جست‌وجوی استاد و پژوهشگر

Monica Kohlerمشاهده پروفایل

استاد پژوهشی

• 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.

Chamith Wijenayakeمشاهده پروفایل

مدرس ارشد

• multidimensional signal processing
• digital hardware architectures
• FPGA based system design
• +۳ مورد دیگر

Dr. Chamith Wijenayake is a Senior Lecturer - Teaching Focused at the School of Electrical Engineering and Computer Science, University of Queensland. He holds a PhD in Electrical and Computer Engineering from the University of Akron (2014) and a BSc (Hons) in Electronic and Telecommunications Engineering from the University of Moratuwa, Sri Lanka (2007). His research focuses on multidimensional signal processing, digital hardware architectures, FPGA-based systems, machine learning accelerators, and engineering education. He has received notable awards, including the 2011 Outstanding Student Research Award and the 2014 IEEE Circuits and Systems Pre-Doctoral Award. Education: BSc (First Class Honours) from University of Moratuwa (2007), PhD from University of Akron (2014). His doctoral work contributed to advancements in signal processing and hardware architectures. Research interests span multidimensional signal processing, FPGA-based system design, and engineering education innovations. He develops low-complexity algorithms for light field processing and multidimensional filters for imaging, sensing, and biomedical applications. His work emphasizes practical implementations in hardware accelerators and educational technologies. Outstanding Student Research Award, University of Akron, 2011 IEEE Circuits and Systems Pre-Doctoral Award, 2014 Teaching and Advising: Focuses on blended learning approaches and project-based instruction in electrical engineering. Prior roles include Lecturer at UNSW Sydney (2015–2019). No explicit student advisee records listed. Grants and collaborations are not detailed in provided texts. Labs/Teams: Involved in multidisciplinary projects integrating signal processing with hardware design, though specific lab affiliations are not specified.

Noah Simonمشاهده پروفایل

دانشیار

• Machine Learning
• High-dimensional Estimation
• Clinical Trial Design
• +۴ مورد دیگر

Noah Simon is an Associate Professor in the Department of Biostatistics at the University of Washington School of Public Health. His research focuses on high-dimensional statistical methods, machine learning, and their applications in biomedicine. He develops computational tools for genomic and clinical data analysis, including penalized regression techniques and adaptive clinical trial designs. Education: B.A. Mathematics, Pomona College (2008) Ph.D. Statistics, Stanford University (2013), advised by Robert Tibshirani Research Interests: Dr. Simon specializes in high-dimensional estimation, algorithm optimization, and clinical trial methodology. His work addresses challenges in biomarker discovery, imaging-based diagnostics, and genomic data analysis. Key areas include sparse-group lasso regularization, adaptive enrichment designs for personalized medicine, and scalable computational methods for big data. Grants & Funding: NIH Director's Early Independence Award ($250k/year, 2014–2019) Amazon and Google Cloud Computing Grants for biomarker research Awards: Forbes 30 Under 30 in Science (2015) NSF Graduate Research Fellowship Honorable Mention (2010) Weiland Fellowship (2011–2013) Advising: He mentors PhD and MS students in biostatistical methodology and data science, with current advisees including Jean Feng, Brayan Ortiz, and Jeremy Roth. Notable collaborations include work on neural activity detection via calcium imaging (SCALPEL) and nonparametric variable importance assessment using neural networks. Lab & Affiliations: Based at the Hans Rosling Center for Population Health, his group develops open-source software (e.g., sgl , standGL ) and contributes to biomedical data science initiatives at UW.

Andrew Linمشاهده پروفایل

مدرس ارشد

• Neuroscience
• Olfactory System
• Neural Circuit Function
• +۳ مورد دیگر

Dr. Andrew Lin is a Senior Lecturer and School Director of One University at the University of Sheffield's School of Biosciences. He holds a PhD from the University of Cambridge and a BA in Biology from Harvard University. His career includes roles as a Lecturer (2019-2022), Vice-Chancellor’s Fellow (2015-2019), and Postdoctoral Fellow at the University of Oxford (2009-2015). Research focuses on how the brain encodes sensory information for memory formation, using Drosophila's olfactory system as a model. Key areas include sparse coding in Kenyon cells, synaptic inhibition/excitation balance, and neural circuit dysfunction links to epilepsy. Teaching includes modules like BMS11004 Introduction to Neuroscience and BMS248 Neural Circuits, Behaviour and Memory. He has secured grants from the European Research Council, BBSRC, and Wellcome Trust. Professional memberships include the FENS-Kavli Network and BBSRC Pool of Experts. Lab research employs techniques like in vivo two-photon imaging, electrophysiology, and genetic manipulation. PhD opportunities are available in neural circuitry and sensory processing.

Lucia Florescuمشاهده پروفایل

مدرس

• Medical Imaging
• Optical Tomography
• Computed Tomography
• +۷ مورد دیگر

Dr. Lucia Florescu is a Lecturer in Medical Imaging and Wellcome Trust Fellow at the University of Surrey, within the School of Computer Science and Electronic Engineering. Her research focuses on developing advanced imaging techniques, including optical tomography, computed tomography, and AI-driven reconstruction algorithms for biomedical applications. She has held roles such as Lead Physicist at Elekta, Associate Research Scientist at Columbia University, and postdoctoral fellowships at institutions like UCLA and NASA's Jet Propulsion Laboratory. Her research interests span optical-CT gel dosimetry, Cherenkov light-based dosimetry, and image-guided radiation therapy. She actively supervises PhD students in topics like deep learning for tomographic reconstruction and CAR-T cell therapy optimization via photoacoustic imaging. Dr. Florescu has published extensively on nonreciprocal broken-ray tomography, Cherenkov light emission in radiation therapy, and interior tomography improvements. Her work integrates physics, AI, and medical applications to enhance diagnostic and therapeutic imaging accuracy. She holds a Wellcome Trust Fellowship and has mentored numerous students in both postgraduate and undergraduate projects, covering areas like sparse-view CT reconstruction and interior tomography challenges.

Magdalena Fuentesمشاهده پروفایل

استادیار

• Machine Listening
• Human-Centered Machine Learning
• Multimodal Representation Learning
• +۳ مورد دیگر

Magdalena Fuentes is an Assistant Professor of Music Technology and Integrated Design & Media at New York University (NYU), affiliated with the Music and Audio Research Lab (MARL) and the Integrated Design & Media (IDM) programs. She holds a Ph.D. from Université Paris Saclay (France) and a B.Eng. in Electrical Engineering from Universidad de la República (Uruguay). Her research focuses on Machine Listening, Human-Centered Machine Learning, and Multimodal Representation Learning, with applications to Music Information Retrieval and Environmental Sound Analysis. Previously, she served as a Postdoctoral Faculty Fellow at NYU’s MARL and Center for Urban Science and Progress (CUSP). Her work bridges technical innovation with cultural and societal contexts, particularly in underrepresented music traditions like Brazilian percussion and Candomblé rituals. She has developed open-source tools such as Soundata to ensure reproducible audio dataset usage. Her research outputs span audio-visual synchronization (e.g., SONIQUE), environmental sound monitoring (e.g., SONYC projects), and rhythm analysis frameworks (e.g., Carat toolbox). These contributions highlight her dual focus on advancing machine learning techniques while maintaining human-centric and culturally sensitive applications.

Tomer Galantiمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Large Language Models
• +۵ مورد دیگر

Tomer Galanti is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on machine learning, deep learning, large language models, and artificial intelligence. He holds a B.S., M.S., and Ph.D. in Computer Science and Mathematics from Tel Aviv University (2015–2021), followed by a postdoctoral position at MIT (2021–2024). His work spans theoretical foundations of neural networks, optimization, generalization bounds, and practical applications in language modeling and self-supervised learning. Education: Postdoctoral Associate, Massachusetts Institute of Technology (MIT), 2021–2024 Ph.D., Computer Science, Tel Aviv University, 2021 M.S., Computer Science, Tel Aviv University, 2017 B.S., Mathematics and Computer Science, Tel Aviv University, 2015 Research Interests: Machine and deep learning, large language models, artificial intelligence, generalization bounds, neural collapse phenomena, and self-supervised learning. His recent work explores optimization techniques (e.g., SGD with weight decay), distributed inference systems, and theoretical guarantees for neural network performance. Articles Trends: His publications emphasize theoretical advancements in neural network dynamics, generalization, and optimization. Notable themes include sparse neural networks, speculative inference acceleration, and fairness in language models. He also investigates representation learning and decision tree integration in language modeling frameworks. Advising & Grants: No advising or grant details are explicitly listed in the provided materials. His research is supported through institutional and collaborative efforts in computational and theoretical machine learning.

Luana Ruizمشاهده پروفایل

استادیار

• Large-scale Graph Machine Learning
• Graph Neural Networks
• Signal Processing
• +۶ مورد دیگر

Luana Ruiz is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science and Artificial Intelligence Institute (DSAI). She earned her Ph.D. in Electrical Engineering from the University of Pennsylvania (2022), and dual B.Sc. and M.Eng. degrees in Electrical Engineering from the University of São Paulo (Brazil) and École Supérieure d’Electricité (France, now CentraleSupélec) in 2017. Her research focuses on machine learning, signal processing, and network science, particularly scalable algorithms for non-Euclidean domains like graphs and data manifolds. She has pioneered work on graph neural networks (GNNs), graph sampling, and theoretical limits of transferability and generalization in graph-based learning. Notably, she has developed methods for efficient GNN training on large graphs and stability analysis of neural networks on manifolds. Affiliations: Johns Hopkins University, MINDS, DSAI Education: Ph.D., University of Pennsylvania (2022); B.Sc./M.Eng., University of São Paulo & CentraleSupélec (2017) Her research interests include large-scale graph machine learning, manifold learning, physics-informed ML, and combinatorial optimization. She has received awards such as the iREDEFINE Fellowship (2019), MIT EECS Rising Star (2021), and Best Student Paper Awards at EUSIPCO (2019, 2021). Her work bridges theoretical foundations and practical applications, with contributions to GNN architecture design, signal processing on graphs, and stability guarantees for neural networks on manifolds. Recent publications highlight advances in graph sampling for scalable GNNs, stability of manifold neural networks, and theoretical analysis of GNN expressivity. She collaborates with institutions like MIT and the Simons Institute, and her interdisciplinary approach addresses challenges in graph data analysis and AI efficiency. She advises on grants related to graph signal processing and has contributed to the development of graphon-based frameworks for large-scale graph analysis. Awards: Eiffel Excellence Scholarship (2013–2015), Best Paper Awards at EUSIPCO (2019, 2021) Grants/Advising: METEOR and FODSI postdoctoral fellowships, Google Research Fellowships

Christian Tetzlaffمشاهده پروفایل

استاد

• Computational Neuroscience
• Synaptic Physiology
• Neuromorphic Engineering
• +۴ مورد دیگر

Prof. Christian Tetzlaff holds a W2 Professorship at the Department for Neuro- and Sensory Physiology, University Medical Center Göttingen. He leads the Computational Synaptic Physiology group, focusing on integrating computational methods to understand neural systems' principles and translating these into technologies like robotics and neuromorphic engineering. His work spans scales from synaptic protein interactions to large neural networks, leveraging disciplines such as nonlinear dynamics and machine learning. Educational Background: 2009: Diploma in Physics, Georg-August University Göttingen 2013: PhD in Physics (Dr.rer.nat.), Georg-August University Göttingen Research Leadership & Grants: Principal Investigator in EU Horizon 2020 Projects (Plan4Act, ADOPD, Human Brain Project's FIPPA) Lead in DFG-funded SFB 1286 (Quantitative Synaptology) INRC member (Intel Neuromorphic Research Community) BMBF-funded KISSKI project (2022–present) Research Interests: His lab explores synaptic mechanisms underlying learning, memory consolidation via synaptic tagging-and-capture, and neuromorphic hardware implementations. Recent work emphasizes calcium signaling in synapses, memristive devices for synaptic working memory, and robust trajectory generation using neuromorphic chips like Loihi. Affiliations: Co-coordinator of Plan4Act (2017–2021) and active in the Göttingen Graduate Center for Neurosciences (GGNB). Collaborates globally with institutions like the Weizmann Institute and Columbia University. Laboratory: Visit tetzlab.com for lab activities and open-source tools like Plastic Arbor .

Morten Nielsenمشاهده پروفایل

استاد

• Harmonic Analysis
• Approximation Theory
• Sparse Signal Processing
• +۷ مورد دیگر

Morten Nielsen is a Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research is centered on harmonic analysis, approximation theory, and sparse signal processing, with strong emphasis on modulation spaces, wavelet systems, and matrix-weighted function spaces. His research interests include: Harmonic and Functional Analysis Approximation Theory and Sparse Representations Wavelet and Time-Frequency Analysis Nonlinear Approximation in Banach Spaces Matrix-Weighted Function Spaces Applications in Signal and Image Processing The recent publications show a consistent focus on theoretical developments in function spaces, particularly α-modulation and Triebel-Lizorkin spaces, with applications to sparse representations and nonlinear approximation. His work often involves deep connections between operator theory, frame theory, and signal decomposition techniques. Although no specific scientific awards are listed, his extensive publication record in top-tier journals and book chapters in prestigious series such as Applied and Numerical Harmonic Analysis indicates significant recognition in the mathematical community. Morten Nielsen has been involved in multiple research projects, including 'Generalized Wavelet Systems', 'Sparse Representation of Data', and 'Muckenhoupt Matrix Weights', often in collaboration with researchers like Hrvoje Šikić. He has supervised at least one PhD student and continues to be actively involved in research, as evidenced by publications in 2025. His work is supported by ongoing research grants and collaborations across Europe. He leads and participates in research groups focused on harmonic analysis and approximation theory, contributing to both theoretical advancements and practical applications in data science and engineering.

Cevdet Aykanatمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing
• Graph and Hypergraph Partitioning
• +۴ مورد دیگر

Prof. Cevdet Aykanat is a Professor of Computer Engineering at Bilkent University, Ankara, Turkey. He earned his BS/MS in Electrical Engineering from METU and PhD from Ohio State University as a Fulbright scholar. His research focuses on parallel computing, sparse matrix algorithms, graph partitioning, and high-performance computing for big data. He has been affiliated with Bilkent since 1989 and has held roles like Associate Provost. Education: BSc/MSc (METU Electrical Engineering), PhD (Ohio State University Electrical & Computer Engineering). Research interests include parallel scientific computing, combinatorial optimization, distributed systems, and large-scale data analysis. His work spans over 100 publications in top journals like IEEE Transactions and SIAM, with 5,500 citations and an H-index of 40. Awards: 1996 TUBITAK Investigator Award, 2007 METU Parlar Science Award. He led 6 TUBITAK projects and participated in EU-funded projects like PRACE-1IP to PRACE-6IP. Publications emphasize efficient parallel algorithms for sparse computations, graph partitioning, and distributed systems. His work addresses latency reduction, load balancing, and scalable data processing in HPC environments. Grants: Funded by TUBITAK, Intel SSD, and EU programs. Academic service includes editorial roles at IEEE Transactions on Parallel and Distributed Systems.

Tianqi Hongمشاهده پروفایل

استادیار

• Power Systems
• Power Electronics
• Renewable Energy Integration
• +۴ مورد دیگر

Dr. Tianqi Hong is an Assistant Professor at the University of Georgia, affiliated with the School of Electrical & Computer Engineering and the Department of Electrical and Computer Engineering. He holds a B.Sc. from Hohai University (2011), an M.Sc. from Southeast University and NYU (201?), and a Ph.D. from NYU (2016). Prior to his academic role, he served as a Principal Energy System Scientist at Argonne National Laboratory and a Senior Research Scientist at Unique Technical Services, LLC. His research focuses on power systems, power electronics, renewable energy integration, and AI-driven solutions for smart grids. Dr. Hong’s work emphasizes cybersecurity for power infrastructure, nonlinear dynamics in microgrids, and advanced control strategies. He actively contributes to IEEE Transactions across multiple journals and chairs the IEEE IAS Industrial Power Converters Committee. His recent publications address topics like AI-based photovoltaic inverter modeling, cyberattack mitigation in smart grids, and voltage regulation using distributed optimization. He has authored over 60 peer-reviewed articles, spanning from 2014 to 2025, with a focus on energy system stability, renewable integration challenges, and data-driven methodologies. His professional service includes editorial roles in top-tier journals and leadership in industry-research collaborations.

Theodosia Stratoudakiمشاهده پروفایل

مدرس ارشد

• Laser Ultrasonics
• Remote Sensing and Imaging
• Non-Destructive Evaluation (NDE)
• +۱۲ مورد دیگر

Theodosia Stratoudaki is a Senior Lecturer in the Department of Electronic & Electrical Engineering at the University of Strathclyde, Faculty of Engineering. She joined the university in 2017 as a Strathclyde Chancellor’s Fellow and has since become a leading researcher in laser ultrasonics and remote sensing. She holds a PhD from the University of Warwick and completed postdoctoral research at the University of Cambridge and the University of Nottingham. Her research interests lie at the intersection of optics, acoustics, and materials engineering, focusing on laser-induced phased arrays (LIPAs) for remote ultrasonic imaging, non-destructive evaluation (NDE), and in-process monitoring in extreme environments. She applies these techniques to advanced manufacturing, including additive manufacturing and nuclear applications, collaborating with industrial partners such as the UK Atomic Energy Authority, Sellafield, and Hitachi. The trends in her recent publications show a strong emphasis on improving the resolution, efficiency, and adaptability of laser ultrasound systems—particularly through innovations in array design, grating lobe suppression, signal processing, and machine learning integration. Her work increasingly incorporates robotics and deep learning for automated inspection and tomography. She has received multiple scientific awards, including: EPSRC DTA PhD studentship (2018) BINDT Annual Conference Paper Award (2018) Best Paper Award (2018) Best Paper Award (2015) Dr. Stratoudaki is actively involved in research leadership and mentoring. She is currently recruiting PhD students and supervising multiple funded projects, such as the Robotic Laser Ultrasonic Inspection System and Impact Enhancement for Adaptive Laser Induced Phased Arrays (ALIPA). She also contributes to professional service as co-chair of the departmental Equality, Diversity and Inclusion (EDI) committee, chair of the Institute of Physics’ Physical Acoustics group, and a member of the British Standards Institute’s ultrasonics committee (EPL/87). She leads a research team focused on laser ultrasonics and is part of collaborative networks involving the University of Strathclyde’s Centre for Ultrasonic Engineering and industrial partners. Her lab develops advanced optical systems for non-contact ultrasonic inspection, often integrating robotics and AI for real-time, in-process evaluation.

• Statistical Problems in Marketing
• Functional Data Analysis
• Statistical Methodology
• +۲ مورد دیگر

Gareth James is the John H. Harland Dean and Professor of Information Systems & Operations Management at Emory University's Goizueta Business School, a position he assumed in July 2022. Prior to this, he held significant leadership roles at USC Marshall School of Business, including interim dean (2019-2020), vice dean of faculty, and deputy dean (2020-2022). James received his PhD in Statistics from Stanford University, complemented by a BComm in Finance and BS in Statistics from The University of Auckland. His academic journey reflects a strong foundation in both statistical methodology and business applications. James specializes in statistical problems in marketing, functional data analysis, statistical methodology, and high-dimensional regression. His research bridges theoretical statistics with practical business applications, particularly in the areas of machine learning, advertising optimization, and technology adoption. His work demonstrates a consistent focus on developing methodological innovations that address real-world business challenges. His research contributions have been widely recognized, with over 30,000 citations. James has led multiple National Science Foundation research grants and served as associate editor for five top research journals. His most notable scholarly contribution is co-authoring the highly successful textbook "An Introduction to Statistical Learning," which has become a standard reference in the field. Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Dean's Research Awards (two times) Evan C. Thompson Faculty Teaching and Learning Innovation Award Three-time winner of the Marshall School of Business' Golden Apple Award Dean's Ph.D. Advising Award USC Mellon Award Provost's Mentoring Award James is equally distinguished as a mentor and educator. His teaching excellence is evidenced by multiple Golden Apple Awards, while his mentoring impact is reflected in the Dean's Ph.D. Advising, USC Mellon, Evan C. Thompson, and Provost's Mentoring awards. As Dean, he leverages his expertise in data-informed decision making to strengthen Goizueta's mission of preparing principled leaders to have a positive influence on business and society.

Jie Dingمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Statistics
• Scientific Computing
• +۶ مورد دیگر

Jie Ding is an Associate Professor at the University of Minnesota's School of Statistics with graduate faculty appointments in Electrical Engineering, Computer Science, and the Data Science Program. He serves as a core faculty member of the Data Science and AI Hub and holds an Amazon Scholar position with the Amazon AGI Team focusing on foundation model training. His educational background includes a Ph.D. in Engineering Sciences from Harvard University (2017), postdoctoral work at Duke University (2018), and a B.S. from Tsinghua University where he participated in both the Math & Physics Academic Talent Program and Electrical Engineering program. Ding's research sits at the intersection of artificial intelligence, statistics, and scientific computing, with focus areas including Agentic AI for autonomous data science workflows, AI Foundations for interpretability and trustworthiness, Scalable Modeling for broader AI accessibility, Decentralized and Collaborative AI systems, and AI Safety addressing privacy and security concerns. He developed the STAT 8931 Generative AI course with open-source materials available at genai-course.jding.org . His recent publications demonstrate strong activity across multiple AI subfields, particularly in value alignment (MAP framework), AI safety mechanisms, federated learning innovations, and statistical foundations for modern AI systems. The breadth of venues (ICML, ICLR, NeurIPS) indicates significant impact across the AI research community. NSF CAREER Award (2024) Army Early Career Program (Young Investigator) Award (2023) Cisco Research Award (2022-25) AWS Cloud Credits for Research (2021-22) Meta/Facebook Faculty Research Award (2021-22) UMN Thank-A-Teacher Teaching Award (2019-20) Ding leads the Agentic AI for Data Science Benchmark initiative, collaborating with University of Minnesota colleagues and Minnesota industry partners to evaluate AI agent capabilities across healthcare, insurance, retail, energy and other sectors. His research group actively recruits PhD students interested in AI/Statistics intersections, with focus on developing theoretically grounded yet practically impactful AI systems.