جست‌وجوی استاد و پژوهشگر

Tamer Ölmezمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Brain-Computer Interfaces
• Deep Learning
• +۷ مورد دیگر

Tamer Ölmez is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU), College of Engineering, where he conducts cutting-edge research in biomedical signal processing, brain-computer interfaces (BCI), and deep learning applications in medical systems. His work bridges engineering and neuroscience, with a strong focus on EEG-based motor imagery classification, medical image analysis, and embedded deep learning systems. His research interests include motor imagery EEG signal processing , brain-computer interfaces , feature extraction , deep neural networks , classification algorithms , and medical image analysis . He applies machine learning and signal processing techniques to improve diagnostic accuracy and system performance in neuroengineering and healthcare technologies. The recent publications highlight a consistent trend in leveraging divergence-based deep neural networks , convolutional neural networks , and small-sized models for efficient and accurate classification in BCI and medical imaging. His work emphasizes performance improvement with reduced channel counts, noise elimination, and real-time applicability in embedded systems. Scientific Awards: Excellent Oral Presentation Certificate, June 1, 2015 Advising and Grants: He is actively supervising 26 theses in progress, indicating a strong mentoring role. He has led multiple funded research projects, including those funded by ITU’s Technology Transfer Office (TTO) and Scientific Research Projects (BAP), such as 'Classification of Medical Images with Deep Learning Method in Embedded Systems' and 'New Approaches to Finding Optimal Protein Folding'. Labs and Research Teams: While specific lab names are not mentioned, his collaborative fingerprint and project leadership suggest he leads or is a key member of a research group focused on biomedical signal processing, neural networks, and intelligent systems at ITU.

• Digital Circuits and Systems
• Energy-Efficient Circuits and Systems
• Embedded Systems
• +۷ مورد دیگر

Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)

Reza Zadehمشاهده پروفایل

استاد مدعو

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +۷ مورد دیگر

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.

Tsui-Wei Wengمشاهده پروفایل

استادیار

• Neural Network Robustness
• AI Safety
• Deep Learning Theory
• +۸ مورد دیگر

Tsui-Wei Weng is an Assistant Professor at the Halıcıoğlu Data Science Institute, affiliated with the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). Her research focuses on enhancing the robustness, reliability, and safety of AI systems and deep learning models. Education: Ph.D. in Electrical Engineering and Computer Science (EECS), Massachusetts Institute of Technology (MIT), 2020; M.S. in Communication Engineering, National Taiwan University, 2013; B.S. in Electrical Engineering, National Taiwan University, 2011. Her research interests span neural network robustness, AI safety, adversarial robustness certification, control policy verification, and theoretical machine learning. She has contributed foundational work on probabilistic and deterministic robustness certification frameworks like PROVEN and CNN-Cert, with a focus on improving the scalability and efficiency of verification methods. Her publications from 2018–2021 reveal a trajectory in adversarial robustness, randomized smoothing, deep reinforcement learning, and interpretable AI. Collaborative efforts with institutions like MIT-IBM Watson AI Lab, Google DeepMind, and IBM Research further underscore her interdisciplinary approach. Scientific awards include the Best Paper Award at IEEE Components, Packaging and Manufacturing Technology (2016). She actively collaborates with students and postdocs, emphasizing mathematical and machine learning rigor in their research contributions.

Xiuyuan Chengمشاهده پروفایل

دانشیار

• Applied Mathematics
• High-Dimensional Data Analysis
• Signal Processing
• +۴ مورد دیگر

Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.

Vishal Saxenaمشاهده پروفایل

استاد

• Silicon Photonic Integrated Circuits
• Neuromorphic Computing
• CMOS Electronics
• +۳ مورد دیگر

Professor Vishal Saxena is a faculty member in the Department of Electrical and Computer Engineering at the University of Delaware since 2019. Previously, he held positions at Boise State University (2010–2016) as Assistant and Associate Professor, and served as the Micron Endowed Professor of Microelectronics at the University of Idaho (2016–2019). His research focuses on analog electronic and photonic integrated circuits (ICs), particularly in sustaining IC design advancements post-Moore scaling through hybrid CMOS-photonic integration, neuromorphic computing, and energy-efficient embedded intelligence. Dr. Saxena earned his B.Tech. in Electrical Engineering from IIT Madras (2002), followed by M.S. and Ph.D. in Electrical and Computer Engineering from Boise State University (2007–2010). He has industry experience in semiconductor and telecommunication engineering. His work is supported by NSF, AFOSR, DARPA, NASA, and industry collaborators. Notable awards include the NSF CAREER (2015), AFOSR YIP (2016), and DARPA YFA (2019). His research interests span silicon photonic ICs for optical interconnects, RF photonic systems, neuromorphic circuits using emerging NVM devices, and high-speed analog-to-digital converters. He has pioneered compact modeling techniques for photonic components and developed energy-efficient architectures for spiking neural networks. Dr. Saxena’s publications reflect advancements in photonic integration, neuromorphic hardware, and mixed-signal IC design. He actively contributes to the IEEE community through editorial roles and conference steering committees, including MWSCAS and ISCAS.

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +۵ مورد دیگر

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

Romain Raveauxمشاهده پروفایل

دانشیار

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +۵ مورد دیگر

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

Marko Šaracمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Blockchain Technology
• +۷ مورد دیگر

Marko Šarac is a Professor at Singidunum University in the Faculty of Informatics and Computer Science . He holds a Master's degree in Contemporary Information Technologies (2008) and a PhD in Advanced Protection Systems (2013) from the same institution. His research spans Cybersecurity, Artificial Intelligence, Blockchain, Internet of Things (IoT), Machine Learning, and Data Privacy . Education: Master: Contemporary Information Technologies, Singidunum University, 2008 PhD: Advanced Protection Systems, Singidunum University, 2013 Research Focus: SSL Traffic Security, Virtual Datacenters, Biometric Cryptography, and IoT Healthcare Systems Developed frameworks for Explainable AI in Metaverse Security , Blockchain-based IoT Security Gateways , and Machine Learning for Medical Diagnostics Notable Publications: 2025: CNN-enhanced attack detection for IoT-based Metaverse 2024: Modified Firefly Algorithm for medical dataset classification 2023: Space weather prediction using metaheuristics Projects: Co-author on 9+ books including Internet Marketing (2020) and Computer Network Security (2014) Contributed to 50+ peer-reviewed journals and conference papers on cybersecurity and AI Grants & Collaborations: Active in IEEE , ZINC , and Sinteza conference series Collaborated with researchers across Europe and Asia on IoT, Blockchain, and Cloud Security Contact: msarac@singidunum.ac.rs

Aliyu Abubakarمشاهده پروفایل

مدرس

• Deep Learning
• Image Analysis
• Medical Imaging
• +۵ مورد دیگر

Dr. Aliyu Abubakar is a Lecturer in Computer Science at Teesside University's Department of Computing & Games. He holds a PhD in Computer Science from the University of Bradford (2023), an M.Sc. in Cyber Security (2015), and a B.Sc. in Computer Science from Gombe State University (2012). Prior to his current role, he was a Postdoctoral Research Associate at the University of Liverpool (2022–2024). His research focuses on deep learning applications in medical imaging, cybersecurity, and pattern recognition. He is a member of the Nigeria Computer Society (NCS) and the Cyber Security Experts Association of Nigeria (CSEAN). Education: B.Sc. Computer Science, Gombe State University (2008–2012) M.Sc. Cyber Security, University of Bradford (2014–2015) Ph.D. Computer Science, University of Bradford (2018–2022) Research Interests: Abubakar’s work spans medical image analysis (e.g., burns assessment, kidney transplantation viability, malaria detection), cybersecurity (deepfake detection, P2P network security), and electrical engineering (partial discharge analysis). His deep learning models address challenges in healthcare diagnostics and industrial safety. Recent studies include human deception detection via facial image comparison and automated liver viability assessment using pre-trained neural networks. Key Trends in Publications: His 2020–2024 articles emphasize cross-disciplinary applications of deep learning, particularly in healthcare and electrical systems. Collaborations span institutions like the University of Liverpool and NHS clinical partners, reflecting his commitment to translational research. Awards & Grants: While no specific awards are listed, his high citation counts (e.g., 32 citations for malaria detection work) highlight impactful contributions. Grants and funding details are not disclosed in the provided texts. Labs & Teams: Active collaborations with multidisciplinary teams in medical imaging and cybersecurity, as evidenced by co-authored papers with clinicians and electrical engineers.

Akila de Silvaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Akila de Silva is an Assistant Professor of Computer Science at San Francisco State University (SFSU). He holds a PhD from UC Santa Cruz (advised by Prof. Alex Pang and James Davis) and an MS from Columbia University. His research focuses on artificial intelligence, machine learning, computer vision, and applied AI for environmental monitoring. Key projects include RipViz/RipScout systems for real-time rip current detection using ML and UAVs, and citizen science platforms like SmartCS for creating no-code mobile apps. Education: PhD in Computer Science & Engineering (UCSC), MS in Computer Science (Columbia). Grants include a NOAA-funded coastal observation system (2025-26) and an ethical generative AI in CS education initiative (2025-26). Notable award: Michael Richman Award (2024-25). Research emphasizes interdisciplinary applications: coastal safety via AI-driven monitoring, STEM education through citizen science tools, and improving data quality in facial recognition systems. Active reviewer for IEEE Visualization, CVPR, and other top conferences. Lab opportunities available for motivated students.

• Signal Processing
• Image Processing
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Bilge Günsel is a distinguished Professor at the Electronics and Communication Engineering Department of Istanbul Technical University . With a career spanning over three decades, she has served in academic and administrative roles including Department Head (2007-2010) and Deputy Head (2005-2006). Her research focuses on Signal Processing , Image Processing , and Machine Learning , with recent work on dynamic convolutional neural networks and video object tracking. Education : PhD (1994) and BS (1985) from Istanbul Technical University in Electronics & Communication Engineering. Research Interests include: Dynamic CNNs for scene classification Video object tracking with deep learning Meta-learning approaches in object verification Transfer learning for crowd density estimation Recent Publications (2021-2024) demonstrate expertise in: Target-driven inference in video tracking Dynamic convolution techniques Instance segmentation integration Scientific Achievements : Scopus h-index of 18 Over 1252 citations Principal Investigator (PI) for two major BAP projects (2016-2019) Collaborations span Turkey and international institutions, with research outputs in partnership with IEEE and academic peers like Furkan Gurkan and Yavuz K. Hanoglu. She continues to lead cutting-edge research while maintaining active teaching and administrative duties.

Lydia Bouzar-Benlabiodمشاهده پروفایل

استادیار

• Hardware-based Machine Learning
• Privacy-Preserving Machine Learning
• Explainable AI
• +۳ مورد دیگر

Dr. Lydia Bouzar-Benlabiod is an Assistant Professor at the Jodrey School of Computer Science, Acadia University, Nova Scotia, Canada. Her research focuses on hardware-based machine learning, privacy-preserving techniques, and explainable AI. She holds a Ph.D. in Computer Science from Université d’Artois, France, and an M.Eng from Ecole nationale Supérieure d’Information (ESI), Algiers, Algeria. Her work bridges theoretical advancements with practical applications in cybersecurity, medical imaging, and intelligent systems. Research interests include adversarial machine learning, neural architecture search, and AI-driven healthcare solutions. Her lab explores cost-effective hardware implementations for medical diagnostics and anomaly detection systems. Key projects involve NeuroMem® chip integration for breast cancer detection and RNN-VED models to reduce false positives in cybersecurity. Dr. Bouzar-Benlabiod’s publications span 2013–2024, emphasizing data-driven modeling, case-based reasoning, and AI ethics. She contributes to special issues on heuristic data science and reuse in intelligent systems. Her work is accessible via CILS Lab and personal page .

Shuo Yuمشاهده پروفایل

استادیار

• Health Analytics
• Interpretable Deep Learning
• Text Mining
• +۴ مورد دیگر

Shuo Yu is an Assistant Professor of Information Systems and Quantitative Sciences at the Rawls College of Business, Texas Tech University, and serves as the Wetherbe Professor in Management Information Systems. He directs the MIS Doctoral Program and teaches courses such as Big Data Security, Information Security, and Data Analytics Tools. Yu holds a PhD from the University of Arizona (Management Information Systems) and a BBA from Tsinghua University (Information Management). His research focuses on data science applications in healthcare analytics, cybersecurity, and e-commerce. Key areas include interpretable deep learning, text mining, and mobile health technologies. Notable projects include SilverLink, a smart home monitoring system for senior care using wearable sensors and machine learning. Yu’s publications span journals like MIS Quarterly and IEEE Transactions, emphasizing innovations in health profiling, fall prevention, and recommendation systems. His work integrates techniques such as Hidden Markov Models, Generative Adversarial Networks, and Bayesian networks. No scientific awards are listed. He is actively involved in advising doctoral students and contributes to the Association for Information Systems (AIS).

Mahdi Jampourمشاهده پروفایل

پژوهشگر

• Computer Vision
• Artificial Intelligence
• Digital Humanities
• +۳ مورد دیگر

Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.