Find Academic Supervisors

Olivier FercoqView profile

Professor

• Optimization Algorithms
• Primal-Dual Methods
• Coordinate Descent Methods
• +4 more

Olivier Fercoq is a Professor at Télécom Paris, affiliated with the Laboratoire Traitement et Communication de l’Information (LTCI). He holds a PhD in Applied Mathematics from École Polytechnique (2012) and has held academic roles including Assistant Professor (2015–2021) and Postdoctoral positions at Télécom ParisTech and the University of Edinburgh. Education: PhD in Applied Mathematics, École Polytechnique (2009–2012) Master of Research in Optimization, Paris 6 University (2008–2009) Engineer's Degree in Mathematical Engineering, ENSTA-ParisTech (2006–2009) Research Interests: First-order optimization algorithms, primal-dual methods, and coordinate descent Applications in machine learning, web ranking, and chemotherapy optimization Key Achievements: Second Prize of the IMA Leslie Fox Prize (2015) PhD Thesis Award from the Gaspard Monge Programme (2013) Developed software tools like APPROX and contributions to Scikit-learn Teaching: Advanced courses on optimization, machine learning, and signal processing at Télécom Paris and IPP Master’s programs

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +4 more

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

• Telecommunications
• Intelligent Transportation Systems
• Electrical Engineering
• +4 more

Prof. Halid Žigić is a faculty member at the European University in Brčko District (BiH), affiliated with the Technical Faculty's Electrical Engineering and Information Technology Department. He holds the rank of Professor and specializes in telecommunications, intelligent transportation systems, and signal processing. His teaching includes courses like Electrical Engineering and Multimedia Systems, as evidenced by exam records from 2025. Research interests span transportation optimization, ICT applications in logistics, and environmental monitoring via satellite technology. Notable projects include electronic toll systems and climate change mitigation strategies through geospatial data analysis. He has authored over 20 technical publications focusing on engineering and applied technology solutions.

Richard RandallView profile

Associate Professor

• Music Theory
• Neuroscience of Music
• Cultural Studies
• +5 more

Dr. Richard Randall is an Associate Professor of Music Theory at Carnegie Mellon University's School of Music and holds a faculty appointment at the Center for the Neural Basis of Cognition and the Neuroscience Institute. His research focuses on the cultural, technological, and psychological foundations of musical experiences, with particular emphasis on auditory perception, cognitive processes, and the intersection of music with social and political contexts. He directs the Music Experience Lab (MEL), which explores interdisciplinary questions about music's role in human life, including projects centered on Romani musicians in the Balkans and the ethical dimensions of music technology. Randall co-founded the Listening Spaces Project, examining how technology shapes contemporary musical practices, and led initiatives like the Pittonkatonk festival, advocating for music as a public good. His work bridges neuroscience, media studies, and ethnomusicology, supported by grants such as the Rothberg Research Award and NIH funding. Affiliations: School of Music, Center for the Neural Basis of Cognition, Neuroscience Institute Key Projects: MEL, Listening Spaces Project, Romani Drummers Project, Pittonkatonk Education: (Not explicitly stated in provided text) Randall's research spans neuroimaging studies of auditory perception (e.g., auditory scene analysis) to cultural critiques of digital music distribution. His lab emphasizes collaborations with artists, activists, and technologists to address systemic issues like cultural representation and labor rights in music. He co-edited 21st Century Perspectives on Music, Technology, and Culture and developed educational programs such as the Young Musicians Collaborative, fostering community engagement through music. His recent publications explore topics like predictive coding models of musicality perception and the impact of low-level auditory features on grouping strength. Awards include the NIH grant T32-MH19983. Randall also leads the experimental ensemble Bombici, merging Balkan folk traditions with electronic improvisation. Grants: Rothberg Research Award, NIH grant T32-MH19983, Fine Foundation, Sprout Fund Labs/Teams: Music Experience Lab (MEL), Listening Spaces Project, Bombici collective

Michele MagnoView profile

Senior Lecturer

• Smart Sensing
• Low-Power Machine Learning
• Wireless Sensor Networks
• +4 more

Michele Magno is a Senior Lecturer and Privatdozent at ETH Zürich's Department of Information Technology and Electrical Engineering (D-ITET), leading the D-ITET Center for Project-based Learning (pbl.ee.ethz.ch). He holds a PhD in Electronic Engineering from the University of Bologna (2010) and has held visiting roles at institutions like the University of Nice and Mid Sweden University. His research focuses on low-power systems, wearable devices, energy harvesting, and IoT applications. Magno has authored over 350 peer-reviewed papers, with a Google H-index of 49. Notable awards include the 2024 Best Paper Award at ECCV and multiple best poster/demo recognitions at IEEE conferences. His industrial collaborations include projects with STMicroelectronics, Texas Instruments, and Logitech. Teaching contributions include courses on embedded systems, FPGA programming, and machine learning on microcontrollers. Magno's innovations span smart sensors for wind turbines, bio-medical monitoring, and autonomous racing systems, with patents in touch communication and energy-neutral devices. Recent work emphasizes ultra-low-power solutions for AI-integrated wearables, energy-efficient IoT nodes, and real-time embedded vision systems. His labs and teams pioneer technologies like TinyssimoRadar for in-ear gesture recognition and WakeMod for ultra-low-power IoT connectivity.

• Computer Vision
• Natural Language Processing
• Computational Thinking in Education
• +3 more

Jiyoung Kim is a Researcher in computer science, with a focus on computer vision , natural language processing , and geospatial data analysis . She has collaborated extensively across disciplines, particularly in robotics , educational technology , and signal processing . Her research interests include: Developing advanced diffusion models for high-fidelity talking head generation . Creating automated pipelines for detoxifying Korean language in AI systems. Applying deep learning to haze removal and indoor navigation for accessibility. Exploring 6G communication through computer vision-aided beamforming . Recent publications highlight trends in multi-task learning , unsupervised segmentation , and geospatial knowledge graphs . Her work bridges theoretical and applied domains, from hardware design to educational interventions for computational thinking in early childhood.

Zoran CvetkovicView profile

Professor

• Digital Signal Processing
• Wavelets
• Time-Frequency Analysis
• +9 more

Professor Zoran Cvetkovic is currently based at the Faculty of Natural, Mathematical & Engineering Sciences at King's College London , where he serves as Professor of Digital Signal Processing and Deputy Head of Department (Research) . He is affiliated with the Centre for Telecommunications Research , Engineering Security Hub , and Music Computing Lab . Education: PhD (University of California, Berkeley, 1995), MPhil (Columbia University, 1998), MSc (University of Belgrade, 1992), BSc (University of Belgrade, 1989) Previous Positions: Principal Member of Technical Staff at AT&T Labs (1997-2022), Research Fellow at Harvard University (2002-2003), Research Associate at EPFL (1996-1997) and UCSF (1994), Research/Teaching Assistant at University of Belgrade (1989-1992) Professor Cvetkovic's research spans signal processing theory , wavelets , time-frequency analysis , and their applications in neurophysiology , audio/spoken language technology , and biomedical systems . His recent work focuses on immersive audio technology , real-time acoustic simulation , superdirective beamforming , and multimodal speech recognition . With over 146 research outputs, his publications demonstrate expertise in machine learning for speech processing , acoustic environments , and information theory . His academic activities include organizing IEEE conferences , editorial roles at IEEE Transactions on Signal Processing , and collaborations with institutions like Stanford University , University of California, Berkeley , and SRI International . He supervises 7 research projects totaling over £3.5 million in funding from EPSRC , Leverhulme Trust , and Royal Academy of Engineering . His work contributes to UN Sustainable Development Goals related to education and technological innovation .

• Music Production
• Sound Engineering
• Audio Recording Techniques
• +3 more

Julius Mauranen is a Senior Lecturer at Metropolia University of Applied Sciences, specializing in Music Production and Technology. He holds a Vocational Teacher's qualification (2018), Master of Music (2014), and BA (Honours) in Sound Technology (2003). His primary role involves teaching music production and technology, along with studio engineering and entrepreneurship. He is co-founder and lead engineer at Studio Kekkonen, a renowned Helsinki-based recording facility. Affiliations: Metropolia University, Studio Kekkonen, Music 2 Team Education: BA (Honours) in Sound Technology (2003) Master of Music (2014) Vocational Teacher's qualification (2018) Research interests focus on innovative studio techniques, acoustic instrument capture, and live recording methodologies. Mauranen emphasizes analog gear integration and hybrid digital/analog workflows. His work spans genres from folk to indie rock, with notable projects including platinum-selling albums for Vesa-Matti Loiri and collaborations with artists like Olavi Uusivirta and Delay Trees. Studio Kekkonen sessions highlight his expertise in live band recordings, leakage management, and creative microphone placements. His engineering philosophy blends technical precision with artistic vision, prioritizing organic sound textures over sterile production. Labs/Teams: Studio Kekkonen serves as his primary creative hub, housing a collection of rare analog equipment and state-of-the-art digital tools. Future projects include advancing educational programs in music technology and expanding Studio Kekkonen's international collaborations.

Jaakko Tapio AstolaView profile

Professor

• Signal Processing
• Image Processing
• Nonlinear Signal Processing
• +9 more

Jaakko Tapio Astola is a Professor of Signal Processing at Tampere University of Technology since 1993. He held roles such as Academy Professor (2001–2006), Associate Professor of Applied Mathematics (1988–1993), and various acting professorships. His research focuses on signal processing, bioinformatics, and coding theory. Education: MSc in Mathematics, University of Turku (1973) PhD in Mathematics, University of Turku (1978) Research Interests: Signal processing methodologies Image and audio processing Systems biology and bioinformatics Algebraic coding theory Awards and Honors: IEEE Fellow (2000) Order of the White Rose of Finland (2000) Nokia Award (2010) Contributions: Advised 38 PhD/MSc theses Editorial Board member of nine journals General Chair of conferences like Eusipco 2000 and ISMVL 2011 International Experience: Research at Kyoto University (1976–1977) Visiting Professor at Eindhoven University of Technology (1984–1985) Edward Rotan Visiting Professor at M.D. Anderson Cancer Center (2002)

• Speech Processing
• Machine Learning
• Affective Computing
• +6 more

Professor Julien Epps is the Dean of the Faculty of Engineering at the University of New South Wales (UNSW). He holds a Professorship and previously served as Head of the School of Electrical Engineering and Telecommunications. His academic journey includes roles as Senior Lecturer (2007), Associate Professor (2014), and Head of School (2019). Epps earned his BE (Hons I) and PhD in Electrical Engineering from UNSW in 1997 and 2001, respectively. His research focuses on speech processing, wearable sensor technology, affective computing, and mental health applications. Notable contributions include work on depression detection via speech analysis, multimodal emotion recognition, and the development of tools like the IREye4Task dataset for wearable mental state analysis. He also investigates human-computer interaction through eye-tracking and biometric sensors. Epps' publications (2021–2025) emphasize speech-based health screening, adaptive signal processing for cybersecurity, and the integration of large language models with acoustic landmarks. His work spans engineering education, including a pioneering undergraduate quantum engineering degree program introduced in 2023. His lab and team collaborations include projects on smartphone-based depression screening, real-time task load estimation, and the design of robust spoofing attack detection systems. Epps has led interdisciplinary efforts in healthcare technology and pedagogical innovation in engineering education.

Wei-Shinn KuView profile

Professor

• Data management systems
• Data analytics
• Cybersecurity
• +3 more

Wei-Shinn Ku is a Professor in the Department of Computer Science and Software Engineering at Auburn University . He earned his Ph.D. in Computer Science from the University of Southern California in 2007, following M.S. degrees in Computer Science (2003) and Electrical Engineering (2006), and a B.S. in Information and Computer Education from National Taiwan Normal University (1999). As a program director at the National Science Foundation (2019–2022), he shaped research directions in data science and cybersecurity. Dr. Ku leads the Data Science and Engineering Laboratory (DSE Lab) at Auburn University, focusing on data management systems , data analytics , cybersecurity , and mobile computing . His recent work includes privacy-preserving techniques for IoT networks, spatio-temporal modeling for traffic prediction, and federated learning frameworks for mobile social networks. He has secured multiple NSF grants for projects like Indoor Spatial Query Evaluation and Data Integrity in Clouds . Dr. Ku's 15 most recent articles (2024–2025) span domains including traffic prediction , air quality modeling , secure cloud computing , and IoT security , with subfields like graph neural networks, quadtree-based optimization, and homomorphic encryption. His research has been recognized with awards such as the Auburn Alumni Engineering Council Research Award (2018, 2012) and the AFRL Summer Research Associate (2016). He has advised 18 current and graduated students in Ph.D. and M.S. programs, including Wenlu Wang, Ting Shen, and Kazuya Sakai. His professional service includes chair roles at IEEE/ACM conferences (ICDE, CIC, ICLR) and editorial positions at journals like IEEE Transactions on Knowledge and Data Engineering .

• Telecommunications Engineering
• Electromagnetic Field Theory
• Wireless Sensor Networks
• +3 more

Dr. Jarosław Emilianowicz holds a PhD in Electrical Engineering and serves as a researcher at the Department of Telecommunications and Teleinformatics within Wrocław University of Science and Technology's Faculty of Information and Communication Technology. Doctoral degree in Electrical Engineering Specializes in electromagnetic field applications and wireless communication systems Active in LPWAN technologies and IoT implementations His research spans electromagnetic field shaping, wireless sensor networks, and real-time location systems. Recent work focuses on LoRaWAN-based environmental monitoring in large buildings, demonstrating practical IoT applications for smart infrastructure. Publications show expertise in microwave techniques, WLAN security analysis, and embedded communication systems. Key technical interests include signal processing algorithms, secure wireless transmission mechanisms, and microprocessor-based measurement devices. Active in conference proceedings and book chapters related to telecommunications applications, with contributions appearing in IEEE publications and Polish technical monographs.

• Acoustics
• Finite Element Analysis
• Ultrasonics
• +7 more

Marc Arnela Coll is an Assistant Professor at the Engineering Department of La Salle Digital Engineering School, where he actively contributes to research in acoustics, vibration control, and sustainable building technologies. His work spans interdisciplinary domains, integrating engineering, environmental science, and human comfort in architectural contexts. His research interests focus on acoustics , finite element analysis , ultrasonics , and sustainable materials , with applications in noise and vibration reduction, sound insulation, and eco-friendly construction. He explores innovative solutions such as acoustic black holes, recycled concrete walls, and ultrasonic waste classification. The trends in his recent publications (2022–2025) reflect a strong emphasis on both theoretical and applied acoustics, with increasing integration of sustainability and human-environment interaction. His work appears in high-impact journals such as Applied Acoustics , Waste Management , and Chaos, Solitons and Fractals , covering topics from urban soundscapes to biomedical voice modeling. Scientific Awards: No scientific awards listed in the provided text. Advising and Grants: While no formal students are listed, Marc Arnela Coll leads and participates in multiple funded research projects. He serves as Principal Investigator (PI) for projects such as Bettair V and Aria , and as a key researcher in initiatives like ABHSSYS and Inhabiting Gaudí . These projects are supported by internal grants, research contracts, and collaborative agreements, reflecting strong institutional and interdisciplinary support. Labs and Teams: He is a core member of the Human-Environment Research group at La Salle Digital Engineering School, collaborating closely with researchers such as O. Guasch, R. M. Alsina-Pagès, and M. Freixes Guerreiro. His work is deeply embedded in team-based, project-driven research environments focusing on acoustic innovation and sustainable design.

• Robotics
• Mechatronics
• Biomedical Engineering
• +5 more

Ramin Almasi is an Assistant Professor at AGH University of Science and Technology in Kraków, Poland, affiliated with the Faculty of Mechanical Engineering and Robotics and the Department of Robotics and Mechatronics. His research bridges robotics, biomedical imaging, and signal processing. Research Focus Medical imaging (ultrasound elastography, OCT analysis) Machine learning for retinal diagnostics Signal processing and audio watermarking Publication Trends His recent work (2012–2025) emphasizes cross-disciplinary projects: biomedical applications (retinal imaging, tissue stiffness) and robust signal processing methods (audio watermarking). Keywords span machine learning, medical diagnostics, and real-time systems. Contact Email: ralmasi@agh.edu.pl

• Digital Signal Processing
• Spectral Analysis
• Pattern Recognition
• +7 more

Maria Ragousi is a faculty member in the Department of Electrical and Electronic Engineering at the University of West Attica, affiliated with the School of Engineering. She holds a PhD in Digital Signal Processing and Pattern Recognition from NTUA (1994), an MSc from the University of Virginia (1990), and a Diploma in Electrical Engineering from NTUA (1987). Her academic role is as a Lecturer, focusing on courses such as Digital Signal Processing, Biomedical Signal Processing, and Educational Technologies. Education: PhD (1994): Digital Signal Processing & Pattern Recognition, NTUA MSc (1990): Digital Signal Processing, University of Virginia Diploma (1987): Electrical Engineering, NTUA Her research interests span Digital Signal Processing (DSP), Spectral Analysis, Pattern Recognition (including neural networks and genetic algorithms), Biomedical Signal Processing, Wireless Sensor Networks for environmental monitoring, and E-Learning technologies. She has contributed to over 30 peer-reviewed publications, including work on EEG-based person identification, environmental sound classification, and educational data mining. Recent research emphasizes integrating machine learning into education and developing tools for environmental acoustic monitoring. She has collaborated on projects like the ESOUNDMAPS initiative, deploying wireless sensor networks for real-time environmental sound mapping. Her work also explores active learning methodologies in physics education and visualization of e-learning platform data. Maria advises courses at both undergraduate and postgraduate levels, including specialized programs in medical signal analysis and smart electronic systems. She actively participates in academic governance and has led several educational innovation projects funded by national and international grants.