جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Douglas Downey is a Professor of Computer Science at Northwestern University, affiliated with the Department of Computer Science. He is currently on leave to direct the Semantic Scholar research group at the Allen Institute for AI, focusing on NLP and HCI tools for scientific literature discovery. Education: Ph.D. Computer Science and Engineering, University of Washington, Seattle, WA M.S. Computer Science and Engineering, University of Washington, Seattle, WA B.S./M.S. Computer Science, Case Western Reserve University, Cleveland, OH Research Interests: His work centers on natural language processing and machine learning, specifically the automatic construction of knowledge bases from web text to advance web search capabilities. He develops methods to optimize human input in ML systems via active learning (strategic data selection) and semi-supervised learning (leveraging unlabeled data). Publication Trends: Recent work (2022-2024) emphasizes NLP efficiency, explainable AI, and scientific literature tools. Themes include model optimization (embedding recycling), ethical AI (bias mitigation), and human-AI collaboration (interactive systems like LIMEADE and CiteSee). Publications frequently appear in premier venues like ACL and ACM. Advising: Actively mentors PhD students at Northwestern. Current advisees include Chris Coleman and Mike D'Arcy. Alumni hold positions at Google, Meta, HKUST, and other institutions. Labs/Teams: Leads the Semantic Scholar research team at Allen Institute for AI, building tools to help scholars discover and synthesize scientific literature. Collaborates with Northwestern faculty David Demeter and Larry Birnbaum.

John Ormerodمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Inference
• Data Mining
• +۴ مورد دیگر

Associate Professor John Ormerod is affiliated with the Statistics Research Group at the School of Mathematics and Statistics , University of Sydney . His research focuses on advanced statistical methodologies, including Variational Bayes , Generalized Linear Mixed Models , Splines , and Missing Data analysis. Current research students: Rajan Shankar and Jackson Zhou Key research strengths: Precision and Digital Health , Data and Decisions , National Security His work spans computational statistics, bioinformatics, and applications in health and biological data. Recent publications emphasize scalable Bayesian inference, variable selection, and single-cell data analysis frameworks. Grants include multiple Australian Research Council (ARC) Discovery Projects (2021, 2017, 2012, 2010) supporting research in feature selection, network modeling, and computational efficiency. He has supervised projects on modern regularization techniques and fast expectation propagation , contributing to BMC Bioinformatics , Bioinformatics , and Journal of Computational and Graphical Statistics .

• Machine Learning
• Signal Processing
• Quantum Computing
• +۴ مورد دیگر

Andreas Spanias is a Professor at Arizona State University specializing in Machine Learning , Signal Processing , and Quantum Computing with applications in renewable energy, healthcare, and wireless networks. His research includes Photovoltaic Fault Detection , Quantum Machine Learning , and Real-Time Energy Monitoring . Key research areas: Machine Learning, Quantum Computing, Renewable Energy Systems, Medical Imaging, Wireless Sensor Networks. Notable collaborations: Glen S. Uehara, Cihan Tepedelenlioglu, Sunil Rao, Jayaraman J. Thiagarajan. Recent Publications (2025–2024) focus on quantum machine learning for photovoltaic fault classification, Bayesian optimization in circuit design, and real-time solar array monitoring. Trends include quantum algorithms for signal processing, energy-efficient ML, and educational innovations in quantum computing. Educational Initiatives include REU programs in Quantum Machine Learning and integrating ML into signals and systems courses. He leads international collaborations like the ASU-DCU Sensors and ML Workforce Development Program.

• Wireless Communications
• Polarization Techniques
• Knowledge Graph Completion
• +۷ مورد دیگر

Fangfang Liu is an active researcher with an extensive publication record spanning from 2005 to 2025, demonstrating significant contributions across multiple domains in computer science and engineering. Their work shows consistent collaboration with researchers including Weimin Li, Caili Guo, Zhimin Zeng, and Chunyan Feng, suggesting strong institutional ties within their research community. Fangfang Liu's research spans several key areas including wireless communications, knowledge graph completion, semantic communications, and fake news detection. Their work demonstrates expertise in applying machine learning techniques to solve complex problems in network security, IoT systems, and multimedia analysis. The research portfolio shows a progression from foundational work in wireless communications and polarization techniques toward more recent applications in AI-driven security and knowledge representation. The publication trends reveal a strategic expansion from traditional communications engineering into cutting-edge AI applications. Early work focused on polarization techniques and wireless channel modeling, while recent publications emphasize knowledge graphs, multimodal fake news detection, and semantic communications. This evolution demonstrates adaptability to emerging research frontiers while maintaining technical depth in signal processing and network analysis. Fangfang Liu has made substantial contributions through numerous publications in prestigious venues including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. Their collaborative approach is evident through extensive co-author networks across academia and research institutions. While specific advising information isn't detailed in the publication record, Fangfang Liu appears to lead research groups working on precision assembly, knowledge graph applications, and IoT security. The research program demonstrates strong connections between theoretical foundations and practical implementations in communication systems and AI applications.

Jonathan Wiltonمشاهده پروفایل

مدرس پاره‌وقت

• Machine Learning
• Artificial Intelligence
• Decision Trees
• +۳ مورد دیگر

Jonathan Wilton is a Casual Academic in the School of Mathematics and Physics at the University of Queensland. His email is jonathan.wilton@uq.edu.au . His research interests focus on machine learning and artificial intelligence, particularly in robust training methodologies for decision trees with noisy labels, positive-unlabeled learning using random forests, and the design of neural networks leveraging randomized algorithms. These areas emphasize improving model reliability and efficiency in complex data environments. His recent work includes contributions to the 2024 AAAI Conference on Artificial Intelligence, highlighting advancements in handling noisy data and optimizing decision tree frameworks. His research spans theoretical algorithm development and practical applications in classification and pattern recognition. No scientific awards or grants are explicitly mentioned in the provided texts. He has not listed any advised students, and no specific laboratories or teams are associated with his profile.

Abigail Koayمشاهده پروفایل

پژوهشگر ارشد

• Cyber Security
• Machine Learning
• Industrial Control System (ICS) Security
• +۳ مورد دیگر

Dr. Abigail Koay is an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on cybersecurity, machine learning applications in industrial systems, and healthcare technology. She has contributed to projects such as real-time cyber-attack detection using weakly supervised learning, supported by UQ Cyber Seed Funding (2021–2022). Her work spans multiple disciplines including: Cybersecurity for Industrial Control Systems (ICS) IoT network anomaly detection using fog-assisted frameworks Machine learning for medical imaging (e.g., glaucoma detection) AI-driven cybersecurity strategies for smart grids Recent publications highlight advancements in: Irregular time series analysis using GNNs Positive-unlabeled learning with random forests Domain generalization in retinal image analysis Dr. Koay has authored/co-authored 15+ peer-reviewed articles across journals like Frontiers of Computer Science , IEEE Access , and conferences including NeurIPS and ISGT Asia. She collaborates with industry partners on projects like Plan2Defend for smart grid security and SDGen for synthetic cybersecurity dataset generation. Her research integrates theoretical machine learning with practical cybersecurity challenges, emphasizing real-world applicability in critical infrastructure and healthcare systems.

Christian Gagnéمشاهده پروفایل

استاد

• Deep Learning
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Christian Gagné is a Full Professor in the Department of Electrical and Computer Engineering at Laval University and Director of the Institute for Intelligence and Data (IID). He holds a prestigious Canada CIFAR AI Chair and is an Associate Academic Member at Mila – Quebec Artificial Intelligence Institute. His research spans multiple interdisciplinary domains with significant contributions to machine learning and its applications. Dr. Gagné completed his doctorate in electrical engineering at Université Laval in 2005, followed by a postdoctoral fellowship at INRIA Saclay (France) and the University of Lausanne (Switzerland) from 2005-2006. Prior to his academic appointment, he worked as a research associate in an industrial environment between 2006 and 2008. His research interests focus on deep learning and stochastic optimization, with particular emphasis on the robustness and generalization of deep neural networks, neurosymbolic approaches to enhance interpretability, and the development of multimodal foundational models. His work has practical applications across computer vision, super-resolution microscopy, healthcare, transportation, and energy sectors. Dr. Gagné has developed important methodologies in representation learning, meta-learning, transfer learning, and optimization approaches based on probabilistic models and evolutionary algorithms. Recent publications demonstrate a strong focus on improving the robustness of machine learning models, with significant contributions to data augmentation techniques for medical imaging, adversarial attacks on visual object trackers, and test time adaptation methods. His research group has produced influential work at the intersection of theoretical machine learning and practical applications, particularly in medical imaging and computer vision domains. Canada CIFAR AI Chair As an academic advisor, Dr. Gagné has supervised numerous doctoral and master's students including Frédéric Beaupré, Catherine Bouchard, Fatemeh Nourilenjan Nokabadi, Sabyasachi Sahoo, and Adam Tupper. His research is supported through various grants, including his Canada CIFAR AI Chair position and collaborations with multiple research centers. Dr. Gagné is actively involved with several research entities including the Vision and Digital Systems Laboratory (LVSN), the Center for Research in Robotics, Vision and Machine Intelligence (CeRVIM), the Center for Research in Massive Data (CRDM), as well as strategic groups REPARTI and UNIQUE of the FRQNT, the VITAM center of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technology (OBVIA).

• Cell Death Mechanisms
• Cancer Immunology
• Immuno-Oncology
• +۲ مورد دیگر

Lorenzo Galluzzi is an Assistant Professor of Dermatology at Yale School of Medicine , with an adjunct appointment. His research integrates cell death mechanisms, cancer immunology, and immuno-oncology to uncover therapeutic targets for refractory cancers. Education: While specific degrees aren’t listed, his prolific publication record in high-impact journals (e.g., Cell Death & Differentiation , Nature Immunology ) indicates extensive training in molecular biology and oncology. Research Interests: Cell Death Pathways : Apoptosis, necroptosis, and autophagy in cancer and aging. Immuno-Oncology : Mechanisms of immune evasion and response to therapies like checkpoint inhibitors. Mitochondrial Regulation : Role of VDAC2 and CYPD in cancer immunity. Radiotherapy-Immunotherapy Synergy : Enhancing anti-tumor immunity via radiation. Recent Work Trends: His 2025 publications emphasize spatial transcriptomics in triple-negative breast cancer, metabolic switches in cell death, and the immunological impact of ATM deficiency. These studies bridge basic science and clinical applications, particularly in immunotherapy resistance. Awards: No specific awards are listed, but his leadership in international conferences (e.g., ImmunoRad) and editorial roles suggest recognition in the field. Collaborations & Labs: Works with teams at Yale and Weill Cornell Medicine, evidenced by co-authored papers and his Cornell email. His ORCID (0000-0003-2257-8500) links to further collaborative outputs.

• Computational Social Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Dachun Sun serves as an Assistant Teaching Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), with additional affiliations in the Data Science and Artificial Intelligence programs. Based in Unity Hall 452, he contributes to WPI's academic mission through both teaching innovation and computational social science research. His educational foundation was built entirely at the University of Illinois Urbana-Champaign: Ph.D. in Computer Science M.S. in Computer Science B.A. in Music Technology B.S. in Computer Science Dr. Sun's research sits at the critical intersection of artificial intelligence and societal impact , specializing in computational social science through graph neural networks , large language models , and retrieval-augmented generation . His work analyzes polarization in online communities to detect ideological positions and model responses to real-world events, with direct applications in influence campaign detection and crisis response systems. He simultaneously pioneers the integration of AI tools into computer science education while fostering critical awareness of their ethical implications. His 2023-2024 publications reveal a strong trajectory in temporal reasoning, polarized social graph analysis, and human activity recognition—appearing in top venues like ACM SIGIR and ICCCN. These works demonstrate consistent innovation in knowledge graph reasoning, multimodal representation, and practical AI applications for societal challenges. Dr. Sun actively mentors students through hands-on research opportunities in his computational social science group, emphasizing collaborative problem-solving for real-world issues. His funded projects directly translate research into tools for crisis response planning and misinformation mitigation. Though no formal lab name is specified, his research collective focuses on bridging AI methodology with social analysis, maintaining strong industry and academic connections as evidenced by his multi-institutional publications and upcoming CIKM 2024 tutorial on temporal graph learning.