جستجوی استاد راهنمای دانشگاهی

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

• Unsupervised Learning
• Semi-supervised Learning
• Probabilistic Generative Modeling
• +۴ مورد دیگر

Tian Han is an Assistant Professor at the Department of Computer Science within the Charles V. Schaefer, Jr. School of Engineering and Science at Stevens Institute of Technology. His research focuses on artificial intelligence (AI) and machine learning, particularly in developing statistical learning methods for probabilistic models and building explainable, controllable AI systems. He holds a PhD in Statistics from UCLA (2019) and a degree in Computer Science from HKUST (2013). His research interests span unsupervised/semi-supervised learning, probabilistic generative modeling, explainable AI, and computer vision. Notable contributions include work on latent space energy-based models, hierarchical feature learning, and robust representation techniques. Han has served as an Area Chair/Senior Program Committee member at conferences like CVPR, NeurIPS, and AAAI. Education: PhD in Statistics, UCLA (2019) MSc/BS in Computer Science, HKUST (2013) His publications emphasize advancements in energy-based models, latent space hierarchies, and generative AI. Recent work includes enforcing sparsity in latent representations for robust AI systems (WACV 2024), molecule design via latent space modeling (UAI 2023), and context-aware health prediction (AAAI 2022). He received the NSF CAREER Award (2024) for his research. Han teaches courses on machine learning fundamentals, deep learning, and computing foundations at Stevens.

Isaac Kofi Ntiمشاهده پروفایل

استادیار

• Applied Machine Learning
• Cybersecurity
• Health Informatics
• +۴ مورد دیگر

Dr. Isaac Kofi Nti is an Assistant Professor in the School of Information Technology at the University of Cincinnati, with over 16 years of interdisciplinary research experience in applied machine learning, health informatics, cybersecurity, and data governance. His work emphasizes responsible AI deployment in high-impact domains including healthcare, finance, and education. He concurrently holds affiliations with Ghanaian institutions (University of Energy and Natural Resources and Sunyani Technical University) and serves as a Visiting Assistant Professor at Cincinnati until 2024. Education PhD in Computer Science, University of Energy and Natural Resources, Ghana (2021) MSc in Information Technology, Kwame Nkrumah University of Science and Technology, Ghana (2016) BSc Computer Science, Catholic University of Ghana (2011) Higher National Diploma in Electrical & Electronic Engineering, Sunyani Technical University, Ghana (2007) Research Focus Dr. Nti develops lightweight, explainable AI systems for cybersecurity, healthcare diagnostics, agricultural optimization, and educational technology. His research integrates secure data analytics, ethical AI frameworks, and cloud-based learning infrastructures. Current projects explore generative AI in education, predictive modeling for disease outbreaks, and blockchain applications for health data verification. Publication Trends His 60+ publications (2022-2024) demonstrate strong emphasis on ensemble learning methods, healthcare AI applications, and cybersecurity innovations. Recent works frequently utilize hybrid models (LSTM/XGBoost/StackNet) for fraud detection, disease prediction, and renewable energy forecasting. Awards & Grants Provost Office Strategic Collaborative Faculty Team Award (2024-2025): $19,200 for AI integration in higher education Faculty Development Grant (2023-2024): $10,038 for emerging technology training Faculty Incentive Awards for Research (CECH, UC 2022-2024) Teaching & Service Teaches graduate/undergraduate courses in machine learning, cybersecurity, and data technologies. Leads multiple university committees including PhD Admissions Review and Applied Research teams. Professional memberships include ACM, IEEE, and AAAI.

Frank Woodمشاهده پروفایل

دانشیار

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +۴ مورد دیگر

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

Mikayla Kelleyمشاهده پروفایل

استادیار

• Philosophy of Action
• Epistemology
• Formal Epistemology
• +۳ مورد دیگر

Mikayla Kelley is the Neubauer Family Assistant Professor in the Department of Philosophy at the University of Chicago, where she has been teaching since 2023. She holds a PhD in Philosophy from Stanford University (2023), an MA in Mathematics from UC Berkeley (2019), and a BA in Mathematics from the University of Wisconsin-Madison (2016). Her research focuses on three interconnected areas: Philosophy of Action - particularly theories of control, basic vs. nonbasic action, and the metaphysics of agency Epistemology - with special emphasis on formal epistemology, accuracy-based justification, and probabilistic coherence Ethics - especially metaethics, normative realism, and the epistemology of moral facts Dr. Kelley's work demonstrates how these areas intersect, particularly through her examination of how agency relates to knowledge (as seen in her paper "Separating Action and Knowledge") and how formal methods from social choice theory can illuminate philosophical problems about action and decision-making. Her research on the Advantage-Standard model for social choice represents a significant contribution to overcoming limitations in Arrow's Impossibility Theorem. She has organized academic events including "Chicago Talks on Agency" (2025-2027) and has participated in workshops on non-human agency and constitutivism in ethics and epistemology. Dr. Kelley teaches a diverse range of courses including: Introduction to Philosophy through Taylor Swift Bayesian Epistemology Philosophy of AI: Tools, Technology, and Human Agency Free Will The Metaphysics of Action Agency and Virtual Reality: A Technophilosophical Exploration

• Computational Biology
• Cardiac Electrophysiology
• In Silico Trials
• +۲ مورد دیگر

Blanca Rodriguez is Professor of Computational Medicine at the University of Oxford, heading the Computational Biology and Health Informatics theme. Her research develops computational methods to accelerate therapy development through human-based modeling and simulation, reducing animal testing via digital twins and in silico trials. Key innovations include the PRISM probabilistic model checker, virtual human heart models, and quantitative verification tools for assessing drug cardiotoxicity. Current work focuses on personalized cardiac models, AI-driven therapy optimization, and multi-scale physiological simulations. Wellcome Trust Senior Research Fellow (2013-2025) NC3Rs International Prize (2017) Impact Award, MPLS Division (2018) Professor Rodriguez mentors graduate students in computational medicine and collaborates extensively with pharmaceutical companies and regulators. Her work has generated 150+ patents licensed to 31 companies, and she co-founded two biotechnology companies.

Robert E Rehderمشاهده پروفایل

استاد

• Causal Reasoning
• Cognitive Psychology
• Causal Learning
• +۷ مورد دیگر

Robert E Rehder is a Professor of Psychology at New York University's Department of Psychology within the College of Arts & Science. He holds a B.A. in Physics and B.S. in Computer Science from Washington University, an M.S. in Computer Science (Artificial Intelligence) from Stanford University, and a Ph.D. in Cognitive Psychology from the University of Colorado. His research focuses on causal cognition, including causal-based categories, causal reasoning, and causal learning, with a strong emphasis on computational models such as Bayesian networks. He leads the Concepts & Causality Lab, investigating dynamic systems and developing formalisms to address limitations in traditional causal graphical models. Education: Ph.D. Cognitive Psychology, University of Colorado at Boulder (1998) M.A. Cognitive Psychology, University of Colorado at Boulder (1995) M.S. Computer Science (AI), Stanford University (1990) B.S. Computer Science & B.A. Physics, Washington University (1978) Research Interests: Dr. Rehder explores how causal knowledge is represented, integrated with observations, and influences reasoning. Key areas include causal categories (classification and feature inference), causal reasoning (inference between variables), and causal learning (discovering causal structures). Recent work examines dynamic systems with continuous variables and feedback loops, addressing challenges like temporal abstraction and systematic errors in learner behavior. His recent publications (2021–2025) emphasize process models of causal reasoning, variability in judgments, and the role of inhibitory causal links. While no scientific awards are listed, his work contributes significantly to advancing computational models of causal cognition. Lab & Affiliations: As Department Chair and lab director, Rehder oversees the Concepts & Causality Lab at NYU, located at 6 Washington Place. His lab collaborates on system dynamics and causal representation, with a focus on extending formal frameworks to complex real-world scenarios.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +۴ مورد دیگر

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

• Statistical Learning
• Game Theory
• Decision-Making Algorithms
• +۶ مورد دیگر

David Leslie is a Professor of Statistics and Director of Engagement in the Department of Mathematics and Statistics at Lancaster University. He specializes in statistical learning, decision-making algorithms, and game theory, with applications in real-time website optimization through bandit algorithms. Previously, he served as a Senior Lecturer at the University of Bristol and co-directed a cross-disciplinary decision-making research group. He has led significant projects such as the EPSRC/NERC-funded DSNE initiative and contributed to strategic partnerships like ALADDIN (with BAE Systems and EPSRC) and NG-CDI (with BT). His work emphasizes bridging theoretical research with practical industry solutions. Education details are not explicitly provided, but his career trajectory includes prior roles at prestigious institutions. Research interests focus on statistical methodologies, AI-driven decision systems, and environmental data science. He advises multiple PhD students in areas like Statistical AI and Extreme Value Theory. Notable projects include AI Hub, NABS+, and ProbAI, reflecting his engagement with cutting-edge technologies and interdisciplinary collaboration. David actively participates in research groups such as STOR-i Centre for Doctoral Training and the Statistical Artificial Intelligence group. His contact information includes an office at B73 in the PSC building and a direct email address. No specific scientific awards are listed, but his extensive project leadership and contributions to foundational AI research highlight his academic impact.

Alistair Barrosمشاهده پروفایل

استاد

• Service Computing
• Enterprise Systems
• Business Process Management
• +۵ مورد دیگر

Professor Alistair Barros is Head of School and Academic Program Leader of Service Science at QUT’s School of Information Systems (Faculty of Science). He holds a PhD from the University of Queensland, with extensive ICT experience in academia and industry (e.g., Global Research Leader at SAP AG). His research focuses on enterprise systems design, optimization, and interoperability in cyber-physical environments leveraging cloud, IoT, and blockchain technologies. Key areas include service computing methods (microservices, business process management), model-based systems re-engineering, and distributed service coordination. Education: PhD in Information Systems from the University of Queensland. Research Interests: Enterprise systems modernization, service-oriented architectures, Industry 4.0 applications, and digital transformation in sectors like construction, manufacturing, and supply chains. He has published 180+ peer-reviewed articles (including 6 edited books) and holds 17 US patents. Notable contributions include the BPMN 2.0 standard co-authorship and the 'Workflow Patterns' paper (most cited in BPM). Awards: Test of Time Award (BPM 2015), Wharton Education Award (TeachConnect platform), and impactful industry collaborations (e.g., SAP NetWeaver, Federal Government WPIT project). Grants & Projects: Led ARC, EU FP6, and industry-funded projects. Current focus includes re-engineering legacy systems for microservices, EV charging network design, and adversarial ML in cybersecurity. Labs/Teams: Active in QUT’s Centre for Data Science, focusing on service science and cyber-physical systems integration.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +۵ مورد دیگر

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Michael Burkeمشاهده پروفایل

مدرس ارشد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne

Augusto Gerolinمشاهده پروفایل

استادیار

• Optimal Transport Theory
• Mathematical Physics
• Theoretical and Computational Chemistry
• +۴ مورد دیگر

Augusto Gerolin is an Assistant Professor jointly appointed in the Departments of Mathematics and Statistics and Chemistry and Biomolecular Sciences at the University of Ottawa. He holds a Tier II Canada Research Chair in Artificial Intelligence at the Interface of Chemistry and Mathematics. His research focuses on Optimal Transport Theory, Mathematical Physics, Theoretical and Computational Chemistry, and Machine Learning. Gerolin’s work bridges quantum chemistry, mathematical analysis, and computational methods, with applications in Density Functional Theory and quantum information science. He obtained his PhD from the University of Pisa and was a Marie Skłodowska-Curie fellow at Vrije Universiteit Amsterdam. He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and leads a research group exploring AI-driven solutions in chemistry and mathematics. His current projects include developing optimal transport frameworks for quantum systems, advancing machine learning algorithms in scientific computing, and fostering collaborations across disciplines through initiatives like the OQMG Network. Gerolin’s research has led to advancements in multi-marginal optimal transport, entropy-regularized methods, and the strong-interaction limit of density functional theory. His group actively collaborates with institutions worldwide, including the Fields Institute, IPAM, and the University of Genoa. He has supervised numerous PhD, Master’s, and undergraduate students, many of whom have contributed to cutting-edge studies in computational chemistry, quantum algorithms, and mathematical analysis. Key awards include the Canada Research Chair designation, and his work has been supported by grants from NSERC, MITACS, and the University of Ottawa. Gerolin is also committed to diversity in science, endorsing principles such as the Diversity Axioms, and advocates for academic solidarity with researchers affected by global conflicts.

• Machine Learning
• Climate Science
• Energy Systems
• +۳ مورد دیگر

Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.

Pierre-Alexandre Matteiمشاهده پروفایل

پژوهشگر

• Statistical Machine Learning
• Generative Modeling
• Model Uncertainty
• +۷ مورد دیگر

Pierre-Alexandre Mattei is a research scientist at Inria, affiliated with the Maasai team in Sophia Antipolis and the J.A. Dieudonné Laboratory at Université Côte d'Azur. He holds a chair at the 3IA Côte d'Azur institute and has a strong academic background in applied mathematics, having earned his Ph.D. from Université Paris Descartes (now Université Paris Cité) under Charles Bouveyron and Pierre Latouche, followed by a postdoc at the IT University of Copenhagen with Jes Frellsen. Ph.D. in Applied Mathematics, Université Paris Descartes (2017) Postdoctoral Researcher, IT University of Copenhagen His research lies at the intersection of statistical machine learning, generative modeling, and uncertainty quantification, with a focus on hidden variables, missing data, and model interpretability. He has co-organized major workshops such as Artemiss, GenU, SophI.A Summit, and Statlearn, and teaches at the Generative Modeling Summer School (GeMSS). His recent work spans energy-based models, clustering, semi-supervised learning, and medical AI applications. The 15 most recent publications reflect a consistent trend in developing statistically principled methods for generative modeling, with emphasis on likelihood-based inference, missing data, and information-theoretic approaches to clustering and representation learning. His work frequently appears in top venues like NeurIPS, ICML, ICLR, and AISTATS, as well as in journals such as Statistics and Computing and JACC: Advances. Co-organizer, Generative Modeling Summer School (GeMSS) Co-organizer, Workshop on Generative Models and Uncertainty Quantification (GenU) Co-organizer, SophI.A Summit Co-organizer, Statlearn Mattei has advised several PhD students and postdocs, including Raphaël Razafindralambo, Hugo Senetaire, Louis Ohl, Federico Bergamin, and Hugo Schmutz, many of whom have gone on to research positions in Copenhagen, Grenoble, Linköping, and Marseille. He collaborates extensively with researchers across France and Denmark, particularly with Jes Frellsen, Frédéric Precioso, and Charles Bouveyron. He is actively involved in the development of open-source tools and libraries such as the GemClus Python library for discriminative clustering. He is a key member of the Maasai team at Inria Sophia Antipolis, which focuses on models and algorithms for artificial intelligence, and contributes to the broader 3IA Côte d'Azur initiative aimed at advancing AI research through interdisciplinary collaboration.