جست‌وجوی استاد و پژوهشگر

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

Nicole A. Benedekمشاهده پروفایل

دانشیار

• Materials Theory
• Computational Materials Science
• Complex Oxides
• +۹ مورد دیگر

Nicole A. Benedek is an Associate Professor in the Department of Materials Science and Engineering at Cornell University, part of the College of Engineering. Her research group focuses on theoretical and computational approaches to understanding and designing functional materials, particularly complex oxides and perovskites. She integrates principles of crystal chemistry, symmetry, and density functional theory to uncover mechanisms underlying material properties and to guide the discovery of new materials with targeted functionalities. Her research interests include nonlinear phononics, ultrafast optical control of lattice dynamics, ferroelectricity, magnetism, and thermal transport in materials. She is particularly interested in how materials behave out of equilibrium and how external stimuli such as light can induce dramatic changes in their properties. This work has implications for low-power electronics, data storage, and dynamic optical devices. The recent publications from her group reflect a strong trend in controlling material symmetries and properties using light, especially through infrared and Raman resonant excitation. Her work bridges theory and experiment, often in collaboration with synthetic chemists, to validate predictions and discover new polar and multiferroic materials. She has made key contributions to understanding negative thermal expansion, light-induced phase transitions, and hybrid improper ferroelectricity. Scientific Awards: NSF CAREER Award, National Science Foundation (2015) Ralph E. Powe Junior Faculty Enhancement Award (2014) Journal of Materials Chemistry Emerging Investigator (2016) Australian Postgraduate Award (2003) Dr. Benedek advises graduate students in materials science and engineering and has mentored PhD candidates such as Ethan T. Ritz and Tucker Swenson. Her research is supported by the National Science Foundation (including the MRSEC program), the Department of Energy, and the Cornell Center for Materials Research. She leads the Benedek Group, which develops theoretical frameworks to explain and predict material behavior, emphasizing design rules for next-generation functional materials. The Benedek Group collaborates extensively with experimentalists, notably with Michael A. Hayward at Oxford University, to synthesize and characterize predicted materials. Their joint work has led to the discovery of new ferroelectric Dion-Jacobson phases and a deeper understanding of polar distortions in layered perovskites. The group combines computational modeling with physical insight to push the boundaries of materials design.

Andrew Ngمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

• Supply Chain Risk Management
• Blockchain Technology
• Sustainability & Environmental Initiatives
• +۶ مورد دیگر

Srinivas (Sri) Talluri is the John Hoagland - Richard Metzler Professor in Purchasing and Supply Management at the Department of Supply Chain Management, Michigan State University (Broad College of Business). He holds the title of DSI Fellow and served as the faculty director of the Axia Institute, focusing on advancing supply chain research and education. His work emphasizes supply chain resilience, risk management, and sustainability through innovative applications of blockchain, generative AI, and quantum computing. Dr. Talluri's research spans demand-supply uncertainty modeling, sustainable supply chain design, and strategic sourcing. Notable contributions include frameworks for blockchain integration in logistics, eco-efficient vaccine distribution during pandemics, and data-driven optimization for remanufacturing systems. His work frequently addresses macroeconomic disruptions, geopolitical risks, and environmental compliance under carbon tax schemes. His scholarly recognition includes the Fulbright-Hanken Distinguished Chair (2016-2017) and top 100 Operations Management scholar rankings. His research has been published in leading journals such as International Journal of Production Research and IEEE Transactions on Engineering Management . Key Professional Contributions: Over 140 peer-reviewed articles, leadership in supply chain education, and industry collaborations addressing global supply chain challenges. Labs/Initiatives: Active in the Axia Institute, focusing on supply chain innovation and policy.

Tharindu P. De Alwisمشاهده پروفایل

استادیار

• High-dimensional Multivariate Statistics
• Sufficient Dimension Reduction
• Dimension Reduction
• +۹ مورد دیگر

Dr. Tharindu P. De Alwis is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is actively engaged in teaching and research, with a focus on high-dimensional data analysis and machine learning applications. Ph.D. in Mathematics (Statistics), Southern Illinois University Carbondale M.S. in Mathematics, Southern Illinois University Carbondale B.Sc. in Statistics and Operations Research, University of Peradeniya, Sri Lanka His research centers on dimension reduction techniques, particularly Sufficient Dimension Reduction (SDR), envelope methods, and their applications in multivariate time series and spatial-temporal data. He integrates deep learning and neural networks into statistical modeling, with recent work on stacking-based deep neural networks and Fourier-based SDR methods. His work bridges statistical theory with practical machine learning applications in complex datasets. His recent publications (2021–2024) demonstrate a strong focus on developing innovative statistical and machine learning methods for time series and high-dimensional regression. Key themes include nonlinear modeling, dimension reduction, R package development, and AI-augmented reliability analysis. These works reflect interdisciplinary applications in engineering, data science, and systems safety. Dr. De Alwis has presented his research at national academic conferences in the USA and has publications in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety , as well as preprints on arXiv and software on CRAN. He teaches a variety of courses including Precalculus with Trigonometry, Linear Algebra, Applied Statistics, and Data Science. While no formal advisees or grants are mentioned, his active publication record suggests ongoing research mentorship and scholarly engagement. He previously served as a post-doctoral scholar at Worcester Polytechnic Institute before joining UWF.

Elena Tuzhilinaمشاهده پروفایل

استادیار

• Machine Learning
• Applied Statistics
• Computational Biology
• +۵ مورد دیگر

Elena Tuzhilina is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, specializing in machine learning, applied statistics, and computational biology. Her research focuses on statistical tools for chromatin 3D spatial structure reconstruction and analyzing emotional disorders' impact on brain function. Ph.D. in Statistics from Stanford University Specialist's degree from Moscow State University Two-year Data Science program at Yandex School Her research spans high-dimensional data analysis , dimension reduction , and statistical modeling in biological contexts. She has developed novel algorithms for chromatin conformation reconstruction and pandemic trajectory modeling. Recent publications focus on canonical correlation analysis , low-rank matrix approximation , and 3D genome architecture , with applications in computational biology and neuroscience. Dorothy Shoichet Women Faculty in Science Award JSM Student Travel Award Outstanding Teaching Assistance at Stanford Stanford Teaching Assistant Award Elena supervises PhD students and postdoctoral fellows across disciplines including statistical sciences, biochemistry, and applied mathematics. She has secured multiple grants including a NSERC Discovery Grant and University of Toronto Accelerator Grant .

Johannes Soedingمشاهده پروفایل

پژوهشگر

• Computational Biology
• Bioinformatics
• Protein Structure Prediction
• +۹ مورد دیگر

Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.

Pierre Bellecمشاهده پروفایل

دانشیار

• High dimensional statistics
• Aggregation of estimators
• Shape constrained problems in statistics
• +۴ مورد دیگر

Pierre Bellec is an Associate Professor in the Department of Statistics at Rutgers University, where he has been a faculty member since 2016 and was promoted to Associate Professor with tenure in 2021. His office is located in Hill Center 406 at 110 Frelinghuysen Road, Piscataway, NJ 08854. Dr. Bellec received his PhD from ENSAE ParisTech, France in 2016 under the supervision of Alexandre Tsybakov. Prior to that, he completed a Part III (MASt) at the University of Cambridge, UK in 2012 and earned his Diplôme d'Ingénieur from Ecole Polytechnique, France in 2011. Dr. Bellec's research focuses on high-dimensional statistics, aggregation of estimators, shape constrained problems in statistics, and probability theory. His work has significant implications for machine learning and statistical inference in high-dimensional settings. He has made important contributions to regularization methods, M-estimators, and uncertainty quantification in complex statistical models. His recent publications show a strong focus on asymptotic theory, robust statistics, and high-dimensional inference, with novel methods for error estimation, adaptive tuning, and bias correction in regularized estimators. Dr. Bellec has received several prestigious awards and honors, including: IMS Fellow (2023) NSF CAREER award DMS 1945428: "Post-Differentiation Inference" (2020-2024, $400,000) NSF award DMS 2413679: "Uncertainty quantification for iterative algorithms" (2024-2027, $225,000) NSF award DMS 1811976: "Uncertainty Quantification in High-Dimensional Structured Regression Problems" (2018-2022, $180,000) Blaise Pascal PhD Award (2017) Dr. Bellec has advised several graduate students, including Takuya Koriyama (now at Chicago Booth), Yiwei Shen (now at Meta/Facebook), and Kai Tan (current PhD student). He has also mentored numerous undergraduate students through REU programs. He serves as an Associate Editor for the Annals of Statistics and has been involved in program committees for major conferences including the Conference on Learning Theory (COLT) and the Conference on Neural Information Processing Systems (NeurIPS).

Theodore Kypraiosمشاهده پروفایل

استاد

• Bayesian Inference
• Statistical Methodology
• Infectious Disease Modeling
• +۵ مورد دیگر

Theodore Kypraios is a Professor of Statistics at the School of Mathematical Sciences, University of Nottingham, United Kingdom. He serves as Course Director for the MSc in Statistics and MSc in Statistics with Machine Learning programmes, and as Head of the Statistics and Probability Section. Kypraios joined the University of Nottingham in September 2006 as a Research Fellow and was appointed as Lecturer in 2008. His research focuses on developing novel statistical methodology for Bayesian inference and model selection for high-dimensional complex data, with particular emphasis on stochastic epidemic models and infectious disease outbreak data. Kypraios has made significant contributions to Bayesian nonparametric methods, as evidenced by his PNAS publication on heterogeneously mixing infectious disease models which enables more data-driven approaches to understanding transmission mechanisms without strict parametric assumptions. Kypraios has been actively involved with the Royal Statistical Society, serving as Chair of the Computational Statistics and Machine Learning Section until December 2020, and currently sits on the Academic Advisory Group committee. He has presented his work at major conferences including the International Symposium for Bayesian Analysis and the Bayesian Inference for Stochastic Processes Workshop. As an educator, Kypraios has taught Statistical Inference and Data Analysis and Modelling modules at Nottingham, and has been an instructor for the 'MCMC II for Infectious Diseases' module at the Summer Institute in Statistics and Modeling in Infectious Diseases since 2010. His teaching emphasizes both classical and Bayesian approaches to statistical inference with applications to real-world problems. He has supervised PhD students including Dr. Rowland Seymour, whose thesis work formed the basis of the PNAS publication. His research has practical implications for understanding disease transmission mechanisms, as demonstrated by his analysis of the 2001 UK foot and mouth disease outbreak, and contributes to the development of more effective disease control strategies.

Sameer Deshpandeمشاهده پروفایل

استادیار

• Bayesian hierarchical modeling
• Bayesian regression trees
• Causal inference
• +۳ مورد دیگر

Sameer Deshpande is an Assistant Professor in the Department of Statistics at the University of Wisconsin–Madison. His research bridges Bayesian methodology development with applications in public health and sports analytics. Prior to joining UW–Madison, he completed a postdoctoral fellowship with Professor Tamara Broderick at MIT and earned his Ph.D. in Statistics from the Wharton School under Professors Ed George and Veronika Rockova. His educational background includes undergraduate studies in mathematics at MIT and a year at Jesus College, Cambridge through the Cambridge-MIT Exchange program. His research focuses on advancing Bayesian hierarchical modeling, treed regression, and causal inference techniques, with particular emphasis on flexible tree-based methods like BART variants for complex data structures. Deshpande's recent publications reveal a strong trend toward developing scalable Bayesian methods for high-dimensional data while maintaining rigorous uncertainty quantification. His work frequently applies these techniques to sports analytics (particularly baseball and football) and public health studies examining long-term effects of adolescent sports participation. The consistent focus on methodological innovation paired with substantive applications demonstrates his dual commitment to statistical theory and real-world impact. He actively mentors graduate students at UW–Madison, requiring STAT 775 as preparation for research collaboration. His Deshpande Lab focuses on Bayesian computation and causal inference, though specific grant details are not publicly listed. Notable projects include the NFL Big Data Bowl submission analyzing quarterback decision-making using Expected Hypothetical Completion Probability. Outside academia, Deshpande maintains interests in cooking, cocktail making, and photography, while remaining a devoted fan of Dallas sports teams – often seen wearing a Texas belt buckle.

Martin Aumüllerمشاهده پروفایل

مدرس

• Algorithms
• Algorithm engineering
• Algorithmic privacy
• +۶ مورد دیگر

Martin Aumüller is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen . He serves as Head of Education and Master of Software Design , focusing on algorithm engineering, differential privacy, and similarity search. Research interests include: Algorithm engineering for high-dimensional data Locality-sensitive hashing and nearest neighbor search Privacy-preserving machine learning Fairness in approximate search algorithms Benchmarking and evaluation of similarity search tools Publications trends highlight his work on approximate nearest neighbor search , privacy-preserving techniques , clustering algorithms , and scalable outlier detection in high-dimensional spaces. His recent projects (2024-2025) focus on fairness, differential privacy, and efficient indexing. Grants and projects : DIREC (2020-2025): Digital Research Centre Denmark (Innovation Fund Denmark) DIREC: Bias and Benefit of Approximate Nearest Neighbor Search (2022-2025): Principal Investigator (Innovation Fund Denmark) BARC (2017-2024): Basic Algorithms Research Copenhagen (Villum Fonden) SSS (2014-2019): Scalable Similarity Search (European Commission)

Leif Kobbeltمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +۳ مورد دیگر

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Jian Kangمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +۱۱ مورد دیگر

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

• Computational Imaging
• Computational Photography
• Neuroimaging
• +۷ مورد دیگر

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

Olga Kurasovaمشاهده پروفایل

استاد

• Machine Learning
• Data Visualization
• Cybersecurity
• +۳ مورد دیگر

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.