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Moncef GabboujView profile

Professor

• Machine learning
• Artificial intelligence
• Signal processing
• +9 more

Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.

Duc (David) TranView profile

Associate Professor

• Network Computing
• Machine Learning
• Blockchain Technology
• +3 more

Dr. Duc (David) Tran is a tenured Associate Professor in the Department of Computer Science at the University of Massachusetts at Boston. He directs the Network Computing Laboratory and focuses on network computing, with current projects in blockchain technology, decentralized learning, and edge computing. His research on peer-to-peer and decentralized networks has been widely cited. National Science Foundation funding recipient Best Theory Paper Award at IEEE MASS (2014) Best Paper Award at ICCCN (2008) IEEE Outstanding Graduate Student Award (2002) His research combines machine learning and decentralized techniques to optimize networked applications. Recent publications highlight blockchain for federated learning, edge computing, and automated market-making algorithms. He has also published cross-disciplinary work in medical imaging (2021) and obstetrics (2025). Dr. Tran actively contributes to academic service as an editor for Elsevier Ad Hoc Networks Journal, Springer Journal on Computational Social Networks, and Taylor Francis Journal on Parallel, Emergent, and Distributed Systems. He has served as TPC Chair for WiMAN, Guest-Editor for Pervasive Computing and Communications, and keynote speaker at WiMAN 2013.

Hamid MansoorView profile

Assistant Professor

• Data Visualization
• Digital Health
• Digital Phenotyping
• +6 more

Hamid Mansoor is an Assistant Professor in the Department of Computer Science at the University of Manitoba. He holds a PhD in Computer Science from Worcester Polytechnic Institute under Prof. Emmanuel Agu, and was part of the DARPA-funded WASH project. His research focuses on data visualization, digital health, and smartphone-based behavioral analysis. He previously served as a Postdoctoral Fellow at the VIXI Lab, University of Victoria, Canada, under Prof. Miguel Nacenta. Education: PhD in Computer Science, Worcester Polytechnic Institute Research Interests: Interactive data visualization frameworks for health monitoring Mobile and ubiquitous computing for behavioral analysis Smartphone-sensed human behavior and health informatics Visual representation of text-based and sensor data Publications highlight trends in visual analytics for healthcare, including tools like ARGUS and INPHOVIS for detecting bio-behavioral disruptions and smartphone-based phenotyping. His work integrates machine learning with visualization to address challenges in health data interpretation. Awards: Best short paper honorable mention (EuroVis 2020) His contributions span academic collaborations in health informatics and mobile computing, with a focus on bridging theory and practical applications in healthcare technology.

Heinz KoepplView profile

Professor

• Self-Organizing Systems
• Systems Biology
• Control Theory
• +9 more

Prof. Heinz Koeppl is a Professor in the Department of Electrical Engineering and Information Technology at TU Darmstadt. His research focuses on self-organizing systems, systems biology, and control theory, with applications in synthetic biology, robotics, and stochastic processes. He explores interdisciplinary topics such as genetic circuit design, UAV swarm dynamics, and machine learning-driven modeling of biochemical systems. Key research areas include the development of deep learning frameworks for kinetic modeling, Bayesian optimization for riboswitch design, and mean field control theory for sparse networks. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in molecular communication, gene regulation, and robotic swarm coordination. Publications from 2023–2025 highlight advancements in bio-inspired algorithms, swarm intelligence, and computational biology. Notable contributions include studies on RNA-based circuits, active matter dynamics, and optimization strategies for large-scale systems. His research emphasizes interdisciplinary collaboration, leveraging tools from electrical engineering, mathematics, and life sciences. No scientific awards are explicitly listed in the provided text. Advising and grants details are not available. Prof. Koeppl’s lab focuses on integrating systems biology approaches with engineering principles to solve complex problems in healthcare, environmental sustainability, and technological innovation.

Pierre BaldiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +7 more

Pierre Baldi is a Distinguished Professor of Computer Science and Director of the Institute for Genomics and Bioinformatics at the University of California, Irvine (UCI). He is affiliated with the Donald Bren School of Information and Computer Sciences. His research spans artificial intelligence, machine learning, bioinformatics, and communication networks, with notable projects in protein structure prediction, gene expression modeling, and neutrino physics collaborations like DUNE. Baldi’s work bridges theoretical foundations (e.g., neural network theory) and applied domains, including medical imaging and fusion technology. Key research interests include AI-driven biomedical applications, neural network theory, and interdisciplinary projects such as the DUNE neutrino experiment. His contributions to neural network engineering were recognized with the 2023 INNS Dennis Gabor Award, highlighting his paradigm-changing impact on computational neuroscience and physics. Baldi’s academic leadership includes directing UCI’s Institute for Genomics and Bioinformatics, fostering collaborations in computational biology and AI. His recent work explores AI’s role in healthcare, climate modeling (e.g., ClimSim-Online), and fundamental physics challenges like neutrino oscillation studies.

Lucy HedermanView profile

Associate Professor

• Health Informatics
• Clinical Decision Support Systems
• Healthcare Data Integration
• +3 more

Dr. Lucy Hederman is an Associate Professor in Computer Science at Trinity College Dublin (TCD), affiliated with the O'Reilly Institute. Her research focuses on leveraging data and documents to support clinical decision-making, particularly in healthcare knowledge work. She has led interdisciplinary projects addressing data integration for rare diseases (e.g., ANCA-vasculitis, MND) and socio-technical challenges in adopting patient-generated health data (PGHD) into clinical practice. Dr. Hederman has secured over €xxxk in research funding and leads the Heterogeneity and Interoperability (H&I) challenge in the SFI-funded ADAPT 2 Centre. Her educational background includes advanced studies in computer science and healthcare informatics, though specific degree details are not explicitly stated in the text. She has supervised 4 PhDs, 2 research MScs, and co-supervised 6 PhDs, while currently mentoring 8 graduate students. Her career includes founding TCD spinouts PBOC and BIOLOGIT, which align with her research in health informatics and technology. Key research interests include: Interdisciplinary collaboration between clinicians, researchers, and technologists Data harmonization for multi-national clinical studies (e.g., FAIRVASC, Precision-ALS) Development of clinical decision support systems (CDSS) Design of mobile health (mHealth) tools for underserved populations Recent work emphasizes FAIR principles for healthcare data and socio-technical factors influencing PGHD adoption. She has contributed to over 70 peer-reviewed publications and actively participates in initiatives like the EU-funded TRANSFORM project and HRB Primary Care Research Centre. Dr. Hederman’s professional memberships include the Irish Computer Society, ACM, and Healthcare Informatics Society of Ireland. Her research has impacted healthcare practices in Ireland, with many MSc student projects influencing local health services.

Heesung WooView profile

Assistant Professor

• Robotics in Forestry
• Sensor Integration
• Precision Forestry
• +4 more

Dr. Heesung Woo is an Assistant Professor of Advanced Forestry at the College of Forestry, Oregon State University , specializing in robotics, sensor integration, and precision forestry. His work focuses on autonomous forestry machinery, AI-driven forest management, and sustainable practices. He advises two graduate students and collaborates internationally through research projects. Research Interests: Autonomous Forest Machinery Development Sensor Integration & ICT Solutions Precision Forestry via Remote Sensing/LiDAR/GIS Machine Learning for Forest Inventory Advanced Forestry Practices for Sustainability Publications emphasize innovative applications of technology in forestry, including LIDAR integration, harvester data analytics, and carbon offset project modeling. His work bridges engineering, environmental science, and policy. Dr. Woo leads the Advanced Forestry Lab at Oregon State, focusing on real-world deployment of cutting-edge technologies to address challenges in forest operations, sustainability, and resource optimization.

• RF IC Design
• Mixed-Signal IC Design
• Wireless Communication Systems
• +4 more

Bertan Bakkaloglu is the On Semiconductor Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has been since 2004. Prior to ASU, he worked at Texas Instruments focusing on analog, mixed-signal, and RF SoC development for communication transceivers. His research expertise spans RF and mixed-signal IC design, wireless/wireline communication systems, and broadband communication systems. Education: Ph.D. in Electrical Engineering, Oregon State University (1995) M.S.C. in Electrical Engineering, University of Houston (1992) Research Interests: RF and mixed-signal integrated circuits Power management ICs (including LDO regulators and DC-DC converters) High-efficiency power delivery systems Radiation-hardened electronics for space applications MEMS-based sensor systems Biomedical circuits for implantable devices Grants & Collaborations: Over 40+ funded research projects with institutions like NASA/JPL, BAE Systems, and NSF, focusing on power electronics, space systems, and biomedical applications Key projects include radiation-hardened converters, self-calibrating DACs, and implantable medical device circuits Industry partnerships with Texas Instruments, Space Micro, and FLIR Professional Activities: Technical committee member for IEEE Radio Frequency Integrated Circuits Conference Founding chair of IEEE Solid-State Circuits Society Phoenix Chapter

• Networked Control Systems
• Cyber-Physical Systems
• Cybersecurity
• +4 more

Professor Daniel Quevedo is a leading academic in Electrical and Computer Engineering at The University of Sydney. Previously, he held positions at Queensland University of Technology and Paderborn University, Germany, where he founded the Chair in Automatic Control. He earned his PhD from the University of Newcastle (Australia) and MSc/Ing. degrees from Universidad Técnica Federico Santa María (Chile). His research focuses on networked control systems, cyber-physical systems, and cybersecurity, with contributions to state estimation, control of power converters, and human-in-the-loop systems. He has pioneered work integrating machine learning, behavioral economics, and advanced mathematics to address challenges in interconnected digital-physical environments. Quevedo serves as Associate Editor for IEEE Transactions on Control of Networked Systems and IEEE Control Systems. He chairs the Committee of Experts for Germany’s Excellence Strategy on Digital Methods and has held leadership roles in IEEE technical committees. Notable awards include the IEEE Axelby Outstanding Paper Award (2018) and multiple fellowships. Teaching includes advanced control systems courses like Reinforcement Learning and Optimal Control. He is a Fellow of the IEEE and has published over 200 peer-reviewed articles, with recent work emphasizing privacy-preserving state estimation, resilient control systems, and energy-efficient wireless control. His research labs explore topics such as human-machine collaboration, cybersecurity in Industry 5.0, and data-driven control strategies. Current projects include secure remote state estimation frameworks and adaptive control under adversarial conditions.

Mikkel N. SchmidtView profile

Associate Professor

• Statistical Modeling
• Machine Learning
• Bayesian Methods
• +3 more

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

Shintaro OkazakiView profile

Professor

• Marketing Communications
• Digital Marketing (AI, Mobile, Social Media)
• Social Issues in Marketing (Diversity, Sustainability)
• +2 more

Shintaro Okazaki is a Professor of Marketing at King’s Business School, King’s College London. He holds a PhD from the Autonomous University of Madrid and has over 20 years of industry experience, including roles at a multinational corporation in Tokyo. His research focuses on marketing communications, digital marketing, social issues in marketing, international marketing strategies, and tourism marketing. He has authored over 100 articles and chapters, with a strong emphasis on advertising, AI ethics, sustainability, and consumer behavior. Research Interests: Marketing Communications: Advertising, Branded Entertainment, Cross-Cultural Advertising, Emotions in Advertising Digital Marketing: AI, Mobile Marketing, Social Media Marketing, Digital CSR Social Issues: Diversity, LGBTQ+ Inclusion, Disaster Resilience, Media Addiction, Sustainability International Marketing: Deglobalisation, Global Brand Positioning, Marketing Standardisation Travel & Tourism: Sustainable Tourism, Greenhushing, Peer-to-Peer Accommodation Awards & Roles: Former Editor-in-Chief of Journal of Advertising (2014–2019) Past-President of the European Advertising Academy Editorial Board Member for Journal of Public Policy & Marketing , International Marketing Review , and others Recipient of multiple awards, including the 2010 Journal of Advertising Best Article Award Grants & Conferences: Grant panelist for over 20 international organizations Organized/chaired 30+ academic conferences globally Labs & Teams: Research Lead of the Department of Marketing at King’s Business School, leading interdisciplinary projects on AI ethics, crisis communication, and sustainable marketing strategies.

Gabriel A. SilvaView profile

Professor

• Neuroscience
• Neural Computing
• Quantum Algorithms
• +7 more

Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.

• Wireless Communications
• Optical Communications
• Radar Signal Processing
• +6 more

Saleh A. Alshebeili is a Professor in the Department of Electrical Engineering at King Saud University's College of Engineering, Riyadh, Saudi Arabia. With over 139 publications spanning from 1991 to 2024, his research demonstrates significant contributions across multiple engineering disciplines. His academic profile shows consistent collaboration with Saudi research institutions and international partners, particularly in communications and signal processing fields. Dr. Alshebeili's research interests span wireless communications, optical networks, radar systems, and biomedical signal processing. His work bridges theoretical signal processing with practical applications in 5G/6G communications, IoT security systems, and healthcare monitoring. The interdisciplinary nature of his research connects electrical engineering with computer science, particularly through machine learning applications for signal analysis and system optimization. His publications demonstrate expertise in both traditional signal processing techniques and emerging AI-driven approaches to engineering problems. Analysis of his recent publications (2021-2024) reveals a strong focus on next-generation communication technologies including 6G systems, optical wavelength conversion, and OAM-SDM communication. Simultaneously, he maintains active research in biomedical applications, particularly EEG signal processing for seizure detection and biometric authentication using physiological signals. His work consistently appears in top IEEE journals including IEEE Access, IEEE Transactions on Wireless Communications, and IEEE Journal of Biomedical and Health Informatics, reflecting the high quality and relevance of his research. Dr. Alshebeili has established extensive collaborations with researchers across King Saud University, particularly with Fathi E. Abd El-Samie (29 co-authored papers), Turky N. Alotaiby (22 papers), and Amr Ragheb (21 papers). These long-term collaborations suggest leadership in research groups focusing on communications systems and biomedical signal processing. His work spans theoretical development, simulation, and experimental validation, as evidenced by publications with 'Experimental Investigation' and 'Experimental Demonstration' in their titles.

Sabine SüsstrunkView profile

Professor

• Computational Imaging
• Computational Photography
• Color Image Processing
• +4 more

Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.

Ebrahim Bedeer MohamedView profile

Assistant Professor

• Signal Processing
• Wireless Communications
• Artificial Intelligence
• +3 more

Ebrahim Bedeer Mohamed is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan. He joined in July 2019, following roles as an Assistant Professor (Lecturer) at Ulster University, UK, and postdoctoral fellowships at Carleton University and the University of British Columbia. He holds a Ph.D. (Distinction) from Memorial University of Newfoundland (2014), with expertise in signal processing and wireless communications. His research focuses on optimizing communication systems through advanced signal processing techniques, including faster-than-Nyquist signaling, IoT network design, AI integration, and energy-efficient protocols. Key areas include next-generation communication networks, non-orthogonal modulation, and MIMO systems. Notable contributions include work on channel estimation for FTN signaling, RIS-aided wireless systems, and LR-FHSS protocols in IoT. His publications span spectral efficiency, interference minimization, and energy management in 5G/6G contexts. He actively seeks Ph.D. students with strong backgrounds in signal processing fundamentals. Awards and grants are not explicitly listed in the provided texts. His work emphasizes practical applications, such as UAV trajectory optimization for IoT data collection and energy-efficient caching strategies in dynamic networks.