جست‌وجوی استاد و پژوهشگر

George G. Cabralمشاهده پروفایل

استادیار

• machine learning
• online learning
• concept drift
• +۸ مورد دیگر

George G. Cabral is an Assistant Professor (Lecturer) in the Department of Computing at the Federal Rural University of Pernambuco (UFRPE) in Recife, Brazil. His academic career spans research and teaching in machine learning, with a particular focus on applications to software engineering and other real-world domains. Ph.D. in Computer Science, Federal University of Pernambuco (2014) M.Sc. in Computer Science, Federal University of Pernambuco (2008) B.Sc. in Computer Engineering, University of Pernambuco (2005) PostDoc, University of Birmingham (2019) Dr. Cabral's research primarily focuses on machine learning algorithms applied to real-world problems, with special emphasis on online learning and concept drift in data streams. His work explores class imbalanced learning techniques, ensembles of learning machines , and novelty detection methods. A significant portion of his research applies these techniques to software defect prediction , where he investigates just-in-time approaches that can identify potential bugs as software is being developed. He also applies similar methodologies to intrusion detection systems and public accounting domains, demonstrating the versatility of his research interests across different application areas. Analysis of Dr. Cabral's publication history reveals a consistent trajectory from foundational work on one-class classification techniques toward increasingly applied research in software engineering contexts. His most recent publications (2022-2023) demonstrate expansion into public sector applications, particularly in data auditing for Brazilian municipal governments, while maintaining his core focus on concept drift challenges in software defect prediction. The publications consistently address methodological challenges like verification latency and class imbalance evolution in streaming data environments. Dr. Cabral actively contributes to the academic community as a committee member for the Artifact Evaluation track at major software engineering conferences including ASE (Automated Software Engineering) since 2021 and ICSE (International Conference on Software Engineering). He serves as a reviewer for journals such as Neurocomputing and Applied Soft Computing, and has taught undergraduate courses including Artificial Intelligence, Neural Networks, Computing Theory, and Programming at UFRPE.

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۳ مورد دیگر

Ana Valeria Gonzalez is a researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning with a focus on human-centered natural language processing. Her work bridges computational linguistics and cognitive science within the department's Machine Learning research section. Her research investigates bias mitigation in multilingual systems, interpretable AI evaluation frameworks, and affect-aware dialogue generation. Key contributions include developing testbeds for gender bias analysis in coreference resolution, novel methodologies for evaluating model interpretability through reverse Turing tests, and retrieval-based approaches for goal-oriented conversational agents. She employs techniques spanning attention mechanisms, BiLSTMs, and domain adaptation to address challenges in low-resource NLP settings. Analysis of her 2019-2021 publications reveals a cohesive research trajectory toward building transparent, equitable NLP systems that account for human cognitive factors. Her work consistently addresses real-world deployment challenges, particularly in dialogue systems requiring emotional intelligence and cross-lingual fairness. Collaborations with Anders Søgaard, Isabelle Augenstein, and other Copenhagen researchers demonstrate strong integration within the university's AI ecosystem. Gonzalez completed her Ph.D. in 2021 with the dissertation "Towards Human-Centered Natural Language Processing". She maintains active research in the Machine Learning group, though specific advising roles and grant details aren't documented in the available materials. Her publication record shows significant impact with multiple Scopus-cited works and substantial reader engagement across platforms.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +۵ مورد دیگر

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

• Computational Linguistics
• Dependency Parsing
• Universal Dependencies
• +۵ مورد دیگر

Joakim Nivre is a Professor of Computational Linguistics at Uppsala University in the Department of Linguistics and Philology. His research primarily focuses on grammatical analysis, studying how to analyze typologically different languages in a uniform way within the Universal Dependencies project, and developing better computational models for automatic analysis within this framework. His teaching is associated with the international Master's Program in Language Technology. His research interests include: Computational Linguistics and Natural Language Processing Dependency Parsing and Universal Dependencies Cross-linguistic Analysis and Multilingual NLP Syntax Analysis and Grammatical Frameworks Language Resource Development Nivre's publication record demonstrates significant contributions to dependency parsing, treebank development, and cross-linguistic analysis. His recent work (2023-2024) shows expansion into evaluating generative language models, resource-efficient NLP techniques, climate change impact analysis, causality detection in governmental documents, and historical text processing. His research bridges theoretical linguistics with practical computational applications, with particular emphasis on creating frameworks that work across diverse languages. His work on the Universal Dependencies framework has been particularly influential, establishing a standard resource that enables consistent syntactic annotation across numerous languages, significantly impacting the field of multilingual natural language processing.

Seong Joon Ohمشاهده پروفایل

استاد

• Trustworthy AI
• Explainable AI
• Robust Machine Learning
• +۷ مورد دیگر

Seong Joon Oh is a Professor at the University of Tübingen leading the Scalable Trustworthy AI (STAI) research group. He also advises Parameter Lab alongside his main academic position. His research career spans both academia and industry, having previously served as a research scientist at NAVER AI Lab for 3.5 years. Oh received his PhD in computer vision and machine learning from the Max-Planck Institute for Informatics in 2018 under the supervision of Bernt Schiele and Mario Fritz. His doctoral research focused on privacy and security implications of computer vision and machine learning. He completed his earlier education at the University of Cambridge, earning a Master of Mathematics with Distinction in 2014 and a Bachelor of Arts in Mathematics as a Wrangler in 2013. Professor Oh's research concentrates on training reliable AI models with emphasis on explainability, robustness, and probabilistic modeling. His work explores methods to obtain necessary human supervision in a cost-effective manner, with significant contributions to uncertainty quantification, out-of-distribution generalization, and privacy-preserving techniques. His research intersects computer vision, natural language processing, and machine learning theory, particularly focusing on compositionality, trustworthy AI, and model interpretability. Analysis of Oh's recent publications reveals a strong focus on practical challenges in deploying trustworthy AI systems. His work spans multiple domains including computer vision, natural language processing, and foundational machine learning. Key themes include compositionality in vision-language models, privacy concerns in large language models, domain adaptation techniques, and methods for improving model robustness. His research demonstrates a consistent trajectory toward building AI systems that are not only high-performing but also reliable, interpretable, and secure. Professor Oh has made significant contributions to several research communities as evidenced by his publication record across top-tier conferences including NeurIPS, ICML, ICLR, CVPR, and ACL. His work on membership inference attacks, object-centric learning, and trustworthy machine learning has influenced the field. He co-authored the textbook "Trustworthy Machine Learning" which covers out-of-distribution generalization, explainability, uncertainty quantification, and evaluation of trustworthiness in machine learning models. Oh leads the Scalable Trustworthy AI research group at the University of Tübingen, focusing on developing AI systems that are explainable, robust, and probabilistic. His team investigates fundamental questions about how AI models understand compositionality, handle distribution shifts, and maintain privacy when processing sensitive information. The group's work bridges theoretical insights with practical applications, aiming to create AI systems that can be safely deployed in real-world scenarios where reliability is critical.

Seunghoon Hongمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Seunghoon Hong is an Associate Professor at the School of Computing, Korea Advanced Institute of Science and Technology (KAIST), where he leads the KAIST Vision and Learning Lab (VLLab@KAIST). His research focuses on advancing machine learning and computer vision with emphasis on reducing human supervision requirements and enhancing model interpretability. Prior to joining KAIST, he was a visiting faculty researcher at Google Brain and completed a postdoctoral fellowship at the University of Michigan working with Professor Honglak Lee. Education: PhD in Computer Science and Engineering, 2017, POSTECH, Korea (Supervised by Professor Bohyung Han) BS in Computer Science and Engineering, 2011, POSTECH, Korea Professor Hong's research primarily explores scaling up machine learning algorithms for visual perception while minimizing human supervision requirements. He investigates techniques to make machine learning algorithms more interpretable, enabling users to better understand and participate in the decision-making process. His work spans few-shot learning, generative models, vision transformers, and equivariant neural networks, with applications across various computer vision tasks. His recent publications reveal a strong focus on advancing foundation models for computer vision with emphasis on data efficiency, interpretability, and generalization. Key themes include developing novel approaches for few-shot learning, improving model efficiency through token merging and compositionality, and creating robust vision systems capable of handling real-world challenges like weather degradation. His work frequently appears in top-tier conferences including NeurIPS, ICLR, CVPR, and ECCV. Scientific Awards: Outstanding Paper Award at ICLR 2023 Professor Hong actively mentors a substantial group of students, including multiple Ph.D. candidates, master's students, and undergraduates. He serves in leadership roles for major conferences, including as Senior Area Chair for NeurIPS 2025 and Workshop Chair for ICCV 2025. His research has significant practical implications for developing more efficient, interpretable, and adaptable computer vision systems. He leads the KAIST Vision and Learning Lab, which focuses on cutting-edge research at the intersection of machine learning and computer vision, with particular emphasis on developing algorithms that require minimal human supervision while maintaining high performance and interpretability.

Shafi Goldwasserمشاهده پروفایل

استاد

• Cryptography
• Theoretical Computer Science
• Complexity Theory
• +۴ مورد دیگر

Shafi Goldwasser is the RSA Professor (Post Tenure) of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and holds a professorship in the Electrical Engineering and Computer Sciences (EECS) department at the University of California, Berkeley. She is a core member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), particularly within the Theory of Computation Group and Complexity Theory Group. Her work focuses on advancing cryptographic foundations, computational complexity, and algorithmic security. Goldwasser has pioneered research in areas such as zero-knowledge proofs, homomorphic encryption, and secure multiparty computation, with applications in privacy, cybersecurity, and AI alignment. Research Interests: Goldwasser’s research spans cryptography, complexity theory, and the intersection of computer science with societal challenges. Her work emphasizes: Designing provably secure cryptographic systems Algorithmic robustness and adversarial machine learning Privacy-preserving technologies for healthcare and data collaboration Formal verification of AI models Cross-disciplinary applications in law and biology Scientific Contributions: A 2017 ACM Fellow, Goldwasser has led major initiatives like the Homomorphic Encryption Standard project. Her research groups at MIT and CSAIL explore foundational problems in computational complexity while addressing real-world challenges such as securing emerging communication systems and reconciling legal secrecy with transparency through cryptographic tools. Collaborations & Teams: She co-leads the Cryptography and Information Security Group and Complexity Theory Group at MIT, collaborating with experts like Vinod Vaikuntanathan and Yael Kalai. Her work integrates theoretical breakthroughs with practical applications, such as secure genomic analysis and AI alignment frameworks.

Steven Moradمشاهده پروفایل

مدرس

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Steven Morad is a Lecturer and PhD student in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on reinforcement learning, long-term memory models, and robotics applications, with an emphasis on partially observable environments and multi-agent systems. He has taught Deep Reinforcement Learning (Lent 2024) and served as a Teaching Assistant for Mobile Robot Systems (2021–2022). His work includes developing frameworks for embodied navigation, memory-augmented algorithms, and cooperative multi-robot systems. Notable contributions include the POPGym benchmark for POMDPs and the NASA-recognized Improving Visual Feature Extraction in Glacial Environments (2019). Research themes span robotics in extreme environments (e.g., lunar caves, low-gravity terrains) and graph-based methods for partial observability. His publications address challenges in decentralized control, topological priors, and language-conditioned navigation. Education : MSc Thesis on The Spinning Projectile Extreme Environment Robot (2019). Awards : NASA New Technology (NTR NPO 51401) for glacial robotics work. Collaborations : NASA/JPL, IEEE, and aerospace industry partnerships.

Horst Bischofمشاهده پروفایل

استاد

• Computer Vision
• Medical Computer Vision
• Object Recognition
• +۴ مورد دیگر

Horst Bischof is a Professor at the Institute for Computer Graphics and Vision at Graz University of Technology, Austria, and serves as Vice Rector for Research. He holds an M.S. and Ph.D. from Vienna University of Technology and a Habilitation (venia docendi) in applied computer science. His research focuses on computer vision, medical image processing, and robot vision, with over 750 peer-reviewed publications. He has organized major conferences like CVPR 2015 and ECCV 2018, and serves on editorial boards of prestigious journals. Key awards include the Most Influential Paper over the Decade Award (MVA 2019), Jan Konderink Award (ECCV 2018), and the 29th Pattern Recognition Award (2002). His work spans object recognition, medical computer vision, and visual learning. He leads research teams in robot vision and collaborates with industry partners like Infineon Technologies. Current projects include LiDAR-based sensing systems, autonomous vehicle technologies, and medical imaging solutions like MedEyeTrack for eye tumor treatment. His research emphasizes practical applications in robotics, automotive, and healthcare sectors.

• Data Mining
• Machine Learning
• Geospatial Data
• +۷ مورد دیگر

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He holds a Ph.D. from Rutgers University (2016), a B.E. from the University of Science and Technology of China (2008), and an M.E. from the Chinese Academy of Sciences (2011). His research focuses on data mining, machine learning, geospatial data, and interdisciplinary applications, particularly in AI-driven urban planning and time series forecasting. He has received prestigious awards, including the NSF CAREER Award (2021), NAE Grainger Early Career Engineer Award (2023), and recognition as a Top 2% Global Scientist (2024). His work bridges theoretical advancements and practical applications, such as generative AI for urban design and reinforcement learning for feature transformation. Recent research emphasizes data-centric AI, including tabular data optimization, feature selection via generative models, and LLM-driven teamwork. He advises Ph.D. students who have transitioned to academic roles and collaborates on initiatives like the IEEE BIGDATA conference and the AAAI workshop on time series modeling.

Günter Neumannمشاهده پروفایل

استاد

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

Qiang Yeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Numerical Analysis
• +۳ مورد دیگر

Qiang Ye is a Professor in the Department of Mathematics at the University of Kentucky. His research focuses on machine learning, deep learning, numerical analysis, and optimization algorithms, with a particular emphasis on neural network architectures, generative adversarial networks (GANs), and numerical methods for solving large-scale linear systems and eigenvalue problems. He has contributed to advancements in recurrent neural networks, batch normalization preconditioning, and symmetry-exploiting convolutional networks. Ye's work bridges theoretical foundations and practical applications, including speech enhancement, molecular representation learning, and industrial welding process monitoring. His research often integrates insights from numerical linear algebra to improve the stability and efficiency of machine learning models. Recent efforts include developing adaptive optimization techniques and regularization methods to address challenges in deep learning training and model accuracy. Awards and grants are not explicitly listed in the provided information. Ye collaborates on interdisciplinary projects, as evidenced by co-authored papers with researchers from engineering and computational chemistry domains. His lab's activities likely center on computational methods for scientific and engineering problems, though specific lab names or teams are not mentioned.

• Machine Learning
• Computer Vision
• Neural Networks
• +۵ مورد دیگر

Mohit Prabhushankar is a Research Fellow at the Georgia Institute of Technology's Department of Electrical and Computer Engineering, part of the College of Engineering. His work focuses on machine learning, computer vision, and explainable AI with applications in medical imaging, seismic interpretation, and uncertainty quantification. He has contributed to developing algorithms for active learning, self-supervised learning, and robust model evaluation. Research interests include hierarchical multimodal systems, counterfactual explanations, and domain adaptation in geoscience applications. His recent work addresses challenges in interpretability metrics, active learning strategies, and biomedical data analysis through projects like the OLIVES dataset for ophthalmic biomarkers and CRACKS for subsurface fault analysis. Publications span topics from action anticipation in robotics to neural network dynamics, emphasizing practical applications of AI in geoscience and healthcare. His research bridges theoretical advancements with real-world deployment considerations through frameworks like gradient-based anomaly detection and deployable clinical active learning systems. No scientific awards or grants are explicitly listed in available materials. Advising activities and student collaborations remain undocumented in the provided content.

Xi Heمشاهده پروفایل

استادیار

• Privacy and Security
• Big Data
• Machine Learning
• +۲ مورد دیگر

Xi He is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. Her research centers on privacy and security for big data, with a focus on developing trustworthy tools for machine learning and data exploration. She holds a Ph.D. from Duke University (2018) and an M.Sc. from Duke University (2015). Her work spans privacy-preserving data analytics, secure ML frameworks, and algorithmic fairness. Recent publications emphasize explainable AI for brain aging , neural tangent kernels , and graph-structured learning . She actively investigates transferability of covariance networks in neuroimaging contexts. Dr. He's articles reveal a strong emphasis on theoretical ML foundations applied to neuroscience, with innovations in model interpretability and high-dimensional data analysis. She leads research on graph-constrained algorithms and adaptive statistical methods.

James Sharpnackمشاهده پروفایل

دانشیار مدعو

• Statistical Machine Learning
• Graph Signal Processing
• Anomaly Detection
• +۱ مورد دیگر

James Sharpnack is an Adjunct Associate Professor in the Statistics Department at UC Davis and an AI Research Scientist at Duolingo. His research focuses on statistical machine learning applied to anomaly detection, graph signal processing, and epidemiological modeling. His methodological work includes developing scan statistics for network data, trend filtering techniques, and domain adaptation approaches. Applied projects involve COVID-19 wastewater monitoring, gravitational lens detection in astronomy, and transportation data analysis. Sharpnack contributes to educational AI systems through his industry role.