جست‌وجوی استاد و پژوهشگر

DeeAnn Reederمشاهده پروفایل

استاد

• Disease Ecology
• Biodiversity
• Conservation Biology
• +۷ مورد دیگر

DeeAnn Reeder is a Professor of Biology at Bucknell University, where she teaches courses in Wildlife and Emerging Infectious Diseases and Mammalogy. She holds a Research Associate appointment at the Smithsonian Institution's National Museum of Natural History. Her research spans disease ecology, biodiversity, and conservation, with a particular focus on bat immunology and ecology. PhD, University of California, Davis M.S., University of California, Davis B.A., University of California, Berkeley Professor Reeder specializes in bat disease ecology, particularly white-nose syndrome (WNS) and viral pathogens including Ebola. Her lab employs diverse methods from traditional fieldwork to transcriptomics, examining the relationships between bat health, ecosystem health, and human disease risk. She has conducted extensive research in Africa (particularly Uganda and South Sudan) and North America, investigating bat immune responses to pathogens and biodiversity conservation. Her research program has received funding from the National Science Foundation, National Institutes of Health, National Geographic, US Fish & Wildlife Service, and the Woodtiger Fund. She is the editor of Mammal Species of the World (3rd edition) and author of 'The Lives of Bats: A Natural History' (2025). Her work includes the South Sudan Diversity Cam citizen science project cataloging animals from over 475,000 camera trap photos. Presidential Professor of Biology (2015-2018) Extensive publication record in leading journals including Nature Communications, PLoS Pathogens, and Science Editor of Mammal Species of the World (3rd edition) Author of 'The Lives of Bats: A Natural History' (2025) Professor Reeder mentors numerous students in her research, with many appearing as co-authors on publications. Her lab conducts field work in partnership with the Smithsonian Institution, Wildlife Services, Muni University, and others, focusing on biodiversity surveys in understudied habitats. She is actively involved in policy discussions about preventing future pandemics through ecological approaches, as featured in eCornell webinars and National Geographic.

Phillip D. Voegelمشاهده پروفایل

دانشیار

• Environmental Chemistry
• Electroanalytical Chemistry
• Sensor Development
• +۵ مورد دیگر

Dr. Phillip D. Voegel serves as Associate Professor of Chemistry at Kentucky Wesleyan College, teaching core courses including Concepts in Chemistry, General Chemistry, Analytical Chemistry, Instrumental Techniques, and Advanced Environmental Chemistry. Previously, he held faculty positions at Southeastern Louisiana University (where he coordinated undergraduate chemistry and chaired the safety committee), Midwestern State University (as environmental science program coordinator), and Southern Illinois University at Carbondale as an instructor. B.A. in Chemistry, Wabash College (1991) Ph.D. in Chemistry, University of Louisville (1997) under Dr. Richard P. Baldwin His research spans environmental chemistry and electroanalytical techniques, evolving from fundamental electrochemical detector development to applied environmental monitoring. Early work focused on carbohydrate detection using active metal electrodes and reverse osmosis effluent impacts on river microbiology. At Southeastern Louisiana University, he investigated nutrient changes in Lake Maurepas post-Hurricane Katrina and hydrocarbon contamination from the Deepwater Horizon spill. Current research examines spatial distribution of coal-derived metal pollutants (cadmium, mercury, lead) along railway corridors using atomic spectroscopy of soil samples. Publications reveal a trajectory from capillary electrophoresis/electrochemical detection methods to environmental impact assessments, consistently featuring undergraduate co-authors. Key themes include sensor development for pollutants, disaster response monitoring, and community-focused educational outreach. His work bridges instrumental analysis with real-world environmental challenges. Finalist, American Chemical Society ChemLuminary Award for Student-to-Student Chemistry Initiative Dr. Voegel secured over $370,000 in research funding from Louisiana Board of Regents, NOAA, EPA, ACS Project SEED, and internal university grants. These projects engaged 50+ undergraduate and high school students, producing seven publications and 88 conference presentations, with over 60% of students presenting their work. His grant portfolio emphasizes student involvement in field sampling and instrumental analysis. He directs environmental research projects on coal transport pollution, training students in microwave plasma atomic spectroscopy and field sampling techniques. His laboratory synthesizes metal pyridinoporphyrazine compounds for electrochemical sensors and catalysis, maintaining strong connections to environmental monitoring applications in Kentucky and Louisiana watersheds.

Chulhee Yunمشاهده پروفایل

استادیار

• Deep Learning Theory
• Optimization
• Machine Learning Theory

Chulhee Yun is an Ewon Assistant Professor at KAIST Kim Jaechul Graduate School of AI (KAIST AI), with a joint affiliation starting September 2025 at KAIST Graduate School of AI for Math and a part-time Visiting Faculty Researcher position at Google Research. He directs the Optimization & Machine Learning (OptiML) Laboratory at KAIST AI. Education: PhD in Elec. Eng. & Comp. Sci., 2016–2021, Massachusetts Institute of Technology MSc in Electrical Engineering, 2014–2016, Stanford University BSc in Electrical Engineering, 2007–2014, KAIST Chulhee Yun's research focuses on deep learning theory, optimization, and machine learning theory. His work bridges theoretical foundations with practical applications in neural network training, addressing fundamental questions about optimization dynamics, generalization capabilities, and the mathematical properties of deep learning models. His research spans from theoretical analyses of gradient-based optimization methods to practical techniques for improving neural network training and performance. His recent publications demonstrate a strong focus on understanding the fundamental properties of neural network training dynamics, optimization algorithms, and generalization. The research spans theoretical analyses of stochastic optimization methods like SGD, investigations into the properties of transformers and language models, and practical techniques for improving neural network training efficiency. A notable trend is the combination of rigorous theoretical analysis with practical implications for deep learning systems. Scientific Awards: KAIA Outstanding Paper Award at KAIA Summer Conference 2025 (CKAIA 2025) KT Best Paper Award at KAIA Summer Conference 2025 (CKAIA 2025) Best Paper Award at KAIA Fall Conference 2024 (JKAIA 2024) KT Best Paper Award at KAIA Summer Conference 2024 (CKAIA 2024) KAIA Outstanding Paper Award at KAIA Summer Conference 2023 (CKAIA 2023) NAVER Outstanding Theory Paper Award at KAIA-NAVER Joint Fall Conference 2022 (JKAIA 2022) Honorable Mention at NYAS Machine Learning Symposium 2020 Poster Awards Notable AC for NeurIPS 2023 Top Reviewer at ICML 2025 Professor Yun actively mentors PhD, Master's, and undergraduate students through the OptiML Laboratory. His research group includes several PhD students (Jaeyoung Cha, Hanseul Cho, Yujun Kim, Junghyun Lee, Junsoo Oh, Baekrok Shin), Master's students, and undergraduate interns. He has successfully guided former students such as Geonhui Yoo, Jaewook Lee, and Dongkuk Si to completion. His service to the academic community includes serving as Social Chair for ICML 2026, Conference Area Chair for ICLR 2025-2026 and NeurIPS 2023-2025, and as a reviewer for numerous top conferences and journals. Chulhee Yun directs the Optimization & Machine Learning (OptiML) Laboratory at KAIST, which focuses on advancing the theoretical foundations of machine learning and optimization. The lab investigates fundamental questions about neural network training dynamics, optimization algorithms, and generalization properties, with the goal of bridging theoretical insights with practical applications in deep learning systems.

Hidekata Hontaniمشاهده پروفایل

استاد

• Image Processing
• Medical Image Analysis
• Computational Anatomy
• +۳ مورد دیگر

Hidekata Hontani is a Professor at the Graduate School of Engineering, Nagoya Institute of Technology, specializing in image processing and medical image analysis. His research focuses on computational anatomy, deep learning applications in medical imaging, and pathological image analysis, particularly for cancer diagnosis and treatment. Professor Hontani's research interests center around medical image processing , with specific focus on computational anatomy , deep learning for medical applications , pancreatic cancer analysis , and malignant lymphoma grading . His work bridges computer science and medical diagnostics, developing innovative algorithms for analyzing pathological images and creating 3D models of tumors. His recent publications (2023-2024) show a strong trend toward applying diffusion models and weakly supervised learning techniques to medical image analysis, particularly for cancer diagnosis. Key areas include pathological image analysis , tumor microenvironment modeling , anomaly detection , and multi-modal image registration , with increasing focus on lymphoma grading and pancreatic cancer analysis. Scientific Awards: Multiple Japanese Society of Medical Imaging Technology Conference Encouragement Awards (2012-2024) JAMIT Young Medical Imaging Engineering Symposium Encouragement Awards (2023) Cum Laude Poster Award, SPIE Medical Imaging (2018) Student Award, VISAPP 2017 Professor Hontani has secured multiple significant research grants, including an ongoing Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science titled Comprehensive Characterization of All Two Million Nuclei in Pathology Images for Quantitative Assessment of Atypia in Malignant Lymphoma (2025-2028). He serves on key academic committees including as Secretary of the Medical Imaging Research Group at IEICE since 2010 and as a committee member of the Pattern Recognition and Media Understanding Research Group since 2013.

Felix Klotzscheمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cognitive Science
• Virtual Reality
• +۳ مورد دیگر

Felix Klotzsche is a Research Scientist at the Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, and an affiliated researcher at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin. His work focuses on cognitive neuroscience, with a particular emphasis on short-term memory, attention, and virtual reality (VR) applications in clinical and experimental settings. Education: B.Sc. Psychology & B.A. Philosophy (2009–2015), Universität Regensburg M.Sc. 'Mind and Brain' (Track 'Brain', 2015–2017), Berlin School of Mind and Brain | Humboldt-Universität zu Berlin Research Interests: Neural mechanisms of visual short-term memory EEG analysis in VR environments Clinical VR applications for neurological assessment Emotional arousal decoding via neurophysiological signals 3D face perception and deep encoding models Cardiac-phase modulated perception Publications reflect a focus on integrating VR with neuroscience methodologies, particularly in evaluating spatial memory, threat perception, and emotional processing. Key tools include Python, R, Unity3D, and advanced EEG signal processing. No scientific awards explicitly noted. No listed advisees or grants. Active in the Research Group Mind-Body-Emotion, contributing to labs and teams developing VR-based cognitive assessment tools.

• Quantum Computing
• Machine Learning
• Combinatorial Optimization
• +۳ مورد دیگر

Dr. Roberto Bondesan is a Senior Lecturer in Quantum Computing at the Department of Computing, Faculty of Engineering, Imperial College London. He holds a PhD in theoretical physics from UPMC Paris and has conducted research at the Universities of Cologne and Oxford on topological phases of quantum matter, focusing on materials for quantum computing and electronics. He later led machine learning research at Qualcomm AI, developing quantum neural networks and optimizing chip design using Bayesian optimization, reinforcement learning, and graph neural networks. His current research integrates quantum computing and machine learning to address computational challenges in optimization and quantum system simulation. He is affiliated with Imperial X and actively supervises PhD students through departmental scholarships. His research interests include quantum algorithms for combinatorial optimization, quantum error correction, and machine learning-driven quantum system simulation. Notable projects involve applying neural networks to quantum error correction and leveraging ML techniques for efficient quantum Gibbs sampling of complex systems like the Fermi-Hubbard model. Education: PhD in Theoretical Physics, UPMC Paris His publications span quantum computing applications, including quantum machine learning frameworks, optimization algorithms, and lattice gauge theories modeled via generative flow networks. He has contributed to advancing quantum advantage assessment in Gaussian processes and probabilistic numeric convolutional neural networks. Current work emphasizes bridging quantum algorithms with practical engineering solutions. Awards: No awards explicitly listed in provided texts. He advises students through Imperial College's scholarships and maintains collaborations with industry (e.g., Qualcomm) and academic networks (Imperial X). His lab focuses on translating theoretical quantum computing breakthroughs into real-world applications.

Otar Akanyetiمشاهده پروفایل

مدرس ارشد

• Bio-inspired Robotics
• Health Informatics
• Underwater Robotics
• +۳ مورد دیگر

Dr. Otar Akanyeti is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. His research integrates engineering, physics, computer science, and biology to advance underwater robotics and health informatics. He holds a BSc in Electronic Engineering, an MSc in Embedded Systems and Robotics, and a PhD in robot learning from the University of Essex. He co-founded the Aberystwyth Stroke Research Group and leads projects on AI-driven healthcare interventions. His research focuses on two primary strands: 1) Bio-inspired Robotics , developing sensors and control systems for underwater autonomy using biological principles; and 2) Health Informatics , employing wearable technology and AI to improve stroke rehabilitation and chronic disease management. Current investigations include hydrodynamic imaging, collective intelligence for data mining, and predictive tools for neurorecovery. Akanyeti's recent publications emphasize interdisciplinary approaches, with articles spanning gait analysis using smartphone sensors, clinical outcome prediction in stroke, and biomimetic robotics. His work consistently bridges biomechanics, machine learning, and clinical applications. He leads multiple funded projects including 'Miscanthus AI' (plant phenotyping for Net Zero), 'Intelligent Exercise Practitioner for Stroke Survivors', and 'iNavigate' (brain-inspired navigation technologies). His lab utilizes advanced equipment like instrumented treadmills and eye-tracking systems.

Daniela Hesseمشاهده پروفایل

استادیار

• Paediatric Dentistry
• Dental Caries
• Restorative Dentistry
• +۳ مورد دیگر

Daniela Hesse is an Assistant Professor in the Department of Paediatric Dentistry at the Academic Centre for Dentistry Amsterdam (ACTA). Her ORCID identifier is 0000-0002-0912-4152. She teaches courses including Cariology and Preventive Dentistry and contributes to clinical education programs. Her research focuses on evidence-based paediatric dentistry with emphasis on: Clinical management of dental caries in children Restorative techniques including atraumatic treatments and glass ionomer cement applications Saliva composition analysis across developmental stages Clinical trial methodologies and systematic reviews Management of enamel defects like molar-incisor hypomineralisation Recent publications (2024-2025) demonstrate strong focus on paediatric oral health assessment methods, restorative treatment outcomes, and practice patterns analysis. Research methodologies prominently feature systematic reviews, scoping reviews, and survey-based studies across dental disciplines. She maintains active editorial roles as peer reviewer for multiple dental journals including European Journal of Oral Sciences , Community Dentistry and Oral Epidemiology , Evidence-Based Dentistry , Caries Research , and International Journal of Paediatric Dentistry .

Gabriel Eilertsenمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Associate Professor Gabriel Eilertsen is affiliated with Linköping University's Department of Science and Technology (ITN) and the Media and Information Technology (MIT) research group. He holds a PhD in computer graphics and image processing, with expertise in high dynamic range (HDR) imaging and machine learning. As a core member of the Analytic Imaging Diagnostics Arena (AIDA) and affiliated researcher at CMIV (Center for Medical Image Science and Visualization) , he bridges academia and healthcare innovation. Research focuses on machine learning applications in computer vision , medical imaging diagnostics , and data-centric deep learning . Current projects include synthetic image generation for neural network training, evaluation of HDR reconstruction methods, and enhancing fairness in GANs. He leads a CENIIT-funded project on generative deep learning for medical imaging. Key contributions span synthetic data synthesis techniques, HDR imaging protocols, and AI-driven healthcare solutions. Notable work includes leveraging deepfake-like technology for generating AI-based medical images from the SCAPIS study. His interdisciplinary approach addresses challenges in domain adaptation, uncertainty quantification, and perceptual evaluation metrics for imaging systems. Affiliations include the Wallenberg AI, Autonomous Systems and Software Program (WASP) as an AI/MLX Assistant Professor, reflecting his leadership in national AI initiatives. His research emphasizes practical applications of AI in healthcare diagnostics and fundamental advancements in image generation and processing algorithms.

• Causal Machine Learning
• Healthcare Informatics
• Medical Data Analysis
• +۳ مورد دیگر

Dr. Vinod Kumar Chauhan is a Research Fellow at the Institute of Biomedical Engineering, University of Oxford. His work focuses on causal machine learning and healthcare applications, including individualized treatment effects estimation and addressing sample selection bias in medical data. Previously, he held a postdoctoral position at the University of Cambridge and earned his PhD from Panjab University, Chandigarh, India. Education: BSc, MCA, PhD (Panjab University, India). Research interests include causal inference, healthcare informatics, and graph neural networks for EHR analysis. Recent projects involve postoperative atrial fibrillation prediction and optimizing treatment effects for composite outcomes. Key Awards: 2021 Institute for Manufacturing Postdoctoral Award (Cambridge), Travel Grant for Asian Conference on Machine Learning 2017. Selected Talks: Keynote on HyperNetworks (2023 Panjab University), invited talks on treatment effects estimation (IISER Pune, 2023). Labs/Teams: Computational Health Informatics group under Prof. David Clifton. Active in departmental EDI committee roles.

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +۶ مورد دیگر

Seong Tae Kim is a Professor at Kyung Hee University's Department of Biomedical Engineering. He previously held affiliations at the Technical University of Munich (Germany) and KAIST's Image and Video Systems Laboratory in South Korea. His research focuses on computer vision, medical imaging, and AI applications in healthcare. Kim's work emphasizes explainable AI, neural network interpretability, and deep learning for medical diagnosis. He has contributed to advancements in knowledge graph-based reasoning, video object segmentation, and generative models for medical data synthesis. His research often bridges computer vision techniques with biomedical applications, such as cancer diagnosis and surgical phase recognition. Over 100 publications since 2003 highlight his expertise in neural networks, federated learning, and robust training methods. Notable projects include developing frameworks for COVID-19 CT analysis and generating realistic biomedical datasets. He collaborates widely with institutions like the Max Planck Institute for Informatics (via Nassir Navab's lab) and Samsung Research. Current research interests include interpretable machine learning models, longitudinal medical image analysis, and AI-driven healthcare solutions. His work frequently appears in top conferences like CVPR, MICCAI, and ECCV.

• Theoretical Computer Science
• Complexity Theory
• Machine Learning Theory
• +۲ مورد دیگر

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University. His research focuses on theoretical computer science, particularly complexity theory and its intersections with machine learning and algorithm design. He holds a Ph.D. from Columbia University, advised by Rocco Servedio, and has been recognized with prestigious awards including the Sloan Fellowship and best paper awards at FOCS, CCC, and SAT conferences. His work bridges foundational theoretical contributions with practical algorithmic innovations, emphasizing topics like decision tree learning, computational hardness, and pseudorandomness. Notable achievements include polynomial-time decision tree learning algorithms, hardness results for learning problems, and contributions to coding theory and property testing. He collaborates extensively with researchers such as Guy Blanc, Caleb Koch, and Carmen Strassle on projects addressing core challenges in computational complexity. Tan's publications span top venues like FOCS, STOC, and JACM, reflecting his impact on areas such as query complexity, certification algorithms, and parallel computation lower bounds. His research also extends to cryptographic primitives and information-theoretic methods, showcasing a broad yet deeply technical approach to theoretical computer science.

Nicholas Priebeمشاهده پروفایل

استاد

• Neuroscience
• Neurophysiology
• Sensory Processing
• +۳ مورد دیگر

Nicholas Priebe is a Professor at the University of Texas at Austin, affiliated with the Center for Perceptual Systems within the College of Liberal Arts. He holds a Ph.D. in Physiology from the University of California, San Francisco (2001), and completed postdoctoral training at Northwestern University. His research focuses on understanding the neural mechanisms underlying sensory processing in the cerebral cortex, particularly in the visual system. Key areas include cortical circuitry, neuronal computations for motion/depth perception, and binocular integration in primary visual cortex (V1). Education: Ph.D. in Physiology, UCSF, 2001 Postdoctoral Fellowship, Northwestern University (with David Ferster) Research Interests: Dr. Priebe's lab investigates how sensory inputs are transformed into perceptual outputs through cortical circuits. Specific topics include: Neuronal response mechanisms in primary sensory cortex Binocular vision and depth perception Neural coding of visual features (orientation, motion) Impact of thalamic inputs and synaptic integration His work combines electrophysiological recordings, imaging, and computational modeling to dissect circuit-level processing. Articles Trends: Recent publications (2023-2025) emphasize thalamic contributions to visual processing , neuronal synchrony dynamics , and luminance-invariant encoding in rodent and primate models. He explores how internal states and neural variability shape sensory representations. Lab and Collaborations: Hosts the Priebe Lab, collaborating on studies of cortical circuits and visual neuroscience. Active in UT Neuroscience and theoretical computational neuroscience initiatives.

• Machine Learning
• Statistics
• Causal Inference
• +۴ مورد دیگر

Arthur Gretton is a Professor at the Gatsby Computational Neuroscience Unit and director of the Centre for Computational Statistics and Machine Learning at University College London (UCL). He is also a Research Scientist at Google DeepMind, focusing on causal inference, representation learning, generative models, and nonparametric hypothesis testing. Research Interests: Causal modeling with hidden confounders, distributional representations via kernel methods, gradient flows for statistical divergence optimization, and robustness in machine learning. Recent Publications: His 2025-2024 work includes causal effect estimation with proxies, kernel-based generative model training (e.g., MMD gradient flows), and foundational work in distributional reinforcement learning. Scientific Recognition: Includes Top 5% paper, ICLR 2023 Spotlight presentations at ICML 2024, NeurIPS 2023, and UAI 2023. Students: Current advisees include Jakub Wornbard, Zikai Shen, Zhe Wang, and others co-supervised in causal and statistical learning projects. Labs: Leads the Gatsby Unit and UCL Centre for Computational Statistics and Machine Learning, with software tools for kernel tests, gradient flows, and distribution embeddings.

Michael Littmanمشاهده پروفایل

استاد

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

Michael L. Littman is a University Professor of Computer Science at Brown University, specializing in machine learning and decision-making under uncertainty. He previously served at Rutgers University as department chair. His research focuses on reinforcement learning, probabilistic planning, and algorithms for uncertainty management. Littman holds a PhD from Brown University (1996) and degrees from Yale University (BS and MS, 1988). He has received multiple awards, including the Warren I. Susman Award for Teaching and induction into AAAI and ACM Fellowships. His work includes contributions to POMDPs, reinforcement learning frameworks, and applications in robotics and human-AI interaction. Education: PhD in Computer Science, Brown University, 1996 BS & MS in Computer Science, Yale University, 1988 Research emphasizes reinforcement learning, algorithmic decision-making, and applications in smart systems. Notable contributions include the Proverb crossword solver and foundational work on partially observable Markov decision processes. He has served as program chair for ICML and co-founded Oneacross.com. Teaching includes courses on machine learning and discrete structures. Awards: Classic Paper Award (AAAI-13) Fellow of AAAI and ACM Best Paper Awards (ICML, UAI, IJCAI) Collaborations include roles as adjunct professor at Rutgers and Georgia Tech, and affiliations with robotics initiatives at Brown. Current work explores human-AI interaction and end-user programming via reinforcement learning.