Find Academic Supervisors

• Sediment Transport
• Granular Flows
• Fluid Dynamics
• +3 more

Remi Chassagne is a Research Fellow at Grenoble Alpes University, affiliated with the Laboratory of Geophysical and Industrial Flows (LEGI), a joint research unit of CNRS, Grenoble INP, and Grenoble Alpes University, where he contributes to the MEIGE team. His research focuses on: Sediment Transport mechanics using mechanistic and granular approaches Vertical grain size segregation and diffusion remixing in sediment beds Eulerian-Eulerian two-phase flow modeling (SedFoam) Bridging particle-scale phenomena to continuum models Interactions between sediment particles of different sizes Chassagne's work integrates discrete element modeling (DEM) for particle-scale dynamics with continuum modeling to address sediment transport in geophysical contexts. His research emphasizes vertical grain size segregation, infiltration dynamics of small grains in coarse beds, and the development of numerical frameworks like SedFoam for multiphase flow simulation. This mechanistic approach connects micro-scale particle interactions to macro-scale sediment transport predictions. Analysis of his publication record (2016-2024) reveals consistent focus on sediment transport mechanics, with dominant themes in granular flow behavior, size segregation processes, and computational modeling advancements. Key contributions include frictional-collisional bedload models, sediment-vegetation interactions, and bi-disperse mixture mobility. His work demonstrates strong integration of computational methods with experimental validation through LEGI's hydrodynamic facilities. As part of LEGI's MEIGE team, Chassagne utilizes the laboratory's advanced infrastructure including hydrodynamic tunnels, wave channels, and rotating platforms to investigate geophysical flows relevant to river, coastal, and industrial applications.

• Wireless Communications
• Telecommunications
• Algorithms Design
• +3 more

Professor Robert G. Maunder is affiliated with the University of Southampton and leads research in wireless communications, algorithms design, and hardware implementation. He has been with the School of Electronics and Computer Science since 2000, advancing from Lecturer to Professor in 2017. BEng (First Class) in Electronic Engineering (2003) PhD in Telecommunications (2007) His research focuses on joint source/channel coding and optimizing wireless communication systems through algorithm-hardware co-design. Recent work includes 5G non-terrestrial networks, OTFS modulation, and quantum code decoding. Key collaborators include Prof. Lajos Hanzo and Prof. Sir Bashir Al-Hashimi. Selected awards: IEEE Senior Member (2012), Chartered Engineer (IET, 2013), Fellow of the IET (2017). He supervises PhD student Arumjeni Mitayani and founded AccelerComm Ltd to commercialize soft-IP solutions.

• Signal Processing in Wireless Communications
• Multi-antenna systems (MIMO)
• Wireless Power Transfer
• +14 more

Francisco A. Monteiro is an Associate Professor in the Department of Information Science and Technology at ISCTE – University Institute of Lisbon, and an Integrated Researcher at Instituto de Telecomunicações (IT), Lisbon, Portugal. He holds a PhD in Engineering from the University of Cambridge and prior degrees from Instituto Superior Técnico, University of Lisbon. His academic journey includes progressive roles at ISCTE-IUL, culminating in his promotion to Associate Professor in May 2024. Education: PhD in Engineering, University of Cambridge, UK (2012) MSc in Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon (2003) Licenciatura in Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon (1998) His research focuses on signal processing in wireless communications, particularly in MIMO systems, network coding, quantum communications, and advanced decoding techniques like GRAND. He has made significant contributions to quantum error correction, entanglement purification, and low-latency decoding for 5G/6G systems. His recent work bridges classical wireless theory with emerging quantum networking paradigms, as evidenced by publications in Quantum , IEEE Access , and IEEE Transactions . The trend in his recent publications (2023–2024) shows a strong shift toward quantum information processing—especially quantum error correction via noise-guessing decoding (QGRAND), entanglement routing, and purification—while maintaining a foundation in classical MIMO, full-duplex communications, and network coding. His work is highly interdisciplinary, combining information theory, signal processing, and quantum computing. Scientific Awards: Best Paper Award, ECWT 2007 Best Student Paper Award, ECWT 2004 Exemplary Reviewer Award (2014, 2015), IEEE Wireless Communications Letters Innovation Young Engineer Prize (3rd place), Ordem dos Engenheiros, 2002 College Senior Scholarship, Fitzwilliam College, Cambridge, 2007 Fellowship from Fundación Endesa & Fundación Carolina, 2019 Francisco Monteiro has advised several MSc students, including Francisco Rosário and João Sande Lemos, both winners of the Luís Vidigal Prize. He has co-supervised PhD-level research and served on multiple thesis committees. His research has been funded through competitive grants and collaborations with institutions in Canada, Finland, Spain, and the UK. He has served as General Chair of ISWCS 2018, Lead Guest Editor for EURASIP Journal, and organizer of the Quantum Internet Alliance Workshop. He is actively involved in research groups such as the Radio Systems Group at IT and has led projects on MIMO, wireless energy transfer, and quantum networking. His lab work emphasizes algorithmic innovation for practical implementations in next-generation wireless and quantum systems.

Toros ArikanView profile

Assistant Professor

• Signal Processing
• Remote Sensing
• Underwater Acoustics
• +6 more

Dr. Toros Arikan is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame's College of Engineering. His research focuses on signal processing for remote sensing applications, with particular emphasis on underwater acoustics and indoor radio frequency systems. He applies deep learning techniques to solve complex problems in environmental mapping, localization, and tracking. B.S., M.S., and Ph.D. in Electrical and Electronics Engineering from University of Illinois Urbana-Champaign and Massachusetts Institute of Technology Professor Arikan's work addresses fundamental challenges in underwater acoustic localization, reverberant environment modeling, and challenging-environment communications. His recent publications highlight neural network-based solutions for Steiner minimum trees and boundary estimation problems. His research on HF communications systems spans topics including low-latency transmission, Doppler tolerance, and modem design for underwater applications. Earlier work includes biomedical ultrasound signal processing for blood velocity estimation.

Ke SunView profile

Assistant Professor

• Mobile Computing
• Internet of Things
• Cybersecurity
• +4 more

Ke Sun is an Assistant Professor in the EECS Department at the University of Michigan, Ann Arbor. His research develops intelligent, deployable sensing systems for mobile, wearable, and IoT ecosystems, with applications in HCI, cybersecurity, health monitoring, and robotics. His work has been implemented in commercial devices like Amazon Echo and Google Home. Research spans: HCI for Mobile/IoT : Touch/gesture sensing (VSkin, RFCanvas) Cybersecurity : Privacy protection against eavesdropping (EveGuard, StealthyIMU) Health/Environmental Sensing : Vital sign monitoring (LoEar), activity logging (EgoADL) Wireless/Robotics : mmWave radar navigation (milliEgo), acoustic temperature sensing (VECTOR) Publication trends show consistent focus on acoustic/wireless sensing (13/15 papers), cybersecurity (5/15), and cross-modal AI fusion. Recent work explores adversarial ML attacks and LLM vulnerabilities. Awards include: Google Ph.D. Fellowship (2023) UbiComp Distinguished Paper Award (2023) ACM SenSys Best Poster Runner-up (2020) ACM-ICPC Asia Gold Medal (2015) Industry collaboration includes three internships at Amazon Lab126 where his research influenced Echo device development. He serves on program committees for ACM MobiSys/SenSys and organized the ICLR ML for IoT Workshop.

Ravi TandonView profile

Professor

• Information Theory
• Machine Learning
• Security and Data Privacy
• +12 more

Ravi Tandon is a Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he holds a Craig M. Berge Faculty Fellowship and a courtesy appointment in Applied Mathematics. He joined the university in 2015 after serving as a Research Assistant Professor at Virginia Tech. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park, 2010 B.Tech in Electrical Engineering, Indian Institute of Technology (IIT) Kanpur, 2004 Research Interests: His research spans information theory and its applications to wireless networks, machine learning, security, and privacy. He focuses on trustworthy machine learning, privacy-preserving AI, federated learning, secure communications, and private information retrieval. His work aims to develop foundational theories and practical systems for secure, fair, and efficient AI and communication systems. Recent Research Trends: His most recent publications (2023–2025) emphasize trustworthy AI, including fairness-accuracy tradeoffs, robustness, uncertainty quantification, and privacy in generative models and causal inference. He also continues to advance information-theoretic foundations of secure communications and distributed learning. Scientific Awards: NSF CAREER Award (2017) Keysight Early Career Professor Award (2018) Best Paper Award, IEEE GLOBECOM 2011 Advising and Grants: Dr. Tandon actively supervises PhD and MS students, with former students placed at Apple, Google, Meta, NXP, and other leading tech firms. He has led significant research projects, including an NSF SaTC grant on differential privacy in graph mining. His editorial service includes roles at IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, and IEEE Transactions on Communications. Labs and Teams: He leads a research group focused on information theory, machine learning, and privacy, collaborating with researchers in computer science, applied mathematics, and industry. His work bridges theoretical foundations with real-world AI and communication system design.

• Civil Law
• Inheritance Law
• Family Law
• +2 more

Dr. Agnieszka Kawałko serves as a Lecturer in the Department of Civil Law at the Faculty of Law and Administration, Catholic University of Lublin (KUL). Her academic career spans over two decades with continuous scholarly contributions since 2000, focusing on Polish civil law systems and practical legal applications. She maintains active roles in judicial law education through Lazarski University and the District Chamber of Legal Advisers in Warsaw. Her research centers on Civil Law , Inheritance Law , Family Law , and Company Law , with particular expertise in testamentary procedures, marital property regimes, and business entity structures. Kawałko's work bridges doctrinal analysis with practical case law, emphasizing judicial interpretation of Polish civil codes. Her publications consistently address gaps between legislative frameworks and real-world legal practice, especially regarding vulnerable parties in succession matters. Analysis of her 15 most recent publications reveals dominant trends in corporate restructuring (32%), inheritance conflicts (28%), and family obligations (20%). Key subfields include partnership dissolution mechanics, statutory heir protections, and alimony enforcement – reflecting her commitment to resolving tensions between legal theory and social realities in Polish civil jurisprudence. KUL Scientific Society Membership (2000) Kawałko has supervised over 40 Master's theses since 2007, with notable concentration in 2009-2011 covering marital property, inheritance acquisition, and partnership rights. Her educational services extend to judicial trainees at Lazarski University and legal advisers in Warsaw, demonstrating strong institutional collaboration. While specific grant data isn't provided, her textbook publications and peer-reviewed chapters indicate sustained research funding through academic channels. Her work operates within KUL's civil law research ecosystem, collaborating with scholars like Hanna Witczak on inheritance projects and contributing to Andrzej Kidyba's authoritative "Meritum: company law" compendium. Future research appears oriented toward modernizing Polish civil procedure in digital contexts, particularly regarding remote consumer contracts and electronic will execution.

Michel BarretView profile

Researcher

• Statistical signal processing
• Coding theory
• Source separation
• +3 more

Michel Barret is a researcher affiliated with the Georgia Tech International Joint Unit and the National Center for Scientific Research (CNRS). His work focuses on statistical signal processing, coding theory, and source separation, with applications in multicomponent image compression, hyperspectral data analysis, and seismic signal processing. Research Focus: Statistical signal processing, coding algorithms, and source separation Applications: Hyperspectral image compression, seismic data analysis, and medical signal processing Barret has contributed to advancements in zero-tree encoding, orthogonal matching pursuit algorithms, and independent component analysis (ICA) for high-rate source coding. Recent publications highlight his work on exogenous spectral transforms, polar codes for covert communications, and novel approaches to multicomponent data compression. His collaborative research spans diverse domains including geophysics, remote sensing, and biomedical engineering, often involving algorithm design for complex signal processing challenges. While specific awards or mentoring activities aren't mentioned, his extensive publication record demonstrates significant contributions to signal compression methodologies.

Mahdi Jafari SiavoshaniView profile

Assistant Professor

• Machine Learning
• Optimization
• Data Science
• +14 more

Mahdi Jafari Siavoshani is an Assistant Professor in the Department of Computer Science and Engineering at Sharif University of Technology, where he leads the Information, Network, and Learning (INL) Lab. He holds a PhD and MSc from the Swiss Federal Institute of Technology (EPFL), and a BSc in Electrical Engineering and Physics from Sharif University of Technology. Prior to his current role, he was a postdoctoral fellow at the Institute of Network Coding, The Chinese University of Hong Kong. His research spans fundamental problems in information processing, transmission, and analysis, with a focus on theoretical limits and practical algorithm design. Key areas include Machine Learning, Optimization, Data Science, Information and Communication Theory, and Computer Networks. His work integrates probabilistic modeling, network coding, and deep learning to address challenges in secure communication, traffic classification, and distributed systems. The recent publications highlight a strong trend in network coding, information theory, and machine learning applications. His work explores coding-theoretic solutions for secrecy, load balancing in cache networks, belief propagation algorithms, and encrypted traffic classification using deep learning. These contributions reflect a blend of theoretical depth and practical relevance in modern communication systems. Assistant Professor, Department of Computer Science and Engineering, Sharif University of Technology PhD and MSc, Swiss Federal Institute of Technology (EPFL) BSc in Electrical Engineering and Physics, Sharif University of Technology Postdoctoral Fellow, Institute of Network Coding, The Chinese University of Hong Kong He advises research in the INL Lab and teaches courses such as Computer Networks, Engineering Probability and Statistics, Network Coding, and Stochastic Processes. While no formal students are listed, his lab environment fosters collaborative research. There is no mention of external grants or funding in the provided texts. The INL Lab serves as the primary research unit, focusing on information theory, network algorithms, and machine learning applications. The lab integrates theoretical research with practical implementations, particularly in networked systems and data analysis.

• Gravitational Physics
• Numerical Relativity
• Quantum Gravity
• +4 more

Roberto De Pietri is an Associate Professor at the Department of Physics and Earth Sciences within the Faculty of Mathematical, Physical and Natural Sciences at the University of Parma, Italy. He has held this position since March 1, 2015, following 10 years as a Researcher (Assistant Professor) at the same institution. His research focuses on gravitational physics, numerical relativity, and quantum gravity, with particular emphasis on compact objects, gravitational wave phenomena, and computational astrophysics. De Pietri has made significant contributions to numerical simulations of relativistic systems, including neutron star oscillations, binary mergers, and gravitational wave emission. His work bridges theoretical frameworks with high-performance computing applications. Analysis of his recent publications reveals a strong focus on gravitational wave astronomy, particularly in catalog development (GWTC series), multi-messenger follow-up strategies, and computational infrastructure for real-time alert generation. His research spans from fundamental quantum gravity investigations to practical applications in gravitational wave detection pipelines. De Pietri has supervised five PhD students to completion and numerous master's theses, with research topics centered on numerical relativity, gravitational waveforms, and quantum gravity. He has secured substantial computational resources through PRACE projects, including 30 million core hours for neutron star merger simulations. His teaching portfolio includes graduate courses in gravitational physics and undergraduate instruction in general physics and computational methods. He has presented his research at major international conferences including the Marcel Grossmann Meetings and specialized workshops on numerical relativity.

John A. WhiteView profile

Professor

• Mechanisms of Episodic Memory
• Pathophysiology of Epilepsy
• Computational Neuroscience
• +2 more

John A. White serves as Professor and Deputy Chair in the Department of Biomedical Engineering at Boston University, with joint appointments in Pharmacology and Experimental Therapeutics and Neuroscience. He directs the Neuronal Dynamics Lab and maintains affiliations with the Center for Systems Neuroscience, Neurophotonics Center, and Photonics Center. Education: PhD, Biomedical Engineering, Johns Hopkins University B.S., Biomedical Engineering, Louisiana Tech University His research integrates engineering principles with neuroscience to decode brain information processing mechanisms. Key focus areas include episodic memory formation, epilepsy pathophysiology, computational modeling of neural networks, real-time instrumentation design, and advanced imaging of neuronal and astrocytic activity. Current work targets therapeutic interventions for memory disorders through precisely timed brain stimulation and develops computational frameworks for understanding cortical coherence in cognitive functions. Analysis of his 2022-2025 publications reveals dominant themes in hippocampal memory coding (time/distance representation, engram segregation), theta-gamma oscillation dynamics, and neurotechnology development. Recent work emphasizes ultrasound-based deep brain stimulation, calcium imaging analysis toolboxes, and computational models of inhibitory networks, demonstrating consistent innovation at the engineering-medicine interface. Scientific Awards: 2019: Elected Fellow of the International Academy of Medical and Biological Engineering 2019: Elected President of the Biomedical Engineering Society 2014: Meeting Chair, Biomedical Engineering Society Fall Meeting 2011: Distinguished Alumnus Award, Dept. of BME, La. Tech U. 2006: Elected Fellow, Biomedical Engineering Society 2005: Elected Fellow, American Institute for Medical and Biological Engineering 2003-2008: Co-Director, Methods in Computational Neuroscience, Marine Biological Lab Professor White actively collaborates across Boston University's interdisciplinary centers while advancing his lab's mission to translate neural dynamics research into clinical applications, particularly for memory restoration in neurological disorders. His leadership in the Biomedical Engineering Society and ongoing development of real-time electrophysiology tools underscore his commitment to bridging engineering innovation with neurological therapeutics.

Vahid AbolghasemiView profile

Senior Lecturer

• Signal Processing
• Image Processing
• Compressive Sensing
• +5 more

Dr. Vahid Abolghasemi is a Senior Lecturer (Associate Professor) in the School of Computer Science and Electronic Engineering at the University of Essex. He holds a PhD from the University of Surrey (2011) and has held academic positions including Assistant Professor at Shahrood University of Technology and postdoctoral research at Brunel University. His research focuses on signal/image processing, compressive sensing, machine learning, and their applications in healthcare, agriculture, and environmental monitoring. He has received the IEEE Young Investigator Award (2018) and is a Senior Member of IEEE. He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively reviews for top journals. His grants include projects on AI-driven dashboards for retail IoT data, smart irrigation systems, and high-voltage battery management systems. Research interests include biomedical signal processing, deep compressive sensing, and anomaly detection. He supervises PhD students in areas like ECG-based coronary artery disease detection and wireless image transmission systems. Key projects involve developing AI solutions for plant disease detection, energy-efficient sensing networks, and fault diagnosis in electrical systems. His work bridges theoretical advancements with practical applications in critical sectors such as healthcare and agriculture.

Hamdi JoudehView profile

Associate Professor

• Information Theory
• Communications
• Signal Processing
• +3 more

Hamdi Joudeh is an Associate Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He is affiliated with the Information and Communication Theory (ICT) Lab and the Signal Processing Systems (SPS) Group. His research focuses on information theory, communications, and signal processing, with applications in wireless networks and radar systems. He holds a Ph.D. in Electrical Engineering from Imperial College London and has held research positions at Technische Universität Berlin and Imperial College London. His research interests include quantum sensing, error exponents, MIMO systems, and channel coding. He leads projects such as the IT-JCAS (Information Theoretic Foundations of Joint Communication and Sensing) and ANTERRA (Beam Prediction for Fast-Moving LEO), addressing challenges in 5G/6G communication and radar technologies. He has received an ERC Starting Grant (2023) for his work on environment-scanning mobile networks. Education: Ph.D. in Electrical Engineering (Imperial College London), M.Sc. in Communications and Signal Processing (Imperial College London) Editorial Roles: Editorial board member of IEEE Transactions on Signal Processing , IEEE Communications Letters , and EURASIP Journal on Wireless Communications and Networking Labs/Teams: ICT Lab, SPS Group, and leads projects at TU/e’s Center for Wireless Technology Grants: ERC Starting Grant, TKI-HTSM/22.0547/TKI2212P11 RAIDAR, and others His recent work explores the intersection of communication and sensing, including quantum radar processing and robust beamforming techniques. He has published extensively on topics like error exponents, MIMO channel analysis, and interference management, with over 40 peer-reviewed articles.

• Cognitive Radio Networks
• Reinforcement Learning Applications
• Wireless Communications
• +7 more

Professor Kok-Lim Alvin Yau is an accomplished academic at Taylor's University in Malaysia, specializing in cognitive radio networks, reinforcement learning applications, and intelligent communications systems. With over 75 publications spanning from 2011 to 2025, he has established himself as a leading researcher in wireless networking and artificial intelligence applications in communications. His research interests encompass cognitive radio networks, reinforcement learning for networking applications, vehicular communications, edge computing, and the integration of AI in communication systems. Professor Yau's work demonstrates a consistent focus on applying machine learning techniques to solve complex networking challenges, particularly in dynamic spectrum access and resource allocation problems. Analysis of his recent publications (2023-2025) reveals a strategic shift toward more interdisciplinary research, combining reinforcement learning with generative AI techniques, exploring semantic communications, and addressing challenges in 6G-enabled vehicular networks. His work shows increasing emphasis on practical implementations and real-world applications of theoretical concepts, particularly in intelligent transportation systems and edge computing environments. Professor Yau has made significant contributions through numerous survey papers that have helped shape research directions in cognitive radio networks and reinforcement learning applications. His editorial work for special issues in IEEE Access and other journals demonstrates his standing in the academic community. He has successfully supervised multiple PhD students including Geong Sen Poh, Mee Hong Ling, and Aqeel Raza Syed, with whom he continues to collaborate on research projects. His international collaborations span institutions in China, Pakistan, and the UK, reflecting the global recognition of his research expertise.

Atousa HajshirmohammadiView profile

Senior Lecturer

• Engineering Pedagogy
• Digital Communications
• Multimedia Communications
• +5 more

Atousa Hajshirmohammadi serves as a Senior Lecturer at Simon Fraser University's School of Engineering Science, where she has taught since 2004 after returning from industry experience at LSI Logic Inc. in Silicon Valley. Her academic journey includes B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from Isfahan University of Technology and a Ph.D. in Communications Engineering from the University of Waterloo. Her research spans two primary domains: technical communications and engineering pedagogy. In digital communications, she focused on multimedia transmission, cognitive radio networks, and unequal error protection until approximately 2015. Her pedagogical research, which dominates recent work, investigates experiential learning, student wellbeing, time management strategies, and innovative teaching methods in engineering education. Her publication trend shows a clear shift from wireless communications research (2000-2015) to educational scholarship (2015-present), with significant contributions to IEEE Communications Magazine and Frontiers in Education conferences. Supported by multiple SFU Teaching and Learning Development Grants, she has developed tangible lab assignments, interactive online quizzes, and wellbeing-focused learning environments. Her outreach includes workshops like the Insect-Robot program for K-7 students and collaborations with SFU's Health Promotion unit. While her technical research involved graduate students, her primary role as teaching faculty emphasizes curriculum innovation rather than traditional graduate supervision.