جستجوی استاد راهنمای دانشگاهی

Andrés Bruhnمشاهده پروفایل

استاد

• Computer Vision
• Optical Flow
• Scene Flow
• +۵ مورد دیگر

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

Anuran Makurمشاهده پروفایل

استادیار

• Machine Learning Theory
• Information Theory
• Applied Probability
• +۴ مورد دیگر

Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.

Monica Kohlerمشاهده پروفایل

استاد پژوهش

• Earthquake Engineering
• Seismic Tomography
• Structural Health Monitoring
• +۵ مورد دیگر

Monica Kohler is a Research Professor in the Department of Mechanical and Civil Engineering at the California Institute of Technology (Caltech). She holds a B.A. from Harvard University (1988) and a Ph.D. from Caltech (1995). Prior to her current role, she served as an Assistant Research Engineer at UCLA (1998–2011), Visiting Associate at Caltech (2007–2011), and Research Assistant Professor (2011–2017). Her work focuses on earthquake engineering, seismic tomography, and structural health monitoring using advanced sensor technologies. Her research integrates observational seismology and engineering principles to detect damage in buildings and infrastructure through dense sensor networks like the Community Seismic Network (CSN). Projects include developing methods for damage identification using seismic data, tsunami early warning systems, and real-time monitoring of urban vibrations from human activity (e.g., concerts). Key areas of exploration include earthquake-induced building response, wave propagation analysis, and the application of Bayesian learning and Gaussian process regression for ground-motion modeling. She advises graduate students at UCLA in civil engineering and collaborates on interdisciplinary projects involving oceanography, structural dynamics, and computational modeling. Her contributions span over 150 publications, including studies on the 2019 Ridgecrest earthquakes, tsunami wavefront mapping, and high-rise structural identification. She also leads efforts to deploy seafloor sensors and improve early warning systems for natural disasters. Labs/Teams: Active in the Caltech CSN project, structural health monitoring initiatives, and tsunami hazard research groups. Her team includes researchers like Viviana Vela, Gloria Hyun, and Richard Guy.

Archer Yangمشاهده پروفایل

دانشیار

• Statistical machine learning
• High-dimensional statistics
• Computational statistics
• +۸ مورد دیگر

Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning. Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare. His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory. ICML Spotlight Paper (top 2.6%, 313/12,107) Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields. His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.

Piya Palمشاهده پروفایل

استاد

• High Dimensional Statistical Signal Processing
• Compressive Sensing
• Tensor Methods
• +۴ مورد دیگر

Prof. Piya Pal is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Her research focuses on high-dimensional statistical signal processing, energy-efficient sampling techniques, and covariance-driven inference. She previously held an Assistant Professor position at the University of Maryland, College Park, and was affiliated with the Institute for Systems Research. Education: Ph.D. in Electrical Engineering from California Institute of Technology (2013). Notable achievements include the NSF CAREER Award (2016) and the 2014 Charles and Ellen Wilts Prize for her thesis on sparse sampling and estimation. Her work emphasizes structured sampling and robust algorithms for undersampled data analysis, with applications in sensor arrays, compressive sensing, and optical imaging. Research Interests: Energy-efficient sparse array design Correlation-aware sparse estimation Covariance compression and statistical inference Tensor methods in machine learning High-resolution imaging systems Publications highlight advancements in sparse array geometries (nested/coprime samplers), Cramér-Rao bound analysis, and hybrid beamforming. Recent work explores super-resolution imaging and millimeter-wave channel sensing with learned empirical priors. Her contributions address fundamental trade-offs between sample size, resolution, and domain knowledge integration. Scientific Awards: NSF CAREER Award (2016) 2014 Charles and Ellen Wilts Prize (Caltech) Advising & Grants: Current research is supported by NSF CAREER funding. Her lab focuses on interdisciplinary projects combining signal processing with medical imaging and wireless communication challenges.

Shunqiao Sunمشاهده پروفایل

استادیار

• Statistical Signal Processing
• Sparse Signal Processing
• Mathematical Optimization
• +۷ مورد دیگر

Dr. Shunqiao Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Alabama, College of Engineering. He joined the faculty in August 2019 as a tenure-track professor after working at Aptiv’s radar core team in Malibu, California. His research focuses on advanced signal processing, machine learning, and optimization for automotive and MIMO radar systems in autonomous vehicles. Ph.D. : Electrical and Computer Engineering, Rutgers University, 2016 M.S. : Electrical Engineering, Fudan University, 2011 B.S. : Electrical Engineering, Southern Yangtze University, 2004 Dr. Sun's research lies at the intersection of statistical and sparse signal processing , mathematical optimization , and machine learning , with applications in automotive radar , MIMO radar , and autonomous driving . His work emphasizes sparsity-oriented frameworks, AI-powered radar perception, and high-resolution 4D sensing. He leads a dynamic research group focused on next-generation radar technologies for intelligent transportation systems. His recent publications demonstrate a strong trend in deep learning for radar signal recovery , collaborative radar imaging , direction-of-arrival estimation with sparse arrays , and integrated sensing and communication . Several of his papers are among the most downloaded and cited in IEEE journals, including top articles in IEEE Signal Processing Magazine and IEEE Journal of Selected Topics in Signal Processing. Scientific Awards and Honors: NSF CAREER Award (2024) NSF CRII Award (2022) IEEE AESS Robert T. Hill Best Dissertation Award (2016) Best Student Paper Award at IEEE SAM Workshop (2020) Rutgers ECE Academic Achievement Award (2015–2016) University of Alabama Hewson Engineering Faculty Fellow (2025) Dr. Sun is actively involved in academic service and leadership. He is an Associate Editor for IEEE Signal Processing Letters and IEEE Open Journal of Signal Processing . He serves as Vice Chair of the IEEE Signal Processing Society’s Autonomous Systems Initiative and is an elected member of the IEEE Sensor Array and Multichannel (SAM) Technical Committee and the Integrated Sensing and Communication (ISAC) Technical Working Group. He has co-organized numerous workshops and special sessions at ICASSP, EUSIPCO, and IEEE Radar Conference. His lab has secured significant research funding from the National Science Foundation , NXP Semiconductors , MathWorks , and NOAA . He mentors multiple Ph.D. students, several of whom have interned at leading industry labs such as NXP and GM Cruise. He has co-organized the Workshop on Signal Processing for Autonomous Systems (SPAS) at ICASSP and EUSIPCO and delivered invited seminars at institutions including TU Delft, UC Davis, and Lehigh University.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۴ مورد دیگر

Dr. Marc Habermann is a Tenured Senior Researcher and Scientific Manager of the Real Virtual Lab at the Max Planck Institute for Informatics (Department 6: Visual Computing and Artificial Intelligence). He leads the Graphics and Vision for Digital Humans research group, focusing on cutting-edge technologies in Computer Vision, Computer Graphics, and Machine Learning. His work emphasizes real-time human performance capture, photorealistic animation synthesis, and generative 3D human models derived from video data. Research Interests: Computer Vision, Computer Graphics, Machine Learning, Human Performance Capture, Non-Rigid Deformation Reconstruction, Neural Rendering, and Motion Capture. His contributions span topics like Gaussian splats optimization, sparse-view avatar synthesis, and physics-based cloth simulation. Awards: Saarland University Associate Fellow (2025) Key Publications (2025): Second-order Optimization of Gaussian Splats with Importance Sampling (arXiv) GIGA: Generalizable Sparse Image-driven Gaussian Avatars (arXiv) EVA: Expressive Virtual Avatars from Multi-view Videos (Siggraph 2025) Labs & Projects: Manages the Real Virtual Lab and heads the Graphics and Vision for Digital Humans group, advancing technologies for digital human avatars and immersive telepresence systems.

Nima Kalantariمشاهده پروفایل

استادیار

• Computer Graphics
• Computational Photography
• Rendering
• +۵ مورد دیگر

Nima Kalantari is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University. He previously held a postdoctoral position at UC San Diego under Ravi Ramamoorthi. His academic journey includes a Ph.D. from UC Santa Barbara (ECE), an M.S. and B.S. from Amirkabir University of Technology (Electrical Engineering). His research focuses on computer graphics, computational photography, rendering, and deep learning, with emphasis on machine learning techniques for image synthesis and material generation. Education: Ph.D., Electrical and Computer Engineering, University of California, Santa Barbara, 2015 M.S., Electrical Engineering, Amirkabir University of Technology, 2009 B.S., Electrical Engineering, Amirkabir University of Technology, 2007 Research Interests: Kalantari's work bridges computer graphics and deep learning, particularly in computational photography, rendering, and view synthesis. Recent projects include 3D relightable face generation, sparse view synthesis with Gaussians, and physics-guided neural networks for fluid dynamics. Awards: 2024 Frontiers of Science Award (SIGGRAPH 2019) TEES Young Faculty Fellow Award (2024) Best Master Thesis Award, IEEE Iran Section (2010) Advising & Labs: Active mentor for 8 current Ph.D./M.S. students and advisor to alumni including Xilong Zhou (Postdoc at MPI for Informatics). His team focuses on projects like PanoDreamer (3D panorama synthesis) and PhotoMat (material generation from flash photos).

P. P. Vaidyanathanمشاهده پروفایل

استاد

• Digital Signal Processing
• Sparse Sensor Arrays
• Network Signal Processing
• +۸ مورد دیگر

P. P. Vaidyanathan is the Kiyo and Eiko Tomiyasu Professor of Electrical Engineering at the California Institute of Technology , where he has served since 1983. His career spans over four decades with appointments as Assistant Professor (1983-88), Associate Professor (1988-93), Professor (1993-2018), and Tomiyasu Professor (2018-present). He has also held administrative roles as Executive Officer (2002-05). B.Sc., University of Calcutta (1974) B.Tech. (1977) and M.Tech. (1979) in Radiophysics & Electronics Ph.D., University of California, Santa Barbara (1982) Dr. Vaidyanathan's research focuses on Digital Signal Processing with emphasis on sparse sampling, network signal processing, number-theoretic signal processing, and applications in communications, radar, genomics, and multirate systems. His work on sparse arrays, coarray theory, and Ramanujan subspace signals has transformed direction-of-arrival estimation and genomic signal analysis. Key trends in his 2023-2024 publications include: Optimization of sparse arrays for mutual coupling reduction Advancements in mmWave MIMO channel estimation Mathematical foundations of beamspace methods Applications of number theory in signal processing Scientific recognition includes: IEEE Jack S. Kilby Signal Processing Medal IEEE Gustav Robert Kirchhoff Award EURASIP Athanasios Papoulis Award Membership in U.S. National Academy of Engineering and Indian National Academy of Engineering His academic legacy extends through 30+ PhD graduates including Tsuhan Chen (1993), Truong Nguyen (1989), and recent advisees Pranav Kulkarni (2025) and Po-Chih Chen (2024). His textbooks like Multirate Systems and Filter Banks and Signals, Systems, and Signal Processing (2024) remain foundational references in the field.

• Historical Climatology
• Climate Dynamics
• Atmospheric Circulation Reconstruction
• +۴ مورد دیگر

Prof. Dr. Stefan Brönnimann serves as a Professor and Unit Leader of Climatology at the Institute of Geography, University of Bern. His extensive research portfolio spans historical climatology, climate dynamics, and atmospheric circulation reconstruction, with particular focus on the past 400 years. He plays a leadership role in major international climate initiatives including the IPCC assessment reports and various climate reanalysis projects. Brönnimann's research centers on reconstructing historical weather and climate patterns through the innovative combination of early instrumental data, proxy records, and climate models. His work examines large-scale climate variability, interannual-to-decadal atmospheric circulation patterns, volcanic eruption effects on climate, and climate-society interactions. His methodologies often involve transforming historical documents into usable climate datasets and applying advanced machine learning techniques to weather reconstruction challenges. Recent publications reveal a strong emphasis on European climate patterns, hydroclimate extremes, and the development of novel datasets and tools for climate research across high-impact journals in climate science, paleoclimatology, and climate informatics. Notable scientific achievements include: Lead author for Chapter 2 of the IPCC Working Group I 5th Assessment Report President of the Commission 'Atmospheric Chemistry and Physics' (ACP) of sc.nat Editorial leadership for multiple prestigious journals including Meteorologische Zeitschrift, Climate of the Past, and Geographica Bernensia Leadership roles at the Oeschger Centre for Climate Change Research Active participation in international initiatives like the Twentieth Century Reanalysis Project and Atmospheric Circulation Reconstructions over the Earth (ACRE) Brönnimann has secured substantial funding through numerous national and international projects including SNF, NCCR Climate, EU FP-7, HORIZON2020, COST, and ERAnet.RUS. He has organized multiple international workshops on weather and climate extremes, atmospheric circulation variability, and historical climate events like the Tambora eruption. His work connects closely with the Oeschger Centre for Climate Change Research at the University of Bern, where he leads Work Package 2 and serves on the Steering Group, demonstrating his significant institutional leadership within Bern's climate research community.

Serkan Gugercinمشاهده پروفایل

استاد

• Model Reduction
• Dynamical Systems
• Numerical Analysis
• +۶ مورد دیگر

Serkan Gugercin is the Class of 1950 Professor of Mathematics and Deputy Director of the Division of Computational Modeling and Data Analytics (CMDA) at Virginia Tech's College of Science. He is also affiliated with the Department of Mechanical Engineering. His research focuses on model reduction, dynamical systems, numerical analysis, and scientific computing, with applications in engineering and data-driven methods. Gugercin has held prestigious titles such as the A.V. Morris Professorship (2016–2019) and received awards like the NSF Early CAREER Award (2007) and Alexander von Humboldt Fellowship (2016). He earned his Ph.D. in Electrical Engineering from Rice University (2003) and has secured over $5.5M in research funding. His work bridges numerical methods, control theory, and optimization, emphasizing high-fidelity reduced models for complex systems. Education: B.S., Middle East Technical University (1997); M.S. and Ph.D., Rice University (1999, 2003). Key contributions include co-authoring the textbook *Interpolatory Methods for Model Reduction* (SIAM) and advancing structure-preserving interpolation techniques for nonlinear systems. Research areas include data-driven modeling, parametric systems, and energy-based approximation methods. Labs/Teams: CMDA Program, part of Virginia Tech’s Academy of Integrated Science. Collaborates on interdisciplinary projects involving power networks, fluid dynamics, and mechanical systems. Active in editorial roles for SIAM Journal on Scientific Computing and Systems & Control Letters.

Marten Wegkampمشاهده پروفایل

استاد

• Applied Mathematics
• Probability and Statistics
• Mathematical Statistics
• +۴ مورد دیگر

Marten Wegkamp is a Professor of Mathematics and Professor of Statistics & Data Science at Cornell University, located in Ithaca, NY. He holds dual affiliations within the College of Arts and Sciences, contributing to both the Department of Mathematics and the Department of Statistics & Data Science. His research focuses on applied mathematics, probability, and statistics, with a strong emphasis on high-dimensional statistics, statistical learning theory, and empirical process theory. He has developed methodologies in latent factor regression, sparse topic models, and interpretable statistical frameworks. His work frequently addresses challenges in high-dimensional data analysis and machine learning. Education: PhD in Mathematics from Leiden University (1996). Research Interests: Wegkamp’s research spans mathematical statistics, empirical process theory, and the development of novel statistical learning techniques. He explores areas such as latent factor models, high-dimensional inference, and the theoretical foundations of machine learning algorithms. His contributions include advancements in prediction methods, discriminant analysis, and optimal estimation strategies for complex data structures. Publications: His recent work includes studies on latent factor regression, sparse topic models, and interdisciplinary applications in genomics and multi-omic data analysis. Key themes across his publications involve high-dimensional data analysis, latent structure discovery, and algorithmic optimization for statistical models. Professional Contributions: He is affiliated with the Statistical Learning and High Dimensional Inference Group at Cornell, and his research has led to software packages like STRS, LOVE, and LoveER, which implement his methodologies. He teaches advanced courses such as Statistical Learning Theory (MATH 7740) and supervises research projects.

• Global Navigation Satellite System (GNSS)
• GNSS Integrity
• High Accuracy Positioning
• +۱۲ مورد دیگر

Dr. Shaojun Feng is an Honorary Principal (Professorial) Research Fellow at the Centre for Transport Studies within the Department of Civil and Environmental Engineering, Faculty of Engineering, Imperial College London. He is a leading expert in Global Navigation Satellite Systems (GNSS), with over thirty years of research experience focusing on GNSS integrity, high-accuracy positioning, augmentation systems, and functional safety for autonomous and safety-critical applications. His research interests span a wide range of topics including GNSS integrity monitoring, precise point positioning (PPP), real-time kinematic (RTK), ionospheric modeling, spoofing detection, software-defined receivers, and integrated navigation systems. He has made pioneering contributions to the development of GNSS correction services with integrity, enabling lane-level navigation in smartphones and certified use in autonomous vehicles. His work bridges theoretical advances with real-world deployment, notably through a commercial service benefiting over 1.5 billion users. The recent publications highlight a strong focus on ionospheric modeling and correction using machine learning (e.g., LSTM, ConvLSTM), sparse reconstruction techniques in tomography, and integrity-aware positioning for autonomous systems. There is a clear trend toward integrating AI with GNSS, improving real-time accuracy, and ensuring safety through robust integrity monitoring—especially in multi-constellation environments (GPS, Galileo, BeiDou). Dr. Feng has been recognized with several prestigious honors: Michael Richey Medal, Royal Institute of Navigation ESA certification for leading Galileo receiver development Recognition by UK Space Agency for UK leadership in Galileo adoption SGS certification for safety-critical GNSS correction service He is actively involved in the academic and standards communities, serving as Associate Editor for the Journal of Navigation and GPS Solutions , and as Chairman of WG3 in RTCM Special Committee 134, which develops international standards for GNSS integrity monitoring. He is a Chartered Engineer (CEng) and Fellow of both the Institution of Engineering and Technology (FIET) and the Royal Institute of Navigation (FRIN), the latter presented by HRH Prince Philip. His leadership in research, editorial roles, and standardization underscores his significant impact on the field of navigation and positioning. Dr. Feng leads research initiatives at Imperial College London with strong industry and agency collaborations, including with the European Space Agency and SGS. His work on integrity-certified GNSS services represents a major advancement in positioning for autonomous systems, with implications for future smart mobility, transportation safety, and resilient navigation infrastructure.

• Computational and Applied Mathematics
• Image Reconstruction
• Computational Imaging
• +۳ مورد دیگر

Dr. Gregery Buzzard is a Professor of Mathematics and Director of the Center for Computational and Applied Mathematics at Purdue University, within the College of Science. His research focuses on computational imaging, inverse problems, and biological systems modeling, with significant contributions to image reconstruction techniques like Plug-and-Play Priors and Consensus Equilibrium. He holds the SIAM Imaging Sciences Best Paper Prize (2020) and led collaborations on algorithms for electron microscopy, CT, and hyperspectral imaging. Current students include Haley Duba (Mathematics), Samin Nur Chowdhury (ECE), and Karl Weisenburger (Mathematics). His work bridges applied mathematics and engineering, emphasizing uncertainty quantification and optimal experimental design. Key projects involve dynamic sampling strategies for microscopy and tomography, as well as multi-agent consensus frameworks for distributed imaging systems. Buzzard also contributed to cellular signaling research, particularly T-cell and B-cell receptor dynamics. Education: Ph.D. in Mathematics (not explicitly stated but inferred from career path) Grants: Multiple grants supporting imaging and computational research (details omitted) Labs/Teams: Directs the Center for Computational and Applied Mathematics (CCAM) Publications: Over 50 peer-reviewed articles, including foundational work on PnP-MACE frameworks

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۳ مورد دیگر

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.