جست‌وجوی استاد و پژوهشگر

Tom Hanikaمشاهده پروفایل

مدرس

• Knowledge Discovery
• Machine Learning
• High-Dimensional Data Analysis
• +۷ مورد دیگر

Tom Hanika is a Lecturer at the Institute of Library and Information Science, Humboldt University, within the Faculty of Philosophy. His research focuses on Knowledge Discovery, Machine Learning, and Formal Concept Analysis, with applications to high-dimensional data, databases, and social network analysis. He actively contributes to Open Science initiatives and scientometric studies. He is a senior developer of the BibSonomy publication sharing system and maintains the FCA-tool conexp-clj. Hanika has held roles as Program Chair for ICCS and ICFCA conferences, and has served on program committees for ISWC, ECML/PKDD, and others. His work bridges theoretical foundations (e.g., lattice theory, ordinal data science) with practical applications in biology, geography, and digital humanities. Recent research includes studying reproducibility in graph neural networks, ordinal motifs in lattices, and intrinsic dimensionality analysis. His contributions span over 50 peer-reviewed publications across venues like Information Sciences , Scientometrics , and Tohoku Mathematical Journal . Professional Memberships: German Computer Science Society, German Mathematical Society, Free Software Foundation Labs/Teams: Knowledge & Data Engineering Group (KDE), Intelligent Information Systems group

Peter van Ooijenمشاهده پروفایل

استاد

• Medical Imaging Informatics
• AI in Radiology
• 3D Visualization
• +۳ مورد دیگر

Professor Peter van Ooijen is Scientific Researcher and Professor at Faculty of Medical Sciences, University of Groningen. He coordinates the Machine Learning Lab at Data Science Center in Health (DASH) and serves as Theme Coordinator for Digital Healthcare at Jantina Tammes School. His research develops AI applications for medical imaging including radiological segmentation, prognostic modeling, treatment planning, and toxicity prediction. Projects focus on deep learning for oncology applications in radiotherapy and diagnostic imaging. As President of the European Society of Medical Imaging Informatics, he promotes AI integration in radiology practice. He leads research projects on 3D visualization for surgical planning, AI-based radiological biomarker development, and federated learning implementations.

PABLO GARCIA TAHOCESمشاهده پروفایل

استاد

• Medical Imaging
• Computer-Aided Diagnosis (CAD)
• Digital Mammography
• +۳ مورد دیگر

PABLO GARCIA TAHOCES is a Professor at the Department of Electronics and Computing, Higher Technical School of Engineering, University of Santiago de Compostela, Spain. His research focuses on medical imaging technologies, particularly computer-aided diagnosis (CAD), digital mammography, computed tomography (CT), and machine learning applications in radiology. He holds a Doctorate from the University of Santiago de Compostela, with a thesis on breast cancer risk assessment through radiographic analysis. Key contributions include the development of automated systems for detecting breast masses and microcalcifications, image compression techniques for mammograms, and algorithms for aortic geometry analysis. He has collaborated on the Galician Telemammography System project, integrating CAD tools into telemedicine platforms. His work spans over 30 years of research in digital radiography and PACS systems, with a focus on enhancing diagnostic accuracy and workflow efficiency. His publications emphasize advanced imaging techniques, including ellipse-based motion estimation, CT visualization tools, and deep learning applications for cardiovascular and dental imaging. His research has been applied in clinical settings, improving the detection of pulmonary nodules, interstitial lung disease, and aortic pathologies. He maintains active collaborations with institutions like AIMEN Technological Center and Siemens Medical Systems.

Leslie Valiantمشاهده پروفایل

استاد

• Theoretical Computer Science
• Complexity Theory
• Computational Learning
• +۴ مورد دیگر

Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. He previously held positions at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His research spans theoretical computer science with significant contributions to complexity theory, computational learning, and parallel computation. He introduced the #P-completeness concept to explain why enumeration and reliability problems are intractable, developed holographic algorithms, and created the influential "probably approximately correct" (PAC) model that helped establish machine learning theory as a formal discipline. His work extends to computational neuroscience, evolution theory, and artificial intelligence, where he has formulated evolution as a form of computational learning. Nevanlinna Prize (1986) Knuth Award (1997) EATCS Award (2008) A.M. Turing Award (2010) Fellow of the Royal Society Member of the National Academy of Sciences (USA) Valiant's interdisciplinary approach connects computer science with biological evolution, proposing that protein circuits in living organisms evolve through processes analogous to machine learning algorithms. His work on evolvability distinguishes function classes that can evolve with polynomially bounded resources from those that cannot, addressing fundamental questions about how complex biological systems can emerge through natural selection.

Leslie G. Valiantمشاهده پروفایل

استاد

• Theoretical Computer Science
• Machine Learning
• Computational Complexity
• +۳ مورد دیگر

Leslie G. Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences. He holds dual British-American nationality and has been affiliated with Harvard since 1982, prior to which he taught at Carnegie Mellon University, the University of Leeds, and the University of Edinburgh. Valiant earned his PhD in Computer Science from the University of Warwick in 1974, preceded by studies at King's College, Cambridge and Imperial College London. His research focuses on theoretical computer science, including complexity theory (#P-completeness), machine learning (PAC model), holographic algorithms, and computational neuroscience. He pioneered the bulk synchronous parallel processing model and introduced foundational algorithms in automata theory. Notable honors include the ACM Turing Award (2010), Nevanlinna Prize (1986), and Fellowship in the Royal Society. His work bridges computational theory with natural learning processes, as exemplified in his 2013 book on PAC learning principles.

Aloni Cohenمشاهده پروفایل

استادیار

• Cryptography
• Privacy
• Law
• +۳ مورد دیگر

Aloni Cohen is an Assistant Professor of Computer Science and Data Science at the University of Chicago. Previously, he served as a Postdoctoral Associate at Boston University, with a joint appointment at the Hariri Institute for Computing and the School of Law. He is also a former affiliate at the Berkman Klein Center for Internet & Society and a Fellow at the Aspen Tech Policy Hub. Aloni earned his PhD in Electrical Engineering and Computer Science from MIT, advised by Shafi Goldwasser, supported by a Facebook Fellowship and an NSF Graduate Student Research Fellowship. His research bridges theoretical cryptography, privacy, law, and policy, focusing on real-world applications like differential privacy, encryption, and legal frameworks such as GDPR and the Fifth Amendment. Key research themes include privacy-preserving data analysis, cryptographic watermarking, and ethical implications of AI. He explores tensions between cryptographic theory and legal regulations governing data privacy and surveillance. His recent publications address topics such as the societal impacts of differential privacy, legal challenges in compelled decryption, and policy questions around multiparty computation. Notable awards include the 2024 Outstanding Paper Award at the International Conference on Algorithmic Learning Theory and the 2022 Distinguished Paper Award at USENIX Security Symposium. Awards: 2024 Outstanding Paper Award, International Conference on Algorithmic Learning Theory 2022 Distinguished Paper Award, USENIX Security Symposium 2014 NSF Graduate Student Research Fellowship Aloni’s work involves interdisciplinary collaborations, addressing both technical and policy dimensions of modern computational challenges. Details on grants or specific advising roles are not explicitly listed in the provided texts.

Pia Nyengمشاهده پروفایل

دانشیار

• Developmental Biology
• Molecular Biology
• Endocrinology
• +۴ مورد دیگر

Pia Nyeng is an Associate Professor at Roskilde University's Department of Science and Environment. Her research focuses on developmental biology, particularly the formation of endocrine organs like the pancreas and thyroid during embryonic development. She employs advanced bioimaging techniques and machine learning to analyze live tissue dynamics, emphasizing neuro-organ crosstalk and cell differentiation mechanisms. Trained at the University of Copenhagen (PhD, 2008), she has held visiting research roles there (2019–2022). Her work integrates transgenic mouse models with 3D cell culture systems, addressing how cellular behavior shapes organ morphology and function. Key projects include DECONET (2025–2028), investigating biological network coordination, and PAC (2022–2024), exploring pancreatic innervation and beta-cell formation. Research interests span endocrinology, tubulogenesis, and stem cell biology, with applications to diabetes and neurological disorders. Her lab develops novel imaging tools and collaborates internationally. Recent work links glucagon receptor signaling to cognitive decline, demonstrating interdisciplinary impacts across neuroendocrinology. Publications highlight innovations in live imaging analysis, including real-time tracking of apical polarity proteins and topology-aware machine learning for tubular network quantification. She actively disseminates findings through talks and media, emphasizing translational potential in healthcare and education.

• Machine Learning
• Graph Neural Networks
• Differential Privacy
• +۳ مورد دیگر

Patrick Indri is a PreDoc Researcher at the Department of Informatics, Technische Universität Wien , specializing in machine learning and its intersections with formal methods and data privacy. His work focuses on graph neural networks, robustness verification, and temporal logic applications. Education : MSc degree holder Research Interests : Expressive Graph Neural Network architectures for specialized graph types Differential Privacy integration in graph-based learning systems Robustness Certification with probabilistic guarantees Temporal logic applications in Cyber-Physical Systems anomaly detection Key Projects include the StruDL (2023–2027) initiative exploring structured deep learning. His publications demonstrate expertise in both foundational machine learning and applied formal verification techniques.

• Coding Theory
• Information Theory
• Forward Error Correction (FEC)
• +۵ مورد دیگر

Mohsen Moradi is a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at Northeastern University. His research focuses on advanced coding theory, particularly in the development of Polarization-Adjusted Convolutional (PAC) codes, forward error correction, and decoding algorithms. He explores intersections between reinforcement learning and signal processing to enhance code performance. His work emphasizes practical applications such as bounded-complexity sequential decoding, tree pruning techniques, and Monte-Carlo-based code construction. Key contributions include optimizing PAC codes through search-constrained algorithms and analyzing their performance alongside Reed-Solomon and Reed-Muller codes. Recent publications (2020–2025) highlight trends in PAC/SC/Polar code design, with a focus on computational efficiency, metric optimization, and algorithmic innovation. His research bridges theoretical insights with real-world implementation challenges in communication systems. Moradi’s explorations extend to hybrid coding systems (e.g., concatenated codes) and stochastic methods like guessing-based decoding. He collaborates with academic and industry partners to advance next-generation communication protocols requiring robust error correction at high data rates.

Katelyn Kirkمشاهده پروفایل

مدرس ارشد

• Exercise Science
• Sport Psychology
• Physical Activity and Health
• +۴ مورد دیگر

Katelyn Kirk, also known as Coach KK, is a Senior Lecturer and the Director of Physical Activity Programs at Oregon State University's College of Health, where she is affiliated with the School of Exercise, Sport, and Health Sciences. She plays a key role in the Physical Activity Course (PAC) and the Office of Physical Activity and Health Programs, contributing to student wellness and fitness education. Her academic background includes a Master of Science in Sport and Exercise Psychology from Oregon State University (2015) and a Bachelor of Science in Psychology from the University of Oregon (2013). MS, Sport and Exercise Psychology, Oregon State University, 2015 BS, Psychology, University of Oregon, 2013 Her research and professional interests focus on exercise science, sport psychology, and health promotion. She integrates behavioral health principles into physical activity programming, emphasizing holistic wellness, outdoor recreation, and community engagement in fitness. Her work supports student success through experiential learning and wellness initiatives. Katelyn has been actively involved in fitness instruction since 2015, serving as a personal trainer and group fitness coach in Albany, Oregon. She also contributes to public service through her board membership with the American Society for the Positive Care of Children, reflecting her commitment to child well-being and positive health outcomes. She is based at Langton Hall 123C, 2450 Jefferson Way, Corvallis, OR 97331, and can be reached at Katelyn.Kirk@oregonstate.edu or (541) 737-3222.

• Physical Activity
• Exercise Science
• Health Education
• +۲ مورد دیگر

Matthew Hogan is an Instructor in the School of Exercise, Sport, and Health Sciences at the College of Health, Oregon State University. He is affiliated with the Physical Activity Course Program (PAC), contributing to physical activity education and student wellness initiatives. His work focuses on promoting physical activity and healthy lifestyles through structured course programming. His teaching and programmatic efforts align with broader goals in exercise science and community health education. The trends in his professional focus suggest engagement with practical, experience-based learning in exercise and health promotion, though no formal publications are listed. Scientific Awards: No awards listed. Advising and Grants: There is no public information available regarding student advising, grant funding, or research mentorship. Labs and Teams: Matthew Hogan is actively involved with the Physical Activity Course Program (PAC), a key unit dedicated to delivering physical activity courses and fostering student engagement in health and wellness.

Cressey Merrillمشاهده پروفایل

مدرس

• Aquatics Safety
• Recreational Sports
• Physical Activity Education
• +۳ مورد دیگر

Cressey Merrill is a PAC Instructor and Activity Area Leader for Aquatics & SCUBA at Oregon State University. They also serve as the Coordinator of the Academic & Recreational Dive Center. Their roles focus on physical activity education, aquatic safety, and recreational dive operations. Affiliations include the Physical Activity Course Program (PAC) and the School of Exercise, Sport, and Health Sciences. Office: Langton Hall 121 D, Corvallis, OR 97331 Contact: 541-737-3222 Merrill’s work emphasizes practical training in aquatic environments, SCUBA certification programs, and promoting health sciences through experiential learning. They coordinate facilities and programs that integrate academic and recreational diving activities.

Sangam KCمشاهده پروفایل

مدرس

• Outdoor Adventure Education
• Mountaineering Techniques
• Climbing Safety
• +۳ مورد دیگر

Sangam KC is a PAC Instructor and Climbing & Challenge Course Coordinator at Oregon State University, affiliated with the School of Exercise, Sport, and Health Sciences. His role focuses on delivering practical and instructional expertise in climbing, mountaineering, and outdoor adventure safety. With extensive experience in high-altitude climbing and outdoor leadership, KC’s work emphasizes risk management and experiential learning in outdoor environments. He coordinates programs that integrate technical climbing skills with safety protocols for academic and recreational activities. KC’s professional contributions are centered on advancing outdoor education through hands-on instruction and course development. His affiliations include the Physical Activity Course Program (PAC), where he designs and oversees climbing-related curricula.

• Learning Environments
• Action Research for School Improvement
• Multicultural Education
• +۳ مورد دیگر

Professor Jill Aldridge holds a faculty position at Curtin University's School of Education within the Faculty of Humanities. She serves in the Office of the Provost and has been a prominent figure in educational research for decades. Her work focuses on learning environments, action research for school improvement, and multicultural education. With over 170 publications and $2.6M in funded research, her contributions bridge academic research and practical classroom application. Her research emphasizes participatory action research using stakeholder feedback to improve student outcomes at classroom, school, and systemic levels. Notable projects include developing instruments like the Parent and Caregiver Survey (PaCS) and the Teacher School Climate Questionnaire. She has supervised over 40 HDR students, fostering collaborative research with international partners in UAE, Indonesia, South Africa, and Jordan. Key areas of exploration include multicultural learning environments, teacher professional development efficacy, and the intersection between school climate and student mental health. Her work often employs mixed-methods approaches, validating tools such as the What's Happening in This Class (WHITS) questionnaire. Recent studies investigate church school climates and the impact of inquiry-based learning on girls' attitudes in mathematics. Jill's publications span journals like Learning Environments Research and International Journal of Educational Research , with book contributions through Sense Publishers. Her grants include large-scale projects addressing educational change barriers and cross-cultural applications of learning environment frameworks. Professional contributions include ORCID (0000-0003-0742-0473) and impactful collaborations with institutions like UNESCO and the South African Department of Education. She advocates for practitioner-led evaluation in teacher training and has designed professional learning programs for school leadership teams.

Omar Rivasplataمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +۶ مورد دیگر

Omar Rivasplata is a Senior Lecturer in Machine Learning and Robotics, specializing in theoretical and applied machine learning. He is affiliated with the MCAIF: Centre for AI Fundamentals, focusing on advancing foundational aspects of artificial intelligence. His research spans reinforcement learning, Bayesian analysis, neural networks, and generalization bounds. Key research interests include the theoretical underpinnings of deep learning architectures, optimization strategies for large-scale models, and probabilistic methods in machine learning. Notable contributions include work on gradient clipping for wide/deep networks and semi-pessimistic reinforcement learning frameworks. Leading the MCAIF project (2021–2026), a multidisciplinary initiative exploring core AI principles. Active collaborations with institutions worldwide, emphasizing open-access publications (e.g., Transactions on Machine Learning Research). His articles explore topics like PAC-Bayesian theory, convergence of diffusion models, and meta-analyses of Bayesian methods. He advises a diverse cohort of postgraduate researchers in AI fundamentals.