جست‌وجوی استاد و پژوهشگر

Nuria Oliverمشاهده پروفایل

پژوهشگر

• Computational Models of Human Behavior
• Human-Computer Interaction
• Intelligent User Interfaces
• +۱۲ مورد دیگر

Nuria Oliver, PhD is a pioneering computer scientist and ACM Fellow , recognized as the first female Spanish computer scientist to achieve both ACM Distinguished Scientist and IEEE Fellow status. Current affiliations include Microsoft Research and former roles at MIT Media Lab. First female Spanish ACM Fellow IEEE Fellow Academia Europaea member Her research spans human behavior modeling , intelligent user interfaces , and mobile computing , with notable work in: Wearable context-aware systems (DyPERS, HealthGear) Music-adaptive exercise platforms (MPTrain, TripleBeat) Social network analysis (information propagation, community link modeling) Computer vision interfaces (LAFTER facial expression recognition) Scientific achievements include: 41 patents 15+ years of continuous multimodal systems research Recognized for LAFTER system (2000 paper) She actively promotes technology accessibility through: Media collaborations Technical/non-technical keynotes STEM outreach for girls

Peter Kieferمشاهده پروفایل

مدرس

• Eye tracking
• Spatial decision making
• Human-computer interaction
• +۶ مورد دیگر

Peter Kiefer is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, specifically within the Institute for Cartography and Geoinformation. He leads the geoGAZElab research group since 2011 and has established himself as a prominent researcher in eye tracking applications for spatial decision-making. His work bridges geography, computer science, and human factors with a focus on creating cognition-aware systems. Dr. Kiefer's research spans multiple domains with a consistent focus on how eye tracking technology can enhance spatial decision situations. His primary interests include gaze-based interaction with maps, location-based services, mobile interfaces, and aviation applications. He has pioneered work in intention recognition from gaze patterns, gaze-adaptive interfaces, and the application of eye tracking in outdoor environments. His research aims to make information systems cognition-aware by inferring users' cognitive states and adapting interfaces accordingly. Analyzing his recent publications reveals strong trends toward practical applications of eye tracking in real-world scenarios, particularly in aviation safety and spatial decision-making. His work increasingly integrates machine learning with gaze data to create intelligent systems that can predict user needs and adapt interfaces dynamically. The research shows a clear progression from theoretical foundations to applied solutions in domains like weather visualization for pilots, 3D sketch mapping, and unobtrusive interaction techniques. Dr. Kiefer has been actively involved in numerous research projects as Principal Investigator or key contributor, including IGAMaps (Swiss National Science Foundation), PEGGASUS (EU Horizon 2020), EFDISA (Federal Office of Civil Aviation), and FRS 2 (National Research Foundation Singapore). These projects demonstrate his ability to secure competitive funding and lead interdisciplinary research teams across international boundaries. As an educator, Dr. Kiefer teaches Mobile GIS and Location-Based Services for Geomatics Master students, GIS I for Environmental Engineers, and contributes to courses on Mobile GIS, Human-Computer Interaction in GIS, and GIS III. He also participates in continuing education programs including the Certificate of Advanced Studies on Spatial Information Systems and the Master of Advanced Studies on Mobility of the Future. His teaching is deeply integrated with his research, particularly through the geoGAZElab where students gain hands-on experience with cutting-edge eye tracking technologies. The geoGAZElab under Dr. Kiefer's leadership serves as a hub for innovative research at the intersection of geography, computer science, and cognitive science. The lab has developed specialized tools and methodologies for outdoor eye tracking, gaze-based map interaction, and aviation interface design. The lab's work has significant implications for improving spatial decision-making across multiple domains including transportation, urban planning, and emergency response.

• Educational Technology
• Digital Games
• Computer Graphics and Simulations
• +۳ مورد دیگر

Veljko Aleksić is an Associate Professor at the Department of Information Technology, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. With over 15 years of academic experience, he specializes in Educational Technology, focusing on the intersection of digital games, computer graphics, and teaching methodologies. His work bridges theoretical educational frameworks with practical technological applications, making significant contributions to both national and international educational technology communities. Awarded the "Dr Milivoje Urošević" foundation award as the best graduate student, Aleksić earned his Bachelor's degree in Teacher of Technics and Informatics from the Technical Faculty in Čačak with an average grade of 9.59. He continued his academic journey with a Master's degree from the University of Kragujevac (average grade: 10.00) and completed his Doctoral studies at the University of Novi Sad, Faculty of Sciences, with a dissertation titled "Relation between digital gameplay and multiple intelligences profile of higher grade elementary school students." Professor Aleksić's research spans multiple interconnected domains within educational technology. His primary focus lies in investigating how digital games can enhance learning experiences, particularly examining the relationship between gameplay preferences and multiple intelligences profiles of students. This work has led to significant contributions in understanding how different learning styles interact with digital educational tools. His expertise extends to computer graphics and simulations, where he explores innovative approaches to visual learning and virtual environments. Additionally, he has made substantial contributions to teaching methodology, particularly in technics and informatics education, developing frameworks that integrate modern technologies into traditional educational settings. His work on neuroscience connections demonstrates his interdisciplinary approach to understanding educational processes. A review of Professor Aleksić's recent publications reveals a clear trajectory toward increasingly sophisticated applications of technology in education. His work has evolved from foundational studies on digital game-based learning to more complex investigations involving artificial intelligence, virtual reality, and cross-cultural educational comparisons. Recent publications demonstrate a growing emphasis on practical applications of educational technology, with several studies focusing on implementing these technologies in real-world educational settings across Serbia and internationally. His comparative studies between Serbian and Greek educational contexts highlight his expanding international research collaborations and the global relevance of his work. The thematic progression shows increasing sophistication in research methodology and broader educational impact. While specific major scientific awards aren't prominently listed in his profile, Professor Aleksić's extensive involvement as a reviewer for the National Accreditation and Quality Review Body in Higher Education and his participation in numerous international conference program committees represent significant professional recognition. His leadership roles in various educational projects funded by the Ministry of Education, Science and Technological Development of Serbia further demonstrate the value placed on his expertise by national educational authorities. His membership in the IEEE Computer Society Technical Committee on Learning Technology underscores his standing in the international educational technology community. Professor Aleksić has supervised numerous educational initiatives and projects, including "Osnaživanje kompetencija studenata - master profesora tehnike i informatike za preduzetničko obrazovanje i podršku profesionalnom razvoju učenika (MPTIPoPr)" and "Digitalna transformacija predmeta obrazovna tehnologija (DTPOT)" funded by the Ministry of Education. He has served as head of study programs including IAS Tehnika i informatika and MAS Predmetna nastava at the Faculty of Technical Sciences in Čačak. His extensive work with the Institute for Education Quality and Evaluation, and the Institute for the Advancement of Education demonstrates his significant contribution to national educational standards development. His involvement in international projects like ALLVET shows his commitment to European educational cooperation. As an active member of the IEEE Computer Society Technical Committee on Learning Technology and participant in numerous international research collaborations, Professor Aleksić contributes to cutting-edge educational technology development. His work with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak has fostered an environment for innovative research in educational technology, particularly in the areas of digital games development and computer graphics applications for learning. His leadership in organizing the Technics and Informatics in Education (TIE) conference series has created important platforms for scholarly exchange in the region.

Catherine Dominguèsمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Cartography
• Data Mining
• +۳ مورد دیگر

Catherine Dominguès is a senior researcher at the French National Mapping Agency (IGN) specializing in Natural Language Processing for Cartography within the MEIG (Mediation and Enrichment of Geographic Information) research team. Her work bridges computational linguistics and geographic information science, focusing on the intersection of language analysis and spatial data representation. Education : Habilitation à diriger des recherches (HDR) in Natural Language Processing (2020), PhD in Computer Science (2001), DEA in Theoretical Foundations of Computer Systems (1989) Research Interests center on places and placenames , expressive variations in toponym spelling , location-based information retrieval , and sensitive cartography . Her methodology combines data mining , fuzzy spatial reasoning , and geovisualization techniques. Key publication trends show her work connects NLP for map analysis , mountain rescue support systems , and narrative-based mapping . She contributes to heterogeneous data integration and fuzzy logic applications in geographic contexts. Supervision : Co-supervised Dong Chuanming (PhD) and previously supervised Firas Bessadok (PhD) Projects : Core member of the CHOUCAS project (2017-2022) integrating heterogeneous data for mountain rescue, and involved in MATRICIEL (2016-2017) mapping migrant perceptions

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۹ مورد دیگر

Prof. Dr. Hayri Sever is a full-time faculty member at Çankaya University, Department of Computer Engineering, since 2018. He previously held professorship roles at Hacettepe University (1997-2018), University of Massachusetts Amherst (2000-2002), and Başkent University (2003-2007). His academic journey includes a PhD in Computer Science from the University of Louisiana at Lafayette (1995), a Master's from the University of Maine (1991), and a BSc in Computer Engineering from Hacettepe University (1987). Education: PhD (1995), Computer Science, University of Louisiana at Lafayette MS (1991), Computer Science, University of Maine BSc (1987), Computer Engineering, Hacettepe University Prof. Sever's research spans Artificial Intelligence, Data Mining, Natural Language Processing, Cybersecurity, and Machine Learning . His recent work focuses on applications in violence detection in videos, sentiment analysis, blockchain security, medical decision support systems, and Turkish language processing. He has supervised over 40 graduate students in topics ranging from deep learning to blockchain and IoT security. His 15 most recent publications (2019-2024) highlight expertise in deep learning for video analysis , blockchain reputation systems , 3D CNN applications , and sentiment analysis in Turkish/Arabic . Collaborations include institutions like Hacettepe University, University of Massachusetts, and international projects funded by TÜBİTAK, UNESCO, and the UN. He has led projects such as "KGYS: Smart Support Software Development" and "DiCoMa: Disaster Control Management" , with roles as researcher, coordinator, and advisor. Professional memberships include IEEE (2005-), ACM (2002-), and Tübitak (2005-).

• Data Science
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Howard J. Hamilton is a Professor at the Department of Computer Science, Faculty of Science, University of Regina. His research spans Data Mining, Machine Learning, and Human-Computer Interaction. University of Regina Department of Computer Science Faculty of Science Research Interests: Hamilton focuses on Data Science , Machine Learning , Speech Recognition , and Augmented Reality applications in healthcare. His recent work includes the My Daily Routine (MDR) system using HoloLens for dementia care and blockchain-based IoT security mechanisms. Scientific Contributions: He has co-authored 15 recent papers on topics spanning Time Series Forecasting , GANs for Game Design , and Smart Grid Security . His work has been presented at top venues like IEEE ICC, ICAART, and IEEE VR. Awards: Recipient of the Best Paper Award at INTENSIVE'12 and Second Place Best Paper at FLAIRS'99. Student Training: As a supervisor, he has mentored over 30 graduate students and project assistants in Computer Science and related fields, including Ph.D. candidates in Multi-Agent Systems and AI for Cooperative Games .

Florian Huberمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Visual Analytics
• +۴ مورد دیگر

Prof. Dr. Florian Huber serves as Professor of Data Science and Visual Analytics at the Department of Media, Faculty of Media, Hochschule Düsseldorf University of Applied Sciences (HSD), a position held since September 2021. He is additionally affiliated with the Center for Digitalization and Digitality (ZDD), where he bridges data science methodologies with real-world applications in scientific and cultural domains through machine learning and visualization techniques. His academic foundation includes: Diploma in Physics, University of Leipzig PhD in Biological Physics, University of Leipzig (2012), thesis: "Emergent structure formation of the actin cytoskeleton" Prior to HSD, he worked as a postdoctoral researcher at AMOLF (Amsterdam) and TU Delft, founded the sustainable sweets company KÄNDI, and served as Data Scientist at the Netherlands eScience Center (2018-2021). Huber's research focuses on developing data science algorithms for comparative analysis of complex datasets, combining machine learning with domain expertise to enable meaningful comparisons through latent space transformations. Key areas include mass spectrometry-based metabolomics (chemical identification/similarity prediction), natural language processing (e.g., lyrical evolution analysis), and audio data science, with emphasis on interpretable and reproducible computational approaches for biology, medicine, and cultural studies. Analysis of his 2023-2025 publications reveals dominant themes in mass spectrometry data science (tools like matchms, MS2Query, Mass2SMILES) and NLP applications in musicology. His work increasingly integrates deep learning for cross-domain similarity prediction while creating educational resources, reflecting a trend toward versatile open-source solutions for interdisciplinary data challenges. Huber actively contributes to open science through GitHub repositories and reproducible pipelines, suggesting collaborative funding models centered on software sustainability. His teaching includes the "Data Science" module (MMI 05.35) with practical components for engineering programs, offered in German or English. He leads initiatives within HSD's Center for Digitalization and Digitality, fostering collaborations between data scientists and domain experts. Current projects involve interdisciplinary teams tackling metabolomics, cultural analytics, and educational tool development using Python-based open-source ecosystems, with future work likely expanding into new application domains while advancing methodological rigor.

Giacomo Tarroniمشاهده پروفایل

مدرس

• Medical Image Analysis
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Giacomo Tarroni is a Lecturer in Artificial Intelligence at City St George's, University of London, where he is a member of the CitAI Research Centre within the Department of Computer Science. He has previously held research positions at Imperial College London (as a Research Fellow and Marie Skłodowska-Curie Fellow), University of Padova, and University of Bologna. His educational background includes a PhD in Medical Image Analysis from the University of Bologna (2009-2012), an M.S. in Electronic Engineering from the University of Bologna (2005-2008), and a B.S. in Electronic Engineering from the University of Bologna (2002-2005). During his PhD, he collaborated with the University of Chicago. Dr. Tarroni's research focuses on medical image analysis, with particular emphasis on unsupervised anomaly detection , self-supervised image classification/segmentation , and federated learning for medical applications. His work spans cardiovascular, brain, and fetal imaging, with significant contributions in cardiac MRI analysis. His research bridges computer vision and healthcare, developing AI solutions that address practical clinical challenges while respecting data privacy constraints through techniques like federated learning. His publication record demonstrates consistent high-impact contributions to medical imaging conferences (MICCAI, ISBI) and journals (IEEE Transactions on Medical Imaging, Medical Image Analysis). Recent work shows increasing focus on foundational AI techniques like diffusion models and self-supervised learning applied to medical imaging challenges. Fellow of Advance Higher Education (HEA), 2021 Marie Skłodowska-Curie Fellowship from the European Commission for the JUNO project (Principal Investigator) Board member of the UK Chapter of Marie Curie Alumni Association (MCAA-UK) since 2018 Associate Editor for IEEE ISBI 2019 conference Dr. Tarroni has secured significant research funding, generating over 183k€ through his Marie Skłodowska-Curie Fellowship. He has collaborated with major medical image analysis projects including SmartHeart (EPSRC, 5M£), iFind (Wellcome Trust and EPSRC, 10M£), and CHIRON (EU Artemis-JU, 18M€). As an active reviewer, he contributes to leading journals including IEEE Transactions on Medical Imaging and Medical Image Analysis, as well as top conferences like MICCAI and IEEE ISBI. His laboratory work centers around developing robust AI methods for medical imaging that can operate effectively with limited or distributed data sources.

Ning Maمشاهده پروفایل

مدرس

• Speech and Hearing Technologies
• Machine Learning
• Healthcare
• +۴ مورد دیگر

Dr. Ning Ma is a Lecturer in Medical Computing at the Department of Computer Science , University of Sheffield, with a dual appointment as an Academic Directorate of Medical Imaging and Medical Physics at the Sheffield Teaching Hospitals NHS Foundation Trust. He holds a PhD in hearing-inspired automatic speech processing from the University of Sheffield and earned his first degree in Computer Science from South China University of Technology. Member of Pervasive Computing and Speech and Hearing research groups Research Theme Co-Director for Healthcare Data/AI at the Insigneo Institute His research focuses on speech and hearing technologies applied to healthcare , particularly in developing AI systems for acoustic monitoring of sleep-disordered breathing and respiratory diseases . He pioneers methods for interpreting sounds and low-cost sensor data to screen conditions like obstructive sleep apnea and tuberculosis through multimodal machine learning . His recent work explores music AI and its intersection with mental health. Dr. Ma has led grants totaling over £500,000 from UKRI, MRC, EPSRC, and Innovate UK, including projects like AI-Enabled Cough Sound Analysis for TB Screening and SOMNUS: Sleep Monitoring by Unobtrusive Sensors . He serves on the Technical Programme Committee for INTERSPEECH and reviews for journals and funders. He is a member of the British Sleep Society , British Thoracic Society , and IEEE . His publications (over 60 peer-reviewed papers) explore topics such as deep learning for sleep apnea screening , binaural sound localization , and noise-robust speech recognition . Notable collaborations include work with Guy Brown on home-based OSA detection and with Tu Zhen on hearing aid optimization using differentiable hearing loss models.

Yoshi Gotohمشاهده پروفایل

مدرس

• Speech and Language Processing
• Video Analysis
• Information Retrieval
• +۲ مورد دیگر

Dr. Yoshi Gotoh is a Lecturer and Student Projects Officer in the Department of Computer Science at the University of Sheffield's School of Computer Science . He holds a PhD from Brown University and a first degree in Engineering from the University of Tokyo. As a member of the Speech and Hearing (SpandH) research group, his work bridges audio-visual processing and language technologies. His core research explores: Video analysis and retrieval systems Natural language generation for video content 3D visual speech animation Crowd behavior modeling through trajectory clustering Medical imaging enhancements via colorization techniques Analysis of his 15 most recent publications reveals strong emphasis on multimodal systems combining computer vision with speech/language processing. Dominant themes include egocentric video analysis, human activity recognition, and cross-modal translation between visual and textual domains. He has secured significant research funding as Co-Principal Investigator: £218,226 from Innovate UK (2021-2024) for fake imagery detection £393,115 from Innovate UK (2018-2021) for unsupervised dubbing systems £284,248 from EPSRC (2001-2005) for spoken language summarization He leads projects within the Speech and Hearing laboratory, focusing on developing computational methods for audiovisual integration and video understanding systems.

Salman Avestimehrمشاهده پروفایل

استاد

• Information Theory
• Federated Machine Learning
• Decentralized Learning
• +۳ مورد دیگر

Salman Avestimehr is a Dean’s Professor at the University of Southern California (USC) in the Viterbi School of Engineering, jointly affiliated with the Electrical and Computer Engineering and Computer Science Departments. He is the co-founder and CEO of FedML, an open-source framework for federated learning, and serves as director of both the USC-Amazon Center on Secure & Trusted ML and the vITAL Research Lab. His research focuses on information theory, decentralized machine learning, and secure/privacy-preserving computing. His recent research explores federated learning for healthcare applications, uncertainty estimation in LLMs, multimodal distillation for heterogeneous systems, and secure aggregation techniques. The vITAL Lab under his leadership has produced numerous publications in 2024-2025 across top conferences like ICLR, CVPR, PETS, and NAACL. Notable achievements include the Andrea Goldsmith Young Scholars Award received by his lab member Batu. His work spans practical frameworks (FedML), theoretical advances (coded computing), and interdisciplinary applications (LANTERN for biomedical prediction, CryptoMamba for cryptocurrency modeling).

Ahmet Onur Durahimمشاهده پروفایل

مدرس

• Artificial Intelligence
• Big Data Analytics
• Social Network Analysis
• +۴ مورد دیگر

Ahmet Onur Durahim is a Lecturer at Boğaziçi University , specializing in Artificial Intelligence, Big Data Analytics, and Cybersecurity. With a PhD in Computer Science and Engineering from Sabancı University, his research focuses on social media analysis, cryptographic protocols, and network security. Education: Sakarya Anadolu Lisesi (High School) Boğaziçi University: B.S. in Industrial Engineering and Construction Engineering (Double Major) Boğaziçi University: M.S. in Civil Engineering Sabancı University: PhD in Computer Science and Engineering Durahim's research spans AI applications in social media, big data processing, and security protocols for wireless networks. His work includes sentiment analysis on Twitter, spam detection frameworks, and cryptographic authentication systems. Recent publications highlight his contributions to cryptocurrency interest identification, music emotion classification, and spam review detection. His methodological focus includes semi-supervised learning and imbalanced data handling.

Dario Allegraمشاهده پروفایل

دانشیار

• Image Processing
• Machine Learning
• Computer Vision
• +۲ مورد دیگر

Dario Allegra is an Associate Professor of Computer Science at the Department of Mathematics and Computer Science (DMI) at the University of Catania. He has been teaching in Computer Science programs since 2018, delivering courses on image, audio, and video processing algorithms. His research focuses on: Image Processing Machine Learning Computer Vision Food Image Analysis Cultural Heritage Preservation and Valorization Dr. Allegra's research addresses challenging problems requiring imaging algorithms, including image and video compression, document fingerprint extraction, food recognition and classification, nutritional value estimation from visual media, and artifact detection and correction. His work has applications in food image/video analysis and cultural heritage enhancement. He is an active member of the IEEE Signal Processing Society and Eurographics association, and has participated in numerous research projects funded by national/international institutions and multinational companies including ST Microelectronics. Dr. Allegra has authored over 50 scientific articles in international journals and conference proceedings, served as guest editor for special issues in international journals, and chaired multiple international workshops. He teaches courses including 'Multimedia and Laboratory,' 'Interaction and Multimedia and Laboratory,' and 'Audio Processing' for both bachelor's and master's programs in Computer Science. He also serves as an academic tutor for first-year students.

Renaud Seguierمشاهده پروفایل

استاد

• Computer Vision
• Signal Processing
• Human-Computer Interaction
• +۵ مورد دیگر

Renaud Seguier is a Professor in the Department of Electronics and Telecommunications at the Institute of Electronics and Telecommunications of Rennes, School of Engineering. His research spans computer vision, signal processing, and human-computer interaction with a focus on facial analysis and speech processing. His primary research interests include computer vision (specializing in micro-expression recognition, 3D facial modeling, and gaze estimation), signal processing (speech emotion recognition and source-filter decomposition), and affective computing (emotion detection from multimodal inputs). Key methodologies involve deep learning architectures like variational autoencoders, GANs, and specialized metric learning techniques. His recent publications demonstrate strong trends in multimodal emotion analysis combining audiovisual inputs, unsupervised learning for human activity recognition , and generative model applications for facial editing and deepfake detection. The work shows increasing sophistication in temporal modeling of facial dynamics and disentangled feature representations. As an academic advisor, he supervises numerous students including Jingting Li, Samir Sadok, and Mouath Aouayeb who frequently appear as first authors on publications. His collaborative network includes Catherine Soladie, Simon Leglaive, and Amine Kacete across multiple institutions. His laboratory work centers on the 3D facial analysis pipeline involving texture reconstruction, motion denoising, and micro-expression spotting systems. Current projects integrate RGB-D sensing with deep learning for unconstrained environments, particularly focusing on health diagnostics from facial cues and real-time gaze estimation.

Sachith Dassanayakaمشاهده پروفایل

استادیار

• Social Media Disinformation Analysis
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Sachith Dassanayaka serves as Assistant Professor of Data Science within the Department of Mathematics at Wittenberg University, teaching statistics and data science courses to both majors and non-majors from his office in BDK Science Center 329P. Contact details include phone (937) 327-7456 and email dassanayakas@wittenberg.edu . His academic credentials feature a Ph.D. in Mathematics (Statistics concentration) from Texas Tech University (2022), an M.S. from the same institution (2019), and a B.S. in Computer Science from the University of Kelaniya, Sri Lanka (2015). Dr. Dassanayaka's research specializes in detecting disinformation campaigns through machine learning and NLP applications, with additional focus on data visualization, machine intelligence, and network security. His doctoral work developed AI algorithms for activity pattern detection in information operation networks, establishing the foundation for current investigations into social media manipulation tactics. Analysis of his 2016-2025 publications reveals a consistent trajectory in cybersecurity and social media forensics, particularly in fraudulent review detection, troll network mapping, and audio-based covert data transmission. These works demonstrate interdisciplinary integration of artificial intelligence, network science, and information security to address modern digital deception challenges. While actively engaged in teaching and student advising, specific details regarding research grants, formal student supervisees, or laboratory affiliations remain undisclosed in publicly available materials.