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Martin Wulf GerdesView profile

Associate Professor

• eHealth
• Artificial Intelligence
• Machine Learning
• +9 more

Martin Wulf Gerdes is an Associate Professor at the Department of Information and Communication Technology, University of Agder, Norway. With a Ph.D. in eHealth and over two decades of experience in telecommunications and healthcare technology research, he focuses on developing user-friendly, interoperable eHealth solutions using ICT and IoT. His career spans roles at Ericsson Eurolab and academic leadership in Norway. Education: M.Sc. equivalent in General Electrical Engineering (1998) from RWTH Aachen, emphasizing ICT. Ph.D.: Information and Communication Technologies with eHealth specialization (2019) from University of Agder. Research Interests: Gerdes explores AI-driven decision support systems, distributed eHealth infrastructure, telemedicine, and assistive technologies for chronic disease management. His work bridges IoT, data privacy, and user-centered design in healthcare robotics. Teaching: Since 2014, he has taught at bachelor's and master's levels, currently leading courses in Communication & Cooperation, Technology Understanding, and seminar projects in eHealth. Scientific Publications: His 15 most recent articles (2020-2024) span AI in dental radiology, blood glucose prediction, digital twins, and ethical considerations in healthcare algorithms. Topics include IoT integration, data privacy, and pandemic analytics.

• Signal Processing
• Machine Learning
• Neural Networks
• +3 more

Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.

Martina NARDONView profile

Assistant Professor

• Financial Mathematics
• Actuarial Science
• Option Pricing
• +5 more

Martina NARDON is an Assistant Professor of Mathematical Methods for Economics and Actuarial and Financial Sciences at the Department of Economics, Ca' Foscari University of Venice. She is also affiliated with the Interdepartmental School of Economics, Languages and Entrepreneurship for International Exchanges based in Treviso. Her academic work spans teaching, research, and service as a member of the Economics Degree Commission. Education: Ph.D. in Mathematics for Economic Decisions, University of Trieste, Italy (2002) Laurea degree in Economics (Economia e Commercio), Ca' Foscari University of Venice, Italy (1998) Dr. NARDON's research focuses on quantitative methods in finance, with particular expertise in option pricing, American options, Monte Carlo simulation, stochastic processes, and credit risk. Her work increasingly incorporates behavioral finance perspectives, examining how psychological factors influence financial decision-making and risk assessment. She applies mathematical modeling to practical financial problems, with recent work extending into fundraising management and neural network applications. Analysis of her recent publications (2023-2024) reveals a strong trend toward behavioral finance applications, particularly in probability weighting functions and their implications for portfolio selection and insurance pricing. Her work bridges traditional quantitative finance with behavioral economics, often employing advanced computational methods including artificial neural networks. The research demonstrates consistent focus on practical financial applications while maintaining rigorous mathematical foundations. Professional Service: Member of the Economics Degree Commission Ad hoc referee for European Journal of Operational Research, Decisions in Economics and Finance, and other journals Participant in numerous international conferences including MAF (Mathematical and Statistical Methods for Actuarial Sciences and Finance) series Dr. NARDON teaches advanced courses in Financial and Insurance Products Technique and Quantitative Methods for Financial Choices at the Master's level. Her teaching reflects her research expertise, emphasizing both theoretical foundations and practical applications of quantitative financial methods. She maintains active research collaborations both within Ca' Foscari University and internationally.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +5 more

Marie Candito is a Lecturer at the School of Linguistics, Paris Cité University. She serves as Deputy Director of the Laboratoire de Linguistique Formelle (LLF, CNRS) since January 2025 and Head of the M2 Computational Linguistics program at Paris Cité University. Her research focuses on natural language processing, computational linguistics, and linguistic abilities of large language models. Current Projects : Co-PI of ANR SELEXINI (2021-2025) and scientific coordinator for LLF in ANR PANTAGRUEL (2023-2025) Past Projects : PI of ANR ASFALDA-French FrameNet (2013-2016), scientific coordinator for ANR PARSEME-FR (2015-2019), and member of ANR SEQUOIA (2010-2013) Her work involves measuring and mitigating biases in language models, inducing semantic lexicons from corpora, and studying human vs. LLM word associations. She supervises PhD students including Maria Andueza Rodriguez, Anna Mosolova, and David Kletz.

Jeongsub ChoiView profile

Assistant Professor

• Machine Learning
• Data Mining
• Data Analytics
• +6 more

Jeongsub Choi is an Assistant Professor in the Department of Management Information Systems at West Virginia University's John Chambers College of Business and Economics. His work bridges machine learning, data mining, and business intelligence with applications in strategic management, patent analysis, and advanced manufacturing. Ph.D. in Industrial and Systems Engineering, Rutgers University M.S. in Statistics, Rutgers University M.S. in Industrial and Systems Engineering, Rutgers University Choi's research focuses on sparse learning models, network analysis, and virtual metrology systems for semiconductor manufacturing. His work spans predictive maintenance, competitor detection, and anomaly identification in dynamic networks. Recent publications highlight trends in sensor optimization, fault diagnosis, and technology lifecycle modeling. Applications span semiconductor manufacturing, financial transaction networks, and patent citation analysis.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Yann LeCun is the Chief AI Scientist at Meta and holds the Jacob T. Schwartz Professorship at New York University in Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering. He is a member of the National Academy of Engineering, National Academy of Sciences, and Académie des Sciences, and a fellow of ACM, AAAI, AAAS, and SIF. Research Interests: AI, Machine Learning, Computer Vision, Robotics, Computational Neuroscience, Physics of Computation. Labs: CILVR Lab (Computational Intelligence, Learning, Vision, Robotics), Computational and Biological Learning Lab at Courant Institute, Meta FAIR (Fundamental AI Research). Scientific Awards include the ACM Turing Award (2018), Queen Elizabeth Prize for Engineering (2025), VinFuture Grand Prize (2024), and multiple honoris causa doctorates. He has mentored numerous PhD students and postdocs, including Ying Wang, Yilun Kuang, and Quentin Le Lidec. Recent Publications focus on self-supervised learning, world models, video analysis, and representation learning, with key contributions to autonomous machine intelligence architectures.

• Wireless Communication
• Communication Systems
• Cellular Networks
• +4 more

Ki Won Sung is an Associate Professor at KTH Royal Institute of Technology, specializing in wireless communication systems and network optimization. His research focuses on 5G/6G technologies, integrated sensing-communication systems, cell-free massive MIMO, and stochastic network modeling. Research interests include: Wireless network architecture design and optimization Resource allocation in multi-user communication systems Integrated sensing and communication (ISAC) Stochastic modeling of ultra-dense networks Energy-efficient communication protocols Millimeter wave and massive MIMO systems His recent publications demonstrate strong emphasis on beyond-5G systems, particularly cell-free massive MIMO deployments and URLLC applications. Research trends show consistent focus on network optimization through advanced signal processing, geometric decomposition methods, and cross-layer protocol design. Teaching activities include course management and examination for multiple degree projects and core courses: Communication Systems (IK2200) Mathematical Statistics (IX1501) Mobile Networks and Services (IK2560) Radio Networks (IK2510) Stochastic Simulation (II2206) Wireless Systems (IK1330)

Chun-Biu LiView profile

Associate Professor

• Computational Mathematics
• Statistical and Computational Methods in Biophysical Systems
• Information Theory
• +4 more

Chun-Biu Li is an Associate Professor at the Department of Mathematics , Stockholm University , specializing in computational mathematics and statistical physics of biophysical systems. His research bridges information theory, machine learning, and nonequilibrium statistical mechanics to understand complex biological processes. Education: PhD in mathematical & statistical physics from the University of Texas at Austin (supervised by Ilya Prigogine), M.S. in mathematical physics from the University of Utah. Academic Experience: Associate Professor (2016–present) at Stockholm University, Associate Professor (2008–2016) at Hokkaido University, and JST/CREST Researcher (2005–2008) at Kobe University. Research Interests focus on data-driven statistical analyses, nonequilibrium biophysical systems, AI explainability, and fluctuation theorems. His work applies these methods to plant morphogenesis, molecular motor dynamics, and machine learning applications in life sciences. Teaching includes advanced courses on deep learning, reinforcement learning, and statistical methods for life science applications. His Supervision has guided over 30 students in topics ranging from protein mutation modeling to causal inference algorithms. Scientific Awards: Professional Development Award, University of Texas at Austin (2003) Summer Research Fellowship, University of Texas at Austin (2001) National Dean's List for Outstanding College Students (1997) Publications (110+) highlight his contributions to plant morphogenesis, single-molecule analysis, and statistical methods. Recent works examine DNA barcode clustering, rotary motor protein dynamics, and shape-aware data visualization techniques.

• Topological Data Analysis
• Dynamical Systems
• Machine Learning
• +7 more

Firas Khasawneh is an Associate Professor at the Department of Computational Mathematics, Science and Engineering at Michigan State University. His research focuses on the intersection of topological data analysis, dynamical systems, and machine learning with applications in engineering, neuroscience, and biological systems. Topological Data Analysis - Persistent Homology, Zigzag Persistence, Data Assimilation Dynamical Systems - Bifurcation Detection, Delay Differential Equations, Nonlinear Dynamics Machine Learning - Applications in Signal Processing, Surface Texture Analysis, Neural Activity Modeling His recent publications (2023-2025) demonstrate a strong emphasis on topological approaches for analyzing complex systems, including neural activity characterization, vibration analysis in manufacturing processes, and data assimilation techniques for forecasting. He has developed computational tools like the Teaspoon Python package for topological signal processing.

Emmanouil Z. PsarakisView profile

Associate Professor

• Image Processing
• Computer Vision
• Machine Learning
• +7 more

Emmanouil Z. Psarakis is an Associate Professor at the Department of Computer Engineering & Informatics , University of Patras, Greece. Born in 1963, he has been affiliated with the university since 2005 and is a member of the Signal Processing and Communications Lab . His academic journey includes teaching courses such as Signals and Systems Theory , Digital Signal Processing , and Computer Vision and Graphics . PhD in Computer Engineering and Informatics, University of Patras (1991) Dr. Psarakis is renowned for his research in image processing , computer vision , and signal processing , with applications in biomedical imaging , seismology , and robotics . His work spans deep learning , filter design , and 3D reconstruction , reflecting a multidisciplinary approach. His 15 most recent publications (2020–2025) focus on sign language recognition , adversarial defense , 3D shape analysis , and medical imaging . These works integrate machine learning , stochastic modeling , and computer vision to address challenges in video summarization , image inpainting , and seismic signal analysis . Dr. Psarakis has supervised over 10 PhD students , including Fotini Fotopoulou (2016–2019) and Panagiotis Georgantopoulos (2020–2023). He has participated in EU-funded , Hellenic , and bilateral R&D projects related to biomedical signal processing , robot vision , and forest fire monitoring . He leads the Signal Processing and Communications Lab , which collaborates with institutions like the Computer Technology Institute and international universities. His contributions include filter design , image alignment , and stereopsis techniques , with a focus on practical implementations in telecommunications and medical diagnostics .

Tsui-Wei WengView profile

Assistant Professor

• Neural Network Robustness
• AI Safety
• Deep Learning Theory
• +8 more

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.

Birsen SirkeciView profile

Professor

• Wireless Communications
• Sensor Networks
• Statistical Signal Processing
• +1 more

Birsen Sirkeci is a Professor in the Department of Electrical Engineering at San Jose State University (SJSU), San Jose, CA. She holds a PhD from Cornell University (2006), an MSc from Northeastern University (2000), and a BSc from Middle East Technical University, Turkey (1998). Prior to joining SJSU, she was a postdoctoral researcher at UC Berkeley. Her educational background includes: PhD in Electrical Engineering, Cornell University, Ithaca, NY (2006) MSc in Electrical Engineering, Northeastern University, Boston, MA (2000) BSc in Electrical Engineering, Middle East Technical University, Ankara, Turkey (1998) Professor Sirkeci's research spans wireless communications , sensor networks , statistical signal processing , and machine learning . Her work focuses on developing advanced algorithms for cognitive radio networks, spectrum sensing, and cooperative communication systems. She has pioneered applications of neural networks in materials science (e.g., stress prediction in porous ceramics) and medical imaging (e.g., colon cancer detection), demonstrating exceptional interdisciplinary innovation. Analysis of her 2013-2021 publications reveals a dominant trend at the intersection of wireless communications and deep learning. Key themes include spectrum sensing via convolutional neural networks, cooperative broadcast strategies in dense networks, and physics-informed machine learning for materials engineering. Her work consistently addresses real-world constraints like channel estimation errors and hardware limitations using USRP SDRs. Her scientific contributions have been recognized with: Best Paper Awards at MILCOM 2005, WCECS 2010, and ICETEC 2013 IEEE ICME Outstanding Organizing Committee Member (2013) Applied Materials Teaching Award at SJSU (2014) As an advisor, she co-led the SJSU Spartans team to the DARPA Spectrum Challenge finals in 2013. While specific grants aren't detailed in source material, her award-winning publications and competition success indicate sustained research funding. She actively mentors students through capstone projects and competitive teams, emphasizing hands-on implementation. Professor Sirkeci leads wireless communications research within SJSU's Electrical Engineering department, with strong ties to industry through awards like Applied Materials. Her DARPA Spectrum Challenge involvement underscores leadership in translating theoretical research into competitive, real-world systems.

Edmund SpencerView profile

Associate Professor

• Space Plasma Physics
• Space Weather
• Radio Frequency Instrumentation
• +3 more

Dr. Edmund Spencer is an Associate Professor in the Department of Electrical and Computer Engineering at the University of South Alabama , with research focused on space plasma physics and space weather . He designs advanced instruments for space science, develops theoretical frameworks for plasma characterization, and applies stochastic optimization algorithms to complex systems. Ph.D. Electrical and Computer Engineering, University of Texas at Austin M.S. Electrical and Computer Engineering, University of Texas at Austin B.S. Electrical and Electronics Engineering, University of Leicester, UK His work bridges space instrumentation with nonlinear magnetospheric dynamics , particularly in geomagnetic substorms and solar wind-earth magnetosphere interactions . Current projects include onboard space weather modules for satellites and advanced antenna systems for CubeSats . Recent research trends from his 15 most recent publications (2019-2025) include: Development of time-domain impedance probes for ionospheric electron density measurements Applications of machine learning in substorm prediction Hybrid physics-black-box modeling for Dst index forecasting Advanced antenna designs for small satellites 3D Particle-in-Cell simulations for RF instruments Collisional effects in plasma probe measurements Scientific contributions include: NSF CAREER Award (2013) for RF impedance probe development Key role in NASA's USIP CubeSat missions (e.g., JAGSAT I) Leveraging WINDMI model for substorm dynamics analysis He teaches graduate and undergraduate courses in electromagnetics and stochastic processes , contributing to the department's space science integration in engineering education.

• Structural engineering
• Steel structures
• Composite structures
• +4 more

Mina Mortazavi is a Senior Lecturer at the University of Technology Sydney's School of Civil and Environmental Engineering with over 15 years of experience specializing in structural engineering. Her academic journey includes a PhD in Structural Engineering from Western Sydney University, an MEng in Structural Engineering from Amirkabir University of Technology in Tehran, and a BSc in Civil Engineering from Shahid Beheshti University in Tehran. Her research interests focus on three interconnected fields: cold-formed steel profile assessment and section optimization, modularization in construction, and prefabrication of seismic mounting systems for building services. Mortazavi has developed expertise in applying machine learning techniques to structural engineering problems, particularly in thermal buckling analysis, seismic performance evaluation, and concrete material behavior prediction. Her publication record demonstrates consistent output in high-impact journals such as Thin-Walled Structures , Automation in Construction , and Journal of Building Engineering . Recent research shows increasing integration of artificial intelligence methods with traditional structural engineering problems, particularly in thermal analysis, seismic performance evaluation, and material behavior prediction. Research Innovation Connection grant recipient Multiple contract research projects with industry partners Active PhD and Masters student supervision Mortazavi's teaching portfolio includes courses in Steel and Composite Design, Steel and Timber Design, Mechanics of Solids, and Application of Timber in Engineering Structures. Her industry collaborations demonstrate strong practical application of research findings to real-world structural engineering challenges.

Athanasios D. PanagopoulosView profile

Professor

• Communications
• Wireless Communications
• Radio Communications
• +9 more

Athanasios D. Panagopoulos is a Full Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in Satellite & Space Communications , Antennas and Propagation , and Quantum Communication . He leads the Division of Information Transmission Systems and Material Technology, with a focus on wireless systems, machine learning, and 5G/6G technologies. Born in Athens (1975), received summa cum laude Diploma and Dr. Engineering from NTUA (1997, 2002). Former roles: part-time Assistant Professor (2003-2007), head of Satellite Division at Hellenic Authority for Information Security (2005-2008). Research Trends emphasize Quantum Key Distribution (QKD) , Reconfigurable Intelligent Surfaces , and Deep Learning Applications in satellite networks. His work bridges atmospheric propagation effects with terrestrial-satellite convergence , including AI-driven excess attenuation prediction and UAV channel modeling. Scientific Awards include URSI General Assembly Young Scientist Award (2002, 2005) Best Paper Awards: IEEE RAWCON 2006, IEEE ISWCS 2015 Grants & Collaborations : Principal Investigator for EU/ESA R&D programs, with editorial roles at IEEE Transactions on Antennas and Propagation, and Elsevier Physical Communication. Member of ITU-R , ETSI Study Groups , and IEEE (Senior) .