جست‌وجوی استاد و پژوهشگر

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)

Chandan Reddyمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Healthcare Analytics
• +۵ مورد دیگر

Chandan Reddy is a Professor in the Department of Computer Science at Virginia Tech. He holds a Ph.D. from Cornell University and an M.S. from Michigan State University. His research focuses on Machine Learning, Natural Language Processing, and their applications in Healthcare, Software, Transportation, and E-commerce. His work has been funded by NSF, NIH, DOE, DOT, and industries, resulting in over 200 peer-reviewed publications. Notable awards include the Best Application Paper at SIGKDD 2010 and the Franz Edelman Award finalist in 2011. Education: Ph.D., Computer Science, Cornell University M.S., Computer Science, Michigan State University Research Interests: Machine Learning and NLP applied to healthcare analytics, big data systems, and complex data challenges. His work emphasizes scalable algorithms, fairness in AI, and generative models. Recent projects include scientific equation discovery via LLMs and bias mitigation in language models. Publications Trends: Recent work explores LLM-driven reasoning, hyperbolic neural networks, and healthcare informatics. Key areas include data privacy (e.g., synthetic medical data), interpretable models (e.g., time-series clustering), and graph-based methods for product search. Awards: Best Application Paper Award at ACM SIGKDD 2010 Best Poster Award at IEEE VAST 2014 Best Student Paper Award at IEEE ICDM 2016 INFORMS Franz Edelman Award Competition Finalist 2011 Advising & Grants: Advises graduate students on machine learning research, with grants supporting interdisciplinary projects in healthcare, transportation, and AI ethics. Leads the GraphZoo toolkit for hyperbolic GNNs and collaborates on large-scale medical data initiatives. Labs/Teams: Directs research in Virginia Tech’s Data Analytics Lab, focusing on big data platforms, healthcare analytics, and explainable AI systems. Active in developing open-source tools like GraphZoo for graph neural networks.

Niraj Jhaمشاهده پروفایل

استاد

• Smart Healthcare
• Machine Learning
• Cybersecurity
• +۵ مورد دیگر

Niraj Jha is a Professor of Electrical and Computer Engineering at Princeton University, affiliated with the School of Engineering and Applied Science. He joined Princeton in 1987 and became a full professor in 1998. His research focuses on smart healthcare, machine learning algorithms, IoT security, and neuro-symbolic AI. He leads projects in wearable medical sensors for disease detection, transformer synthesis, and cybersecurity for cyber-physical systems. Jha has authored/co-authored over 480 papers, 25 patents, and multiple books, including Testing and Reliable Design of CMOS Circuits and Nanoelectronic Circuit Design . He has received prestigious awards like the ACM and IEEE Fellowships, and the Distinguished Alumnus Award from IIT Kharagpur. Education: Ph.D., University of Illinois at Urbana-Champaign, 1985 M.S., State University of New York at Stony Brook, 1982 B.Tech., Indian Institute of Technology Kharagpur, 1981 Research Interests: Jha’s work bridges AI, hardware design, and healthcare. Key areas include: Predictive healthcare via wearable sensors and ML ensembles Transformer acceleration for edge computing Cybersecurity for IoT and 5G systems Neuro-symbolic AI for small-data learning Optimization via reinforcement learning His lab explores body-area networks, synthetic control for clinical trials, and energy-efficient architectures. Publications & Awards: Over 20 papers have won best paper awards. Notable recognitions include the Princeton Graduate Mentoring Award (2004) and the 2025 ACM recognition for Learning Interpretable Differentiable Logic Networks . Advising & Labs: Jha advises graduate students on topics like neural architecture search (e.g., FlexiBERT) and healthcare AI (e.g., DOCTOR framework). He directs the Andlinger Center for Energy and the Environment and has led the Center for Embedded System-on-a-Chip Design. His lab collaborates on projects like SHF: Small grants for transformer synthesis and NSF-funded initiatives. Labs & Teams: Active in Princeton’s Global Health Initiative and collaborates across disciplines, including robotics and data science. His research group develops tools like CODEBench (co-design frameworks) and TUTOR (decision-rule-based ML).

Mohamed Sedkyمشاهده پروفایل

استاد

• Computer Vision
• Video Analytics
• Cyber-Physical Systems
• +۴ مورد دیگر

Professor Mohamed Sedky holds the position of Professor of Cyber-Physical Systems at Staffordshire University. He previously founded SKM Communication Systems and served as a lecturer at the Arab Academy for Science and Technology before joining Staffordshire in 2003. His roles include MSc Computer Science Deputy Award Leader and BSc Internet of Things Award Leader. He leads research at AVA Technologies Ltd, focusing on real-time video analytics algorithms such as Spectral-360®, which addresses security, safety, and business intelligence applications. His patented work in object detection and change detection has been globally recognized. He teaches modules like Fundamentals of Computer Networks and Router Security Technologies. Education: BEng (Hons) Electro-Physics and Communications, Alexandria University (1996) MSc Communications and Electronics, Arab Academy for Science and Technology (2002) PhD Computer Vision, Staffordshire University (2009) PGC Research Supervision, Staffordshire University PGC Higher and Professional Education, Staffordshire University Research Interests: Object recognition/segmentation Video surveillance systems IoT forensic processes AI in healthcare and smart environments Commercial Ventures: Founder of Spectral-360® technology Lead Researcher at AVA Technologies Ltd Consultant for Serco, Centralweighing, and WMTN Labs & Projects: Developed AVA Technologies' Detect/Protect/Count/Summarise solutions Designed OpenSHS (Open Smart Home Simulator) Conducted feasibility studies for buried utility detection and industrial inspection His research bridges academia and industry through patented innovations in video analytics and IoT systems, addressing challenges in security, healthcare, and smart infrastructure.

Xinzhe Luoمشاهده پروفایل

پژوهشگر

• Medical Image Computing
• Artificial Intelligence
• Statistics
• +۳ مورد دیگر

Xinzhe Luo is a Postdoctoral Researcher at the Department of Electrical and Electronic Engineering and I-X Institute at Imperial College London. He collaborates with Dr. Chen Qin, Dr. Yingzhen Li, and Prof. Ben Glocker. Previously, he earned a BSc in Mathematics (2019) and a PhD in Statistics (2024) from Fudan University. His research focuses on interdisciplinary areas including medical image computing, artificial intelligence, statistics, and mathematics. Current priorities include developing trustworthy AI for MRI reconstruction. Key contributions span semi-supervised tabular-image learning, unsupervised MRI reconstruction, and Bayesian-based groupwise registration techniques. Notable achievements include receiving the China National Scholarship 2023 and being honored as a Distinguished Reviewer for IEEE TMI (2023). His work has been recognized with an Honourable Mention for the Francois Erbsmann Prize (IPMI 2023) and multiple prestigious conference acceptances. Research highlights include frameworks like BInGo for Bayesian groupwise registration and STiL for multimodal classification, as well as benchmark studies in cardiac MRI segmentation. His work bridges clinical needs with computational advancements, emphasizing robustness and interpretability in medical AI systems.

Jörn Heesمشاهده پروفایل

استاد

• Data Science
• Deep Learning
• Anomaly Detection
• +۳ مورد دیگر

Prof. Jörn Hees is a Professor for Data Science at the Department of Computer Science, Hochschule Bonn-Rhein-Sieg (H-BRS), and serves as Chairman of the Computer Science Faculty Council. He is affiliated with the Institute for Artificial Intelligence and Autonomous Systems (A2S) and the Institute of Technology, Resource and Energy-efficient Engineering (TREE). His research focuses on Data & Graph Mining, Deep Learning, Anomaly Detection, Explainable AI, and AI-assisted education (e.g., automated grading systems). He teaches courses such as Stochastics, Data Analysis and Visualization, Artificial Intelligence (Bachelor level), and Deep Learning Foundations, Natural Language Processing (Master level). His work bridges theoretical advancements with practical applications in finance, remote sensing, and education. Notable projects include federated outlier detection systems (Fin-Fed-OD), the TreeSatAI benchmark dataset for tree species classification, and AI-driven grading solutions using LLMs. His publications emphasize anomaly detection techniques, multimodal fusion, and interpretable machine learning. Despite no explicit awards listed, his contributions to AI education and computational methods are evident through his extensive publication record. He coordinates research teams and advises on infrastructure for AI education and infrastructure monitoring via Earth observation data.

Haohui Luمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Graph Machine Learning
• Health Informatics
• +۱۲ مورد دیگر

Haohui Lu is a Research Fellow at the Molly Wardaguga Research Centre within the Faculty of Health. He is registered to supervise postgraduate research and holds a PhD from The University of Sydney, awarded in March 2024. His work bridges artificial intelligence and public health, with a focus on ethical and explainable AI systems. Research Interests: Artificial Intelligence and Machine Learning Graph Machine Learning and Knowledge Graphs Health Informatics and Clinical Decision Support Bias Mitigation and Fairness in AI Large Language Models for Healthcare Statistical Modeling and Real-World Data Analysis His recent publications demonstrate a strong trend in applying machine learning—particularly graph-based and language models—to public health challenges such as disease progression, policy analysis, and undernutrition. He frequently publishes in high-impact journals like PLoS One , Scientific Reports , and Neurocomputing , and presents at top venues such as EMNLP. His work emphasizes fairness, interpretability, and practical impact for policymakers and communities. Scientific Contributions and Recognition: Active contributor to UN Sustainable Development Goals, particularly in health and well-being. Presented research at EMNLP 2024 on LLMs for disease prediction. Research disseminated through news outlets and widely shared on academic platforms like Mendeley. Advising and Leadership: Registered as a postgraduate research supervisor, indicating a role in mentoring future researchers. Provides technical leadership in AI tool development, research data platforms, and LLM applications, supporting multidisciplinary and international collaborations. Laboratories and Teams: Based at the Molly Wardaguga Research Centre, a hub for health research, where he contributes to projects integrating AI with real-world health data and policy.

John P. Dickersonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence Ethics
• Economics of Complex Systems
• +۳ مورد دیگر

John P. Dickerson is a tenured Professor of Computer Science at the University of Maryland and Chief Scientist at Arthur AI. He holds a PhD from Carnegie Mellon University and is affiliated with UMIACS, AMSC, HCIL, and MTI. His work bridges machine learning and economics, focusing on fairness, robustness, and incentive design in complex systems. Research areas include algorithmic fairness, healthcare optimization (e.g., organ allocation), and AI ethics. Education: PhD in Computer Science, Carnegie Mellon University Affiliations: UMIACS, AMSC, HCIL, MTI His research is supported by NSF CAREER, NIST, DARPA, and industry grants. Notable contributions include fairness in clustering, robustness in face detection, and kidney exchange optimization. He advises startups like GovShop and has been recognized with Siebel Scholarship and Facebook Fellowship. Key projects include Arthur AI's enterprise AI monitoring systems and collaborations with UNOS on organ allocation. Current focus is growing Arthur and mentoring students in machine learning and operations research.

Gjergji Kasneciمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence Ethics
• Algorithmic Fairness
• +۴ مورد دیگر

Prof. Dr. Gjergji Kasneci is a Professor of Responsible Data Science at the Technical University of Munich (TUM), leading the Chair of Responsible Data Science. He holds affiliations with the TUM School of Social Sciences and Technology and the TUM School of Computation, Information and Technology. His research focuses on ethical, legal, and societal aspects of AI, emphasizing transparency, fairness, and robustness in machine learning algorithms. Prof. Kasneci’s academic journey includes a PhD in Computer Science from the University of Marburg (2009), postdoctoral research at Microsoft Research Cambridge, and leadership roles at the Hasso Plattner Institute and SCHUFA Holding AG. He was an Honorary Professor at the University of Tübingen (2018–2023) and currently serves as Vice Dean and Information Officer at TUM. Key awards include the Seoul Test of Time Award (2018) and an Honorary Professorship from the University of Tübingen (2019). He leads initiatives like the AI in Finance Lab and contributes to AI policy through projects such as the EU-funded AI4POL initiative.

Olivier Bernardمشاهده پروفایل

استاد

• Medical Image Processing
• Artificial Intelligence in Cardiac Imaging
• Domain Adaptation
• +۴ مورد دیگر

Olivier Bernard is a Professor of Image and Signal Processing at Université de Lyon (INSA), France, and Deputy Director of the CREATIS laboratory. He leads the MYRIAD research team and holds editorial roles in IEEE Transactions on Medical Imaging and the European Heart Journal . His work bridges artificial intelligence and medical imaging, focusing on cardiac applications. Education: HDR (2019), PhD in Medical Image Analysis (2006), M.S. in Imaging (2003), Engineering Degree (2003) – all from Université de Lyon (INSA). His research explores representation learning , domain adaptation , and physics-guided AI for cardiac imaging. Key areas include uncertainty modeling, echocardiographic segmentation, and clinical data fusion. Recent publications highlight reinforcement learning frameworks (RL4Seg) and diffusion models for cardiac diagnosis. Scientific contributions include domain adaptation techniques, open-source tools like CMRSegTools , and synthetic data generation pipelines. Awards include Senior Membership in the Institut Universitaire de France (IUF).

Ian Laneمشاهده پروفایل

دانشیار

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +۵ مورد دیگر

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

Danny Chenمشاهده پروفایل

استاد

• Algorithm Design
• Computational Geometry
• Computational Biomedicine
• +۵ مورد دیگر

Danny Chen is a Professor in the Department of Computer Science and Engineering at the University of Notre Dame, with a concurrent appointment in the Department of Applied and Computational Mathematics and Statistics. His research focuses on algorithm design, computational geometry, biomedical imaging, and machine learning. Ph.D., Computer Science, Purdue University (1992) M.S., Computer Science, Purdue University (1988) B.S., Computer Science and Mathematics, University of San Francisco (1985) Dr. Chen has developed over 330 publications and 5 U.S. patents, with applications in radiation therapy, biomedical imaging, and VLSI design. His recent work explores advanced deep learning architectures for medical image segmentation and tabular data analysis. Key awards include the NSF CAREER Award, Kaneb Teaching Award, James A. Burns Award, and Computerworld Honors Program Laureate. He is an IEEE Fellow and ACM Distinguished Scientist. Visiting Professor at HKUST (2003), Tsinghua University (2012), and Zhejiang University (2012) Research funded by NSF and NIH Active in program committees and NSF review panels

Hajar Homayouniمشاهده پروفایل

استادیار

• Data Science
• Database Systems
• Applied Machine Learning
• +۲ مورد دیگر

Hajar Homayouni is an Assistant Professor in the Department of Computer Science at San Diego State University's College of Sciences. She specializes in Data Science, Database Systems, Applied Machine Learning, and Big Data Quality Assurance. Her work bridges theoretical research with practical applications in healthcare informatics, data privacy, and educational technology. Ph.D. in Computer Science from Colorado State University (2020) MS in Computer Science (Colorado State University, 2017) MS in Artificial Intelligence (Alzahra University of Tehran, 2013) BS in Software Engineering (University of Kashan, 2008) Her research focuses on developing advanced methodologies for anomaly detection, synthetic data generation, and data quality assurance, with applications in public health (particularly COVID-19 analysis), cardiovascular research, and medical imaging. She has pioneered approaches using transformer architectures and federated learning frameworks for improved data analysis and privacy preservation. Recent publications highlight her work in machine learning interpretability , multimodal medical data generation , and time-series anomaly detection . Her research has been recognized with multiple awards including the Harvard PingPong Generative AI Tutor Award and Teach Access Fellowship . Harvard PingPong Generative AI Tutor Award ($1,000) Teach Access Fellowship ($4,000) SDSU Inclusive Excellence Faculty Fellowship STARS Computing Corps Fellowship ($1,000) Grace Hopper Celebration Scholarship She supervises numerous graduate and undergraduate students in research projects covering synthetic data generation, visual phishing detection, and medical data analysis. Her grants portfolio includes over $1 million in federal and private funding, with notable awards from NSF, NIH, and Microsoft Research.

Héctor Gibrán Ceballos-Cancinoمشاهده پروفایل

استاد

• Social Network Analysis
• Machine Learning
• Process Mining
• +۵ مورد دیگر

Héctor Gibrán Ceballos-Cancino serves as Director of the Living Lab & Data Hub of the Institute for the Future of Education (IFE) at Tecnologico de Monterrey and is a full-time faculty member of the Computer Science Graduate Program (DCC). Previously, he led the Scientometrics office at the Research Vice-Rectory of Tecnologico de Monterrey for 18 years, establishing himself as a key figure in research evaluation at the institution. His educational background includes: Computer Systems Engineer from Instituto Tecnológico De Veracruz Master of Science with specialization in Intelligent Systems from Tecnologico de Monterrey PhD in Information Technology and Communications from Tecnologico de Monterrey (2010) Dr. Ceballos-Cancino's research program bridges artificial intelligence with practical applications in education and organizational contexts. His work spans Social Network Analysis, Machine Learning, Process Mining and Agent theory, applied specifically to Research Analytics and Educational Data Mining. He has developed significant expertise in applying AI techniques to solve real-world problems across multiple domains, with particular focus on educational improvement and research evaluation. Analysis of his recent publications reveals a strong trajectory toward quantum machine learning applications for education, learning analytics in Latin American contexts, and sophisticated student success prediction models. His work demonstrates consistent innovation in applying advanced computational techniques to educational challenges, while also maintaining strong contributions to scientometrics and building technology applications. His scientific recognition includes: Premio Tecnos 2023 conferred by Gobierno de Nuevo León Mexican Researcher Certification - Level 1 Member of the Mexican National System of Researchers (SNI) Adherent member of the Mexican Academy on Computing (AMEXCOMP) Dr. Ceballos-Cancino has supervised thesis work as evidenced by his teaching activity in 'Design of Intelligent Agents Thesis II.' His research has been supported by institutional initiatives at Tecnologico de Monterrey, including predictive analytics platforms for public administration evaluation. He has also provided expert consulting services for banks and IT companies, and promoted Semantic Web technologies adoption across multiple sectors. As Director of the Living Lab & Data Hub, he leads initiatives focused on 'Engaging and Motivating Learning Models,' connecting his technical expertise with educational innovation. His work aligns with multiple UN Sustainable Development Goals, particularly Quality Education (SDG 4) and Industry, Innovation and Infrastructure (SDG 9), demonstrating his commitment to technology for social impact.