Find Academic Supervisors

• Power Systems
• Smart Grids
• Distribution System State Estimation
• +4 more

Dr. Barry Hayes is a Senior Lecturer (Associate Professor) in Power Systems Engineering at University College Cork (UCC), Ireland. He leads a research team focusing on grid integration of sustainable energy technologies and future power system operation/planning. Previously held roles include Lecturer at University of Galway (2016-2018) and Marie Sklodowska-Curie Research Fellow at IMDEA Energy (2013-2016). Holds a PhD in Electrical Power Systems Engineering from University of Edinburgh (2013). Research interests include smart grids, distribution network management, demand-side flexibility, non-intrusive load monitoring, and energy communities. Active in IEEE standards development (P2030 smart grid interoperability) and serves as Associate Editor for IEEE Transactions on Energy Markets, Policy and Regulation. Has secured €1.03M SFI grant (2024-2029) for disruptive energy tech research, plus other grants including €506K from Disruptive Technologies Innovation Fund. Supervised PhD students in power systems and energy markets. Collaborates with Irish energy industry and international partners through MaREI Centre. Publications emphasize grid integration challenges, P2P energy trading, and smart meter applications. Notable recognition includes Elsevier's Most-Cited Article award (2022). Engages in public science communication via RTÉ and advises community-owned renewable energy projects.

Mateja NovakView profile

Assistant Professor

• Model Predictive Control
• Power Electronics
• Multilevel Converters
• +6 more

Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.

Mohammad FanaeiView profile

Teaching Professor

• Machine Learning
• Wireless Sensor Networks
• Automotive Systems
• +3 more

Mohammad Fanaei is an Associate Teaching Professor in the Department of Electrical and Computer Engineering at Northeastern University, affiliated with the College of Engineering. His research focuses on machine learning applications, wireless sensor networks, automotive systems, and network security. He has contributed to advancements in vehicular communication protocols, distributed estimation techniques, and cybersecurity measures against tunneling attacks. Research Interests Fanaei’s work spans deep learning for autonomous systems , vehicular networks , and sensor fusion . He explores challenges in distributed signal processing, including power allocation strategies and robust communication protocols for automated vehicles. His cybersecurity research addresses vulnerabilities in vehicular ad hoc networks and sensor infrastructure. Key Contributions His publications highlight trends in 3D vehicle localization , DSRC receiver modeling , and modulation-channel coding interplay . Earlier work includes foundational studies on distributed detection algorithms and spatial randomness in sensor networks. Advising & Grants While specific advising/grants are not detailed, his academic role suggests involvement in teaching and mentoring in electrical engineering and computer science disciplines.

• Asphalt
• Bituminous materials
• Pavements
• +8 more

Davide Dalmazzo is an Associate Professor in the Department of Environment, Land and Infrastructure Engineering (DIATI) at Politecnico di Torino, Italy. He specializes in Roads, Railways, and Airports (SSD ICAR/04) and serves as the technical manager of teaching and research activities for asphalt binders at the High Quality Experimental Laboratory of Innovative and Recycled Materials for Civil Engineering Infrastructures. He has been affiliated with Politecnico di Torino since his post-doctoral research (2009–2014), progressing from Assistant Professor (2014–2017) to his current role. His research is centered on sustainable and innovative materials for transportation infrastructure, with a strong emphasis on asphalt technology, recycling, and rheology. Key areas include: Characterization and modeling of bituminous materials Use of recycled plastics and reclaimed asphalt pavement (RAP) Development of sustainable pavement solutions Rheological testing and performance modeling of binders and mixtures Low-temperature behavior and fracture resistance of asphalt His recent publications reveal a consistent focus on circular economy principles in pavement engineering, particularly through projects like Green Roads, which explores plastic-modified asphalt. He frequently employs advanced testing methods such as the Monotonic Torsional Loading (MTL) test and contributes to RILEM standardization efforts. His work bridges laboratory research with practical applications in road construction and maintenance. Scientific contributions include: Co-inventor of a national patent on workability testing equipment for asphalt and granular materials Active participation in competitive and commercial research projects Regular publication in high-impact journals such as Construction and Building Materials , Materials , and Road Materials and Pavement Design Davide Dalmazzo advises several PhD students, including Davide Cimenti, Aliasghar Akbari Nasrekani, Joseph Nicolas La Macchia, and previously Sadegh Yeganeh. He is also involved in multiple research grants and contracts, particularly those focused on sustainable materials and infrastructure resilience. He leads or contributes to teaching in Bachelor’s, Master’s, and PhD programs in Civil Engineering, with courses on pavement engineering, rheology, and infrastructure maintenance. He is a key member of the Interdepartmental Center SISCON, focusing on infrastructure safety. He is actively engaged in laboratory research and leads experimental efforts in innovative material development, particularly for rural and low-volume roads. His work supports UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 13 (Climate Action).

• Interactive Digital Storytelling
• Children's Literature
• Media Education
• +7 more

Dr. Jim Pope is a Principal Academic in English and Communication at Bournemouth University, Faculty of Media and Communication. He is based at Talbot Campus and has been a key figure in advancing interactive digital storytelling in both academic and public spheres. His work bridges creative writing, media education, and digital technology, with a strong focus on reader engagement and narrative design. His research interests include: Interactive Digital Storytelling Children's Literature and Reader Response Media Education and Digital Literacy Narrative Design in New Media Arts-based Research Hyper-narrative and Non-linear Texts Jim Pope's recent publications reflect a consistent focus on digital narrative tools like Genarrator and the New Media Writing Prize, exploring how interface design, user experience, and interactivity shape reading and writing practices. His work spans journals in digital humanities, media studies, and creative writing education, demonstrating interdisciplinary reach and practical impact in both pedagogy and public engagement. He is actively involved in outreach, including co-chairing the digital art exhibition at the International Conference for Interactive Digital Storytelling (2019–2020) and contributing to the British Library’s Emerging Formats archive. He founded and maintains the Genarrator platform and the New Media Writing Prize, key resources in the digital storytelling community. Jim Pope supervises undergraduate dissertations and teaches courses in Children's Literature, Interactive Digital Storytelling, and Creative Writing. He holds a PhD from Bournemouth University (2007) on readers' responses to interactive fiction and is a member of NAWE and MeCCSA.

• Operations Research
• Data Analytics
• Optimization
• +6 more

Geunyeong Byeon is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). She previously held postdoctoral positions at Argonne National Laboratory and Los Alamos National Laboratory. Her research focuses on large-scale optimization methodologies for decision-making in energy systems and interdependent infrastructure networks. Byeon teaches courses in operations research, optimization, and data-driven methods at both undergraduate and graduate levels. Education Ph.D. in Industrial & Operations Engineering (2020), University of Michigan, Ann Arbor M.S. in Industrial Engineering (2016), Seoul National University, South Korea B.S. in Industrial Management Engineering (2014), Korea University, South Korea Research Interests Byeon specializes in developing optimization frameworks for complex systems, including energy grids and interdependent infrastructures. Her work emphasizes distributionally robust optimization , Benders decomposition , and federated algorithms , with applications to power systems, resilient distribution networks, and multi-agent decision-making under uncertainty. She bridges theoretical advancements with practical computational implementations. Teaching Highlights Optimization I (2022-2024) Computing for Data-Driven Optimization (2021-2025) Probability and Statistics for Engineering (2021) Awards MICDE Fellowship (University of Michigan, 2018) Rising Stars in Computational & Data Sciences Nominee (2020) Richard & Eleanor Towner Prize for Outstanding Ph.D. Research (Honorable Mention, 2019) Professional Service Byeon has reviewed for prestigious journals including INFORMS Journal on Computing , Management Science , and IEEE Transactions on Power Systems , and conferences like the Power Systems Computation Conference.

• Operations Management
• Statistics
• Robust Regression
• +5 more

Avi Giloni is an Adjunct Associate Professor in the Leonard N. Stern School of Business at New York University, where he has been teaching since 2004. He is also an Associate Professor of Operations Management and Statistics at the Sy Syms School of Business, Yeshiva University. His academic work bridges robust statistical methods and operational applications. Ph.D. in Statistics and Operations Research, New York University, 2000 B.A., New York University, 1994 Professor Giloni's research centers on robust regression , optimization , and stochastic system design , with applications in revenue management and supply chain operations . His expertise spans statistical modeling , decision-making under uncertainty , and operations analytics , contributing to both theoretical and applied domains in business and industry. The 15 most recent publications reflect a consistent focus on robust statistical techniques and stochastic modeling in business contexts. Key thematic areas include robust regression under outliers and heteroscedasticity , optimization in supply chains under uncertainty , and design of service systems . The keywords span operations research, statistics, and applied mathematics, while subfields reveal deep technical engagement with estimation, simulation, risk, and efficiency. While no formal scientific awards are listed in the provided text, Professor Giloni's sustained publication record in top-tier journals such as Management Science and SIAM Journal on Optimization indicates scholarly recognition. He has advised students in statistics, operations, and business analytics, though specific names are not listed. His founding of Del V.I., LLC, a consulting firm in statistics and operations research, demonstrates real-world application of his research and suggests involvement in industry grants or contracts. His teaching includes foundational courses such as Statistics for Business Control and Regression and Forecasting Models. Professor Giloni is affiliated with research activities through his consulting firm Del V.I., LLC, which functions as an applied research team focusing on statistical solutions and operational improvements for clients. His collaborative work across NYU and Yeshiva University suggests active participation in interdisciplinary teams addressing business analytics challenges.

Prashant MhaskarView profile

Professor

• Model Predictive Control
• Process Systems Engineering
• Fault Detection and Isolation
• +6 more

Prashant Mhaskar is a Professor in the Department of Chemical Engineering at McMaster University. His research focuses on model predictive control (MPC), fault detection and isolation (FDI), and data-driven modeling of chemical and bioprocess systems. He has established a four-year partnership with Sartorius to advance biomanufacturing technologies for antibody and virus-based treatments. Key research areas include: Batch and continuous process control Hybrid modeling integrating first-principles and data Stochastic and nonlinear control systems Bioreactor optimization for monoclonal antibody production Safe-parking frameworks for fault-tolerant control Economic model predictive control for industrial processes Recent work emphasizes practical implementations using recurrent neural networks, autoencoders, and physics-informed neural networks to address plant-model mismatch and sparse data challenges.

DJEMAI MohamedView profile

Professor

• Control Theory
• Hybrid Systems
• Multi-Agent Systems
• +9 more

Dr. Mohamed Djemai is a Full Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Cergy, and INSA Hauts-de-France. He is affiliated with the Quartz Laboratory (EA 7393) and LAMIH UMR CNRS 8201 at University Polytechnic Hauts-de-France. His research focuses on nonlinear control systems theory, with emphasis on hybrid and variable structure systems, sliding mode approaches, fault detection, and applications to power systems, robotics, and vehicle dynamics. IEEE Senior Member Associate Editor for Nonlinear Analysis: Hybrid Systems Co-Facilitator of National Working Group GT-SDH (2014–present) Member of IFAC-TC-1.3 (Discrete Event and Hybrid Systems) since 2001 Member of IFAC-TC-2.1 (Control Design) since 2005 His recent publications address fractional-order control of multiagent systems, stability analysis on time scales, fault-tolerant satellite attitude control, and robust consensus algorithms for nonlinear systems. Key methodologies include sliding mode control, event-triggered control, and observer-based fault detection. Current teaching activities encompass diagnostics, linear systems, signal processing, and sensor conditioning. The trend in Dr. Djemai's research since 2022 involves advanced control strategies for cyber-physical systems, distributed fault detection mechanisms, and time scale theory applications to intermittent communication problems. Notable collaborations include work with Michael Defoort, Stefano Di Gennaro, and international institutions like Kyungpook National University and University of Reims. Scientific contributions include: IEEE Senior Member recognition Development of robust exact filtering differentiators Innovations in fixed-time consensus protocols Leadership in IFAC technical committees Editorial role in hybrid systems analysis His laboratory work at Quartz and LAMIH supports applications in aerospace systems, renewable energy conversion, and industrial risk management architectures.

• Control Systems
• Signal Processing
• Automation Engineering
• +1 more

Professor Minyue Fu is an Honorary Professor in the School of Engineering at the University of Newcastle, Australia, specializing in Electrical and Computer Engineering. With over 30 years of research experience, he has established himself as a leading expert in control systems and signal processing, having published over 500 research papers with an H-index of 55. His academic journey began with a Bachelor's degree from the University of Science and Technology of China, followed by M.S. and Ph.D. degrees from the University of Wisconsin-Madison. Prof. Fu's research interests span a broad range of topics in control theory and signal processing. His work consistently focuses on fundamental theoretical problems with practical applications in diverse fields including power systems, sensor networks, multi-agent systems, and cyber-physical systems. He has made significant contributions to distributed control algorithms, stochastic systems, quantization effects in control, and networked systems. His recent publications (2021-2024) demonstrate continued productivity and relevance in the field, with research spanning decentralized optimal control, anomaly detection in cyber-physical systems, cart-pole control systems, and mean-field games. These works reflect his ability to bridge theoretical control concepts with practical engineering challenges, particularly in the context of modern networked and distributed systems. Fellow of IEEE (2004) Fellow of IFAC (2022) Fellow of Engineers Australia Fellow of Chinese Association of Automation (2018) Throughout his career, Prof. Fu has held significant editorial positions including Editor of IEEE Transactions on Signal Processing (2010-2014) and Associate Editor for several prestigious journals. His research has been supported by numerous grants, though specific details aren't provided in the current text. His laboratory work has focused on practical implementations of control algorithms in various systems, demonstrating the real-world applicability of his theoretical contributions. Prof. Fu has also supervised numerous students throughout his career, though specific names aren't listed in the available information.

Gerd VandersteenView profile

Professor

• Nonlinear Systems Analysis
• RF Circuit Design
• System Identification
• +5 more

Gerd Vandersteen is a Professor at the Vrije Universiteit Brussel (VUB) in the Department of Electronics and Informatics within the Faculty of Engineering. He serves as part-time Director (30%) of the Doctoral School of Natural Science and (Bio)Engineering (NSE) at VUB. His career spans academic research and industry collaboration, with significant contributions to nonlinear systems analysis and RF circuit design. His research interests focus on Nonlinear Systems Analysis , RF Circuit Design , System Identification , and Microwave Engineering . Vandersteen combines theoretical expertise in modeling nonlinear systems with practical experience in analog and RF design. His work emphasizes simulation-based analysis techniques, particularly using the Best-Linear-Approximation method for nonlinear system characterization. Vandersteen's publication record shows consistent output with 363 research outputs, including 103 articles and 164 conference papers. His recent work spans diverse areas from Arabic language processing to advanced RF measurement techniques, demonstrating interdisciplinary reach while maintaining core expertise in electronic systems. Key trends include nonlinear distortion analysis, frequency response characterization, and system identification methodologies. Best Paper Award (2002) Best Paper Award for 'Synchronizing modulated NVNA measurements on a dense spectral grid' (2012) IEEE Senior Member (2007) IEEE Instrumentation and Measurement Society Outstanding Reviewer (2014) As an educator, Vandersteen teaches CAE tools for electronic design, microwave design, and telecommunication techniques at VUB. He mentors PhD students through the international spring school on system identification and modeling techniques. His research is supported by multiple active projects including SRP78 (Center for Model-Based Systems Improvement), HERC61 (110 GHz real-time oscilloscope development), and VLOV106 (Flemish AI Academy establishment).

Daniel R. NascimentoView profile

Assistant Professor

• Theoretical Chemistry
• Computational Chemistry
• Quantum Chemistry
• +8 more

Daniel R. Nascimento is an Assistant Professor and UMRF Research Professor in the Department of Chemistry at the University of Memphis. He received his PhD in Theoretical Physical Chemistry from Florida State University in 2017 and held postdoctoral positions at Georgia Tech and Pacific Northwest National Laboratory. BS in Chemistry, Federal University of Ouro Preto, Brazil (2013) MS in Chemistry, Florida State University (2015) PhD in Physical Chemistry, Florida State University (2017) His research focuses on quantum mechanical methods for light-matter interactions, particularly in X-ray spectroscopy , time-dependent DFT , and electronic structure theory . The group develops algorithms for core-level spectroscopies and confined environments like optical cavities. Recent publications (2024-2020) highlight his work on metal-ligand covalency , iSPECTRON software , and confined electric field modeling in transition metal complexes. Articles span TD-DFT benchmarks , resonant X-ray scattering , and nonlinear optical response . 2024 CAS Early Career Research Award The Nascimento Lab includes graduate students Sarah Pak, Muhammed Dada, and Nathaniel Gillispie. He has secured NSF CAREER and Collaborative grants for method development in Psi4 and NWChem software. His group collaborates with institutions like Stanford, University of Bologna, and SLAC National Accelerator Laboratory.

• Explainable Artificial Intelligence
• Machine Learning
• Data Clustering
• +4 more

Nicolas Labroche serves as a Researcher in the Department of Computer Science at the Polytechnic School of the University of Tours. He is affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) within the UFR of Sciences and Technology. His academic work spans multiple research domains with significant contributions to machine learning methodology and applications. His primary research focuses on Explainable Artificial Intelligence , particularly for medical applications where model interpretability is critical. He has pioneered methods in clustering of local attributive explanations and discernibility in medical ML models . Additional expertise includes exploratory data analysis through mathematical programming for comparison queries, time series forecasting for environmental monitoring, and preference-based explanations in recommender systems. His work bridges theoretical computer science with practical applications in healthcare, environmental science, and business intelligence. Analysis of his publication trends reveals consistent innovation in making data exploration more efficient and interpretable. Recent work emphasizes user-centric explainability (2024-2025), mathematical optimization for analytics (2022-2023), and synthetic data generation (2022). His research demonstrates strong interdisciplinary connections between computer science, medicine, and environmental science. His laboratory affiliation with LIFAT provides the infrastructure for his computational research. While specific grant details aren't public, his publication record indicates sustained research productivity across multiple European-funded projects focused on data science applications.

Carsten SchürmannView profile

Professor

• Information Security
• Cryptographic Voting Protocols
• Security Analysis
• +5 more

Carsten Schürmann is a Professor of Theoretical Computer Science at IT University of Copenhagen, where he serves as Center Manager for the Center for Information Security and Trust. His research spans information security, cryptographic voting protocols, identity management, and digital democracy, with significant contributions to security ceremonies and formal verification of protocols. Professor, Department of Computer Science Center Manager, Center for Information Security and Trust Principal Investigator for multiple DIREC projects through 2025 Active researcher with 64 publications and 20 projects listed His research focuses on the intersection of theoretical computer science and practical security challenges, particularly in voting systems and security ceremonies. Schürmann has developed formal methods for analyzing security protocols, with emphasis on human factors in security implementations and cryptographic voting systems. His work bridges logical frameworks with real-world security applications, addressing both technical and socio-technical aspects of security. Analysis of his recent publications reveals a strong emphasis on voting security, with multiple papers on risk-limiting audits, receipt-free voting, and election integrity. His work increasingly incorporates formal logical frameworks to verify security properties, while also addressing human factors in security ceremonies. The research spans theoretical foundations in linear logic to practical applications in election systems. As Principal Investigator, Schürmann leads several major projects funded by the Innovation Fund Denmark, including DIREC initiatives focused on Capacity Building, PhD School, Voting, and Entrepreneurship (2020-2025). He has also established working groups in Adversarial AI and Machine Learning. Organized workshops on Code Scanning (2014) and Verifying Security Protocols in Tamarin (2016) Active media commentator on security issues with 311 media appearances through 2025 Principal Investigator for 7 ongoing and 13 completed research projects Schürmann directs the Center for Information Security and Trust, which serves as a hub for interdisciplinary security research connecting theoretical computer science with practical security applications. His center focuses particularly on voting systems security and security ceremonies, bringing together researchers from multiple disciplines to address complex security challenges.

• Construction Engineering
• Structural Health Monitoring
• Concrete Structures
• +5 more

Muhammad Usman Hanif is an Assistant Professor at the Department of Technology and Innovation within the Faculty of Engineering at the University of Southern Denmark (SDU). His research focuses on structural health monitoring , concrete durability , and damage assessment in civil engineering systems. Research Highlights Advanced signal processing for bridge damage detection Acoustic Emission techniques in CFRP-concrete debonding Machine learning applications in concrete durability prediction Augmented Reality integration with bridge monitoring systems Recent Publications (2023-2025) 2025: Machine learning in concrete durability 2024: Novel ΔT mapping for debonding detection 2023: FBG sensors in CFRP retrofitted beams 2022: MEMS accelerometers for damage assessment Teaching Activities Finite Element Method (2025) Non-linear Finite Element Method (2025) Advanced Finite Element Analysis (2024)