جست‌وجوی استاد و پژوهشگر

• Computational Social Science
• Social Media Analysis
• Machine Learning for Social Good
• +۶ مورد دیگر

Kenny Joseph is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Associate Director of the AI and Society Institute for Artificial Intelligence and Data Science. PhD in Societal Computing from Carnegie Mellon University (2016) MS in Societal Computing from Carnegie Mellon University (2012) BS in Computer Science from Carnegie Mellon University (2010) His research focuses on computational social science , examining stereotypes and prejudice dynamics , social inequality through computational tools , and gender disparities in organizations . He develops machine learning frameworks for neighborhood change prediction and predictive models for child welfare systems , with work featured in the New York Times . Scientific contributions include: UB Exceptional Scholar—Young Investigator Award (2021) He advises students Yuhao Du (Meta data scientist), Jason Yan (Michigan Ph.D. student), Arjunil Pathak (Amazon researcher), and Navid Madani (former Ph.D. student). His Computation and Equity Lab (cubelab) produces interdisciplinary work spanning social media analysis, urban equity, and computational methods for marginalized communities.

Zhiyao Duanمشاهده پروفایل

استاد

• Computer Audition
• Audio-Visual Scene Understanding
• Machine Learning
• +۳ مورد دیگر

Zhiyao Duan is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester, with affiliations to the Goergen Institute for Data Science. He holds a B.S. and M.S. from Tsinghua University and a Ph.D. from Northwestern University. Primary: Department of Electrical and Computer Engineering Secondary: Department of Computer Science Affiliated: Goergen Institute for Data Science His research focuses on Computer Audition, exploring audio-visual scene understanding, source separation, and human-computer collaborative music systems. He has received prestigious awards including an NSF CAREER Award and best paper honors at SMC 2017. His work is funded by NSF, NIH, Adobe, Kwai, and others. Recent publications emphasize audio-visual integration, speech synthesis, and deep learning architectures. He actively participates in academic service, serving on editorial boards and technical committees. The Audio Information Research (AIR) lab, which he leads, has hosted numerous students and collaborated with institutions like Northwestern University, Tsinghua University, and Stanford University. Scientific Awards: NSF CAREER Award Best Paper Award at SMC 2017 Best Paper Nomination at ISMIR 2017 Grants: NSF BIGDATA Grant No. 1741472 NSF Grant No. 1846174 for Human-Computer Collaborative Music Making University of Rochester AR/VR and Health Analytics Grants Students: Yapeng Tian Lele Chen Sefik Emre Eskimez Bochen Li Rui Lu Yujia Yan

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +۵ مورد دیگر

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

• Affective Computing
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Jonny O'Dwyer is a Lecturer in the Department of Accounting and Business Computing at the Faculty of Business and Hospitality. His research focuses on affective computing, leveraging eye tracking, speech, and head cues for continuous emotion prediction. He employs machine learning and computer vision techniques to advance human-computer interaction and emotion recognition systems. O'Dwyer has published four peer-reviewed conference papers, with two in 2017 and two in 2019, exploring multimodal data fusion and real-time affect prediction models. His work emphasizes interdisciplinary approaches, combining artificial intelligence with bioinformatics to address challenges in emotion analysis. Recent studies highlight applications in facial expression analysis, convolutional neural networks, and discrete-time feature extraction. O'Dwyer’s contributions aim to enhance communication environments and human-computer interfaces through multimodal sensing and open-source software integration.

Tommaso Caselliمشاهده پروفایل

استادیار

• Natural Language Processing
• Computational Linguistics
• Event Extraction
• +۵ مورد دیگر

Tommaso Caselli is an Assistant Professor in the Faculty of Arts at the University of Groningen, specializing in Computational Linguistics. His work focuses on advanced NLP techniques including event extraction, storyline analysis, sentiment detection, and generative AI applications. He leads the 'AI and Language' theme at the Jantina Tammes School of Digital Society and contributes to initiatives like the Dutch Abusive Language Corpus (DALC) and the Event Storyline Corpus (ESC). His research addresses societal challenges such as climate communication, misinformation detection, and ethical AI use. Caselli has received awards for his contributions to NLP, including the Outstanding Area Chair (2023) and Best Paper Awards at COLING 2022. His recent projects explore generative AI's potentials and risks in healthcare and social media contexts. Affiliations: Faculty of Arts, Computational Linguistics Department, University of Groningen External Roles: Theme Coordinator (Jantina Tammes School), Former Board Member (Senso Comune) Research Interests: Event processing, temporal reasoning, causal relation extraction, abusive language detection, and the societal impact of NLP technologies. His work intersects with UN Sustainable Development Goals related to climate action and responsible innovation. Key Contributions: Developed benchmark corpora like EXCEPTIUS for legal texts analysis and TEXT-CAKE for evaluating language models. Active in CLEF labs (CheckThat!) addressing misinformation and check-worthiness detection. Supervised datasets such as the Dutch Abusive Language Corpus and the Content Type Dataset. Awards & Recognition: Recipient of multiple academic accolades including Outstanding Paper Awards (2022-2023), Best Student Paper (2022), and leadership in organizing NLP workshops (e.g., CLEF, CASE).

• Wireless Communication
• Machine Learning
• Internet of Things (IoT)
• +۳ مورد دیگر

Aderemi Aaron-Anthony Atayero is an academic affiliated with the Department of Electrical Engineering and Information Engineering at Covenant University, Nigeria. His research focuses on telecommunications, wireless communication systems, IoT applications, and machine learning, with a strong emphasis on path loss modeling, signal processing, and smart city technologies. He has contributed to over 30 publications since 2005, collaborating extensively with researchers like Segun I. Popoola, Sanjay Misra, and Simon K. Hinga. His work spans theoretical and applied domains, including 5G mmWave models, botnet detection in IoT networks, and smart city infrastructure. Notable projects include the development of a low-intensity light imaging probe for medical applications and a cellular RSS dataset for roaming analysis. His research has practical implications for urban communication systems, healthcare technology, and cybersecurity.

• Data Science
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Taiseer Abdalla Elfadil Eisa is a researcher actively contributing to data science, machine learning, and academic integrity. His work spans plagiarism detection in scientific figures and digital media, robotic manipulation in cluttered environments, and Arabic speech dataset development for demographic analysis. He has collaborated with scholars from institutions in Malaysia, Saudi Arabia, and Yemen. His research interests include: Text and image-based plagiarism detection Machine learning for speech and facial analysis Computer vision for academic integrity Data mining for scientific publications Recent publications focus on: Arabic speech datasets (2024) Deepfake detection (2023) Robotic manipulation (2022) Systematic literature reviews (2015-2020)

• Audio Signal Processing
• Beamforming
• Speech Enhancement
• +۴ مورد دیگر

Kouei Yamaoka is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Tokyo Metropolitan University's College of Engineering, specializing in advanced audio signal processing techniques. With over 24 publications from 2017-2024, Yamaoka has established a strong research presence in the international signal processing community. Yamaoka's research focuses on innovative beamforming techniques, time delay estimation methods, and speech enhancement algorithms. Their work consistently addresses challenging problems in multichannel audio processing, particularly in underdetermined scenarios where the number of sources exceeds available microphones. Key contributions include causal distortionless response beamforming, minimum-spanning-tree-based time delay estimation robust to outliers, and sound field interpolation for rotation-invariant processing. Analysis of Yamaoka's publication trends reveals a consistent focus on practical audio processing solutions with applications in source separation, speech enhancement, and acoustic scene analysis. The research demonstrates strong theoretical foundations combined with practical implementation considerations, particularly for real-time and online processing scenarios. Recent work has expanded into deep learning applications for bioacoustic analysis, as evidenced by the 2023 publication on marmoset vocalization analysis. Yamaoka maintains a productive collaboration network, with frequent co-authorship with Nobutaka Ono (20 joint publications), Shoji Makino (9 joint publications), and other researchers in the Japanese signal processing community. Publications appear consistently in top venues including IEEE/ACM Transactions on Audio Speech and Language Processing, APSIPA, EUSIPCO, and ICASSP.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۳ مورد دیگر

João Paulo Papa is a Professor at the Department of Computing within the Institute of Biosciences, Humanities and Exact Sciences at São Paulo State University (UNESP), Brazil. His research spans machine learning, computer vision, and quantum computing with significant applications in medical diagnostics and environmental monitoring. Over the past three years, he has published extensively in top-tier journals including IEEE Access, ACM Computing Surveys, and Neural Computing and Applications. His research interests focus on developing innovative machine learning approaches for healthcare applications, particularly in Parkinson's disease detection through speech and facial analysis, medical image processing for cancer detection, and quantum-classical hybrid models. His work demonstrates strong integration of theoretical machine learning advancements with practical medical and environmental applications. Papa has established a productive research group that has produced numerous publications in computer vision conferences and medical informatics venues. Analysis of his recent publications reveals a strong emphasis on medical applications of AI, with approximately 60% of his work focused on healthcare diagnostics, 25% on fundamental machine learning advancements, and 15% on environmental and remote sensing applications. His research shows increasing collaboration with international partners while maintaining strong roots in Brazilian academic networks. Among his notable contributions is the development of specialized machine learning architectures for medical image analysis, including TransConv for esophageal cancer detection and quantum-classical hybrid models for breast cancer diagnosis. He has also made significant contributions to the Portuguese language processing community through adaptations of large language models for Brazilian Portuguese medical applications. Prof. Papa actively supervises graduate students and collaborates with medical professionals across Brazil, translating AI research into practical clinical tools. His research group maintains strong connections with hospitals and medical research centers to ensure clinical relevance of their technical developments.

Hakan Altınçayمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Pattern Recognition
• +۳ مورد دیگر

Prof. Hakan Altınçay is a Full Professor in the Department of Computer Engineering at Eastern Mediterranean University (EMU). He holds a PhD from Middle East Technical University (METU), Turkey (2000), and has been at EMU since 2000, progressing from Assistant Professor (2000–2005) to Full Professor (2012–present). His research focuses on machine learning, including imbalance learning, feature selection, ensemble methods, and biomedical data analysis. He has supervised 10+ PhD and Master’s theses and authored over 50 journal and conference papers. Education: BEng in Electrical and Electronics Engineering (METU, 1993), MSc and PhD in Electrical Engineering (METU, 2000). Awards include the IEEE Third Best Paper Award (2003) and TÜBİTAK PhD Scholarship. He serves on editorial boards (e.g., Applied Intelligence Journal) and chairs academic committees at EMU. Key contributions include algorithms for imbalanced data classification (MaMiPot), feature selection techniques, and biomedical applications like diabetes prediction. His work addresses challenges in text categorization, speaker verification, and ensemble classifier design.

• Biomedical Signal Processing
• Decision-Making Methodologies
• Frailty Prevention in Elderly
• +۳ مورد دیگر

Aly CHKEIR is an Associate Professor at the University of Technology of Troyes (UTT), holding the 'SilverTech' chair focused on technologies for healthy aging and elderly autonomy support. He earned his PhD in biomedical sciences from the University of Technology of Compiègne (UTC) in 2011, specializing in uterine electrical activity modeling. His research emphasizes biomedical signal processing, decision-making methodologies, and applications in frailty prevention among the elderly. Education: PhD in Biomedical Sciences, UTC, France (2011). Research interests include signal segmentation/classification, biomedical engineering, and solutions for aging populations. Notable projects include the SilverTech chair's holistic approach to aging technologies and the PiCADo initiative for in-home medical monitoring systems. Recent work focuses on AI-driven diagnostics (e.g., MRI segmentation, cough analysis), radar-based mobility assessment, and voice recognition for health monitoring. His contributions span sensor fusion, gait analysis, and non-invasive health tracking tools. His lab collaborations include Doppler radar systems for gait speed measurement, thermal imaging for health indicators, and AI-enhanced geriatric assessment tools. Projects like ARPEGE and PiCADo highlight his commitment to deploying innovative healthcare solutions for aging populations.

• Natural Language Processing
• Computational Social Science
• Machine Learning
• +۳ مورد دیگر

Aiqi Jiang is a Research Associate in the School of Mathematical & Computer Sciences, specifically in the Computer Science department, at Heriot-Watt University in the United Kingdom. Their work focuses on natural language processing and computational social science, with an emphasis on detecting online gender-based violence. Jiang's research interests span natural language processing, computational social science, and machine learning. They have contributed to the field through systematic reviews of resources for automated identification of online gender-based violence and revisiting annotation practices for such content. Their work also touches on data science, social media analysis, and ethical AI, aiming to leverage technology for social good in addressing gender-based violence. Jiang's recent publications (2023-2024) demonstrate a strong focus on combating online gender-based violence through computational methods. They have conducted systematic reviews to compile resources and evaluated annotation practices for gender-based violence content. Their research integrates insights from social sciences and lived experiences, and utilizes large language models and machine learning techniques to develop more effective detection systems. Scientific Awards: No scientific awards were mentioned. Advising and Grants: No information on students or grants was provided.

Eduardo Blancoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۷ مورد دیگر

Eduardo Blanco is an Associate Professor in the Department of Computer Science at the University of Arizona. His research focuses on advancing Natural Language Processing (NLP) with an emphasis on understanding negation, hate speech dynamics, and computational linguistics applications. He explores how language models can better interpret ambiguous statements, predict conversation incivility, and counteract harmful online content through structured responses. Blanco’s work spans theoretical and applied NLP domains, including semantic role labeling, sentence embedding evaluation, and biomedical text analysis using protein language models. His research often integrates large language models with task-specific frameworks to address challenges in zero-shot learning and negation robustness. Key areas of investigation include: Negation processing and affirmative interpretation Social media discourse dynamics and hate speech detection NLP for biomedical applications (glycosylation site prediction) Semantic similarity alignment in sentence embeddings His recent articles highlight advancements in counterhate speech strategies, temporal QA systems, and LLM-based peer review tools. Notably, he secured the NSF CAREER Award for studying negation through positive interpretations (2022). Collaborative efforts focus on interdisciplinary teams addressing challenges in computational linguistics, with grants supporting projects like the ALIGN-SIM benchmarking framework and BEMEAE event argument extraction system.

• Neuroscience
• Brain Pathophysiology
• Speech and Language Disorders
• +۴ مورد دیگر

Nabin Koirala is an Associate Research Scientist at Yale University's Child Study Center, where he conducts interdisciplinary research at the intersection of neuroscience, engineering, and clinical applications. His work focuses on understanding brain pathophysiology in neurological and neuropsychological disorders and developing disease-specific brain biomarkers. Dr. Koirala's educational background reflects a strong interdisciplinary foundation: PhD in Neuroscience from Johannes Gutenberg University (2019) MSc in Engineering & Neuroscience from Christian Albrechts University (2014) BE in Engineering from Tribhuvan University (2010) Postdoctoral Researcher at Haskins Laboratories (2022) His research program investigates the neural correlates of speech, hearing, and language-related disorders using multi-modal brain imaging and electrophysiological data. He applies state-of-the-art frameworks of complex network algorithms and machine learning approaches to analyze brain connectivity and function. Dr. Koirala's work spans several key areas including cochlear implant research, brain network analysis in neurological conditions, machine learning applications in neuroscience, deep brain stimulation mechanisms, and speech and language processing in the brain. His publication record demonstrates significant contributions to understanding how socioeconomic status influences brain circuitry for literacy, neural characteristics affecting literacy outcomes in children with cochlear implants, and the application of artificial intelligence in epilepsy care, particularly in resource-limited settings. His research often employs advanced neuroimaging techniques including EEG and functional near-infrared spectroscopy to examine cross-modal plasticity and sensory integration. Dr. Koirala collaborates extensively with researchers across disciplines, with frequent co-authorship with Vincent Gracco and Nicole Landi. His work bridges basic neuroscience with clinical applications, particularly in pediatric populations and neurological conditions including Parkinson's disease, multiple sclerosis, and stroke.

• Sound Synthesis
• Physical Modeling
• Virtual Reality Audio
• +۶ مورد دیگر

Cumhur Erkut is an Associate Professor in the Sound and Music Computing group within the Department of Architecture, Design and Media Technology at Aalborg University, Copenhagen, Denmark. With a publication record spanning over two decades from 2000 to present, his research bridges computer science, audio engineering, and creative arts. His primary research interests include Sound Synthesis, Physical Modeling of musical instruments, Virtual Reality Audio, Sonic Interaction Design, and Embodied Interaction. His work demonstrates a consistent focus on the intersection of technology and human experience, particularly in how sound and movement interact in digital environments. His recent publications show a strong shift toward AI-driven audio processing, voice conversion, and differentiable digital signal processing techniques. Dr. Erkut's publication trends reveal an evolution from traditional physical modeling of musical instruments (particularly string instruments like tanbur, clavichord, and guitar) toward more contemporary applications in virtual reality, embodied interaction, and AI-powered audio processing. His work consistently emphasizes real-time performance considerations and human-centered design principles. He has served in significant academic roles, including organizing the 17th International Conference on New Interfaces for Musical Expression (NIME 2017) at Aalborg University, demonstrating his leadership within the international research community. His collaborative network is extensive, with frequent co-authorship with Stefania Serafin, Vesa Välimäki, Antti Jylhä, Rolf Nordahl, and other prominent researchers in the sound and music computing field. These collaborations span multiple institutions across Europe, reflecting the international nature of his research.