جست‌وجوی استاد و پژوهشگر

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.

Qin Luمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Communications
• +۳ مورد دیگر

Qin Lu is an Assistant Professor at the University of Georgia's School of Electrical & Computer Engineering. Her research focuses on robust and adaptive Bayesian methods for decision-making using sequential/networked data, including online Gaussian processes, Bayesian optimization, reinforcement learning, and spatio-temporal inference over graphs. Applications span autonomous systems and mobile edge computing. Education details are not explicitly listed in the provided text. Her work emphasizes scalability and uncertainty quantification, with contributions to edge computing resource management, active learning strategies, and multi-agent systems in IoT. Recent publications highlight advancements in Gaussian process dynamical modeling, adaptive Bayesian optimization, and ensemble learning techniques for online systems. No scientific awards are mentioned in the provided materials. Advising and grants details are not provided, though her research indicates collaborations in autonomous systems, underwater acoustics, and sensor networks. She is affiliated with the Boyd GSRC building and reachable at Qin.Lu@uga.edu.

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 .

Cesar Ruizمشاهده پروفایل

استادیار

• Metal Additive Manufacturing
• Process-Informed Machine Learning
• Quality Control
• +۳ مورد دیگر

Cesar Ruiz is an Assistant Professor in the Department of Industrial & Systems Engineering at The University of Oklahoma. His research focuses on integrating domain knowledge with machine learning and stochastic modeling for engineering applications, particularly in metal additive manufacturing, predictive maintenance, and quality control. He holds a Ph.D. and M.S. in Industrial Engineering from the University of Arkansas and a B.A. in Business Engineering from the Higher School of Economics and Business in El Salvador. His research domains include metal additive manufacturing processes, process-informed machine learning frameworks, and reliability evaluation in complex systems. Notable works address challenges in layer segmentation for quality assessment, predictive maintenance strategies, and degradation-based reliability analysis. Ruiz has received prestigious awards such as the RAMS Golomski Award (2020, 2022), ASQ Best Reliability Paper (2021), and IEEE CASE Best Conference Paper (2021). His affiliations include SRE, INFORMS, and ASME. His research spans aerospace and defense systems, with recent publications emphasizing advanced manufacturing techniques, functional data analysis, and Bayesian modeling for reliability growth. He collaborates on projects involving biosensor optimization and large-scale system maintenance frameworks.

Elizabeth Chinمشاهده پروفایل

استادیار

• Causal Inference
• Algorithmic Fairness
• Machine Learning
• +۱۲ مورد دیگر

Elizabeth Chin is an Assistant Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. Her work bridges biostatistics, causal inference, and public health, with a strong focus on equity and ethical decision-making systems. PhD in Biomedical Data Science, Stanford University (2022) BS in Applied Mathematics, UCLA (2017) Her research interests include causal inference, algorithmic fairness, machine learning, and their application to public health challenges such as health inequities, environmental justice, and policy evaluation. She develops statistical methodologies to improve the robustness and fairness of data-driven decisions, particularly in vulnerable populations including incarcerated individuals. Elizabeth Chin's recent publications span high-impact areas such as mental health care disparities, forensic statistics, environmental lead exposure, and pandemic response. Her work often appears in top journals like Nature Machine Intelligence , NEJM , JAMA Pediatrics , and PNAS . A key trend across her articles is the use of advanced statistical methods to address structural injustices and inform equitable policy. National Science Foundation Graduate Fellowship Stanford Graduate Fellowship She has advised and collaborated with leading researchers in biostatistics and public health. Her work has received significant media attention and has been referenced in policy discussions, indicating real-world impact. Elizabeth Chin is actively contributing to national conversations on data ethics, health equity, and scientific rigor in public health.

• Survival Analysis
• Spatio-Temporal Modeling
• Bayesian Statistics
• +۷ مورد دیگر

Maria Antonia Barceló Rado is a Professor at the Department of Economics, University of Girona, and a member of the Research Group on Statistics, Econometrics and Health (GRECS). She is also affiliated with the Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) and collaborates with the Centre de Recerca en Economia i Salut (CRES) at Pompeu Fabra University. Her research spans methodological advancements in survival analysis, spatio-temporal modeling, Bayesian statistics , and applied work in clinical epidemiology, environmental epidemiology, cardiovascular risk factors, chronic diseases , and cancer epidemiology . She has developed a mixed-methods line combining Real World Data with Real World Evidence for epidemic prevention and detection. Licenciate in Mathematics (Universitat Autònoma de Barcelona, 1995) PhD in Mathematics (University of Girona, 2001) Accredited as Catedràtica d’Universitat (ANECA, 2018) Her recent publications focus on environmental stressors , air pollution effects , and spatio-temporal modeling across Spain. Articles address mental health in adolescents , vaccination equity , Covid-19 impacts , and chronic disease epidemiology . Collaborations include institutions like Johns Hopkins University, Pompeu Fabra University, and hospitals in Bellvitge and Barcelona.

Cameron McLemanمشاهده پروفایل

دانشیار

• Algebraic Number Theory
• Elliptic Curves
• Class Field Tower Problem
• +۴ مورد دیگر

Cameron McLeman is an Associate Professor of Mathematics at the College of Innovation and Technology , University of Michigan-Flint. His academic career spans roles including Division Director and Department Chair , with a focus on algebraic number theory , elliptic curves , and data science . Ph.D. in Mathematics, University of Arizona (2008) B.Sc. in Mathematics, Harvey Mudd College (2002) His research bridges algebraic number theory with data science , emphasizing pedagogical innovations. Recent work explores heavenly elliptic curves , class field tower problems , and network dynamics , while contributing to graph theory and K–12 mathematics education . Selected publications highlight trends in imaginary quadratic fields , graph invertibility , and mixing times for stochastic processes. His Teaching Excellence Award (2018) underscores a commitment to integrating linear algebra and statistics into real-world modeling.

Yoav Zemelمشاهده پروفایل

مدرس

• Geometrical statistics
• Point processes
• Shape theory
• +۳ مورد دیگر

Yoav Zemel is a Lecturer at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and Department of Mathematics . He specializes in statistical aspects of optimal transport and related geometric methods. B.Sc. Mathematics & Economics, Hebrew University of Jerusalem (summa cum laude, 2010) M.Sc. Applied Mathematics, EPFL (2012) PhD Mathematical Statistics, EPFL (2017), advised by Victor M. Panaretos His research focuses on geometrical statistics , point processes , shape theory , and optimal transportation . Recent work explores covariance operators, Gaussian processes, and stochastic algorithms in Wasserstein spaces. Publications bridge theoretical advancements with applications in ecology, genetics, and machine learning. Scientific awards include the Robert May Prize , Swiss government scholarship , and multiple Hebrew University honors. He has taught courses on probability, statistical machine learning, and optimal transport at EPFL, Göttingen, and Cambridge.

• High-Frequency and Algorithmic Trading
• Stochastic Games between Takers and Makers of Liquidity
• Mathematical Finance
• +۲ مورد دیگر

Prof. Leandro Sanchez Betancourt is a faculty member at the University of Oxford , affiliated with the Mathematical Institute . His work bridges Mathematical Finance and Stochastic Games , with a focus on high-frequency trading and market microstructure. He has been recognized with prestigious awards, including the Bruti Liberati Prize for Quantitative Finance research and the Gabino Barreda medal from UNAM. Education Doctor of Philosophy (DPhil) Master of Science (MSc) Bachelor of Science (BSc) Research Contributions Prof. Sanchez Betancourt’s research addresses critical challenges in algorithmic trading , latency impacts , and toxic order flow . His recent publications explore Nash Equilibrium in broker-trader dynamics, Mean Field Games for informed trading, and innovative applications of Lévy-Ito Processes and Quantum Measurement in financial modeling. These works highlight his expertise in integrating advanced mathematics with real-world trading strategies. Scientific Awards Bruti Liberati Prize for best PhD thesis in Quantitative Finance Best overall performance student award from King’s College London Gabino Barreda medal from Universidad Nacional Autónoma de México Labs and Teams He is a member of the Mathematical and Computational Finance research group at the University of Oxford, collaborating with leading experts like Álvaro Cartea, Sebastian Jaimungal, and others.

Milos Nikolicمشاهده پروفایل

مدرس

• Databases
• Data Management
• Incremental Computation
• +۷ مورد دیگر

Milos Nikolic is a Lecturer in Database Systems at the School of Informatics, University of Edinburgh. He is a member of the Database Group and the Laboratory for Foundations of Computer Science. Prior to joining Edinburgh, he was a Departmental Lecturer in the Department of Computer Science at the University of Oxford. He holds a PhD in Computer Science from EPFL. Research Interests: Milos Nikolic's research focuses on databases and large-scale data management, with emphasis on incremental computation, in-database learning, stream processing, and query compilation. His work explores how complex analytical queries—such as SQL, linear algebra, and machine learning tasks—can be efficiently maintained and evaluated in dynamic and distributed environments. He develops novel techniques in query optimization and compilation to enable real-time analytics over evolving data. His recent publications demonstrate a strong trend in dynamic query evaluation, particularly around conjunctive and hierarchical queries under updates, incremental view maintenance (e.g., F-IVM), and scalable processing of nested and biomedical data. His work often leverages theoretical foundations to achieve worst-case optimal performance, grounded in conjectures like the Online Matrix-Vector Multiplication (OMv) hypothesis. Scientific Awards: Best Paper Award, ICDT 2019 Advising and Grants: Milos is actively seeking PhD students to work on data management topics. While no formal list of advisees is provided, he collaborates extensively with researchers such as Dan Olteanu, Ahmet Kara, and Haozhe Zhang. His projects, including Adaptive Query Processing , Incremental Maintenance of Complex Analytics , and Declarative Data Pipelines (industry-supported), suggest ongoing grant and industry collaborations. Labs and Teams: He is a key member of the Database Group and the Laboratory for Foundations of Computer Science at the University of Edinburgh, contributing to cutting-edge research in foundational and applied database systems.

• Structural integrity of materials
• Advanced manufacturing techniques
• Fracture mechanics
• +۷ مورد دیگر

Reza Talemi is a Professor and Head of the Elooi Research Laboratory within the SCALINT Division at the Department of Materials Engineering (MTM), Faculty of Engineering Technology, KU Leuven, Belgium. His research focuses on structural integrity of materials fabricated through advanced manufacturing techniques, with particular expertise in impact dynamics, fracture mechanics, and fatigue analysis of metallic materials. His educational background shows extensive specialization in materials science and engineering, with research focusing on structural integrity assessment of advanced materials. His work bridges experimental and numerical approaches to understand material behavior under various loading conditions. Talemi's research interests span structural integrity of advanced materials, tribo-mechanical fracture, innovative testing methods for material behavior assessment, and advanced numerical modeling of material failure. His work has significant applications in aerospace, energy, and manufacturing sectors where material integrity is critical for safety and performance. His recent publications demonstrate a strong focus on additive manufacturing technologies, particularly examining fretting fatigue behavior of additively manufactured components, residual stress characterization, and microstructural analysis. The research shows consistent advancement in understanding how advanced manufacturing processes affect material properties and structural performance. Among his notable contributions are development of specialized testing apparatus for evaluating material performance in dovetail joint configurations, advanced numerical modeling techniques for predicting material behavior, and innovative approaches to characterize material integrity under complex loading conditions. Professor Talemi actively supervises numerous PhD students and leads multiple research projects, including 'Precise advanced material processing via controlled fatigue fracture' and 'Hybrid Experimental and Numerical Framework for Monitoring Structural Integrity in Aerospace Structures,' demonstrating his leadership in both academic and applied research domains.

Vincent Sitzmannمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Scene Representations
• +۷ مورد دیگر

Vincent Sitzmann is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS), where he leads the Scene Representation Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on building machines that learn to understand and interact with the world autonomously through 'world models' - mental simulators that enable agents to predict environmental outcomes and the consequences of their actions. His educational background includes a PhD from Stanford University under Gordon Wetzstein and a Bachelor's degree from the Technical University of Munich. Sitzmann's research spans computer vision, graphics, and robotics, with pioneering contributions to neural scene representations. He introduced Scene Representation Networks (SRNs) that enable continuous 3D-structure-aware scene modeling from 2D images. His work on implicit neural representations with periodic activation functions has become foundational to the field. Recent research focuses on scaling 3D reconstruction techniques, improving generative models for visual content, and developing methods for robot control through neural Jacobian fields. His approach emphasizes both theoretical rigor and practical applications across multiple domains. His publication record shows a clear progression toward more sophisticated diffusion models applied to video generation, robotics, and 3D reconstruction. The 2025 Nature paper on robot control via Jacobian fields demonstrates his expanding influence beyond traditional computer vision into robotics. His work consistently bridges theoretical advances with practical implementations, as evidenced by the CVPR 2023 Best Paper Runner-Up for pixelSplat, which offers scalable solutions for 3D reconstruction. His notable scientific achievements include: CVPR Best Paper Runner-Up (2023) for 'pixelSplat' Multiple papers with 'Spotlight' or 'Oral' presentations at NeurIPS and CVPR 2023 Amazon Research Award for '2D and 3D Animation via Image-Conditional Generative Flow Models' NeurIPS Outstanding New Directions Honorable Mention (2019) As leader of the Scene Representation Group, Sitzmann mentors researchers working at the intersection of computer vision, graphics, and AI. The group has secured funding from prestigious sources including Amazon Research Awards. Their work has practical applications in virtual reality, robotics, and content creation industries. Sitzmann teaches advanced courses at MIT, including 'Advances in Computer Vision' (6.8300). The Scene Representation Group focuses on developing novel methods for 3D scene understanding and manipulation. Current projects include research on neural radiance fields, diffusion models for 3D content creation, and methods for autonomous scene understanding. The group maintains active collaborations with industry partners and academic institutions to advance visual computing research.

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).

• Time Series Analysis
• Statistics
• Regression Modeling
• +۳ مورد دیگر

Rongning Wu is an Associate Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, City University of New York . Holding a Ph.D. and M.S. in Statistics from Colorado State University and a B.S. in Applied Mathematics from Southeast University, Dr. Wu specializes in advanced statistical modeling with particular focus on time series analysis and count data models. Education : Ph.D./M.S. in Statistics (Colorado State University), B.S. in Applied Mathematics (Southeast University) Current Roles : Associate Professor, Committee Chair (Statistics Faculty Recruiting), Co-organizer (Department Research Seminar Series) Dr. Wu's research explores robust statistical methodologies for complex time series data, including Least Absolute Deviation estimation , single-index models with time series errors , negative binomial models for count series , and tail-trimmed absolute deviation techniques . His work addresses challenges in parameter estimation, variance modeling, and structural change detection for both finite and infinite variance processes. Recent publications highlight advancements in change-point estimation for count time series , conditional maximum likelihood for INGARCH models , and semiparametric approaches for discrete-valued processes . Dr. Wu's methodological contributions find applications in financial data analysis and public health research, particularly in modeling HIV transmission dynamics related to incarceration. Scientific Honors : Honored Faculty, Baruch College (2009-2011) IMS Travel Award (2009) James L. Madison Memorial Award (2004) Franklin A. Graybill Award (2003) As an active researcher, Dr. Wu has secured multiple PSC-CUNY grants (2008-2022) totaling over $52,000 for projects spanning time series estimation, regression modeling, and empirical likelihood methods. He has served on various academic committees including the ZSB Graduate Curriculum Committee and the Statistics Faculty Recruiting Committee, while maintaining editorial reviewing roles for 15+ international journals.

• Carbon Materials
• Biochar
• Activated Carbon
• +۷ مورد دیگر

Mauro Giorcelli is an Associate Professor at Politecnico di Torino, Italy, affiliated with the Department of Applied Science and Technology (DISAT). He holds a PhD in Physics (2009) and previously earned a degree in Electronic Engineering (2004). Dr. Giorcelli is a carbon materials specialist with over 10 years of experience, focusing on low-cost carbon materials derived from recycled biomasses, particularly biochar. His research interests span carbon materials, biochar, activated carbon, biopolymers, and Raman spectroscopy. Dr. Giorcelli's work falls under the scientific branch of Experimental Physics of Matter and Applications (PHYS-03/A) with ERC sectors in environmental chemistry, spectroscopic techniques, and surface science. His research aligns with multiple UN Sustainable Development Goals including clean water, industry innovation, sustainable cities, responsible consumption, and climate action. Dr. Giorcelli's publication record shows a consistent focus on biochar applications, particularly in water purification, composite materials, and electromagnetic properties. His most recent work (2023-2025) demonstrates increasing sophistication in biochar applications, moving from fundamental characterization to real-world implementation in water treatment and advanced composite development. The research shows strong interdisciplinary connections between materials science, environmental engineering, and sustainable technology development. His research is conducted through the Institute of Fundamental Physics and Materials for Nanotechnology at DISAT, utilizing specialized laboratories including the Carbon Materials Characterization Laboratory, Carbon Materials Synthesis Laboratory, and Nanomaterials for composites and photocatalysis Laboratory. Dr. Giorcelli actively teaches Physics I and II across multiple engineering programs at Politecnico di Torino, including Aerospace Engineering, Mechanical Engineering, and Computer Engineering. He teaches in both Italian and English, demonstrating international engagement. His current research projects include ICELESS (2023-2025) as Scientific Responsible, focusing on innovative biochar applications.