جستجوی استاد راهنمای دانشگاهی

Charlotte Frenkel is a Tenure-Track Assistant Professor in the Microelectronics Department at Delft University of Technology (TU Delft), where she leads research in neuromorphic engineering and low-power AI hardware. Her work bridges the gap between biological intelligence and artificial neural networks, focusing on energy-efficient computing at the edge. Dr. Frenkel's research spans digital and mixed-signal IC design, computer architecture, learning algorithms, and neuroscience. She directs the Cognitive Sensor Nodes and Systems (CogSys) lab, which develops neuromorphic processors like ODIN, MorphIC, SPOON, and ReckOn that demonstrate competitive advantages over conventional neural network accelerators. Her publications reveal a strong focus on spiking neural networks, event-based processing, and on-chip learning. Key trends include developing hardware that leverages sparsity for energy efficiency, creating bio-inspired learning algorithms that solve weight transport and update locking problems, and establishing frameworks for benchmarking neuromorphic systems through initiatives like NeuroBench. Scientific Awards: IBM Innovation Award 2021 Nokia Bell Labs Scientific Award 2021 IEEE ISCAS 2020 Best Paper Award NEUROTECH/NICE Best Early Researcher Presentation 2021 AiNed Fellowship Grant Dr. Frenkel is actively expanding her research group through PhD and postdoc positions. She serves as Associate Editor for IEEE Transactions on Biomedical Circuits and Systems and Frontiers in Neuroscience, and has held numerous leadership roles in conference organization including Program Chair for tinyML Research Symposium 2024 and Neuro-Inspired Computational Elements conference 2023-2024. Her service includes extensive reviewing activities for top IEEE journals and conferences in her field.

• Neuromorphic Engineering
• AI Processor Design
• Digital and Mixed-Signal IC Design
• +۵ مورد دیگر

Julien Mairal is a Senior Research Scientist at Inria Grenoble within the Thoth research team, focusing on machine learning, optimization, and computer vision. His research spans theoretical foundations and practical applications across multiple domains including signal processing, bioinformatics, and neuroimaging. His primary research interests include machine learning , particularly optimization algorithms for large-scale problems, computer vision with applications in image restoration and super-resolution, and kernel methods for structured data. His work demonstrates strong connections between theoretical optimization and practical implementations, as evidenced by his Cyanure toolbox for empirical risk minimization. Analysis of his recent publications reveals a consistent focus on optimization techniques for machine learning, with increasing emphasis on large-scale applications, self-supervised learning, and connections between kernel methods and deep learning architectures. His work shows strong interdisciplinary applications spanning computer vision, neuroscience, and remote sensing. ERC Consolidator Grant (2023) for the Apheleia project ICML Test of Time Award (2019) ERC Starting Grant (2016) for the SOLARIS project Mairal serves extensively in editorial capacities as area chair for major conferences including ICML, NeurIPS, CVPR, and ECCV. He is an associate editor for the Journal of Machine Learning Research (JMLR) and previously served for IEEE PAMI and IJCV. His Cyanure toolbox has become a significant contribution to the field of large-scale optimization in machine learning. His research demonstrates strong connections between theoretical foundations and practical implementations across multiple application domains.

• Machine Learning
• Optimization
• Computer Vision
• +۲ مورد دیگر

Dr. Xin Yang is an Assistant Professor in the Department of Computer Science at Middle Tennessee State University (MTSU) . With a focus on Machine Learning , Deep Learning , and Neuroimaging Data Analysis , he bridges computational methods with biomedical applications. His teaching responsibilities include CSCI-2170 Computer Science II , CSCI-3080 Discrete Structures , and CSCI-4410 Web Technologies . PhD, MTSU (2016) MS, MTSU (2014) ME, North China University of Technology (2012) BE, Qingdao University (2008) Dr. Yang’s research explores Autism Spectrum Disorder (ASD) classification using functional MRI data, image fusion techniques (visible and infrared), and imbalanced data handling . His work applies methods like Group ICA , Dictionary Learning , and Spearman’s Rank Correlation to analyze brain networks. Recent publications span 2024 to 2010 , with a focus on ASD classification , fMRI analysis , and image processing . Trends include convolutional neural networks , adversarial defense , and signal processing for biometric applications. USDA grant ($181,819, Co-PI, 2023) MT-IGO award ($10,000, PI, 2022) MTSU URECA and CBAS Scholar Week awards NSF EPSCoR and REU Site grants He mentors students in machine learning research and software development , including projects like Dijkstra’s algorithm for pathfinding and web-based calculators . His lab emphasizes hands-on AI applications and interdisciplinary collaboration in healthcare and data science.

• Machine Learning
• Deep Learning
• Neuroimaging Data Analysis
• +۸ مورد دیگر

Alex Cayco Gajic is a Junior Professor and Principal Investigator in the Department of Cognitive Studies at École Normale Supérieure (ENS), part of PSL University in Paris. She leads a research team within the Group for Neural Theory (GNT), which is embedded in the Laboratoire de Neurosciences Cognitives et Computationelles. Her work sits at the intersection of machine learning, mathematical modeling, and systems neuroscience, with a particular focus on understanding how neural populations represent behavior and change over learning. Dr. Cayco Gajic received her Ph.D. in Applied Mathematics from the University of Washington in 2015, where she worked under Eric Shea-Brown, followed by a postdoctoral fellowship in Angus Silver's lab at University College London. She established her independent laboratory at ENS in 2019, integrating her mathematical training with experimental neuroscience to study cerebellar function and neural dynamics during learning. Her research interests span computational neuroscience, cerebellar function, neural coding, dimensionality reduction techniques, reinforcement learning, and dynamical systems. Dr. Cayco Gajic aims to identify fundamental principles of how task-relevant neural dynamics emerge over learning, particularly focusing on the cerebellum but also exploring its interactions with the motor cortex and basal ganglia during motor and cognitive learning. Her recent publications reveal a strong trend toward developing novel dimensionality reduction methods for analyzing large-scale neural recordings, with particular emphasis on understanding how neural representations evolve during learning. Her work bridges theoretical modeling with experimental neuroscience, often collaborating closely with experimental teams to validate computational approaches with real neural data, as evidenced by her development of slice tensor component analysis (sliceTCA) for identifying multiple classes of covariability in neural data. Dr. Cayco Gajic currently supervises several PhD students in her lab, including Helen Todd (working on cerebellar interneuron synchronization), Leonardo Agueci, Mattia Della Vecchia, and Hugo Ninou. Her team is actively working on understanding how interacting circuits in the brain control behavior, with particular focus on the cerebellum's role in both motor and cognitive functions. She maintains affiliations with multiple research centers including the ENS Quantitative Biology Centre and the Paris Artificial Intelligence Research Institute, reflecting the interdisciplinary nature of her work that spans neuroscience, mathematics, and artificial intelligence.

• Cognitive Neuroscience
• Cerebellum
• Motor Control
• +۷ مورد دیگر

Hyun Oh Song is an Associate Professor in the Department of Computer Science and Engineering at Seoul National University , focusing on machine learning, combinatorial optimization, and algorithms. Previously, he was a Research Scientist at Google Research and a Postdoctoral Fellow at Stanford University . Education : Ph.D. in Computer Science (2014) from UC Berkeley , B.S. from Hanyang University Research Interests : Solving combinatorial problems in AI, with applications in neural network compression, adversarial robustness, and reinforcement learning. Teaching : Courses include Deep Learning, Machine Learning, and Probability & Computing at SNU. His startup DeepMetrics raised $2.2M in combined VC and government funding. Scientific Awards : Samsung Lee Kun Hee Scholarship Foundation (5-year Ph.D. fellowship) Publications : 15 recent works span efficient CNN compression (ICML2022), adversarial attack mitigation (ICML2022), neural relation graphs for label noise detection (NeurIPS2023), and Co-Mixup data augmentation (ICLR2021).

• Machine Learning
• Combinatorial Optimization
• Algorithms
• +۷ مورد دیگر

Christoph Ortner is a Professor in the Department of Mathematics at the Faculty of Science, University of British Columbia. His research bridges rigorous mathematical analysis, numerical methods, and practical applications in molecular simulation and materials science. His primary research interests include: Numerical Analysis & Scientific Computing Applied Analysis Multi-scale Modelling and Coarse-graining Molecular Simulation Scientific Machine Learning, particularly for applications in multi-scale modelling Professor Ortner's work focuses on several interconnected areas including Hybrid Mechanistic & Data-driven Modelling of atomistic systems, Material Defects analysis, QM/MM Multiscale Methods, and Atomistic/Continuum Coupling techniques. His research group develops both theoretical foundations and practical algorithms for molecular simulation, with emphasis on mathematical rigor combined with computational efficiency. Recent work increasingly integrates machine learning techniques with traditional numerical methods, particularly through the Atomic Cluster Expansion framework. His publications demonstrate consistent contributions to the field of computational mathematics and materials science, with recent focus on machine learning applications in molecular simulation and the development of mathematically sound interatomic potentials. Professor Ortner is affiliated with the Institute of Applied Mathematics at UBC and is available for collaborations, including interdisciplinary research projects and supervision of graduate students in Mathematics programs.

• Numerical Analysis
• Scientific Computing
• Applied Analysis
• +۱۲ مورد دیگر

Patrick Schmidt is a researcher in the Department of Computer Science at RWTH Aachen University, Germany, specializing in geometry processing and computer graphics. His work focuses on developing robust algorithms for surface mapping, mesh processing, and automatic differentiation, with significant contributions to both theoretical foundations and practical applications. He actively collaborates with the Computer Graphics group led by Prof. Leif Kobbelt, producing high-impact research published in premier venues including SIGGRAPH and Eurographics. His research spans Geometry Processing, Computer Graphics, and Computational Biology, with core interests in distortion-minimizing surface maps, mesh parametrization, and biomedical applications. Schmidt's innovative approaches include adaptive triangulation methods for bijective surface correspondence and lightweight automatic differentiation frameworks like TinyAD that simplify non-linear optimization in geometry processing. His interdisciplinary work on heart tube formation demonstrates how geometric techniques can advance biological understanding of mammalian organ development. Analysis of Schmidt's publication trajectory reveals a consistent focus on improving the robustness and efficiency of surface mapping techniques. His work progressively addresses challenges in distortion control, topological consistency, and computational performance, culminating in award-winning contributions like adaptive triangulation methods and constant-curvature metric representations. The recurring theme across his research is the unification of geometric theory with practical implementation, emphasizing reproducibility through open-source tools and Graphics Replicability Stamps. Scientific recognition includes: Günter Enderle Best Paper Award at Eurographics 2023 for pioneering adaptive triangulation techniques Consecutive Best Paper Awards at SGP 2022 and 2021 for TinyAD library and homology inference methods Honorable mention for Eurographics 2021 work on layout embedding optimization While specific student advising records aren't documented in public sources, Schmidt's role involves mentoring through collaborative research within RWTH Aachen's Computer Graphics group. His projects frequently secure institutional support for high-impact work, evidenced by consistent publication in top-tier venues and development of widely adopted tools like TinyAD. The group maintains strong industry and academic partnerships focused on advancing geometry processing fundamentals. As a core member of RWTH Aachen's Computer Graphics research team, Schmidt contributes to a vibrant ecosystem centered around Prof. Leif Kobbelt's lab. The team emphasizes both theoretical innovation and practical software development, with recent work bridging computer graphics and biomedical modeling. Current efforts focus on intrinsic optimization frameworks and expanding automatic differentiation applications, positioning Schmidt at the forefront of next-generation geometry processing techniques.

• Geometry Processing
• Computer Graphics
• Mesh Processing
• +۳ مورد دیگر

Greg Ver Steeg is an Associate Professor in the Computer Science & Engineering Department at University of California, Riverside (UCR) , with an Adjunct Research Associate Professor title at University of Southern California (USC) . He is also a Part-Time Visiting Academic at Amazon Alexa AI (2023). His research bridges machine learning , information theory , and complex systems , focusing on fair representation learning, diffusion models, and causality. Education: Ph.D. in Physics from Caltech Research Interests: His work spans: Information-Theoretic Fairness: Removing group-identifying information to ensure algorithmic fairness with guarantees on accuracy trade-offs. Diffusion Models: Connecting log likelihood ratios and classification for robust generative modeling. Graph Neural Networks: Innovations like MixHop for neighborhood weighting in graphs. Neuroimaging: Federated learning for MRI harmonization across sites. Complex Systems: Macro-causal discovery in social and biological data through coarse-graining. Scientific Contributions: Key papers include the Information Sieve (ICML 2016) for hierarchical factor discovery, MixHop (ICML 2019) for graph convolutional networks, and information-theoretic generalization bounds (ICML 2020) for label noise correction. His lab has produced multiple best paper awards and high-impact preprints on diffusion bridges and material science. Advising: Mentored students like Rob Brekelmans (ICML Best Paper), Sami Abu-el-Haija (Google Research), and Hrayr Harutyunyan (NeurIPS 2021), with ongoing supervision of Xianghao Kong (ICLR 2023/2024) and Shaorong Zhang (diffusion bridges).

• Machine learning
• Information theory
• Complex systems
• +۵ مورد دیگر

Arnaud Giacometti is a Professor at the University of Tours, affiliated with the Faculty of Science and Technology and the Computer Science Department. He is also the President of the University and leads research at the Tours Fundamental and Applied Computer Science Laboratory (LIFAT). Research Interests: His work focuses on data mining, pattern discovery, and knowledge graph analysis. He has developed innovative methods for frequent pattern mining, sequential pattern sampling, and constraint-based clustering, emphasizing efficiency and scalability in large datasets. Recent Publications: His research trends include trie-based sampling, reservoir pattern sampling in data streams, and semantic web-driven comparison tables. Key subfields span distributed databases, knowledge graph representativeness, and memory-aware parallelization for big data. Scientific Roles: As President of the University of Tours, he oversees institutional leadership while maintaining active research contributions. His laboratory (LIFAT) serves as a hub for fundamental and applied computer science research.

• Data Mining
• Pattern Mining
• Database Systems
• +۳ مورد دیگر

Donald Sheehy is an Associate Professor in the Department of Computer Science at North Carolina State University's College of Engineering, where he conducts research at the intersection of computational geometry and topological data analysis. His work focuses on developing efficient algorithms for analyzing geometric and topological structures in data. B.S.E. in Computer Science from Princeton University (2005) Ph.D. in Computer Science from Carnegie Mellon University (2011) Postdoctoral researcher at Inria Saclay, France Former faculty member at University of Connecticut Sheehy's research spans computational geometry, topological data analysis, and algorithm design. He develops mathematical frameworks for understanding shape, structure, and complexity in data, with particular expertise in persistence diagrams, Voronoi diagrams, Delaunay triangulations, and metric space analysis. His work bridges theoretical computer science with practical applications in data analysis, often developing sparse representations of complex geometric structures. He has made significant contributions to the understanding of approximation algorithms in geometric settings and has applied his techniques to problems in scientific computing and simulation analysis. Analysis of his recent publications reveals a consistent focus on developing efficient algorithms for topological data analysis, particularly in creating sparse representations of complex structures like persistence diagrams and Delaunay filtrations. His work demonstrates a progression from fundamental geometric algorithms toward more sophisticated applications in data analysis, with increasing attention to practical computational efficiency while maintaining theoretical guarantees. A notable pattern is his development of approximation techniques that dramatically reduce computational complexity while preserving essential topological features. 2022 Fall Workshop on Computational Geometry ($14,700 NSF grant) CAREER: Algorithmic Challenges and Opportunities in Spatial Data Analysis ($277,465 NSF grant) Sheehy has secured significant research funding from the National Science Foundation, including a CAREER award focused on developing new data structures and algorithms for spatial data analysis. His work on the Fall Workshop on Computational Geometry demonstrates leadership in the computational geometry community. While specific student advising information isn't provided in the source material, his research program appears to involve substantial collaboration with graduate students and postdoctoral researchers, as evidenced by his numerous co-authored publications.

• Computational Geometry
• Topological Data Analysis
• Algorithms
• +۳ مورد دیگر

Dongkuan Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University, leading the Generative Intelligent Computing (GIC) Lab. His research spans Artificial Intelligence , focusing on scalable, efficient, and reliable AI systems for large language models, diffusion models, and generative frameworks. Education Ph.D. in Computer Science, Pennsylvania State University (2022) M.S. in Optimization and Data Mining, University of Chinese Academy of Sciences (2017) B.E. in Information Management and System, Renmin University of China (2014) His research explores test-time adaptation, computational efficiency, and trustworthy AI through uncertainty-aware learning and robust generalization. Applications include Education , Robotics , Agriculture , Networking , and Healthcare . Recent work emphasizes hardware-algorithm co-design and domain-specific AI platforms like Synthora and Gentopia. His publications from 2024–2025 highlight advancements in Vision Transformers , Diffusion Models , Edge Computing , and Trustworthy AI , with grants from NVIDIA , Microsoft , and the National Science Foundation . Awards include the AAAI Best Demonstration Award (2025), NVIDIA Academic Grant (2025), and the Carla Savage Award (2024). He actively mentors undergraduate researchers and co-chairs workshops at top venues like DAC , CVPR , and KDD , aiming to democratize AI access and enhance its reliability across real-world domains.

• Machine Learning
• Natural Language Processing
• Computer Vision
• +۷ مورد دیگر

Jose Picheral is an active researcher at the Signals and Systems Laboratory specializing in signal processing with expertise in source localization, spectral analysis, and vibration analysis. His work bridges theoretical methodologies with practical applications in aerospace engineering, acoustics, and mechanical systems monitoring. His primary research interests include: Signal Processing Source Localization Spectral Analysis Vibration Analysis Acoustic Imaging Array Signal Processing Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on sparse signal processing techniques applied to aircraft engine vibration monitoring through tip-timing analysis, high-resolution acoustic imaging methods, and non-uniform antenna array processing. Key trends include the development of OMP-based spectral analysis for blade vibration, manifold learning approaches for impulse response reconstruction, and robust super-resolution techniques for correlated source localization in noisy environments. No scientific awards were documented in the provided source material. The text contains no information regarding student advising activities or research grant funding. He operates within the Signals and Systems Laboratory framework, which concentrates on advanced signal processing solutions for engineering challenges, particularly in aerospace vibration analysis and acoustic source mapping through innovative sparse recovery and manifold-based methodologies.

• Signal Processing
• Source Localization
• Spectral Analysis
• +۳ مورد دیگر

Diego Lopez De Ipiña Gonzalez De Artaza is a researcher at the Department of Computing, Electronics and Communication Technologies within the University of Deusto's College of Engineering. His work spans interdisciplinary domains such as the Internet of Things (IoT), Artificial Intelligence (AI), and Ambient Intelligence, focusing on human-centric systems and sustainable technologies. Research Trends : Recent publications highlight AI applications in geriatric health assessment, energy-efficient building systems, and citizen science platforms. His work integrates sensor data, context-aware reasoning, and machine learning for smart cities and edge intelligence. Scientific Awards : No specific awards mentioned in the provided text. Advising : Supervised 15+ theses in areas like IoT protocols, context-aware systems, AI interpretability, and smart mobility solutions. Congress Participation : Active in conferences addressing human-AI collaboration, micro-volunteering for environmental monitoring, and adaptive educational technologies. His contributions emphasize scalable architectures for remote laboratories, uncertainty reasoning in intelligent environments, and privacy-preserving IoT frameworks, reflecting a commitment to bridging theoretical research with real-world applications in smart communities.

• Internet of Things (IoT)
• Artificial Intelligence
• Sustainable Computing
• +۷ مورد دیگر

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +۴ مورد دیگر

Olivier Fercoq is a Professor at Télécom Paris in the Information Processing and Communication Laboratory (LTCI) . He holds a PhD in Applied Mathematics from École Polytechnique (2012) and a Master's degree from Paris 6 and ENSTA Paris. His research focuses on first-order optimization algorithms , primal-dual methods , coordinate descent , and applications in web ranking , chronotherapy for cancer , and statistical learning . Education : PhD in Applied Mathematics (École Polytechnique), Master's (Paris 6), Engineering degree (ENSTA Paris) Research Themes : Optimization for machine learning, primal-dual algorithms, web ranking, chemotherapy scheduling His recent work spans stochastic optimization , nonconvex-nonconcave minimax problems , and scalable semidefinite programming . Articles highlight advancements in primal-dual coordinate descent , power-aware signal processing , and duality gap analysis . Awards include the IMA Leslie Fox Prize (2015) and the Gaspard Monge PhD Thesis Award (2013) . Olivier has supervised numerous PhD students including Eyal Vayness (2024-), Iyad Walwil (2022-), and Eugène Ndiaye (2016-2018). He organized the Paris-Saclay Junior Conference on Data Science and Engineering (2018) and co-led the workshop Optimization, Distributed Computation, and Machine Learning (2016) . His software contributions include APPROX for parallel coordinate descent and tools for scikit-learn .

• First-order optimization algorithms
• Primal-dual methods
• Coordinate descent methods
• +۷ مورد دیگر