جست‌وجوی استاد و پژوهشگر

• Signal Processing
• Image Processing
• Remote Sensing
• +۹ مورد دیگر

JAVIER CALPE MARAVILLA is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is a key member of the Image and Signal Processing (ISP) research group and affiliated with the ERI Image Processing Laboratory (IPL). His work bridges academic research and industrial applications in electronics and signal processing. Research Interests: His research spans signal and image processing, remote sensing, hyperspectral imaging, machine learning for classification, biophysical parameter estimation, analog microelectronics, and sensor systems. He focuses on developing algorithms for cloud screening, vegetation stress detection, ECG signal analysis, and industrial sensing applications. The recent publications (2009–2021) highlight a strong trend in applying kernel methods, neural networks, and adaptive filters to remote sensing and biomedical signals. His work emphasizes practical implementations in industrial and environmental monitoring, including touchscreens, AOTF-based imaging, and low-power circuit design. Education: Ph.D. in Electronics, Universitat de València (1994), with thesis on spectral estimation techniques. Scientific Awards: Advising and Grants: While no explicit list of students or grants is provided, his academic rank and publication record suggest experience in supervising postgraduate research. He has contributed to educational curriculum development, particularly in analog microelectronics. No funding sources are mentioned in the text. Labs and Teams: He is actively involved in the ISP (Image and Signal Processing) research group and the ERI Image Processing Laboratory (IPL) at the University of Valencia, focusing on both theoretical and applied research in electronic systems and data analysis.

• Gut Microbiota
• Microbiota-Gut-Brain Axis
• Irritable Bowel Syndrome (IBS)
• +۸ مورد دیگر

Premysl Bercik is a Professor in the Department of Medicine at McMaster University's Faculty of Health Sciences. His research focuses on the interaction between gut microbiota and the host's nervous and immune systems, particularly in disorders like irritable bowel syndrome (IBS), celiac disease, and inflammatory bowel disease (IBD). He has led studies on dietary interventions, microbial metabolites (e.g., histamine, phospholipids), and their impact on visceral pain and behavioral changes using humanized and gnotobiotic mouse models. Institution: McMaster University School: Faculty of Health Sciences Department: Department of Medicine Academic Rank: Professor Research Interests: Bercik's work explores the microbiota-gut-brain axis, emphasizing how gut bacteria influence chronic gastrointestinal disorders, mood, and immune responses. Key areas include gluten/wheat effects on IBS, tryptophan metabolism via the aryl hydrocarbon receptor, proteolytic microbiota in Crohn's disease, and stress-induced bacterial translocation. His studies often bridge microbiology, immunology, and neuroscience to uncover mechanisms linking gut health to systemic and psychological conditions. Recent Research Trends: His 2025-2024 publications highlight microbial phospholipids (LPC/LPA) as pain triggers in IBS, TRPC5/LPAR1-3 signaling in visceral hypersensitivity, and microbiota-mediated CGRP regulation. He investigates dietary interventions (low-FODMAP, tryptophan supplementation) and microbial metabolites' role in schizophrenia, Crohn's disease, and post-infectious IBS. His team also examines how rodent diet variability affects experimental reproducibility in microbiome research. Collaborations: Bercik collaborates with institutions like Queen's University and leverages McMaster Experts for scholarly activity. His work spans clinical trials (e.g., gluten-free diet efficacy), mechanistic studies on receptors like PAR2 and AHR, and translational models of stress and dysbiosis.

• Machine Learning
• Computer Vision
• Affective Computing
• +۷ مورد دیگر

Antonio Fernández-Caballero is a Full Professor at Universidad de Castilla-La Mancha in the Departamento de Sistemas Informáticos , with a career spanning since 1996. His academic contributions focus on interdisciplinary research at the intersection of Computer Science, Neuroscience, and Human-Computer Interaction. Primary affiliation: Universidad de Castilla-La Mancha Department: Sistemas Informáticos Academic rank: Professor His research interests include affective computing, machine learning, computer vision, and applications in healthcare and smart cities. He has published extensively on emotion recognition using EEG/fNIRS signals, robot gesture interpretation for autism therapy, and domain-specific generative AI for medical simulations. Recent publications highlight his work in extended reality for rehabilitation, IoT solutions for elderly care, and systematic reviews on deep learning for defect detection and smart city societal aspects. Collaborations span institutions in Spain and Portugal, with cross-disciplinary applications in medical informatics, robotics, and smart environments.

• Psycholinguistics
• Language and Cognition
• Gender Studies
• +۴ مورد دیگر

Sayaka Sato is a Part-Time Lecturer at the Department of Psychology, Faculty of Philosophy, University of Fribourg. She works within the Psycholinguistique & Psychologie Sociale team, focusing on cognitive and psycholinguistic research. University: University of Fribourg School: Faculty of Philosophy Department: Department of Psychology Academic Rank: PartTime Lecturer Her research explores the intersection of psycholinguistics and cognitive psychology, particularly how grammatical structures influence gender perception, inclusive language, bilingualism, and the embodiment of action-related language. Key themes include linguistic relativity, second language acquisition, and the cognitive processing of grammatical gender systems. Recent publications highlight her interdisciplinary approach, combining neuroscience, psycholinguistics, and social psychology to analyze topics like grammaticalization of the future tense in health communication, inclusive language impacts on gender visibility, and neural correlates of embodied semantics in multilingual contexts. Sayaka Sato collaborates with researchers across Switzerland and Europe, contributing to experimental linguistics and gender studies. Her work has appeared in journals such as Cognition , Bilingualism: Language and Cognition , and Frontiers in Psychology . As a lecturer, she contributes to teaching activities in the Faculty of Philosophy, with a focus on psychology-related courses. She is affiliated with the Psycholinguistique & Psychologie Sociale team, advancing research on language-cognition interactions.

• Econometrics
• Operational Research
• Statistics
• +۷ مورد دیگر

Prof. Tuba Yakıcı Ayan is a Professor of Econometrics at Karadeniz Technical University (KTU), Faculty of Economics and Administrative Sciences, where she has served since 1994. She currently acts as Deputy Head of the Department of Econometrics and coordinates both the Erasmus and Farabi exchange programmes for her faculty. Education Doctorate (1997–1999) – Atatürk University, Business Administration (Quantitative Methods) Postgraduate (1988–1990) – Karadeniz Technical University, Business Administration Undergraduate (1981–1986) – Istanbul Technical University, Business Engineering Undergraduate (1989–1992) – Karadeniz Technical University, Civil Engineering Research Interests Her scholarly work centres on econometrics , operational research and statistics , with particular emphasis on fuzzy decision-making models , efficiency and performance analysis , transportation optimisation , financial risk modelling , and energy-economic policy evaluation . A recurring theme is the application of advanced quantitative and artificial-intelligence techniques to socio-economic and managerial problems. Publication Trends Across more than thirty-nine refereed articles (2002-2024), a clear evolution emerges: early studies focused on Turkish banking efficiency and export performance; recent contributions integrate fuzzy logic, neural networks and gravity models into tourism demand, health-system benchmarking, sovereign credit rating, and sustainable-development assessments. Scientific Awards & Recognition Editorial board member and frequent reviewer for several SSCI/SCI journals (e.g., American Journal of Operations Research, Uluslararası İktisadi ve İdari İncelemeler Dergisi) Organiser and scientific committee member of 18th International Symposium on Econometrics, Operations Research and Statistics (2017) Advising & Grants Prof. Yakıcı Ayan has supervised five completed theses (one PhD and four master’s) on topics ranging from public-transport route optimisation to adaptive neuro-fuzzy credit-rating systems. She has participated in one funded TÜBİTAK-supported symposium project and regularly mentors students within the Erasmus and Farabi mobility programmes. Laboratories & Teams While no dedicated laboratory is listed, she actively leads the research group on quantitative methods within the Department of Econometrics, collaborates with colleagues across Turkey and Europe on multi-criteria decision-making projects, and supports data-driven policy analysis for regional development agencies.

Stefano Alberto Malanمشاهده پروفایل

استادیار

• Automatic Control
• Control Engineering
• Systems Engineering
• +۱۰ مورد دیگر

Stefano Alberto Malan is a Tenured Assistant Professor at the Department of Electronics and Telecommunications (DET) , Politecnico di Torino, and a member of the CARS@PoliTO - Center for Automotive Research and Sustainable Mobility . His academic rank is Assistant Professor in the scientific disciplinary sector IINF-04/A - Systems and Control Engineering (Area 0009 - Industrial and Information Engineering). Educational Background : Laurea in Electrical Engineering (1988) from Politecnico di Torino. Control Engineer at FIAT Research Center (1988-1990). Research Interests : Stefano's work spans Automatic Control , Control Systems Engineering , and Dynamic Modeling , with a focus on model-based and sensor-based algorithms for combustion and emission control in diesel engines and Direct Virtual Sensors . He contributes to Robust Control via uncertainty model sets identification, Building Energy Management (e.g., WiFi4Energy, EcoThermo projects), and Automotive Systems (chassis dynamics, engine controls). His methodologies include Computer-Aided Control System Design using H-infinity and interval arithmetic, and Multi-objective Robustness Analysis . Article Trends : His publications since 2015 emphasize Control Systems (e.g., LiDAR for UAS precision spraying), Diesel Engine Emissions Management , Virtual Sensor Design , and Sustainable Mobility . Recent works integrate Machine Learning (e.g., recurrent neural networks) and Optimization Techniques (e.g., genetic algorithms) into automotive and energy systems. Teaching Contributions : He has taught Automotive Control Systems (2019–2025) and Automation and Planning of Production Systems (2020–2021) at Politecnico di Torino’s Master of Science in Computer Engineering . He is a member of the College of Computer, Film and Mechatronics Engineering . Patents : Co-inventor of two national patents: Gruppo contabilizzatore per impianti termici di riscaldamento o raffrescamento (2013, heating/cooling metering unit). Metering unit for heating or cooling systems (2010, PCT/IB2009/055912). Laboratories and Teams : Active in the Automatica Research Group at DET and the CARS@PoliTO center, Stefano collaborates on projects like WiFi4Energy and EcoThermo to advance sustainable mobility and energy efficiency.

Daniele Dell'Aglioمشاهده پروفایل

دانشیار

• Computer Science
• Knowledge Graph Embedding
• Differential Privacy
• +۶ مورد دیگر

Daniele Dell'Aglio is an Associate Professor at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design . His research focuses on privacy-preserving data synthesis, knowledge graphs, and semantic data integration. Academic Rank: Associate Professor Department: Computer Science School: Technical Faculty of IT and Design University: Aalborg University Research Interests : Development of privacy metrics for synthetic data generation Heterogeneous graph representation in knowledge graphs Interactive evaluation tools for data privacy Transformer-based biomedical data extraction Integration of gut-brain axis scholarly data Scientific Awards : Best paper at the Deep Learning for Knowledge Graphs workshop (2022) Collaborative Projects include HEREDITARY (Heterogeneous semantic data integration for gut-brain interplay) as Principal Investigator, and partnerships with NASA on Mars exploration data processing. His work spans privacy-preserving technologies, knowledge graph engineering, and biomedical text mining applications.

Haim Avronمشاهده پروفایل

استاد

• Numerical Computing
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

• Speech Processing
• Biomedical Signal Analysis
• Neural Networks
• +۳ مورد دیگر

Adam Robert Kobus is a Lecturer at the Department of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics, and Computer Science, Maria Curie-Skłodowska University (UMCS) in Lublin, Poland. His academic profile includes roles in research and education, with a focus on speech processing and biomedical engineering. University: Maria Curie-Skłodowska University School: Faculty of Mathematics, Physics, and Computer Science Department: Department of Neuroinformatics and Biomedical Engineering Academic Rank: Lecturer Dr. Kobus specializes in speech recognition algorithms for detecting non-fluencies and disorders, combining techniques like linear prediction coefficients , neural networks , and Continuous Wavelet Transform (CWT) . His work integrates machine learning and biomedical signal processing to develop automated tools for analyzing speech patterns. Research trends include speech disorders diagnostics , neural network applications , and wavelet-based signal analysis . His publications emphasize computational linguistics and biomedical informatics . Dr. Kobus is based at the Institute of Computer Science, room 511, Lublin. He can be contacted via email at adam.kobus@mail.umcs.pl .

Michael Stefan Bittermannمشاهده پروفایل

دانشیار

• Architectural Design
• Computational Intelligence
• Visual Perception
• +۳ مورد دیگر

Assoc. Prof. Dr. Michael Stefan Bittermann is an academic at Istanbul Technical University , Department of Architecture. His career spans multiple institutions including Technical University Delft and Maltepe University, with roles ranging from Doctor Lecturer to research leadership positions. Doctorate from Delft University of Technology (2005) Post-doctoral Fellowship (80k€) at Delft University of Technology (2009) Currently Assistant Professor at ITU Previously held academic positions in Germany and The Netherlands His research focuses on Information Technologies in Architecture and Design , with key contributions in: Architectural Design Computational Intelligence Visual Perception Evolutionary Algorithms Fuzzy Logic Applications Multi-Objective Optimization Scientific activities reflect strong collaboration with Dr. Özer Ciftcioglu and others, with projects like the Interactive Computational Design initiative (2018-2020). Research outputs (42 total) show consistent contributions since 2006 in: IEEE Transactions on Systems, Man, and Cybernetics Nexus Network Journal CAADRIA proceedings IEEE Congress on Evolutionary Computation International collaborations span institutions in: The Netherlands (Delft University) Germany (Bittermann Weiss GmbH) Turkey (Istanbul Technical University) Japan (CEC conferences) USA (SIGGRAPH, NAFIPS)

Amir H Gandomiمشاهده پروفایل

استاد

• Machine Learning
• Evolutionary Computation
• Global Optimization
• +۷ مورد دیگر

Professor Amir H Gandomi is a leading academic in data science and artificial intelligence at the University of Technology Sydney, where he serves as Professor of Data Science at the Data Science Institute within the Faculty of Engineering and Information Technology. An ARC DECRA Fellow with over 450 journal papers and 14 books, his research has garnered more than 70,000 citations with an H-index exceeding 110. Ranked 18th among over 17,000 researchers in Genetic Programming bibliography and 24th in Artificial Intelligence & Image Processing by Stanford University, Prof. Gandomi is recognized as one of the world's most influential scientific minds. His research interests span machine learning, evolutionary computation, global optimization, and big data analytics, with applications across healthcare, structural engineering, environmental science, and cybersecurity. He has developed innovative frameworks like Adaptive Strategy Management for large-scale optimization and Boundary Update methods for constrained optimization problems. His work bridges theoretical advancements with practical implementations in diverse fields including medical diagnostics, renewable energy site selection, and smart infrastructure. Prof. Gandomi's publication portfolio demonstrates consistent high-impact contributions across multiple disciplines, with recent work focusing on AI-driven healthcare solutions, optimization algorithms, and climate modeling. His research shows strong interdisciplinary connections between computer science, engineering, and medical applications, with particular emphasis on practical implementations of theoretical frameworks. The breadth of his work reflects both deep technical expertise and the ability to apply computational methods to solve real-world problems across various domains. 2024 IEEE TCSC Award for Excellence in Scalable Computing (MCR) 2023 Achenbach Medal from Stanford University 2022 Walter L. Huber Prize (highest-level mid-career civil engineering research award) 2025 Sigma Xi Young Investigator Award 6 consecutive years as Clarivate Analytics Highly Cited Researcher AmCham Alliance Award in AI As a dedicated educator and mentor, Prof. Gandomi has supervised numerous research students in evolutionary machine learning, structural health monitoring, and uncertainty-aware AI systems. His funded research projects include Amazon Research Awards for medical report generation, Climate Change AI grants for drought prediction, and Digital Finance CRC projects for cyber threat detection. He leads the Data Science Institute's efforts in developing practical AI solutions while maintaining strong industry partnerships and international collaborations across multiple continents.

• Missing Data Imputation
• Machine Learning
• Healthcare Informatics
• +۳ مورد دیگر

Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.

Raj Rao Nadakuditiمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Random Matrix Theory
• Machine Learning
• +۷ مورد دیگر

Raj Rao Nadakuditi is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, College of Engineering. His research bridges statistical signal processing, random matrix theory, and machine learning with applications in sensing, imaging, and communications. Research Interests: Statistical Signal Processing and Learning Random Matrix Theory and Applications Signal Processing for Sensor Networks Light Transport Through Opaque Media Eigen-wavefront Imaging for Brain Circuit Mapping High-Dimensional Data Analysis His recent work focuses on overcoming fundamental limits in imaging through highly scattering media, such as brain tissue, by leveraging eigen-wavefronts and algorithmic innovations. This has direct implications for the BRAIN Initiative and optical neuroimaging. Publications Trends: His most recent articles reflect a strong emphasis on the application of random matrix theory to signal detection, covariance estimation, and imaging through random media. Key themes include eigenvalue analysis, high-dimensional statistics, spectral methods, and wavefront shaping for deep-tissue imaging. Scientific Awards: DARPA Young Faculty Award (2014) ONR Young Investigator Program Award AFOSR Young Investigator Program Award Advising and Grants: Prof. Nadakuditi has advised students in advanced signal processing and machine learning. His research is generously supported by the National Science Foundation, DARPA, Office of Naval Research, Air Force Office of Scientific Research, and Army Research Office’s MURI program. He has led projects on fundamental limits of imaging and robust signal processing in low-SNR environments. Labs and Teams: He collaborates with Prof. Eric Michielssen on computational electromagnetics and is part of the broader EECS research community at the University of Michigan focused on autonomy, AI, and precision health. His group works on theoretical and applied aspects of sensing and imaging systems.

Hui Liمشاهده پروفایل

دانشیار

• Information Retrieval
• Data Mining
• Data Management
• +۸ مورد دیگر

Hui Li is an Associate Professor in the Department of Computer Science and Technology at the School of Informatics, Xiamen University. He is affiliated with the Media Analytics & Computing Laboratory (MAC Lab) and holds a Ph.D. in Computer Science from the University of Hong Kong. Ph.D., Computer Science, University of Hong Kong His research centers on Information Retrieval, Data Mining, and Data Management, with a strong emphasis on Recommender Systems. Key areas include shilling attacks and defenses, bias analysis and debiasing, large language model-based recommendation, generative information retrieval, code retrieval, and multi-modal large language models. His work bridges theoretical innovation with practical applications in real-world platforms. His recent publications span top-tier journals and conferences such as IEEE TKDE, TNNLS, TOIS, KDD, SIGIR, AAAI, ACL, and ICLR. The publications demonstrate a consistent focus on robustness, fairness, and efficiency in recommender systems, increasingly integrating large language models and graph-based methods. Themes include adversarial attacks, self-supervised learning, and data augmentation using LLMs. Hui Li actively supervises graduate students, many of whom have published in CCF-A and CCF-B ranked venues. Notable students include Zhang Sheng, Liu Hao, and Li Lianyun. He has contributed to open science by releasing datasets like the Dianping datasets for sequential and social recommendation research. His work has practical implications in e-commerce, social platforms, and software engineering tools. He collaborates with industry partners such as Alibaba, Tencent, and Pinduoduo, where several of his students have been placed. His research is supported by publications in high-impact journals and participation in leading AI conferences.

Chao Liمشاهده پروفایل

پژوهشگر

• Tensor Network
• Machine Learning
• Combinatorial Optimization
• +۳ مورد دیگر

Dr. Chao Li is an indefinite-term research scientist at the RIKEN Center for Advanced Intelligence Project (AIP) since 2021, focusing on tensor network learning and its applications in machine learning. His work involves modeling tensor network structure search (TN-SS) as a combinatorial optimization problem, with contributions to algorithms like TNGA and TNLS. He earned his Ph.D. and bachelor’s degrees from Harbin Engineering University in 2017 and 2006, respectively. Dr. Li’s research spans tensor networks, machine learning, and optimization. He has presented at leading conferences like ICML and regularly serves as a senior reviewer for top-tier machine learning venues such as ICML, NeurIPS, and AAAI. The 2022 presentation on TN-SS highlights his work in advancing tensor network methodologies for machine learning tasks, leveraging optimization techniques and exploring applications in neural networks and computational mathematics. Dr. Li is affiliated with the EPFL Center for Intelligent Systems (CIS) through collaborative events with RIKEN AIP, though no specific labs or teams are detailed in the provided text.