جست‌وجوی استاد و پژوهشگر

• Signal Processing
• Acoustics
• Psychoacoustics
• +۷ مورد دیگر

Armin Taghipour is a Professor in Signal Processing and Acoustics at the Institute of Electrical Engineering, Lucerne School of Engineering and Architecture. He leads the Research Group Waves & Signal Processing and oversees Continuing and Executive Education in Innovation & Technology. His roles include MSE Advisor in the HSLU Master of Science program and President of the Swiss Acoustical Society SGA-SSA. Education: B.Sc. Microelectronics (University of Guilan, Iran), M.Sc. Telecommunications (University of Hanover, Germany), Dr. Eng. in Psychoacoustics (International Audio Laboratories Erlangen, Germany). Professional expertise spans audio signal processing, psychoacoustic modeling, noise annoyance evaluation, and acoustic comfort design. He has conducted research at Empa, Fraunhofer IIS, and the Hanover Medical School. Research focuses on urban acoustics, noise mitigation, room acoustics optimization, and psychoacoustic experiments. Recent work addresses soundscape quality in residential areas, cognitive impacts of noise, and AI-driven odor classification systems. Over 50 publications span environmental acoustics, audio engineering, and multidisciplinary applications. Lab leadership: Head of Research Group Waves & Signal Processing Key projects: Soundscape design, autonomous acoustic measurement systems, and green healthcare building acoustics Grants: IDN Performance Lab project leadership (2022) Labs/Teams: Active in the Institute of Electrical Engineering's Autonomous Systems & Robotics division, collaborating with DAGA, EAA, and SGA-SSA.

• Human-Robot Interaction
• Affective Computing
• E-commerce Search Algorithms
• +۴ مورد دیگر

Mingming Li is an active professor at the Chinese Academy of Sciences with extensive research contributions across multiple domains including human-robot interaction, e-commerce systems, and wireless communications. With over 110 publications spanning from 2007 to 2025, Dr. Li demonstrates consistent scholarly productivity and leadership in interdisciplinary research. Dr. Li's research interests span Human-Robot Interaction , Affective Computing , E-commerce Search Algorithms , Machine Learning , and Wireless Communications . Recent work focuses on understanding how humanoid robots' multimodal characteristics (appearance, voice, touch) affect users' emotional responses and cognitive processing, employing physiological measures like ECG and fNIRS. Simultaneously, Dr. Li leads research in e-commerce search optimization, developing novel deep learning architectures for improved recommendation systems. The publication record reveals significant trends toward increasingly sophisticated multimodal analysis in HRI and more efficient neural architectures for practical applications. Dr. Li's work bridges theoretical advancements in machine learning with practical implementations in human-centered systems. Dr. Li has advised numerous graduate students including Jiahao Chen, Aung Naing Win, and Songlin Wang, with whom collaborative publications demonstrate strong mentorship. Research has been supported by substantial funding reflected in the volume and quality of publications across top venues. Current research activities include leading projects on digital twin-based thermal error compensation, multimodal affective robot design, and advanced search algorithms for e-commerce platforms, indicating an active laboratory environment with multiple ongoing research threads.

Demetrios Cantzosمشاهده پروفایل

استاد

• Signal Processing
• Pattern Recognition
• Machine Learning
• +۳ مورد دیگر

Demetrios Cantzos is a Professor at the Department of Industrial Design and Production Engineering, University of West Attica. He teaches courses in signal processing, pattern recognition, and machine learning. He holds a Ph.D. (2008) from the University of Southern California (USC) and a B.S. (2002) in Physics from the University of Athens. His research focuses on signal processing applications in seismology, environmental science, and engineering systems. He has participated in US Army/NASA-funded projects (2002-2008) and EU/National programs (2009-present) addressing biomedical, energy, and agricultural challenges. Key research interests include fractal analysis of seismic and environmental signals, electromagnetic precursors, and machine learning for signal interpretation. His work combines geophysics, electrical engineering, and data science to improve earthquake prediction and environmental monitoring systems. Recent publications (2020-2025) emphasize long-memory trends in electromagnetic and radon data, multifractal analysis of seismic activity, and backprojection techniques for rupture modeling. These studies contribute to understanding earthquake dynamics and developing early warning systems. No scientific awards are explicitly listed. He advises students in signal processing and AI-related disciplines through postgraduate programs like the MSc in Artificial Intelligence and Deep Learning.

Elmahedi Mahalalمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Mobile Networks
• +۳ مورد دیگر

Elmahedi Mahalal is an Assistant Professor at the Tagliatela College of Engineering, University of New Haven, specializing in Electrical & Computer Engineering and Computer Science. He holds a Ph.D. in Computer Science from Tennessee Technological University, along with a bachelor's and master's in Telecommunications. His research focuses on future mobile networks, cybersecurity, and AI applications, with industry experience in mobile network engineering and AI-driven solutions. Education: Ph.D. in Computer Science, Tennessee Technological University M.Sc. in Telecommunications B.Sc. in Telecommunications Dr. Mahalal's research interests revolve around secure wireless communication systems, leveraging deep learning and AI for challenges in LiFi networks, terahertz sensing, and dynamic environments. His work addresses concept drift, secure key generation, and network resilience. He actively publishes on topics such as physical layer authentication, occupancy-aware channel prediction, and GAN-assisted security mechanisms. His articles highlight advancements in LiFi networks, terahertz technology, and adaptive machine learning models for wireless systems. Though no specific awards or grants are listed, his contributions bridge academic research with practical industry applications.

Matt Boutellمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Robotics Education
• +۲ مورد دیگر

Matt Boutell is an Associate Professor in the Department of Computer Science and Software Engineering at Rose-Hulman Institute of Technology. His research focuses on multimedia content understanding, robotics education, and computer vision techniques for scene classification. Education: PhD in Computer Science, University of Rochester (2005) MS in Electrical and Computer Engineering, University of Rochester (1989) BS in Electrical Engineering, University of Rochester (1985) His research spans computer vision (semantic scene classification, image orientation detection), educational robotics (simulator development), and computer science pedagogy. Recent publications show strong interest in AI applications for education and security systems. Work integrates low-level vision features with contextual metadata for improved classification. Awards recognize innovations in multimedia analysis and semantic scene understanding. He co-developed Rose-Hulman's multidisciplinary robotics minor and created educational tools like the iRobot Create simulator. As advisor, he guides students in robotics capstone projects and mentors research in vision systems. He coordinates the robotics curriculum and maintains active industry collaborations.

• Migration Studies
• Generative AI
• Innovation Research
• +۳ مورد دیگر

Jussi Jauhiainen is Professor of Geography at the University of Turku, focusing on migration patterns, educational technology, and regional innovation systems. His research examines spatial dimensions of human mobility and technological transformations across educational and cultural contexts. Key research areas: Generative AI applications in education Transnational migration and diaspora networks Innovation ecosystems in Global South contexts Cultural geography of collective events Recent publications explore large language models for educational assessment, AI-driven learning personalization, return migration patterns, and innovation research in Africa. His work demonstrates strong interdisciplinary connections between geography, education, and computer science.

Yi-Chang James Tsaiمشاهده پروفایل

استاد

• Smart Cities Infrastructure
• Transportation Systems Engineering
• Pavement Preservation and Management
• +۵ مورد دیگر

Dr. Yi-Chang James Tsai is a Professor and CISE Group Leader at Georgia Institute of Technology's School of Civil and Environmental Engineering. He specializes in smart cities infrastructure, transportation systems engineering, and pavement preservation using advanced sensing technologies. His work has significantly impacted Georgia’s pavement management systems, including the development of a GIS-based pavement preservation system for GDOT. He holds the prestigious Chinese Chang Jiang Scholar title (2009) and received an innovation award for GIS-enabled ship recognition (2007). His research integrates emerging technologies like 3D laser, LiDAR, and AI to enhance infrastructure resiliency, roadway safety, and asset management. Key projects include predictive safety modeling, autonomous vehicle integration, and sustainability of transportation networks. Dr. Tsai leads the Smart City Infrastructure (SCI) Lab, focusing on infrastructure condition assessment, deterioration analysis, and optimization strategies. Education: Doctorate in Civil Engineering (prior to Georgia Tech faculty role) Grants: Multiple GDOT-funded projects on pavement preservation, HFST implementation, and sensor-based safety systems Dr. Tsai’s work emphasizes data-driven decision-making, cost-effective maintenance, and innovation in transportation infrastructure. His lab has pioneered technologies for automated sign detection, pavement crack analysis, and real-time safety scoring. Key Achievements: Developed GDOT’s pavement preservation system managing 18,000 miles of highways Advanced AI-based crack detection and 3D imaging techniques Leader in roadway safety audits and curve sign optimization The Smart City Infrastructure Lab collaborates with industry partners to address challenges in infrastructure sustainability, resiliency, and smart mobility solutions for aging populations and energy efficiency.

Lianhua Chiمشاهده پروفایل

مدرس

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۷ مورد دیگر

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

• Atmospheric Pollution Modeling
• Air Quality Assessment
• Machine Learning Applications in Environmental Science
• +۵ مورد دیگر

Andreja Stojić is an active academic researcher at Singidunum University in Belgrade, Serbia, specializing in atmospheric pollution modeling and environmental health. Her work integrates advanced machine learning techniques with traditional environmental science methodologies to address urban air quality challenges. Born January 3, 1976 in Jagodina, Serbia Doctoral studies (2007-2015) in Theoretical Physics/Atomic and Molecular Physics at University of Belgrade Bachelor studies (1998-2007) in Applied Physics and Informatics at University of Belgrade Her primary research focuses on atmospheric pollution dynamics, particularly PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and their health implications. She pioneers explainable artificial intelligence approaches for predicting pollutant behavior, including innovative applications of XGBoost, SHAP, and hybrid metaheuristics. Her work bridges environmental chemistry, computational modeling, and public health risk assessment. Analysis of her 15 most recent publications reveals a strong trend toward explainable AI frameworks for environmental systems, with increasing emphasis on PM2.5-bound pollutants, indoor-outdoor pollution transfer mechanisms, and VOC fate prediction. Her research consistently targets urban environments, particularly Belgrade, with growing international collaboration evident in Mediterranean marine pollution studies. While no specific scientific awards are documented in the provided materials, her extensive publication record in high-impact journals demonstrates significant scholarly contribution to environmental science. Dr. Stojić leads collaborative research projects involving multi-institutional teams across Serbia and international partners, focusing on atmospheric modeling and pollution source apportionment. Her work frequently employs receptor modeling techniques and advanced multivariate statistical methods for environmental data analysis. Her research infrastructure includes access to PTR-MS measurement systems and computational resources for developing AI-driven environmental models, with applications spanning urban air quality management to marine ecosystem contamination studies.

• Public Health
• Agent-Based Modeling
• Systems Science
• +۴ مورد دیگر

Dr. Kashif Zia is a Research Associate at the University of Glasgow within the MRC/CSO Social and Public Health Sciences Unit. His work bridges Public Health and Computer Science , focusing on computational models and data-driven interventions. Role : Research Associate (Public Health) Institution : University of Glasgow, Scotland, UK Research Interests Agent-Based Modeling for housing markets and public health Systems Science in tackling socioeconomic crises like cost-of-living Computational methods for secure biometric systems Epidemiological simulations for pandemic response Integration of behavioral economics with AI Uncertainty propagation in complex models Publications highlight cross-disciplinary work combining public health with computational techniques , including models for housing markets, synthetic population datasets, and cybersecurity applications. Notable collaborations include work with the International COVID-19 Research Network. Labs & Teams : Contributed to the SIPHER project , developing tools for public health research through synthetic data and simulations.

Sam Baughمشاهده پروفایل

استادیار

• Spatial Statistics
• Climate Science
• Environmental Statistics
• +۳ مورد دیگر

Sam Baugh is an Assistant Professor of Statistics at Pennsylvania State University's Department of Statistics since Fall 2023. He holds a PhD from UCLA (2022) and MS/BS degrees from the University of Chicago. His research focuses on developing statistical methods for climate change impacts, spatio-temporal modeling, Bayesian hierarchical approaches, and psychometrics, with collaborations in medicine/public health. Education: Ph.D. in Statistics, UCLA (2022) MS in Statistics, University of Chicago (Advisor: Michael Stein) BS in Mathematics, University of Chicago Research interests emphasize climate science applications, including ocean/atmosphere changes and extreme event probabilities. Methodologically, he specializes in: Bayesian hierarchical models Spatio-temporal statistics Computational approximations Psychometric modeling Recent work spans climate attribution studies, medical AI applications, and latent variable modeling. His publications demonstrate cross-disciplinary impact across climate science, surgery outcomes, and educational measurement. Awards include the NSF Postdoctoral Fellowship (2022-2023) and UCLA's Outstanding Ph.D. Student Award (2022). Collaborates with institutions like Lawrence Berkeley National Lab and medical teams in plastic surgery research. Active in both environmental and health-related statistical method development.

Julie Vercelloniمشاهده پروفایل

استاد مهمان

• Coral Reef Conservation
• Machine Learning Applications
• Citizen Science
• +۳ مورد دیگر

Dr. Julie Vercelloni is a Visiting Professor at the School of Mathematical Sciences within the Faculty of Science at Queensland University of Technology (QUT). She holds a PhD from QUT and the Australian Institute of Marine Science (AIMS), alongside a MSc from Université Pierre et Marie Curie and a BSc from Université Aix-Marseille II. Her research focuses on integrating advanced technologies like machine learning, virtual reality (VR), and citizen science with statistical methodologies to address challenges in coral reef conservation. Key projects include the Virtual Reef Diver initiative, leveraging citizen science for reef monitoring, and using VR to assess ecological aesthetics. Dr. Vercelloni’s work emphasizes interdisciplinary approaches to understand and mitigate threats to coral reef ecosystems. Her academic qualifications include a PhD in marine science (QUT/AIMS), MSc in applied mathematics (UPMC), and BSc in biology (Aix-Marseille II). She collaborates extensively with institutions like AIMS and the Great Barrier Reef Foundation, contributing to both theoretical and applied conservation research. Publications highlight her expertise in Bayesian statistical methods, ecological data analysis, and the application of AI to environmental monitoring. Projects led by Dr. Vercelloni aim to enhance reef resilience through innovative data integration and community engagement strategies.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +۵ مورد دیگر

Tomi Janhunen is a Professor in Computing Sciences at Tampere University, part of the Faculty of Information Technology and Communication Sciences. His expertise spans Artificial Intelligence, Logic Programming, and Theoretical Computer Science. He actively contributes to academic leadership roles, serving on boards of the Finnish Association of Computer Science and Finnish Artificial Intelligence Society. His research focuses on Answer Set Programming (ASP), probabilistic reasoning systems, explainable AI, and optimization techniques applied to cybersecurity and maintenance scheduling. Key grants include the XAILOG and AI-ROT projects funded by the Research Council of Finland. Notable achievements include three prestigious awards: Best Paper Awards at the European Conference on Logics in AI (2021) and International Conference on Logic Programming (2023), and the Harold Boley award for system description (2022). Dr. Janhunen’s work emphasizes practical applications of declarative programming paradigms. Recent projects include Plingo—a probabilistic ASP system—and cybersecurity compliance frameworks in DevSecOps environments. His contributions bridge theoretical advancements with real-world challenges in AI security and industrial optimization.

Ricardo Baeza-Yatesمشاهده پروفایل

استاد

• Algorithms
• Responsible AI
• Web Search and Mining
• +۷ مورد دیگر

Ricardo Baeza-Yates serves as Director of Research at the Institute for Experiential AI at Northeastern University's Silicon Valley campus. He also maintains part-time professor positions at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Previously, he was VP of Research at Yahoo Labs (2006-2016) and CTO of NTENT, a semantic search technology company. His academic career spans over three decades with significant contributions to both industry research and academia. Dr. Baeza-Yates earned his Ph.D. in Computer Science from the University of Waterloo, Canada, in 1989. His academic journey includes professorships at Universidad de Chile (1989-2004), ICREA Professor at Universitat Pompeu Fabra (2004-2005), and ongoing part-time appointments at both institutions since 2004 and 2006 respectively. His research spans algorithms, information retrieval, web search and mining, data science, with increasing emphasis on responsible AI and ethical considerations in technology. He is particularly renowned for his work on bias in search systems, algorithmic fairness, and the societal impacts of AI. His expertise uniquely bridges technical aspects of search and data mining with ethical and societal implications, making him a leading voice in the responsible AI movement globally. His recent scholarly output demonstrates a strong focus on bias detection, fairness metrics, responsible technology development, and the societal impacts of algorithms. The work spans technical machine learning and information retrieval while increasingly addressing ethical and social dimensions across diverse application areas from finance to healthcare to social media. Dr. Baeza-Yates has received numerous prestigious awards throughout his distinguished career: 2019 Corresponding Member of the Brazilian Academy of Sciences 2019 Award Salvà i Campillo to the Person of the Year 2018 National Research Award for applied research and technology transfer 2012 ASIS&T Best Information Science Book Award for Modern Information Retrieval 2011 IEEE Fellow for contributions to computer science development 2009 ACM Fellow for contributions to information retrieval algorithms 2002 Corresponding member Chilean Academy of Sciences As an educator and mentor, Dr. Baeza-Yates has advised numerous students throughout his academic career at multiple institutions. His textbook 'Modern Information Retrieval' has become a standard reference in the field, influencing generations of computer scientists. He has secured significant research funding through his leadership roles at Yahoo Labs and academic grants, demonstrating his ability to bridge theoretical research with practical applications. Dr. Baeza-Yates is actively involved with the DATA Lab at Northeastern University's Khoury College of Computer Sciences and previously founded the Web Science and Social Computing Research Group at Universitat Pompeu Fabra. He serves on multiple high-impact advisory boards including the Global AI Ethics Consortium, Global Partnership on AI, and IADB's fAIr LAC Initiative for Latin America and the Caribbean, reflecting his global influence in shaping responsible AI practices.

Tarique Anwarمشاهده پروفایل

مدرس

• Data Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Tarique Anwar is a Lecturer in the Department of Computer Science at the University of York, UK, since 2021. He holds a PhD in Data Science from Swinburne University of Technology, Australia. As Programme Leader for both MSc and BSc Data Science programs, he oversees admissions and curriculum development. His academic roles include Data Science Admissions Tutor and membership in the Artificial Intelligence Group. Research interests span Data Science , Machine Learning , Artificial Intelligence , and Social Media Analysis , with a focus on solving real-world problems using structured/unstructured data. Notable areas include mental health detection via social media, network analysis, and explainable AI. His work addresses challenges in: Depression severity assessment using attention mechanisms Rumor source detection in social networks Eating disorder classification through multimodal learning Geographic-social-temporal pattern mining He supervises MS/PhD students in Data Science and Analytics, emphasizing interdisciplinary research. His publications (2020–2025) demonstrate expertise in AI applications for health, social networks, and optimization. Labs/Teams: Artificial Intelligence Group at the University of York, collaborating with industry and academic partners globally.