جست‌وجوی استاد و پژوهشگر

Shahryar Rahnamayanمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Evolutionary Computation
• +۵ مورد دیگر

Dr. Shahryar Rahnamayan is a Full Professor and Chair of the Department of Engineering at Brock University. He holds a PhD in Systems Design Engineering from the University of Waterloo (2007) and has over 22 years of research experience across institutions like the University of Waterloo, Robarts Research Institute, and Ontario Tech University. His research focuses on machine learning, optimization algorithms, and medical image processing, with over 250 publications and 10,700 citations. He directs the Nature Inspired Computational Intelligence (NICI) Lab and co-directs the Kimia Lab for medical image analysis. Education: PhD (Systems Design Engineering, University of Waterloo), MSc/BSc (Software Engineering). Research interests include opposition-based computation, evolutionary algorithms, and large-scale optimization. He pioneered the 'Opposition-based Computation' framework, leading to over 800 subsequent papers. His work bridges machine learning and medical applications, particularly in bias mitigation in histopathology datasets and feature selection for high-dimensional data. He has supervised/co-supervised 75 highly qualified personnel (HQPs), taught 14 courses (e.g., Advanced Optimization, Machine Learning), and holds over 45 awards/honors, including recognition as a top 2% AI researcher globally (2020–2024). His grants span NSERC, MITACS, and other agencies. Current projects address bias reduction in medical AI and multi-objective optimization in engineering systems. Labs: NICI Lab (Brock University), Kimia Lab (cross-institutional).

Simone Coniaمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Large Language Models
• +۴ مورد دیگر

Simone Conia is an Assistant Professor at Sapienza University of Rome specializing in Natural Language Processing and Machine Learning. He leads research at the intersection of Large Language Models and multilingual systems while maintaining an active research collaboration with Apple as a Research External Collaborator. His research interests focus on Large Language Models , particularly investigating their fundamental capabilities and limitations with emphasis on improving reasoning abilities and reducing hallucinations; Multilingual NLP , developing robust systems for multiple languages with attention to low-resource settings; and Retrieval Augmented Generation , enhancing language models through external knowledge integration to improve factual accuracy. His scholarly contributions include over 40 publications in top-tier conferences with significant citation impact (1,063 total citations, h-index 14). His work has been recognized with multiple awards: Outstanding Paper Award at EMNLP 2024 Outstanding Paper Award at NAACL 2021 Distinguished Program Committee member at IJCAI Honor Student at Sapienza University Dr. Conia's research demonstrates strong industry-academia collaboration, particularly through his ongoing work with Apple on multilingual language models and real-world applications. His citation trajectory shows rapidly growing impact, with citations increasing from 23 in 2020 to 399 in 2024. He actively contributes to the open-source NLP community through GitHub repositories including unify-srl, wsd-hard-benchmark, and multilabel-wsd, focusing on semantic role labeling, word sense disambiguation, and knowledge graph integration with language models.

• Intelligent Video Surveillance
• Person Re-Identification
• Crowd Analysis
• +۳ مورد دیگر

Rita Delussu is a Lecturer and postdoc researcher at the University of Cagliari, affiliated with the Faculty of Humanities. She holds a Master's in Electronic Engineering (2017) and a PhD in Electronic and Computer Engineering (2021), both from the University of Cagliari. Her research focuses on intelligent video surveillance systems, with emphasis on person re-identification, crowd analysis, and synthetic data applications. Education: PhD in Electronic and Computer Engineering (2021) Master's in Electronic Engineering (2017) Research Interests: Rita's work addresses challenges in computer vision for surveillance, including adversarial defense mechanisms, human-in-the-loop systems, and domain adaptation. She explores synthetic data generation techniques to enhance model robustness and generalization across diverse scenarios. Her projects like the IMMAGINA initiative emphasize ethical AI principles in surveillance technologies. Collaborations: Active member of the PRA Lab since 2017, contributing to research funded by grants like 'Study and development of algorithms for remote sensing data analysis.' Labs/Teams: Collaborates with the PRA Lab on video surveillance and computer vision applications.

• Reinforcement Learning
• Multi-Agent Systems
• Artificial Intelligence
• +۳ مورد دیگر

Frans A. Oliehoek is a researcher affiliated with Delft University of Technology, The Netherlands. His work focuses on advancing the theoretical and practical aspects of artificial intelligence, particularly in the areas of reinforcement learning, multi-agent systems, and decision-making under uncertainty. He has contributed to foundational research on influence-based learning, abstracted models, and scalable solutions for large-scale networked systems. His research interests span a broad spectrum of topics including multi-agent reinforcement learning, POMDPs (Partially Observable Markov Decision Processes), and the integration of human-AI collaboration frameworks. He has also explored applications in transportation systems, robotics, and ethical AI through multi-objective optimization. Recent trends in his publications emphasize the development of modular frameworks for human-AI interaction, scalable methods for influence-augmented planning, and ethical considerations in AI decision-making. His work often bridges theoretical advancements with practical implementations, as seen in frameworks like SHARPIE and MORAL. Oliehoek has collaborated extensively with leading institutions and researchers globally, contributing to conferences such as AAMAS, IJCAI, and NeurIPS. His technical contributions include the MADP Toolbox for multi-agent planning and learning, reflecting his commitment to open-source research tools.

• Machine Learning
• Signal Processing
• Hidden Markov Models
• +۳ مورد دیگر

Aggelos Pikrakis is an Associate Professor in the Department of Informatics at the University of Piraeus, with research focus on Signal Processing with emphasis on Sound and Voice . He serves as an affiliate member of the Institute for Language and Speech Processing (ILSP). Specializes in machine learning algorithms for audio analysis Expertise in Hidden Markov Models and Bayesian architectures Supervises doctoral research in audio processing and machine learning His work spans music information retrieval, voice pathology classification, and mobility pattern analysis. With over 50 publications and 1150 citations (h-index=16), he has received best paper awards and first place in algorithm competitions . He has participated in research programs funded by GGET and European Union as part of research teams. Scientific Awards Best Paper Award First Place in Algorithm Competition Active on editorial boards (EURASIP Journal on Advances in Signal Processing since 2008) and conference committees, he contributes to advancing audio processing methodologies.

Wallapak Tavanapongمشاهده پروفایل

استاد

• Data Sciences
• Applied Machine Learning
• Multimedia Systems
• +۳ مورد دیگر

Wallapak Tavanapong is a Professor at Iowa State University, specializing in Data Sciences, Applied Machine Learning, and Multimedia Systems. His work integrates computational techniques with medical imaging and political science applications. He leads research in automated quality assessment for colonoscopy procedures and interpretable AI models for healthcare diagnostics. Research focuses on improving medical imaging analysis through machine learning, including real-time feedback systems during colonoscopies and developing datasets like IDCIA for cellular image analysis. He has pioneered methods for handling class imbalance in medical image classification and leveraging social media data for policy agenda analysis. His recent articles emphasize interpretable AI (e.g., CountXplain), confusion-based training strategies, and visual concept-based active learning. His work bridges technical innovation with clinical and policy applications, addressing challenges in healthcare quality and data-driven decision-making. No scientific awards are explicitly listed in the provided materials. His research has been applied in multi-center clinical trials for colonoscopy improvement and has contributed to advancements in endoscopic procedure monitoring systems.

Ehsan Shareghi Nojehdehمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Deep Generative Models
• +۵ مورد دیگر

Ehsan Shareghi Nojehdeh is an Assistant Professor in the Department of Data Science and Artificial Intelligence at Monash University and an affiliated lecturer at the University of Cambridge. He leads a research team focused on predictive models for language (text and speech), with a background in postdoctoral work at Cambridge and prior roles at UCL. His research interests include probing and augmenting Large Language Models (LLMs), reasoning in legal contexts, safety of multimodal models, speech/text translation, and self-supervised learning. Education: PhD in Computer Science (Scalable Non-Markovian Sequential Modelling) from Monash University (2017). Professional roles include Deputy Course Director for the Master of AI program at Monash, service on academic committees (ECA Committee, 2022–2023), and senior reviewing roles at major conferences (ACL2025, EMNLP2023). He teaches units like FIT5217 Natural Language Processing and FIT5212 Data Analysis for Semi-structured Data. Key projects include a Paul Ramsay Foundation-funded study on media narratives around disadvantage in Australia (2022–2023). His work addresses UN SDGs through advancing education and AI ethics. Research collaborations span global institutions, with a focus on knowledge-intensive tasks like biomedical domain applications, graph-to-text generation, and generative model disentanglement. Publications emphasize safety, multimodal capabilities, and logical reasoning with LLMs, with recent work appearing in ACL, EMNLP, and NAACL venues. His team investigates LLM shortcomings, including alignment with human judgment, speech-specific vulnerabilities, and tool-based reasoning strategies.

• Visual Analytics
• Information Visualization
• Computational Linguistics
• +۳ مورد دیگر

Menna El-Assady is an Assistant Professor at ETH Zurich's Department of Computer Science , leading the Interactive Visualization and Intelligence Augmentation Lab (IVIA) . She works at the intersection of data analysis, visualization, computational linguistics, and explainable AI , focusing on combining machine learning with visual analytics for text data. Current affiliations: ETH Zurich (Department of Computer Science, AI Center) Previous roles: Research Fellow at ETH AI Center, Research Associate at University of Konstanz, Doctoral Student at OntarioTech University Research Interests El-Assady's work focuses on human-AI collaboration , particularly through: Interactive systems for topic model refinement and language model explainability Visual analytics frameworks for argumentation analysis and digital humanities Explainable AI solutions for medical decision support and political deliberation Trust calibration mechanisms in generative AI and recommender systems Scientific Contributions Developed explAIner - a visual analytics framework for explainable machine learning Created LingVis - integrating computational linguistics with visual analytics Designed VALIDA and VisArgue for analyzing political debates and rhetoric Co-organized workshops: ArgVis , Vis4DH , VisLR , and VISxAI Academic Recognition Best Paper Award: Honorable Mention (IEEE Trans. on Visualization and Computer Graphics, 2018) 2024 VGTC Visualization Significant New Researcher Award

• Semantic Web
• Knowledge Graphs
• Linked Data
• +۳ مورد دیگر

Mohamed Ahmed Sherif is an academic affiliated with Paderborn University's Department of Computer Science and the University of Leipzig's Institute of Computer Science. His primary research focuses on semantic web technologies, knowledge graph integration, and machine learning applications in data integration. Key affiliations: Paderborn University, University of Leipzig Specialization: Link discovery, knowledge graph construction, semantic web standards His work emphasizes scalable methods for linking heterogeneous data sources, explainable AI techniques for knowledge integration, and geospatial semantic technologies. Recent contributions include the ANTS system for entity summarization in knowledge graphs and the NELLIE framework for continuous linked data integration. Notable projects include LIMES - a widely-used link discovery framework - and contributions to the HOBBIT benchmarking platform. His research has been applied in disaster management (I-AID system), Quran semantification, and global health data integration.

• Telecommunications
• Wireless Communication Systems
• Machine Learning
• +۵ مورد دیگر

Jianzhong Zhang is a researcher affiliated with Samsung Research America (Plano, TX, USA) , City University of Hong Kong (Department of Mathematics) , and Nankai University (College of Computer and Control Engineering) . His work focuses on 6G networks , machine learning applications in telecommunications , and advanced wireless technologies like THz/mmWave communication and beamforming . Recent publications highlight his contributions to Large Language Models (LLMs) for wireless network optimization , physics-inspired deep learning frameworks for channel state feedback, and ultra-wideband channel measurements for THz/FR3 bands. He also explores AI-driven CSI prediction , network slicing , and in-context learning for power control. His research integrates signal processing , channel modeling , and deep reinforcement learning to address challenges in next-generation wireless systems . Collaborative work includes joint phase-time arrays for frequency-dependent beamforming and end-to-end THz platform demonstrations .

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dan Liu is a prolific academic researcher with affiliations at institutions including University of California at San Diego and Beijing Institute of Technology . His work spans interdisciplinary areas such as computer science , biomedical informatics , and environmental science . Research interests include few-shot learning , multi-agent systems , deep learning for ecology , and data augmentation in imbalanced diagnostics . His publications highlight collaborations in signal processing , bioinformatics , and urban sustainability . Recent articles focus on marine microalgae classification , carbon emissions prediction , and wildlife action recognition , reflecting a trend toward environmental AI and resource optimization across domains.

Ahmed Mohammedمشاهده پروفایل

عضو هیئت علمی

• Text Mining
• Artificial Intelligence
• Digital Humanities
• +۳ مورد دیگر

Dr. Ahmed Mohammed is a Research Assistant at Linnaeus University's Department of Computer Science and Media Technology, holding a postdoctoral research fellowship. He earned his MSc and PhD from Northern University of Malaysia (UUM), focusing on semantic-based text anomaly detection. His research integrates AI and text mining into Digital Humanities (DH) and education, emphasizing computational thinking for non-engineering students. He leads the Computational Thinking and Coding Skills in Schools (CoCoS) research group and contributes to projects like Exten.DT² (combining design thinking with AI) and InKuiS (cultural entrepreneurship).

Guy De Tréمشاهده پروفایل

دانشیار

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +۷ مورد دیگر

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

Lukasz Golabمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Lukasz Golab is a Professor in the Department of Management Science and Engineering at the University of Waterloo, cross-appointed to the David R. Cheriton School of Computer Science. He holds a BSc from the University of Toronto (2001) and a PhD from the University of Waterloo (2006, Alumni Gold Medal recipient). Previously, he was a Senior Member of Research Staff at AT&T Labs (2006–2011) and held the Canada Research Chair (2014–2024). His research focuses on data-centric systems with societal impact, including explainable AI, blockchain systems, and applications in sustainability and education. Key research areas include: Explainable AI and model transparency High-speed data systems (blockchains, stream processing) Hate speech detection via multi-modal analysis Education data mining (co-op programs, mental health) Sustainability analytics (climate policy, energy systems) Notable achievements include Best Demo Award at EDBT 2025 and Best Short Paper at SSDBM 2023. His work spans 180+ publications in top venues like VLDB, ICDE, and AAAI. Current projects emphasize societal impact through data-driven solutions.

Souvika Sarkarمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Information Retrieval (IR)
• Artificial Intelligence (AI)
• +۳ مورد دیگر

Souvika Sarkar is an Assistant Professor at the School of Computing within the College of Engineering at Wichita State University. Her research focuses on enhancing AI and data science accessibility through interdisciplinary work at the intersection of Natural Language Processing (NLP), Information Retrieval (IR), and AI. She aims to develop context-aware and scalable AI systems capable of semantic understanding of natural language, particularly for broader societal benefit. Dr. Sarkar holds a Ph.D. from Auburn University, where she was honored with prestigious awards including the 100+ Women Strong Outstanding Departmental Annual Graduate Award and Auburn University’s Outstanding Doctoral Student Award. She also earned a master’s in software engineering from Jadavpur University. Prior to academia, she worked as an IT Analyst at Tata Consultancy Services, managing Microsoft SharePoint migrations and enterprise process workflows. Her research interests span NLP applications in education, multilingual AI systems, and ethical AI practices. Notable projects include developing conversational frameworks for K-12 physics education and analyzing annotator bias in hate speech detection systems. She has also explored deploying NLP models on embedded devices to address computational constraints. Publications highlight her work on digital twin security, LLM-driven meta-review systems, and Bangla language processing. Her industry experience bridges academic research with real-world enterprise solutions, reflecting a commitment to practical AI applications.