including Control Systems Technology, Dynamics and Control, and Group Van de Wouw. Professor Heertjes specializes in industrial control for high-precision mechatronic systems with particular emphasis on nonlinear control , feedforward and learning control , and data-driven optimization and self-tuning . His research aims to break free from linear control design limitations through robust nonlinear control design and stability analysis. The high-precision motion industry, particularly wafer scanning technology, is largely constrained by linear control design which imposes limitations on bandwidths, overshoot, and settling times. His work provides extra design freedom to overcome these limitations and enable future control system performances needed for advanced high-precision mechatronics. His recent publications demonstrate significant advancements in nonlinear integrators, projection-based integrators, spatio-temporal modeling, and model-free control techniques for precision motion systems. These contributions directly address challenges in wafer scanners and other high-precision manufacturing equipment, showing how nonlinear approaches can surpass traditional linear control limitations. He has served as guest editor of the International Journal of Robust and Nonlinear Control (2011) and IFAC Mechatronics (2014), and currently serves as associate editor of IFAC Mechatronics (since 2016). Marcel Heertjes received both his MSc and PhD in mechanical engineering from Eindhoven University of Technology in 1995 and 1999. After joining Philips Centre for Industrial Technology in 2000, he moved to ASML in 2007 where he continues to work while maintaining his academic appointment at TU/e.

Daniel DelahayeView profile

Professor

• Stochastic Optimization
• Trajectory Optimization
• Dynamic Programming
• +5 more

Daniel Delahaye is a Professor at École Nationale de l'Aviation Civile (ENAC), France, affiliated with the OPTIM research team. His work focuses on applying stochastic optimization, dynamic programming, and reinforcement learning to air traffic management and transportation systems. Key research themes: Trajectory optimization, Airspace design, CO2/contrail mitigation, UAV navigation, and multi-modal transportation Recent publications analyze climate-aware air traffic flow management , UAV trajectory prediction , emergency landing planning , and multi-hub airline scheduling . Methodologies include column generation, convolutional neural networks, and multi-objective optimization frameworks. Applications span sustainable air operations, terminal airspace efficiency, and future urban air mobility systems

Bas J.D. VermulstView profile

Assistant Professor

• Power Electronics
• Automotive Electronic Systems
• Medical Imaging Systems
• +2 more

Dr. Bas J.D. Vermulst is an Assistant Professor at the Eindhoven University of Technology (TU/e) in the Electromechanics and Power Electronics group of the Department of Electrical Engineering. He is also affiliated with the High Tech Systems Center and the Power Electronics Lab, focusing on high-performance, high-precision power electronics systems. His research spans automotive electronics, medical imaging, semiconductor lithography, and renewable energy systems. Key technical interests include power converters, switching frequencies, and thermal stress management. Recent work emphasizes gallium nitride (GaN) transistors for ultrafast switching and nanosecond pulse generation. Publications & Research Trends His publications address bidirectional converters, GaN/SiC transistor comparisons, and optimized control schemes for multi-port systems. Common themes include efficiency improvements, thermal modeling, and applications in plasma reactors and photovoltaics. Awards Best Session Presentation Award (2012) Best Session Presentation Award (2014) Technical Session Winner (2016) Education BSc, MSc, and PhD in Electrical Engineering from TU/e (2010, 2011, 2016)

• AI Algorithms
• Neuromorphic Computing
• Low-Power Electronics
• +7 more

Kaushik Roy is the Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. He leads research in AI algorithms, neuromorphic computing, low-power electronics, and design-technology co-design. His work bridges hardware innovation with advanced computational models. Education: B.Tech, Indian Institute of Technology, Kharagpur (1983) Ph.D., University of Illinois at Urbana-Champaign (1990) Research Focus: Dr. Roy's interests span neuromorphic systems, energy-efficient computing architectures, and AI hardware. Key areas include neuromorphic sensors, spiking neural networks, and ADC-less in-memory computing. His team explores applications in robotics, autonomous systems, and privacy-aware machine learning. Publications Trends: Recent work emphasizes neuromorphic navigation, unlearning algorithms, and hardware-software co-design for transformers. His research often intersects edge AI, security-aware computing, and bio-inspired systems. Awards & Labs: While no specific awards are listed, his leadership in founding the Neuromorphic Research Lab (NRL) at Purdue highlights his impact. The NRL focuses on next-generation computing paradigms. Grants & Collaborations: His projects likely involve multi-disciplinary grants given his work on neuromorphic robotics and secure AI systems. Collaborations span academia and industry, driven by Purdue's engineering partnerships.

Gord FishellView profile

Professor

• Neurobiology
• Developmental Neuroscience
• Neural Circuitry
• +5 more

Gord Fishell, Ph.D., is a Professor of Neurobiology at Harvard Medical School. His research focuses on the developmental mechanisms underlying inhibitory interneuron diversity and their integration into neural circuits, with implications for autism spectrum disorder, schizophrenia, and intellectual disability. His laboratory has pioneered tools like enhancer-AAV viral vectors for targeting interneurons across species, enabling precise manipulation of cortical circuits. Research Themes: Interneuron Specification: Genetic programs governing interneuron subtype generation during embryonic development. Circuit Integration: Local cues guiding interneurons into cortical and subcortical circuits. Pathophysiological Links: Investigating how interneuron dysfunction contributes to neurological disorders. Technological Innovation: Developing viral tools for cell-type-specific manipulation in mammals and humans. Key Contributions: The lab's work on somatostatin (SST) and parvalbumin (PV) interneurons has revealed critical roles in circuit maturation and sensory processing. Their viral toolkit, developed with Paola Arlotta, enables targeted studies of diverse cortical cell types. Future Directions: Translating developmental insights into therapies for neurodevelopmental disorders, particularly through interneuron-based interventions.

• Power Electronics
• Solid-State Transformers
• Medium Voltage Applications
• +7 more

Andrea Cervone is a Professor at KU Leuven, specializing in power electronics and electrical engineering with a focus on solid-state transformers and power conversion systems. Based at the Thor Park 8310 campus in Genk, Belgium, Cervone conducts research at the intersection of medium voltage applications and power system integration. The research group is actively involved in multiple projects including the ADVANCED SOLID-STATE ELECTRONIC TRANSFORMER (ASSET) initiative and the Solid-State Transformers for Medium Voltage Applications PhD project supervised for Marina Vitali. Dr. Cervone's research interests span power electronics, solid-state transformers, medium voltage applications, electrical power systems, power converters, grid integration, electric drives, multiphase machines, DC-DC converters, and renewable energy systems. The work emphasizes practical applications in power distribution networks, with particular attention to harmonic analysis, stability considerations, and efficiency optimization in modern power conversion systems. Current research demonstrates strong focus on addressing challenges in grid integration of renewable energy sources and improving power quality in hybrid AC-DC networks. The publication record shows a clear trend toward solid-state transformer technology, with numerous papers on input-series-output-parallel configurations, harmonic compensation techniques, and stability analysis. Recent work has expanded into grid impedance estimation, DC data center power supplies, and modulation strategies for dual-port converters. The research consistently addresses practical engineering challenges in power conversion while maintaining strong theoretical foundations in control systems and power electronics. As an academic supervisor, Dr. Cervone currently mentors PhD student Marina Vitali on solid-state transformers for medium voltage applications. The research group maintains strong industry connections through collaborative projects focused on practical power electronics applications, particularly in the energy sector. The group's work spans both theoretical analysis and experimental validation of novel power conversion topologies.

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.