جست‌وجوی استاد و پژوهشگر

Hu Caoمشاهده پروفایل

پژوهشگر

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +۵ مورد دیگر

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

Christian Heineمشاهده پروفایل

پژوهشگر

• Data Visualization
• Topology-Based Methods
• Scientific Computing
• +۳ مورد دیگر

Christian Heine is a researcher at the Institute of Computer Science , University of Leipzig. His work focuses on advanced data visualization techniques, particularly those grounded in topological and geometric analysis of scalar fields, ensemble data, and high-dimensional datasets. Key Research Areas: Topological visualization, scalar field analysis, medical imaging, and uncertainty quantification. Methodologies: Bayesian inference, fiber trajectories, volume rendering, and dynamic workflows. Applications: Meteorological data analysis, medical diagnostics, and interactive visualization systems. He has published extensively on these topics, with recent work addressing spatio-temporal trends in climate data and noise-robust visualization techniques. His research often integrates interdisciplinary approaches, bridging computer science and applied sciences.

• Homogenization and Multiscale problems
• Numerical analysis
• Inverse Problems
• +۲ مورد دیگر

Associate Professor Hoang Viet Ha is a faculty member at the School of Physical & Mathematical Sciences, Nanyang Technological University (NTU), Singapore. His research focuses on advanced mathematical methods for solving complex partial differential equations with applications in multiscale modeling and uncertainty quantification. His educational background includes: Undergraduate studies at the University of Wollongong, Australia (1993-1996) Ph.D. at the University of Cambridge, UK (1996-1999) Junior Research Fellow at Gonville and Caius College, Cambridge (1999-2004) Fellow at Emmanuel College, Cambridge (2004-2008) Professor Hoang's research spans Homogenization and Multiscale problems , Numerical analysis , Inverse Problems , Stochastic partial differential equations , and Free boundary value problems . His work develops computational frameworks for high-dimensional approximation, particularly through finite element methods and polynomial chaos expansions. This enables efficient solutions for stochastic PDEs in elasticity and fluid dynamics where traditional methods face the curse of dimensionality. Analysis of his 2009-2015 publications reveals a cohesive trajectory: evolving from foundational homogenization theory to cutting-edge stochastic multiscale algorithms. Key themes include sparse tensor discretizations for high-dimensional problems, Bayesian inverse frameworks for data assimilation, and rigorous analysis of convergence rates for uncertainty quantification methods. His collaborations with leading researchers (Schwab, Stuart, Xia) demonstrate strong interdisciplinary reach across applied mathematics and computational science. Information regarding scientific awards, student advising, research grants, and laboratory affiliations is not provided in the available text.

Daniela Marellaمشاهده پروفایل

استاد

• Statistical matching
• Bayesian networks
• Survey sampling
• +۴ مورد دیگر

Daniela Marella is a Professor at the Department of Social and Economic Sciences, Sapienza University of Rome. She teaches Statistics and holds office hours on Tuesdays from 10:00 AM to 12:00 PM. Her email address is daniela.marella@uniroma1.it . Teaching: Statistics (Sociology, Economics, Development Studies) Research: Statistical matching, Bayesian networks, survey sampling, uncertainty quantification Publications: 15+ articles on non-probability sampling, measurement error, and interrater agreement Her research focuses on statistical matching methodologies, Bayesian network applications in survey data, and handling selection bias in non-probability samples. She explores measurement error modeling, empirical likelihood approaches, and resampling techniques for complex survey designs. Recent publications emphasize uncertainty analysis in statistical matching, graphical models for data integration, and Bayesian structural learning. Key areas include non-ignorable sampling, ordinal categorical data agreement, and pseudo-population resampling frameworks.

Wei Xiongمشاهده پروفایل

دانشیار

• Materials design and discovery
• Integrated computational materials engineering (ICME)
• CALPHAD thermodynamics and kinetics
• +۷ مورد دیگر

Wei Xiong is the William Kepler Whiteford Faculty Fellow and Associate Professor in the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh Swanson School of Engineering . Since founding the Physical Metallurgy and Materials Design (PMMD) Laboratory in 2016, he has led a multidisciplinary team that couples CALPHAD-based ICME with high-throughput experiments and additive manufacturing to create high-performance alloys for extreme environments. Education & Career Path Ph.D., Materials Science – KTH Royal Institute of Technology, Sweden (2012) Research Associate – University of Wisconsin–Madison (2012–2013) Research Associate – Northwestern University (2013–2016) Associate Professor & William Kepler Whiteford Faculty Fellow – University of Pittsburgh (2016–present) Research Interests Dr. Xiong’s research integrates computational thermodynamics (CALPHAD), kinetic modeling , and data-driven materials informatics to accelerate the design of advanced alloys. Core themes include: Phase equilibria and transformations in multi-principal element alloys Process-structure-property relationships in additive manufacturing High-temperature oxidation and corrosion resistance Space-qualified materials and extreme-environment performance Publication Trends & Impact With an h-index of 35 and >150 peer-reviewed papers since 2016, the PMMD Lab’s output spans Nature , Science Advances , Acta Materialia , and leading additive-manufacturing journals. Recent work emphasizes uncertainty quantification, machine-learning-guided alloy design, and real-time process monitoring via in-situ diagnostics. Scientific Awards & Honors ASTM AM CoE Young Professional Award in Additive Manufacturing (2024) NSF CAREER Award (2021) TMS Early Career Faculty Fellow Award (2021) APDIC Best Paper Award (2021) CALPHAD Young Leader Award (2020) Acta Materialia Outstanding Reviewer Award (2020) TMS FMD Young Leader Professional Development Award (2015) CALPHAD Journal Best Paper Awards (2012, 2013) Grants & Funding Dr. Xiong’s research is continuously supported by federal and industrial sponsors, including: NASA – High-performance space alloys via additive manufacturing Air Force Office of Scientific Research (AFOSR) – Extreme-environment materials Office of Naval Research (ONR) – Naval alloy development National Science Foundation (NSF) – CAREER and collaborative awards Department of Energy (DOE) & ARPA-E – Fossil-energy materials Nuclear Energy University Program (NEUP) – Radiation-resistant steels Laboratory & Team The Physical Metallurgy and Materials Design (PMMD) Laboratory operates >20 active research projects, hosts 5-year average PhD timelines, and has produced 3 best-graduate-student awards and 4 best-paper awards. The lab maintains collaborations with national laboratories (Argonne, NIST), federal agencies, and industry leaders (Caterpillar, GM, Thermo-Calc Software Inc.).

Bruna G. Palmمشاهده پروفایل

مدرس

• Statistical Modeling
• Signal Processing
• Remote Sensing
• +۴ مورد دیگر

Bruna G. Palm is a Lecturer at the Department of Mathematics and Natural Sciences, Blekinge Institute of Technology (BTH), Sweden. She holds a BA (2014) and PhD (2020) in Statistics from Brazilian institutions and has held visiting and research fellow positions at BTH and the Aeronautics Institute of Technology (ITA). Education: BA in Statistics (2014), PhD in Statistics (2020) Current Role: Lecturer at BTH Collaborations: Saab AB, Sweden Her research bridges statistical modeling with remote sensing and signal processing , focusing on SAR image analysis , change detection , and machine learning applications. She has also contributed to environmental data forecasting and educational pedagogy in STEM. Key trends include Bayesian inference , autoregressive models , and Rayleigh distribution techniques for SAR imagery. Her work on resin composite masking extends into dentistry , highlighting interdisciplinary reach. She has no listed scientific awards but maintains active profiles on LinkedIn , Google Scholar , and ORCID .

Matthew Wickerمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Formal Verification
• Adversarial Robustness
• +۳ مورد دیگر

Matthew Wicker is an Assistant Professor (Lecturer) at Imperial College London and a Research Associate at The Alan Turing Institute. His research focuses on developing formally verified, trustworthy machine learning systems with provable guarantees for safety, robustness, and fairness. He collaborates extensively with academic and industry partners, including Accenture. Research Interests: Formal verification of ML systems (e.g., neural network robustness/fairness) Bayesian neural networks and uncertainty quantification Adversarial attack/defense strategies Causal structure learning with probabilistic methods Certifiable guarantees for model explanations His recent publications (ICLR, ICML, NeurIPS) converge on theoretical and practical frameworks for trustworthy AI, emphasizing adversarial robustness in Bayesian models, global fairness certification, and scalable uncertainty methods. A consistent theme is bridging formal methods with real-world ML deployment challenges. Awards: Best Paper at TPM 2022 for causal structure learning research He maintains the deepbayes repository, enabling reproducibility of his PhD work on Bayesian neural network robustness.

Victor Bolbotمشاهده پروفایل

پژوهشگر

• Autonomous ships
• Marine systems safety
• Ship propulsion systems
• +۳ مورد دیگر

Victor Bolbot serves as a Postdoctoral Researcher in the Department of Energy and Mechanical Engineering at Aalto University, Finland, affiliated with the Marine and Arctic Technology research group. His work focuses on advancing safety, reliability, and cybersecurity frameworks for autonomous maritime systems through rigorous systems engineering approaches and data-driven methodologies. He maintains active collaborations with international researchers and institutions, evidenced by extensive co-authorship across high-impact publications. Dr. Bolbot's research centers on autonomous ships, marine systems safety, ship propulsion cybersecurity, and risk modeling. He employs systems-theoretic process analysis (STPA), Bayesian networks, and association rule mining to address critical challenges including maritime accident causation, cybersecurity vulnerabilities in dual-fuel engines, safety acceptance criteria for autonomous vessels, and socio-technical implications of maritime automation. His methodological innovations bridge theoretical safety engineering with practical applications in Arctic navigation, inland waterways, and regulatory compliance, emphasizing the integration of cyber-physical risk assessment. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) Development of cyber-physical risk frameworks like STPA-Cyber for maritime cybersecurity; (2) Real-time Bayesian modeling for dynamic operations including remote pilotage and ice navigation; and (3) Socio-technical investigations into regulatory frameworks, educational needs, and workforce skill transformations for autonomous shipping. His work consistently addresses the interplay between technological innovation and safety assurance, with growing emphasis on cybersecurity as a critical maritime safety component. As an active member of Aalto University's Marine and Arctic Technology research group, Dr. Bolbot contributes to interdisciplinary projects tackling complex challenges in marine safety engineering, Arctic operations, and sustainable maritime technologies. The group's collaborative environment supports the development of safer, more efficient, and environmentally conscious maritime systems through experimental validation, computational modeling, and industry partnerships.

• Chemical Process Control
• Biodiesel Production
• Distillation Modeling
• +۴ مورد دیگر

Adel Mhamdi is a Professor at RWTH Aachen University's Department of Chemical Engineering within the Faculty of Mechanical Engineering. His research focuses on process systems engineering with emphasis on nonlinear model predictive control, hybrid modeling, and sustainable process design for chemical and biochemical systems. He leads research in electrified biodiesel production, distillation optimization, and polymerization process control. His research interests span chemical process control, biodiesel production optimization, distillation modeling, hybrid mechanistic-data-driven approaches, polymerization processes, and heat transfer optimization. Mhamdi develops advanced control strategies including economic NMPC, distributed control architectures, and chance-constrained optimization to address challenges in flexible operation of energy-intensive processes. His work integrates computational fluid dynamics with process modeling for reactor design and optimization. Analysis of his recent publications reveals strong trends in electrification of chemical processes, particularly biodiesel production with heat integration. His research increasingly incorporates machine learning for hybrid modeling and employs advanced computational techniques like Bayesian optimization for reactor geometry design. Key application areas include renewable energy systems, polymer manufacturing, and separation processes. Mhamdi actively supervises doctoral researchers including M. El Wajeh, J.M. Faust, and P.J. Joy who appear as first authors on multiple publications. His research group collaborates extensively with the Mitsos group at RWTH Aachen, securing publications in top journals including Industrial & Engineering Chemistry Research and Computers & Chemical Engineering . Current projects focus on real-time optimization of electrified processes and development of open-source modeling platforms like HybridML.

Nadia Lapustaمشاهده پروفایل

استاد

• Computational Mechanics
• Earthquake Physics
• Fracture and Frictional Processes
• +۴ مورد دیگر

Nadia Lapusta is the Lawrence A. Hanson, Jr., Professor of Mechanical Engineering and Geophysics at the California Institute of Technology (Caltech), and serves as Graduate Option Representative for Applied Mechanics, Civil Engineering, and Mechanical Engineering within the Division of Engineering and Applied Science. Her research focuses on computational mechanics of geomaterials, earthquake source processes, frictional and fracture dynamics, and interactions between fluid flow and faulting. She investigates phenomena such as induced seismicity, fault stability, and the physics of earthquakes through advanced numerical modeling and experimental analysis. Her work bridges laboratory experiments and large-scale geophysical observations, emphasizing how fluid-rock interactions influence fault behavior. Notable contributions include studies on supershear rupture dynamics, slow slip events, and the role of pore pressure in fault weakening. Lapusta has been recognized with prestigious awards including AGU Fellowship and the GSC Mentoring Award. Education: Details not explicitly provided in texts, but her academic roles suggest advanced degrees in engineering and geophysics. Research Group: Leads a multidisciplinary team including PhD students (e.g., Taeho Kim, Stacy Larochelle) and postdocs (e.g., Luca Dal Zilio, Elias Heimisson), with former members contributing to fields like fault dynamics and seismology. Lab Activities: Focuses on computational geomechanics, dynamic rupture simulations, and fluid-fault interaction modeling. Current and former students and collaborators have advanced understanding of fault mechanics, seismic hazard modeling, and geophysical fluid dynamics. Lapusta’s work often involves collaborative code verification projects (e.g., the SEAS initiative) to improve simulations of earthquake sequences and aseismic slip.

Ye Hongمشاهده پروفایل

پژوهشگر

• Geoinformatics
• Urban Planning
• Transportation Engineering
• +۳ مورد دیگر

Dr. Ye Hong is a Researcher affiliated with the Institute of Cartography and Geoinformatics at ETH Zürich, specifically within the Department of Geoinformation Engineering. Their work focuses on geospatial data analysis, urban mobility modeling, machine learning applications in transportation systems, and sustainable urban planning. They contribute to open-source tools like Trackintel for mobility analysis and have published extensively on topics such as mobility data synthesis, traffic prediction, and privacy-preserving techniques. Research interests emphasize integrating multi-source geospatial data with deep learning to address challenges in urban sustainability, poverty reduction, and transportation efficiency. Their articles highlight innovations in trajectory generation, uncertainty quantification, and contextual-aware neural networks for spatial-temporal prediction. Dr. Hong’s publications explore the interplay between urban infrastructure, human behavior, and environmental impact. Key themes include accessibility measurement in cities, carbon footprint analysis, and the ethical implications of tracking mobility data. Their work bridges theoretical advancements in geoinformatics with practical applications in urban policy-making and infrastructure design. Notable contributions include frameworks for causal intervention in mobility data, methods to estimate poverty reduction efficiency using remote sensing, and analyses of tracking duration effects on location privacy. These efforts demonstrate a commitment to leveraging geospatial technologies for socially impactful research.

Michele Caprioمشاهده پروفایل

مدرس

• Probabilistic Machine Learning
• Imprecise Probability
• Uncertainty Quantification
• +۳ مورد دیگر

Michele Caprio is a Lecturer (Assistant Professor) in Machine Learning and Artificial Intelligence at The University of Manchester. He holds a PhD in Statistics from Duke University (2022) and completed a postdoctoral fellowship at the University of Pennsylvania’s Department of Computer and Information Science. His research focuses on probabilistic machine learning, particularly imprecise probabilistic techniques for uncertainty quantification. He is actively accepting PhD students for projects such as 'Multimodal Credal Learning Theory' and is a member of the London Mathematical Society. Education: PhD in Statistics, Duke University (2022) Postdoctoral Researcher, University of Pennsylvania (Computer and Information Science) Research Interests: Dr. Caprio’s work bridges theoretical foundations and practical applications in machine learning, emphasizing uncertainty quantification through imprecise probabilities. Key areas include conformal prediction, distributionally robust optimization, and the ergodicity of Markov processes. His methods address challenges in ambiguous ground truth, distribution shifts, and epistemic uncertainty in AI systems. Scientific Awards: IJAR Young Researcher Award (2023) IMS New Researchers Travel Award (2023) IMS Hannan Travel Award (2022) Aleane Webb Dissertation Research Fellowship (2021) Advising & Projects: As PI of the MCAIF: Centre for AI Fundamentals project, he collaborates with researchers and students on AI fundamentals. His advisees include over 15 PhD candidates focusing on topics like distribution shift recovery, imprecise neural networks, and continual learning. Labs/Teams: Central to his work is the MCAIF initiative, a multidisciplinary team advancing theoretical and applied AI research through collaborative projects.

Sergei Kalininمشاهده پروفایل

استاد

• Machine Learning in Microscopy
• Atom by Atom Fabrication
• Scanning Probe Microscopy
• +۴ مورد دیگر

Sergei Kalinin is the Weston Fulton Professor at the Department of Materials Science and Engineering, University of Tennessee, Knoxville, and holds a Corporate Fellow position at Oak Ridge National Laboratory (ORNL). His academic affiliations include adjunct professorships at Sung Kyun Kwan University (South Korea) and Penn State University. He earned his M.S. summa cum laude from Moscow State University (1998) and Ph.D. from the University of Pennsylvania (2002), both in Materials Science. Kalinin's research focuses on transformative applications of machine learning and AI in materials science, particularly in: Atom-by-atom fabrication using electron beams Big data analytics in scanning probe/transmission electron microscopy Nanoscale electromechanical phenomena in functional oxides Development of novel microscopy techniques and autonomous experimentation frameworks His recent publications (2024-2025) demonstrate strong emphasis on AI-driven microscopy, autonomous labs, and computational materials design. Over 85% of his last 15 articles involve machine learning applications in experimental physics, with emerging focus on human-AI collaboration and real-time instrumentation control. Major scientific honors include: Blavatnik National Award (2018) Feynman Prize in Nanotechnology (2022) Presidential Early Career Award (2009) 4 R&D100 Awards (2008-2023) Fellowships in APS, MRS, IEEE, AVS He directs the ORNL Institute for Functional Imaging of Materials and leads research on autonomous experimentation systems integrating high-performance computing with microscopy platforms.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science
• +۷ مورد دیگر

Janardhan Rao (Jana) Doppa is an Associate Professor in the School of Electrical Engineering and Computer Science (EECS) at Washington State University (WSU). He holds the Huie-Rogers Endowed Chair in Computer Science and the Berry Distinguished Professorship in Engineering. His research focuses on artificial intelligence, machine learning, and data-driven science, with applications in structured prediction, Bayesian optimization, and sustainable computing. He chairs the EECS Graduate Studies program and has received awards such as the NSF CAREER Award (2019) and the Voiland College Anjan Bose Outstanding Researcher Award (2024). Education: Ph.D., Computer Science, Oregon State University (2014) M.Tech., Computer Science, Indian Institute of Technology Kanpur (2006) Research Interests: Dr. Doppa's work spans AI-driven adaptive experiment design, sequential decision-making under uncertainty, robust machine learning, and applications in agriculture, health informatics, and electronic design automation. He leads the NSF-USDA AI Institute for Agricultural Decision Support and explores AI methods for nanoporous materials design and sustainable computing. Awards: NSF CAREER Award (2019) Voiland College Anjan Bose Outstanding Researcher Award (2024) Best Paper Awards at multiple conferences (e.g., ACM/IEEE Embedded Systems Week 2023) Outstanding Junior Faculty in Research (2020) and Reid Miller Teaching Excellence (2018) Advising & Grants: He advises a dynamic group of PhD and MS students, many of whom have won awards. His grants include NSF-CAREER and USDA AI Institute funding. He teaches courses like Machine Learning and Structured Prediction at WSU. Labs/Teams: His research group collaborates with industry and academia, advancing AI for engineering and scientific domains through interdisciplinary projects.

Omid Chatrabgounمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Bayesian Machine Learning
• Gaussian Processes
• +۵ مورد دیگر

Dr. Omid Chatrabgoun serves as an Assistant Professor in Data Science and AI at the CEES School of Science, holding a PhD in Data Science from Shahid Chamran University (Iran, 2015). Previously affiliated with Malayer University, he teaches undergraduate and postgraduate courses including Statistical Learning, Data Science, and Machine Learning while actively supervising PhD students. His research focuses on uncertainty quantification and Bayesian machine learning for complex systems. Education: PhD in Data Science, Shahid Chamran University, Iran (2015) Dr. Chatrabgoun specializes in Modelling, Optimization, and Uncertainty Quantification (UQ) using (Deep) Gaussian processes, with applications spanning bioinformatics (Gene Regulatory Networks, Protein-Protein Interactions) and environmental engineering (coastal protection, flood modeling). He develops novel approaches for machine learning of linear operational equations to quantify uncertainty in highly complex systems, emphasizing computational efficiency through sparse approximations and kernel methods. His work bridges theoretical statistics with practical implementations in healthcare, environmental science, and industrial applications. Analysis of his 29 publications (2016-2025) reveals a consistent trajectory in Gaussian process methodologies, evolving from foundational graphical models to advanced deep Gaussian processes and probabilistic surrogate modeling. Key thematic clusters include bioinformatics network inference, environmental risk assessment, and computational optimization techniques, demonstrating interdisciplinary impact across computer science, statistics, and domain-specific applications. Dr. Chatrabgoun has secured research funding and conducted consultancy projects with the Iran National Science Foundation (INSF) and Research Institute for Grapes and Raisin (RIGR). His collaborative network spans international institutions, as evidenced by co-authorships across civil engineering, bioinformatics, and environmental science domains. Current projects involve developing machine learning frameworks for linear differential operators and expanding applications of pair-copula Bayesian models in high-dimensional data analysis. His research group actively explores uncertainty propagation in PDE-based models and spatio-temporal environmental simulations, maintaining strong industry-academia partnerships for knowledge exchange in data-driven decision systems.