جست‌وجوی استاد و پژوهشگر

Arnab Maityمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Kernel Machine Regression
• Semiparametric Regression
• +۵ مورد دیگر

Arnab Maity is an Associate Professor in the Department of Statistics at North Carolina State University (NC State). His research focuses on functional data analysis, kernel machine regression, and semiparametric methods with applications in environmental epidemiology and epigenetics. He holds a Ph.D. in Statistics from Texas A&M University (2008), an M.S. from the same institution (2005), and a B.Stat. (Honors) from the Indian Statistical Institute (2003). Education: Ph.D. in Statistics, Texas A&M University, 2008 M.S. in Statistics, Texas A&M University, 2005 B.Stat. (Honors), Indian Statistical Institute, 2003 Research interests span functional data analysis, kernel methods, semiparametric inference, and their applications in genomic studies, environmental health, and epigenetics. He has contributed to statistical tools for analyzing high-dimensional gene-environment interactions and longitudinal data. Recent work includes advancements in robust kernel association testing and functional concurrent models. His publications reflect interdisciplinary collaboration, addressing challenges in genetic epidemiology, biostatistics, and environmental health. Notable grants include NIH-funded projects on imprint regulatory regions in childhood obesity and functional data integration in genomics. Awards include the ASA Noether Young Researcher Award (2014) and the Cavell Brownie Mentoring Award (2020). He has advised十余名博士生 and served on numerous editorial and review boards. Active in academic service, he chairs committees and organizes seminars on functional data analysis and statistical genetics.

Raktim Bhattacharyaمشاهده پروفایل

استاد

• Uncertainty Quantification
• Robust Control
• Nonlinear Systems
• +۳ مورد دیگر

Raktim Bhattacharya is a Professor in the Department of Aerospace Engineering at Texas A&M University, serving as Director of the Graduate Studies Program. He holds a Ph.D. in Aerospace Engineering from the University of Minnesota (2003) and a B.Tech from IIT Kharagpur (1996). His research focuses on uncertainty quantification, robust control, and nonlinear systems, with applications in aerospace systems design and control. Dr. Bhattacharya leads the Intelligent Systems Research Laboratory (ISRL), advancing algorithms for next-generation aerospace systems operating in uncertain environments. His work integrates optimal control, stochastic modeling, and data-driven methods to enhance system reliability and performance. Notable contributions include probabilistic robustness analysis, model validation frameworks, and sparse sensing architectures. Recent research trends emphasize optimal transport theory for state estimation, privacy-aware machine learning, and sensor-actuator co-design for resource-constrained systems. His publications span topics like UAV configuration optimization, LPV control frameworks, and invariant set estimation using physics-informed neural networks. Dr. Bhattacharya’s lab collaborates on projects involving tensegrity structures, cyber-physical systems, and space situational awareness. His work addresses challenges in hypersonic flight dynamics, celestial navigation, and resilient control under actuator degradation.

Jules van Binsbergenمشاهده پروفایل

استاد

• Corporate Finance
• Asset Pricing
• Macroeconomics
• +۷ مورد دیگر

Jules van Binsbergen is the Anthony L. Davis Director of the Joseph H. Lauder Institute for Management and International Studies and holds the Lauder Chair Professorship at the University of Pennsylvania. He is also the Nippon Life Professor of Finance at The Wharton School and currently a Visiting Professor at MIT Sloan School of Management. His research focuses on asset pricing, financial markets' macroeconomic interactions, and financial intermediaries' performance. He earned a PhD in Finance from Duke University and an MA in Financial Econometrics from Tilburg University, with an undergraduate degree in Jazz Piano from The Hague's Royal Conservatory. Prof. van Binsbergen’s work bridges theoretical and empirical finance. His current research explores the dynamics between financial systems and broader economic trends, including monetary policy impacts, environmental risks, and mutual fund management strategies. He co-hosts the podcast All Else Equal: Making Better Decisions and is affiliated with the NBER and CEPR as a research scholar. His academic trajectory includes teaching roles at Stanford Graduate School of Business and Northwestern’s Kellogg School. Beyond research, he addresses practical finance challenges such as risk-free rate calculation methodologies and the valuation of long-term liabilities. His contributions span asset pricing models, corporate finance, and the evaluation of financial professionals’ skill and incentives. Prof. van Binsbergen’s interdisciplinary approach integrates econometric rigor with real-world financial phenomena, emphasizing both theoretical innovation and policy-relevant insights.

Augusto Gerolinمشاهده پروفایل

استادیار

• Optimal Transport Theory
• Mathematical Physics
• Theoretical and Computational Chemistry
• +۴ مورد دیگر

Augusto Gerolin is an Assistant Professor jointly appointed in the Departments of Mathematics and Statistics and Chemistry and Biomolecular Sciences at the University of Ottawa. He holds a Tier II Canada Research Chair in Artificial Intelligence at the Interface of Chemistry and Mathematics. His research focuses on Optimal Transport Theory, Mathematical Physics, Theoretical and Computational Chemistry, and Machine Learning. Gerolin’s work bridges quantum chemistry, mathematical analysis, and computational methods, with applications in Density Functional Theory and quantum information science. He obtained his PhD from the University of Pisa and was a Marie Skłodowska-Curie fellow at Vrije Universiteit Amsterdam. He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and leads a research group exploring AI-driven solutions in chemistry and mathematics. His current projects include developing optimal transport frameworks for quantum systems, advancing machine learning algorithms in scientific computing, and fostering collaborations across disciplines through initiatives like the OQMG Network. Gerolin’s research has led to advancements in multi-marginal optimal transport, entropy-regularized methods, and the strong-interaction limit of density functional theory. His group actively collaborates with institutions worldwide, including the Fields Institute, IPAM, and the University of Genoa. He has supervised numerous PhD, Master’s, and undergraduate students, many of whom have contributed to cutting-edge studies in computational chemistry, quantum algorithms, and mathematical analysis. Key awards include the Canada Research Chair designation, and his work has been supported by grants from NSERC, MITACS, and the University of Ottawa. Gerolin is also committed to diversity in science, endorsing principles such as the Diversity Axioms, and advocates for academic solidarity with researchers affected by global conflicts.

Mathias Wellerمشاهده پروفایل

استاد

• Parameterized Algorithmics
• Genome Scaffolding
• Phylogenetic Networks
• +۳ مورد دیگر

Mathias Weller is a Professor and Chair of the AKT group at Université Gustave Eiffel (Paris), France. Previously, he held a Full-Time Researcher position at CNRS/LIGM (2018–2022) and postdoctoral roles at LIRMM (2013–2017). He earned his PhD in Theoretical Computer Science from TU Berlin (2012) and a Diploma in Computer Science from Friedrich Schiller University Jena (2009). His research focuses on Parameterized Algorithmics, Genome Scaffolding, Phylogenetic Networks, and Structural Parameterization. He has contributed extensively to algorithm design for computational biology, including preprocessing techniques and kernelization concepts. Weller’s work bridges theoretical computer science and applied bioinformatics, addressing challenges in phylogenetic analysis, genome assembly, and combinatorial optimization. His publications span algorithmic efficiency, network analysis, and complexity theory, with a focus on practical applications in genomics and phylogenetics.

Kirill Simonovمشاهده پروفایل

دانشیار

• Parameterized Complexity
• Algorithm Design
• Graph Theory
• +۳ مورد دیگر

Kirill Simonov is an Associate Professor in the Department of Informatics at the University of Bergen. His research focuses on parameterized complexity, algorithm design, and graph theory, with particular emphasis on clustering algorithms, graph modification problems, and algorithmic graph theory. He has contributed to foundational work in fair clustering, approximate algorithms for graph cycles, and structural analysis of sparse graphs. His notable contributions include studies on coresets for fair clustering, algorithmic extensions of Dirac's theorem, and techniques for building large k-cores from sparse graphs. His work is supported by the Research Council of Norway (Project 314528). He frequently collaborates with leading researchers like Fedor Fomin and Petr Golovach on topics such as parameterized algorithms and combinatorial optimization. Simonov's publications span venues like the Journal of Computer and System Sciences and Leibniz International Proceedings in Informatics. His research bridges theoretical computer science with practical algorithmic solutions for graph problems and data clustering challenges.

Pau Batlle Franchمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Applied Mathematics
• Uncertainty Quantification
• +۴ مورد دیگر

Pau Batlle Franch is a Research Fellow in the Computing and Mathematical Sciences Department at California Institute of Technology (Caltech), working with Professor Houman Owhadi. He holds a PhD from Caltech (June 2025) and was a research affiliate at NASA Jet Propulsion Laboratory (JPL). His research focuses on the intersection of statistics and applied mathematics, including frequentist confidence intervals in inverse problems, game-theoretical uncertainty quantification, and Gaussian processes. He has applied his work to domains like remote sensing, biology, earthquake prediction, and telecommunications engineering. Education : PhD in Computing and Mathematical Sciences (Caltech, 2025); Double undergraduate degree in Mathematics and Engineering Physics from Universitat Politècnica de Catalunya (CFIS program); Research visitor at NYU's Center for Data Science. His research interests include optimization-based statistical methods, Gaussian process frameworks for scientific computing, and uncertainty quantification in physical systems. Notable contributions include resolving the Burrus conjecture and developing computational hypergraph discovery techniques applied to NASA JPL projects. His work bridges theory and application, addressing challenges in ill-posed inverse problems and robust statistical inference. Recent activities include presenting at SIAM conferences and workshops on inverse problems in Earth science. His Gaussian process methods have been published in journals like PNAS and SIMODS, with applications ranging from PDE solving to RNA classification. Collaborations include JPL and the Groningen seismic study. Grants & Collaborations : Ongoing work with NASA JPL on lunar rover control and computational graph discovery; Seismic modeling in the Groningen gas field with epistemic/aleatoric uncertainty frameworks. He maintains an active GitHub profile showcasing projects in machine learning and scientific computing, including repositories like DarwinProjectAnalytics and emb4class .

Francois Trahayمشاهده پروفایل

دانشیار

• High Performance Computing
• Storage Systems
• Parallel and Distributed Systems
• +۲ مورد دیگر

François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.

Tianfeng Luمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Chemical Kinetics
• Turbulent Combustion
• +۳ مورد دیگر

Professor Tianfeng Lu is a faculty member in the School of Engineering at the University of Connecticut, where he joined as an Assistant Professor in 2008 and was appointed as the United Technologies Associate Professor of Engineering Innovation in 2016. His research focuses on computational fluid dynamics, combustion chemistry, and turbulent flow simulations. He earned his B.S. and M.S. in Engineering Mechanics from Tsinghua University and his Ph.D. in Mechanical and Aerospace Engineering from Princeton University. Dr. Lu's work emphasizes reducing complex chemical mechanisms for efficient simulations of multidimensional turbulent flows and engineering systems. His contributions include advancements in ignition dynamics, detonation modeling, and plasma-assisted combustion. Key projects involve exascale simulations through initiatives like PELE and collaborations on real-fuel combustion models for engines. His articles highlight breakthroughs in combustion diagnostics, engine efficiency, and pollutant reduction, with recent efforts addressing hydrogen-methane mixtures and low-temperature combustion strategies. Awards include his endowed chair position, reflecting recognition of his impactful contributions to combustion science.

David S. Wackمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Biomedical Engineering
• Brain Research
• +۵ مورد دیگر

David S. Wack is an Associate Professor at the University at Buffalo's Jacobs School of Medicine & Biomedical Sciences, Department of Radiology. His research focuses on medical image analysis and neuroimaging, particularly in auditory processing and parameter estimation from PET, MRI, and CT images. He has held academic positions since 1992, progressing from instructor roles to his current rank. His work includes NIH-funded projects on imaging technologies and collaborations with institutions like Canon Medical Systems and UBNS. Education: PhD in Communicative Disorders and Sciences (SUNY Buffalo, 2010) MA in Applied Mathematics (SUNY Buffalo, 1992) BA in Applied Mathematics and Music Performance (SUNY Buffalo, 1989) Research Interests: Development of parametric maps from medical images Machine learning applications in neuroimaging Neuroimaging of auditory and language processing Dynamic image noise reduction and segmentation algorithms Neural markers of top-down compensation in speech processing Recent Projects: NIH-funded self-collimating SPECT breast tomography system 4D flow MRI for intracranial atherosclerotic disease assessment Machine learning for neuroimage classification Grants & Service: Member of Jacobs School faculty council and UB Faculty Student Association board Editorial roles at Frontiers in Neuroscience (Brain Imaging Methods, Decision Neuroscience) Labs & Teams: Buffalo Neuroimaging Analysis Center Collaborations with UBNS, Canon Medical, and VA Western New York Health Care System

• Cognitive Neuroscience
• Human Physiology
• Sensorimotor Integration
• +۳ مورد دیگر

Luciano Simone is an Associate Professor at the Department of Medicine and Surgery, University of Parma, Italy. He teaches Human Physiology and Research Methods in Cognitive Neuroscience across multiple degree programs, including Biomedical Laboratory Techniques, Medicine and Surgery (6-year program), and Translational Biomedical Sciences.

Dan Steinbergمشاهده پروفایل

عضو هیئت علمی

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +۷ مورد دیگر

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

• Differential Geometry
• Kähler Geometry
• Symplectic Geometry
• +۲ مورد دیگر

Michela Zedda is an Associate Professor at the Department of Mathematical, Physical and Computer Sciences of the University of Parma. Her research focuses on differential and symplectic geometry, particularly in Kähler metrics, Sasakian manifolds, and geometric quantization. Department: Mathematical, Physical and Computer Sciences (University of Parma) Academic Rank: Associate Professor Email: michela.zedda@unipr.it Her work explores the interplay between Kähler and symplectic structures, with key contributions to projectively induced metrics, immersions into complex space forms, and the geometry of Cartan-Hartogs domains. Recent publications (2024–2025) address scalar flat metrics on line bundles and symplectic cones over Sasakian manifolds. Zedda's research spans geometric analysis, including the Yamabe problem, Ricci solitons, and stability under Lie group actions. She has extensively studied diastasis functions, TYZ expansions, and balanced metrics in both Cartan and Hartogs domains. Teaching appointments include Geometry courses for Mathematics and Management Engineering students at the University of Parma (2022–2025) and previous roles in Mathematics and Dental Medicine programs. No scientific awards or advisees are documented in the provided texts.

Sascha Sporsمشاهده پروفایل

استاد

• Spatial Audio Processing
• Signal Processing
• Medical Signal Processing
• +۴ مورد دیگر

Sascha Spors is a Professor at the Chair of Signal Theory and Digital Signal Processing within the Institute of Communications Engineering at the University of Rostock. His work spans spatial audio processing, medical signal processing, and data-driven methodologies. Chair of the AES Technical Committee on Spatial Audio Ombudsperson at the University of Rostock Research focuses on spatial sound capture/synthesis, perception of synthetic sound fields, and biomedical applications like implant monitoring via electrical impedance tomography. He contributes to open science initiatives and research data management frameworks. Key collaborations include memberships in the IEEE Signal Processing Society, Deutsche Gesellschaft für Akustik (DEGA), and Audio Engineering Society (AES).

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.