جست‌وجوی استاد و پژوهشگر

Elias Bareinboimمشاهده پروفایل

دانشیار

• Causal Inference
• Counterfactual Analysis
• Artificial Intelligence
• +۶ مورد دیگر

Elias Bareinboim is an Associate Professor of Computer Science at Columbia University and Director of the Causal Artificial Intelligence (CausalAI) Laboratory. He holds affiliations with the Data Science Institute (DSI) and multiple centers, including Foundations of Data Science and Health Analytics. His research focuses on causal and counterfactual inference, particularly in data-driven fields like healthcare and social sciences, as well as artificial intelligence and machine learning. Notable contributions include foundational work on data fusion, causal reinforcement learning, and fairness analysis. Before Columbia, he was an Assistant Professor at Purdue University and earned his Ph.D. in Computer Science from UCLA. He has been recognized with prestigious awards, including the NSF CAREER Award and the Dan David Prize Scholarship. His work bridges theoretical advancements with practical applications, emphasizing causal methods for decision-making and equitable outcomes. Bareinboim’s research interests span causal discovery, counterfactual reasoning, and the integration of causal principles into machine learning systems. His lab develops methodologies to address challenges in confounded data, domain generalization, and ethical AI. Current projects include causal approaches to health equity, algorithmic fairness, and robust decision-making under uncertainty.

Jin Tianمشاهده پروفایل

استاد

• Causal Inference
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Jin Tian is a Professor affiliated with the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having previously held a Graduate Faculty position at Iowa State University. His research focuses on causal inference, machine learning, and artificial intelligence. Dr. Tian's work emphasizes causal discovery, causal effect identification, adversarial robustness in machine learning, and graphical models. He holds a Ph.D. in Computer Science from UCLA, an M.S. in Physics from UCLA, and a B.S. in Physics from Tsinghua University. His research interests span causal inference frameworks, including handling hidden variables and developing algorithms for causal model testing. He also explores adversarial machine learning, regularization techniques for robustness, and large-scale data systems like AnnotatedTables. His contributions to causal effect estimation via instrumental variables and combining experimental results highlight his interdisciplinary approach. Recent work includes advancements in polynomial-delay algorithms for causal model testing, unified covariate adjustment methods, and vulnerability-aware adversarial training. His research bridges theoretical foundations (e.g., graph-based complexity) with practical applications in machine learning security and causal reasoning.

Friedhelm von Blanckenburgمشاهده پروفایل

استاد

• Geochemistry
• Isotope Geochemistry
• Cosmogenic Nuclides
• +۳ مورد دیگر

Prof. Friedhelm von Blanckenburg is a Professor of Geochemistry at Freie Universität Berlin and former head of the 'Geochemistry of the Earth's Surface' section at the GFZ German Research Centre for Geosciences. He holds a Diploma in Geology from TU Berlin (1985) and a PhD in Geochemistry/Geochronology from ETH Zurich (1990). His research focuses on Earth surface processes, particularly cosmogenic nuclides, isotope geochemistry, and biogeochemical fluxes. He pioneered methods for quantifying erosion and weathering rates using cosmogenic beryllium-10 and stable isotope systems. Von Blanckenburg founded the HELGES laboratory at GFZ, a globally recognized facility for geochemical analysis. He has received numerous awards, including the ERC Advanced Grant DEVENDRA (2023) and the Ralph Alger Bagnold Medal (2010). His work bridges geology, climate science, and biomedicine, notably applying metal isotope analysis to study human food chain dynamics. He has led major initiatives, such as the EU-funded IsoNose training network and the DFG Earthshape program. Von Blanckenburg's contributions include over 200 publications, with seminal works on slab breakoff tectonics and paleo-erosion models. He remains active in scientific leadership, serving as Chair of the German Mineralogical Society and contributing to editorial roles in journals like Elements . Recent research emphasizes the interplay between vegetation, erosion, and rock weathering, with implications for global carbon cycles. His ERC-funded DEVENDRA project develops novel methods to measure weathering rates in basalt and carbonate rocks, critical for understanding Earth's climate system. Von Blanckenburg's outreach efforts include science films explaining geochemical concepts to the public, with over 100k views on YouTube.

Martin Haughمشاهده پروفایل

دانشیار

• Computational Finance
• Risk Management
• Stochastic Control
• +۲ مورد دیگر

Martin Haugh is an Associate Professor of Analytics and Operations Research at Imperial College Business School. He holds a PhD in Operations Research from MIT and MSc degrees in Mathematics and Applied Statistics. His research focuses on computational finance, stochastic control, and data science applications in sports analytics and causal inference. His educational background includes: PhD in Operations Research - Massachusetts Institute of Technology MSc in Applied Statistics - University of Oxford MSc in Mathematics - University College Cork Research interests span three primary domains: Quantitative Finance: Portfolio optimization with taxes, derivatives pricing, and risk management using deep learning approaches Stochastic Control: Information relaxation techniques for bounding optimal value functions in complex decision processes Data Analytics: Sports performance modeling, causal inference in healthcare, and political election analysis His recent publications (2015-2024) demonstrate a consistent focus on applied mathematical modeling across finance, economics, and competitive strategy domains, with emerging emphasis on counterfactual analysis and machine learning applications. He serves as Associate Editor for Management Science and INFORMS Journal on Computing, and mentors graduate students in the MSc Business Analytics program.

• Geophysics
• Machine Learning
• Differential Equations
• +۵ مورد دیگر

Facu Sapienza is a Postdoctoral Scholar in Geophysics at Stanford University, focusing on interdisciplinary research that combines paleomagnetism , differential equations , machine learning , and quantum thermodynamics . His work bridges computational methods with Earth and planetary sciences, particularly through physics-informed machine learning models for glaciology and magnetospheric studies. His recent publications highlight expertise in Universal differential equations for geophysical modeling Statistical learning for remagnetization analysis Quantum thermodynamic resource theory Planetary magnetic field interactions (Mars) Distance learning algorithms with Fermat principles trends in applying Gaussian processes, causal inference, and differentiable programming to geoscience challenges. He maintains an active research profile with collaborations in computational geophysics and quantum information theory, though no formal awards or student advising details are publicly listed in the provided materials.

Omiros Papaspiliopoulosمشاهده پروفایل

استاد

• Computational Statistics
• Machine Learning
• Probability
• +۵ مورد دیگر

Omiros Papaspiliopoulos is a Full Professor at Bocconi University’s Department of Decision Sciences. He joined Bocconi in 2021, previously serving as an ICREA Research Professor at Universitat Pompeu Fabra in Barcelona. His academic career includes roles at Warwick, Oxford, Lancaster, Berlin, Osaka, Paris, Madrid, and Lima. He has been awarded the Royal Statistical Society’s Guy Medal (2010) and the DeGroot Prize for his influential work with Nicolas Chopin. His research focuses on computational statistics, spanning Bayesian inference, machine learning, probability, and applied mathematics. Notably, he founded Europe’s first Master in Data Science at the Barcelona Graduate School of Economics (2013) and directed the Data Science Center until 2021. Since 2022, he has been Director of Bocconi’s Bachelor of Science in Economics, Management, and Computer Science (BEMACS). Papaspiliopoulos is co-Editor of Biometrika (top-4 in Statistics) and Associate Editor of the Journal of Uncertainty Quantification . He has delivered keynote talks at institutions like Chicago Booth, Duke, and LSE, emphasizing the societal impact of data science. His teaching spans courses on data science, statistics, machine learning, and quantitative methods in social sciences. Education & Academic Leadership PhD in Statistics (Implied by career trajectory) Founded Bocconi’s BEMACS program (2022) Directed the Master in Data Science (2013–2021) Executive course design for SDA Bocconi and Barcelona School of Economics Research Interests Computational Statistics & Bayesian Methods Machine Learning & Applied Mathematics Data Science in Social Sciences Hidden Markov Models & State-Space Systems High-Dimensional Inference & Scalable Algorithms Recent Work Trends His publications emphasize scalable Bayesian computation, treatment effect inference, and applications in social sciences. Recent topics include confounder importance learning, particle filtering, and computational efficiency in hierarchical models. Collaborations bridge theory and practice, addressing challenges in policy, finance, and public administration. Awards & Recognition 2010: Royal Statistical Society’s Guy Medal DeGroot Prize for Nonparametric Hidden Markov Models Onassis Foundation Scholar Grants & Outreach Directed the Barcelona Data Science Center (2016–2021) Outreach activities: Talks for high-school students, policymakers, and the European Commission Editorial roles in top journals: Biometrika , Journal of Uncertainty Quantification Labs & Teams Current affiliation with Bocconi’s Department of Decision Sciences and involvement in interdisciplinary data science initiatives.

Alessio Farcomeniمشاهده پروفایل

استاد

• Statistics
• Statistical Methodology
• Data Science
• +۵ مورد دیگر

Alessio Farcomeni is a Professor of Statistics at the University of Rome Tor Vergata, specializing in statistical methodology development. His work bridges academia and applied research across disciplines including economics, medicine, ecology, and engineering. He has authored/co-authored over 250 peer-reviewed papers and two books, with notable contributions in hidden Markov models, Bayesian analysis, and quantitative social science measurement. His research emphasizes interdisciplinary collaboration, reflected in studies on population health, economic policy evaluation, and surgical outcome optimization. Farcomeni’s research interests span statistical theory, with a focus on methodological innovations for complex data structures (e.g., longitudinal, spatial, and high-dimensional datasets). He has pioneered approaches in quantile regression, semi-Markov processes, and latent variable modeling. His applied work addresses real-world challenges such as estimating material deprivation scales, modeling cardiovascular disease risk factors, and analyzing pandemic dynamics (e.g., COVID-19 forecasting in Italy). His recent articles highlight advancements in statistical techniques for healthcare (e.g., AI-driven dermatology diagnostics, atrial fibrillation prediction models) and socio-economic analysis (e.g., macroprudential policy impacts, cross-country material deprivation comparisons). He is recognized for developing open-source statistical software packages, contributing to reproducible research practices. Farcomeni holds the distinction of being ranked among Italy’s top scientists by VIA-Academy. His work frequently integrates Bayesian methods, machine learning, and big data analytics to address pressing questions in public health, environmental science, and economic policy.

• Quantum Physics
• Atomic, Molecular and Optical Physics
• Electronics and Sensors
• +۳ مورد دیگر

Prof Margaret Reid is a Professor at the School of Science, Computing and Emerging Technologies at Swinburne University of Technology. Her research focuses on quantum foundations, mesoscopic and macroscopic quantum correlations, and quantum optics. She has expertise in phase-space methods for simulating quantum networks and validating quantum computational advantage. Education: BSc (Physics, First Class Honours) from the University of Auckland, New Zealand. Professional roles include ARC Panel of Experts, editorial roles in journals like Physical Review A, and leadership in quantum physics committees. Research interests span quantum correlations in photonic and atomic systems, macroscopic realism tests, and quantum simulation techniques. Her work bridges foundational quantum theory with experimental applications in quantum computing and measurement. Key achievements include the Moyal Medal (2019), Fellowships from the American Physical Society (2017), Australian Academy of Science (2014), and Fellowships in the Optical Society of America (2007). She has supervised multiple PhD students exploring topics like quantum phase-space representations and mesoscopic entanglement. Recent grants include a John Templeton Foundation project on quantum foundations (2023–2026) and collaborations with NTT Research on joint quantum projects (2019–2024). Her publications emphasize quantum validation methods, macroscopic paradoxes, and phase-space simulations in photonic networks.

• Data Analysis
• Statistical Learning Methods
• Structural Equation Modeling
• +۶ مورد دیگر

Laura Trinchera is a Professor of Statistics at NEOMA Business School, specializing in data analysis, statistical learning methods, and structural equation modeling. She holds a Master’s in Economics and Management from the University of Naples Federico II, Italy, and a PhD in Statistics (2008) from the same institution. Her research focuses on advanced statistical methodologies such as Partial Least Squares (PLS), clustering algorithms, and applications in supply chain management and marketing. She has held visiting researcher positions at institutions including University of California Santa Barbara, University of Michigan, Hamburg University, and HEC Paris. Her work spans interdisciplinary fields, including: Development of PLS-based methodologies for complex data structures Analysis of unobserved heterogeneity in longitudinal growth models Applications of AI in environmental performance and retail analytics Exploration of consumer behavior in sustainable diets and eating disorders Recent publications highlight her contributions to: AI-driven dynamic capabilities for environmental management Statistical methods for detecting hidden patterns in longitudinal data Impact of mobile commerce on retail performance Role of marketing capabilities in SMEs Her collaborative network includes partnerships with leading universities worldwide, emphasizing methodological advancements in data science and their practical applications. She actively participates in academic conferences, contributing to the advancement of PLS techniques and composite indicator systems.

Oleg Luninمشاهده پروفایل

دانشیار

• String Theory
• Quantum Field Theory
• Supergravity
• +۳ مورد دیگر

Oleg Lunin is an Associate Professor in the Department of Physics at the University at Albany (SUNY). He obtained his PhD from Ohio State University in 2000, followed by postdoctoral positions at Ohio State University (2000–2002), the Institute for Advanced Study in Princeton (2002–2005), the University of Chicago (2005–2009), and the University of Kentucky (2009–2010). He joined the University at Albany as an Assistant Professor in 2010 and was promoted to Associate Professor in 2016. His research explores fundamental problems in theoretical physics, including quantum gravity, string theory, and gauge/gravity duality. Key interests include the dynamics of supersymmetric gauge theories, black hole quantum properties, and geometric structures in supergravity. His work often bridges mathematical physics with phenomenological implications for strong interactions and spacetime quantization. Lunin's publications predominantly focus on black holes, T-dualities, and deformed geometries in string theory. Recent articles analyze higher-dimensional spacetimes, α′-corrections, and integrable systems, reflecting a consistent emphasis on exact solutions and holographic principles. Trends indicate deepening work on cosmological extensions of black hole geometries and non-abelian dualities. Lab affiliations or team collaborations are not explicitly detailed, but his publications frequently involve co-authors from institutions like SUNY Albany, indicating active research networks.

• Quantum Physics
• Optics
• Quantum Optics
• +۵ مورد دیگر

Florian Marquardt is a Professor at the University of Erlangen-Nürnberg and Scientific Director of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany. His research focuses on the intersection of quantum physics, optics, and machine learning, with a particular emphasis on neuromorphic computing and quantum technologies. He holds a PhD from the University of Basel (2002) and has held roles including Emmy-Noether Fellow (2007-2012) and Full Professor at FAU Erlangen-Nürnberg (2010-2016). His work bridges theoretical physics and applied AI, aiming to advance quantum control, topological photonics, and energy-efficient computing. Education: PhD in Theoretical Physics, University of Basel (2002) Diplom Physics, University of Bayreuth (1998) Research Interests: The Marquardt Group explores quantum optomechanics, neuromorphic computing, and machine learning applications in physics. Current projects include designing analog neuromorphic systems, optimizing quantum error correction, and leveraging deep learning for inverse design in photonics. Recent work emphasizes AI-driven strategies for quantum circuit discovery and topological material engineering. Awards: Walter Schottky Prize (2009) ERC Starting Grant (2011) Emmy-Noether Fellowship (2007-2012) Grants & Collaborations: Leads research networks including the Max Planck School of Photonics and the MPC for Extreme and Quantum Photonics. His lab collaborates on EU-funded projects and organizes workshops on topics like quantum metrology and neuromorphic computing. Labs & Infrastructure: Directs the Theory Division, which hosts interdisciplinary teams working on photonic neural networks, quantum machine learning, and topological transport. The group uses advanced simulation tools like dCG (differentiable connected geometries) for multi-domain optimization.

Zijian Guoمشاهده پروفایل

دانشیار

• High-dimensional statistics
• Causal Inference
• Nonparametric Statistics
• +۴ مورد دیگر

Zijian Guo is an Associate Professor in the Department of Statistics at Rutgers University, School of Arts and Sciences. He holds a PhD in Statistics from the University of Pennsylvania (2017) and a B.S. in Mathematics from The Chinese University of Hong Kong (2012). His research focuses on high-dimensional statistical inference, causal inference with instrumental variables, nonparametric methods, and applications in health and genomics data. Education: Ph.D., Statistics, The Wharton School, University of Pennsylvania (2017) B.S. (First Honor), Mathematics, The Chinese University of Hong Kong (2012) Research Interests: His work addresses uncertainty quantification in high-dimensional models, causal inference with invalid instruments, and machine learning applications in healthcare. Notable contributions include federated meta-learning, semi-supervised risk prediction, and robust Mendelian randomization analysis. Grants: NIH R01LM013614 (Co-PI): Semi-supervised Approaches to Denoising Electronic Health Records NIH R01GM140463 (PI): Predictive Modeling with High-Dimensional Incomplete Data NSF DMS 2015373 (Co-PI): Repro Sampling Method for Discrete Parameter Inference Awards: ICSA New Researcher Award (2019) Bernoulli Society New Researcher Award Honorable Mention (2023) President Gutmann Leadership Award (2017) He has developed R packages like SIHR, RobustIV, and DDL for high-dimensional statistical inference. Teaching includes Financial Data Mining and Advanced Modern Statistical Inference at the PhD level.

Karen Wiltshireمشاهده پروفایل

استاد

• Coastal Ecology
• Climate Change Impacts
• Marine Biodiversity
• +۳ مورد دیگر

Prof. Dr. Karen Wiltshire is a coastal and climate researcher serving as former Vice Director of the Alfred Wegener Institute (AWI) and Director of AWI's Helgoland and Sylt sites until July 2024. Her work focuses on coastal ecosystems' resilience to climate change and human impacts, emphasizing sustainable ocean use. She leads initiatives like Marine Protected Areas strategies and advises international bodies (IPCC, UN Ocean Assessments). Key research interests include North Sea dynamics, shelf sea ecology, and ecostructure development. Notable contributions include advancing observation systems and policy frameworks for ocean governance. Education: Studied coastal and climate sciences, with expertise in marine ecosystems. Awards include recognition for her scientific and administrative contributions. Active in global committees addressing ocean sustainability and policy. Her research integrates physical, chemical, and biological oceanography to assess ecosystem responses to warming, eutrophication, and human activities. She advocates for coordinated marine management, innovative eco-infrastructures, and real-world laboratories to balance conservation and resource use.

• Neuroscience
• Biomedical Engineering
• Machine Learning
• +۳ مورد دیگر

Jeffrey E Markowitz is an Assistant Professor in the Department of Biomedical Engineering at the Georgia Institute of Technology, affiliated with the College of Engineering. His research focuses on understanding the neural mechanisms underlying behavior, particularly the role of dopamine in shaping spontaneous and learned actions. He employs advanced techniques such as motion sequencing, optogenetics, and closed-loop stimulation systems to bridge computational models with experimental neuroscience. His technical expertise spans neurotechnology development, including injectable fluorescent nanoparticles for high-resolution tracking and tapered optical fibers for light delivery. He integrates machine learning approaches like inverse reinforcement learning and hidden Markov models to analyze behavioral patterns and neural dynamics. Key research areas include dopamine’s influence on reinforcement learning, epilepsy-induced behavioral changes, and the structure of social communication in animals. Notable contributions include the Cleo testbed for neurotechnology experimentation and pioneering work on motion sequencing (MoSeq) to dissect complex behaviors. His interdisciplinary work intersects neuroscience, biomedical engineering, and artificial intelligence, aiming to decode how neural circuits translate into observable behaviors. Though no specific grants or awards are listed, his publications reflect sustained innovation in behavioral neuroscience and neurotechnology. His lab likely focuses on developing analytical frameworks to connect molecular, neural, and behavioral levels of analysis, with applications in understanding both normal and pathological brain states.

Sandhya Patidarمشاهده پروفایل

دانشیار

• Data Science
• Climate Change
• Energy Systems
• +۴ مورد دیگر

Sandhya Patidar is an Associate Professor at the School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University. She holds editorial roles in journals like Geosciences and Frontiers in Environmental Engineering, and is a member of the Royal Academy of Engineering. Her research focuses on interdisciplinary applications of data science (mathematical/statistical/machine learning) to energy, water, climate change, and environmental impact assessment. She has led projects funded by grants like EP/F038240/1 and EP/K013513/1, and developed novel methodologies in stochastic processes, complex networks, and bifurcation analysis. Education & Early Career: Previously affiliated with Shri RGP Gujarati Professional Institute (2001–2004), DAVV Indore, and RGPV Bhopal. Holds expertise in programming (Python, R, MATLAB) and has over 70 peer-reviewed publications. Three outputs won best paper awards, including recognition at climate change and low-energy architecture conferences. Research Interests: Machine learning applications in energy systems, flood risk modeling, and climate change adaptation. Specializes in techniques like Hidden Markov Modeling, time series analysis, and deep learning frameworks for energy demand forecasting and hydrological prediction. Key Contributions: Developed a physics-aware machine learning framework for hydrological models, error correction models for reservoir levels, and AI-driven heat pump usage classification. Pioneered methodologies linking climatic trends to energy demand via stochastic modeling and covariance approaches. Awards: Robert Alfred Carr Prize (2022) Sir David Wallace Prize (2008) Best Paper Awards (2011, 2019) Advising & Grants: Supervises interdisciplinary PhD projects on data analytics in climate-energy-water nexus. Collaborates on initiatives like the CEDRI project for community energy demand reduction in India. Actively engages in grant-funded research on flood inundation modeling and probabilistic climate projections. Labs & Teams: Part of the Institute for Infrastructure & Environment, leading cross-disciplinary teams in energy performance assessments, resilience planning, and environmental risk management.