جست‌وجوی استاد و پژوهشگر

Srinivas Akellaمشاهده پروفایل

استاد

• Robotics
• Motion Planning
• Multi-Agent Systems
• +۳ مورد دیگر

Srinivas Akella is a Professor of Computer Science at the University of North Carolina at Charlotte, affiliated with the College of Computing and Informatics. He holds a Ph.D. in Robotics from Carnegie Mellon University (1996). His research focuses on robotics, particularly motion and manipulation planning, multi-robot coordination, and digital microfluidic systems. His work includes developing geometric and optimization algorithms for applications in robotics, biotechnology, and data analytics. Key contributions include coordination of UAVs for coverage/formation tasks, digital microfluidic systems optimization, and manufacturing automation. His research has been supported by NSF, DOD, DARPA, and NCDOT. He received the NSF CAREER Award for outstanding research and education integration. Recent work emphasizes multi-robot path planning, sensor placement for environmental monitoring, and wastewater surveillance systems for public health applications. His projects often bridge theoretical algorithm development with practical implementations in domains like autonomous systems and biomedical engineering. Education: Ph.D. Robotics, Carnegie Mellon University (1996) Funding: NSF, DOD, DARPA, NCDOT Lab Focus: Multi-Agent Systems, Microfluidics, and Data Analytics

Kai Polstererمشاهده پروفایل

استاد پژوهشی

• Astroinformatics
• Machine Learning
• Data Science
• +۴ مورد دیگر

Dr. Kai Polsterer is the Scientific Director of the Heidelberg Institute for Theoretical Studies (HITS) since January 2025, succeeding Tilmann Gneiting. He has been a leading researcher at HITS since 2013, heading Europe's first astroinformatics research group. His work focuses on developing machine learning methods to handle exponential data growth in astrophysics, including projects like the ERC Synergy Grant for universe mapping and exascale computing initiatives (SPACE). He collaborates across disciplines with statisticians and physicians to advance weather forecasting and cardiological analysis. Polsterer is actively involved in international organizations such as the International Astroinformatics Association (IAIA, President since 2023), IEEE Task Force on Mining Complex Astronomical Data, and the German Physical Society's AI working group. His research spans astroinformatics, data science, and interdisciplinary applications of AI. His expertise includes photometric redshift estimation, galaxy classification, and unsupervised learning techniques. Polsterer has pioneered tools like PINK (Parallelized Kohonen Maps) and contributed to projects like the LUCIFER instrument for near-infrared spectroscopy. He emphasizes open science and collaborative frameworks, such as the HiPS ecosystem for astronomical data visualization. His current role as Scientific Director reflects his leadership in advancing theoretical studies through computational and data-driven approaches. Polsterer’s contributions include over 50 peer-reviewed articles, spanning astroinformatics, machine learning applications in astronomy, and computational methodologies. He advocates for interdisciplinary research, bridging astrophysics, computer science, and medical data analysis. His ERC Synergy Grant project exemplifies his commitment to large-scale, collaborative science addressing fundamental questions in cosmology and data science.

Radu Patrice Horaudمشاهده پروفایل

پژوهشگر

• Computer Vision
• Audio Signal Processing
• Robotics
• +۵ مورد دیگر

Radu Patrice Horaud is a Researcher at Inria Grenoble Rhône-Alpes and a member of the RobotLearn team at Laboratoire Jean Kuntzmann, Université Grenoble Alpes. He holds a PhD from 1981 and has been a director of research since 1998. His work focuses on computational vision, audio-visual fusion, robotics, and machine learning, with applications in human-robot interaction and multimodal perception. Notable achievements include coordinating European projects, receiving two ERC grants (Advanced and Proof of Concept), and founding the Perception team. His research spans over 200 publications and includes contributions to 3D sensors, embodied audition, and Bayesian methods in inverse problems. Education: PhD (1981), postdoctoral work at SRI International (1982-1984), and CNRS researcher (1984-1998). Research Interests: Audio-visual scene analysis, robotics, machine learning, and multimodal fusion. His work emphasizes integrating audio and visual data for robotics and healthcare applications, such as the SPRING project in gerontological healthcare. Key Awards: ERC Advanced Grant (2014-2019), ERC Proof of Concept (2018-2019). His lab has also secured funding for projects like Socially Pertinent Robots in Gerontological Healthcare (SPRING). Labs/Teams: Formerly led the Perception team (2007-2021), now part of RobotLearn. Active in the Multidisciplinary Institute in Artificial Intelligence (MIAI) since 2019.

Jungsik Nohمشاهده پروفایل

استادیار

• Bioinformatics
• Biostatistics
• Computational Biology
• +۵ مورد دیگر

Jungsik Noh is an Assistant Professor in the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center. He holds a Ph.D. in Statistics from Seoul National University (2000–2010) and has specialized in statistical methods for analyzing biomedical big data since 2014. His research focuses on integrating statistical modeling with bioinformatics to address complex biological problems, including cell migration dynamics, telomere biology, and pandemic modeling. Notably, his 2021 machine-learning pipeline for estimating hidden SARS-CoV-2 infections gained international attention across 20+ news outlets. Research interests include: Statistical analysis of time-series and biomedical data Machine learning applications in epidemiology Actin cytoskeleton regulation and cell motility Quantitative approaches to cellular aging Key contributions involve developing novel methodologies like the DynamicNeuronTracker for single-neuron analysis (2024) and advancing light-sheet microscopy techniques (2024). His work bridges computational statistics with experimental biology, addressing both fundamental and applied scientific challenges. Lab activities center around the Noh Lab, focusing on systems-level analysis of cellular processes. While no specific grants or awards are listed, his work demonstrates significant societal impact through pandemic-related research and methodological innovations.

Stéphane Mallatمشاهده پروفایل

استاد

• Signal Processing
• Wavelet Theory
• Machine Learning
• +۴ مورد دیگر

Stéphane Mallat is a Professor at the Collège de France, holding the Chair of Data Sciences. He is also affiliated with the École normale supérieure in Paris, where his address is located. His research focuses on signal processing, wavelet theory, and their applications in machine learning and data sciences. Mallat pioneered wavelet transforms for signal decomposition and has contributed to scattering transforms for invariant feature extraction in deep learning. His work bridges applied mathematics, harmonic analysis, and computational methods, with impactful applications in image processing, audio analysis, and quantum chemistry. Publicly listed articles span from foundational wavelet theory (1989) to recent advances in scattering networks (2020). His research trends emphasize geometric signal representations, sparse approximations, and interdisciplinary applications across physics and engineering. His CV and publications are available through institutional links, though specific awards or grants are not explicitly detailed in the provided texts. He collaborates extensively with researchers in mathematics and computer science, advancing theoretical frameworks with practical implementations.

Kuang-Yao Leeمشاهده پروفایل

دانشیار

• Statistical machine learning
• Statistical genomics
• Graphical modeling and causality learning
• +۲ مورد دیگر

Kuang-Yao Lee is an Associate Professor in the Department of Statistical Science at the Fox School of Business and Management, Temple University, holding a tenure-track appointment. Previously, he served as an associate research scientist at Yale University’s Center for Statistical Genomics and Proteomics, where he developed statistical methods for high-dimensional data and conducted collaborative research. Dr. Lee's educational background includes: PhD in Statistics from The Pennsylvania State University Master of Science in Mathematics from National Taiwan University Bachelor of Science in Mathematics from National Taiwan University His research centers on statistical genomics and machine learning with high dimensionality as a unifying theme. He develops advanced methodologies in graphical modeling, causality learning, high-dimension inference, and semi/non-parametric statistics. His work bridges theoretical statistics with practical applications in bioinformatics, image analysis, and functional data, often through collaborations with biologists and computer scientists on genome-wide association studies, gene regulatory networks, and pathway analysis. Dr. Lee's publication trajectory reveals a concentrated focus on functional data analysis and graphical models over the past five years. His contributions include foundational work on functional directed acyclic graphs, nonparametric functional graphical modeling, and sufficient dimension reduction in metric spaces. These innovations demonstrate consistent integration of causal inference frameworks with high-dimensional genomic applications, reflecting his dual expertise in statistical theory and biological data science.

Frederik Hartmannمشاهده پروفایل

استادیار مدعو

• Historical Linguistics
• Computational Linguistics
• Phonology
• +۳ مورد دیگر

Frederik Hartmann is an Adjunct Assistant Professor of Linguistics at the University of North Texas. His work bridges computational methods with historical and theoretical linguistics, focusing on language evolution, phonology, and phylogenetic analysis. He employs agent-based modeling, Gaussian processes, and deep neural networks to study sociolinguistic factors, diachronic changes, and phylogenetic relationships in languages like Germanic, Proto-Indo-European, and Spanish. Key research themes include: Computational modeling of language diversification Environmental influences on phonological systems Reconstruction of ancient languages (e.g., Proto-Indo-European laryngeals) Socio-geographical variation in language contact phenomena Recent work demonstrates methodological innovations in analyzing linguistic phylogenies and sociolinguistic effects, with applications to both modern and historically documented languages. His publication trends reflect a focus on interdisciplinary approaches, combining quantitative methods with traditional linguistic analysis to address foundational questions in historical linguistics. No major awards were explicitly mentioned in the provided text.

• Operations Research
• Logistics Optimization
• Heuristic Algorithms
• +۳ مورد دیگر

Associate Professor Dario Pacino is affiliated with the Technical University of Denmark (DTU), specifically within the Department of Technology, Management and Economics under the Operations Research Section. His research focuses on optimization challenges in logistics and transportation systems, including container stowage planning, berth allocation, and the application of AI techniques like reinforcement learning. He holds a position at DTU's Management Science Division and is based in Lyngby, Denmark. His work emphasizes heuristic and metaheuristic algorithms for solving complex logistics problems. Notable areas include Roll-on/Roll-off (RoRo) ship stowage planning, dynamic berth allocation, and discrete event simulation for healthcare logistics. He has contributed to mathematical models addressing draft limits in shipping and stochastic fleet optimization for urban mobility. Publications from 2022 onward highlight advancements in reinforcement learning integration with traditional operations research methods, demonstrating a trend toward AI-driven logistics solutions. His work bridges theoretical optimization with practical applications in maritime and healthcare sectors. No scientific awards are explicitly listed in the provided text. His advising and grants sections remain unspecified, though his research collaborations are evident through multi-institutional projects. He is part of the Operations Research Section's research team, focusing on decision support systems and optimization under uncertainty.

Russell C. Hardieمشاهده پروفایل

استاد

• Digital Signal Processing
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu

Mohamed Iskandaraniمشاهده پروفایل

استاد

• Oceanography
• Climate Modeling
• Data Assimilation
• +۳ مورد دیگر

Mohamed Iskandarani is a Professor in the Department of Ocean Sciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami. His research focuses on oceanographic modeling, data assimilation, and uncertainty quantification in geophysical fluid dynamics. He specializes in developing advanced numerical methods for climate and ocean circulation studies, including polynomial chaos frameworks, Gaussian process regression, and spectral element models. His work integrates machine learning techniques with traditional oceanography to improve predictions of submesoscale structures, storm surges, and hurricane impacts. Iskandarani has contributed to understanding Loop Current dynamics in the Gulf of Mexico, fish larval connectivity in Florida Keys, and the propagation of uncertainties in coupled atmosphere-wave-ocean systems. Key research areas include: (1) High-resolution ocean velocity field reconstruction using Lagrangian drifter data, (2) Quantifying uncertainties in biophysical and climate models, (3) Developing statistical interpolation codes for ocean forecasting, and (4) Exploring multiscale ocean-atmosphere interactions. His methods are applied to real-world challenges like hurricane forecasting, oil spill trajectory modeling, and coastal engineering design criteria. Collaborations involve institutions such as NOAA, NASA, and international oceanographic consortia. Published extensively in journals like Journal of Marine Science and Engineering and Monthly Weather Review , his work bridges computational science with environmental applications. Recent projects analyze submesoscale turbulence, drifter-based velocity field reconstructions, and ensemble-based statistical emulators for ocean current predictions.

Belinda Nicholsonمشاهده پروفایل

پژوهشگر ارشد

• Exoplanet Research
• Stellar Astrophysics
• Astrophysics
• +۳ مورد دیگر

Belinda Nicholson is a Vice-Chancellor's Postdoctoral Research Fellow for Women in STEMM at the University of Southern Queensland's School of Mathematics, Physics and Computing. She holds a BSc (2011) and MSc (2013) from the University of Melbourne and a PhD (2019) from USQ. Her research focuses on stellar magnetic activity, exoplanet detection, and astrophysical processes in young stars. She is affiliated with the Centre for Astrophysics and contributes to projects like the Terra Hunting Experiment and MINERVA-Australis. Her work includes studies on exoplanet dynamics, stellar magnetic fields, and radial velocity surveys. Research interests span exoplanet characterization, stellar activity modeling, and the interplay between stars and their planetary systems. She has published extensively on topics like super-Earth candidates, sub-Neptune systems, and magnetic field impacts on exoplanet detection. Nicholson collaborates internationally on projects involving telescopes like HARPS and TESS. Her articles highlight contributions to exoplanet discovery (e.g., HD 21520 b, HD152843 b/c) and advancements in stellar activity analysis. She also co-authored studies on white dwarf binaries and T Tauri star magnetism. Nicholson's work bridges observational astronomy with computational methods, such as Gaussian process modeling for noise reduction. As an academic, she serves as an associate supervisor for doctoral research on stellar magnetic behavior. Her lab affiliations include the Centre for Astrophysics, where she engages in collaborative research to advance understanding of stellar and planetary systems.

• Mechanical Engineering
• Fluid Mechanics
• Heat Transfer
• +۶ مورد دیگر

Dr. Khalid Saleh is a Senior Lecturer in Mechanical Engineering at the University of Southern Queensland's School of Engineering. He holds an MSc from Al-Nahrain University and a PhD from USQ. His research focuses on fluid mechanics, heat transfer, computational fluid dynamics (CFD), aerospace icing phenomena, renewable energy systems, and nanotechnology applications. He is affiliated with the Institute for Advanced Engineering and Space Sciences and has contributed to multiple international conferences and peer-reviewed journals. Dr. Saleh has supervised doctoral research on topics including ejector design optimization, composite materials, and ice crystal icing experiments. His work integrates experimental and numerical methods, often employing machine learning and advanced diagnostics like TDLAS. Notable research contributions include studies on radial ejectors, supersonic steam jets, and hybrid nanofluids. He collaborates on projects related to solar energy systems, hydrogen production challenges, and thermal management in aerospace applications. Dr. Saleh's experimental facilities include a specialized ice crystal icing wind tunnel and a hypersonic Ludwieg tube. While no specific awards are listed, his extensive publication record (over 40 outputs) reflects his impactful research in mechanical engineering and thermal sciences. His teaching and supervision roles emphasize hands-on experience with CFD tools, experimental validation, and interdisciplinary approaches to engineering challenges. Ongoing research explores sustainable energy systems and materials science innovations for thermal applications.

Loucas Pillaud-Vivienمشاهده پروفایل

پژوهشگر

• Machine Learning
• Stochastic Processes
• High-Dimensional Statistics
• +۳ مورد دیگر

Loucas Pillaud-Vivien is a researcher at the Applied Probability team within CERMICS (École des Ponts ParisTech), specializing in the high-dimensional stochastic dynamics underlying machine learning optimization algorithms. His work bridges statistical theory and computational methods, focusing on understanding algorithmic behavior in modern ML systems. Education : PhD from ENS/Inria Paris (2020) under Francis Bach; postdoctoral research at EPFL, New York University (Courant Institute), and the Simons Foundation (Flatiron Institute). Research interests include stochastic processes, optimization theory, and high-dimensional statistical learning. Key themes involve analyzing gradient-based methods, implicit regularization effects, and the interplay between computational efficiency and statistical accuracy. Recent publications emphasize topics such as gradient flow dynamics, variational inference, and spectral bias in neural networks. His work often addresses fundamental questions about algorithmic convergence and generalization in overparameterized models. Loucas is affiliated with the CERMICS laboratory, contributing to interdisciplinary projects at the intersection of applied mathematics and machine learning.

Shuangqing Weiمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• Network Security
• +۴ مورد دیگر

Shuangqing Wei is the Michel B. Voorhies Distinguished Professor and Division Chairman in the Division of Electrical & Computer Engineering at Louisiana State University (LSU). He holds a Ph.D. (2003) from the University of Massachusetts and is affiliated with the School of Electrical Engineering and Computer Science. His research focuses on wireless communication systems, including OFDMA networks, multiple antenna systems, and privacy-enhanced communication protocols. Key research interests include: Multi-agent reinforcement learning and cooperative strategies Privacy-preserving techniques in federated learning and data analysis Covert communication over AWGN channels Optimization algorithms for distributed systems Cognitive radio networks and spectrum management Recent work emphasizes communication efficiency in distributed optimization (e.g., ADMM with Gaussian process regression) and trade-offs between privacy and utility in machine learning systems. His publications span over two decades, addressing topics from information theory fundamentals to applied security in vehicular networks and social media systems. He maintains an active research group at LSU, exploring interdisciplinary challenges at the intersection of signal processing, machine learning, and network security. Labs/Teams : Active involvement in LSU's wireless communication and machine learning research initiatives, including collaborative projects on federated learning frameworks and secure multi-agent systems.

Jarad Niemiمشاهده پروفایل

استاد

• Statistical Science
• Bayesian Modeling
• Public Health Forecasting
• +۳ مورد دیگر

Jarad Niemi is a Professor of Statistics at Iowa State University (ISU), serving as Professor-in-Charge of the Data Science Program and Director of Graduate Recruiting and Admissions. He holds affiliations with STRIPS (Science-based Trials of Rowcrops Integrated with Prairie Strips) as Data Management Lead and C-CHANGE (Climate and Carbon Impact of Hydrogen in Agriculture) as Smart Technology Lead. His research focuses on Bayesian methods, statistical modeling for public health (e.g., influenza and COVID-19 forecasting), environmental sustainability (prairie strip impacts on soil and crops), and agricultural systems. Education: Ph.D. in Statistical Science (Duke University, 2009), M.S. in Statistics (University of Minnesota, 2005), B.Chem.Eng. in Chemical Engineering (University of Minnesota, 1999). Research interests include statistical inference, computational methods, and interdisciplinary applications in health and agriculture. He leads initiatives to integrate remote sensing and machine learning into agroecosystem modeling. His work on prairie strips demonstrates ecosystem services like sediment reduction. Niemi also contributes to open-source tools for statistical computing and education.