جست‌وجوی استاد و پژوهشگر

• Genomics
• Statistical Genetics
• Polygenic Risk Scores
• +۷ مورد دیگر

Bogdan Pasaniuc is a Professor in the Departments of Pathology and Laboratory Medicine, Human Genetics, and Computational Medicine at the University of California, Los Angeles (UCLA). His academic career spans multiple disciplines within genomics and computational biology, with a focus on advancing precision medicine through genetic and genomic research. He holds a BSc in Computer Science from A.I. Cuza University of Iasi (2003) and a PhD in Computer Science and Bioinformatics from the University of Connecticut (2008). His postdoctoral training included positions at the Broad Institute of Harvard and MIT (2012), Harvard School of Public Health (2012), and ICSI at UC Berkeley (2010), where he specialized in statistical genetics and computational biology. His research interests encompass statistical genetics, polygenic risk scoring, and the application of computational methods to understand genetic architecture across diverse populations. He focuses on translational research in precision medicine, including the development of tools for polygenic risk assessment and their implications in healthcare disparities. Key areas include the genetic underpinnings of complex diseases such as cancer, neuropsychiatric disorders, and metabolic conditions, alongside the integration of genomic data with electronic health records (EHR) for clinical insights. Notable contributions include leadership in the UCLA ATLAS Community Health Initiative and the PRIMED Consortium, which aim to improve polygenic risk assessment in underrepresented groups. His work also extends to the All of Us Research Program, leveraging large-scale genomic and health data to enhance disease understanding and precision medicine applications. He collaborates across institutions and leads research teams focused on computational methods for admixture mapping, genetic epidemiology, and multi-omics integration. His projects often emphasize reducing disparities in genomic research and ensuring equitable access to precision health solutions.

Maggie Makarمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Healthcare Applications
• +۳ مورد دیگر

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst. Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems. Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications. Education: PhD in Computer Science, MIT (CSAIL), 2018 Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013 Awards: CAREER Award (NSF, 2024) CRII: SCH Grant (NSF, 2022) Research Themes: Causal inference in machine learning Data-efficient methods Healthcare applications Algorithmic fairness

Amin Rezaeiمشاهده پروفایل

استادیار

• Computer Security
• Machine Learning
• Computer Architecture
• +۳ مورد دیگر

Amin Rezaei is an Assistant Professor in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He directs the Computer Architecture, Reliability, and Security Laboratory (CARS-Lab) and serves as the undergraduate advisor for Computer Engineering. His expertise spans computer security, machine learning, and computer architecture, with a focus on hardware security and protecting hardware intellectual property. Rezaei holds a Ph.D. in Computer Engineering from Northwestern University. He is a senior member of IEEE and a lifetime member of ACM and AAAI. His research has been funded by NSF grants totaling $500,000, and he has published over 50 peer-reviewed articles in top venues like DAC, ICCAD, and DATE. He has also served on technical program committees for major conferences such as IPDPS and GLSVLSI. His research interests include hardware security, machine learning applications in secure systems, and novel logic locking techniques. Key projects include developing CycPUF (a cyclic physical unclonable function) and frameworks for detecting hardware Trojans using multimodal deep learning. His lab emphasizes democratizing research by publicly sharing source code. Education: Ph.D. in Computer Engineering, Northwestern University Awarded Academic Excellence Award, University of Louisiana at Lafayette Awards: Walter P. Murphy Fellowship, Northwestern University Royal E. Cabell Fellowship, Northwestern University Grants: NSF CISE-MSI and CRII programs. Rezaei’s advising focuses on undergraduate Computer Engineering students, with Spring 2025 hours on Zoom. His lab’s current research includes projects on hardware Trojan prediction, ML-based security evaluations, and dynamic logic locking.

Shinichi Nakajimaمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Generative Modeling
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.

Dan M. Frangopolمشاهده پروفایل

استاد

• Life-cycle civil engineering
• Structural reliability
• Infrastructure sustainability
• +۵ مورد دیگر

Prof. Dan M. Frangopol is the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture at Lehigh University's P.C. Rossin College of Engineering and Applied Science. He is a global leader in life-cycle civil engineering, focusing on probabilistic methods, infrastructure resilience, and sustainability. His research spans structural reliability, risk-based decision-making, and multi-hazard mitigation under climate change. Affiliations: Lehigh University (current), University of Colorado Boulder (23 years), and institutions in Romania and Belgium. Education: Dipl.-Ing. from Bucharest (1969), Doctor of Applied Sciences (summa cum laude) from University of Liège (1976), and multiple honorary doctorates. His research interests include life-cycle cost optimization, probabilistic mechanics, and infrastructure systems management. He pioneered the International Association for Bridge Maintenance and Safety (IABMAS) and the International Association for Life Cycle Civil Engineering (IALCCE). Key contributions include frameworks for resilient infrastructure under climate change and extreme events. Frangopol has authored/co-authored over 500 journal articles, 5 books, and 70 book chapters. He has supervised 50 PhD and 56 M.Sc. students, many of whom are now leading academics and practitioners. His awards include the inaugural Dan M. Frangopol Medal (2023), ASCE Noble Prize (2015, 2024), and multiple honorary memberships in national and international academies. He has advised numerous high-profile projects funded by NSF, FHWA, NASA, and others. His labs and initiatives include the Fazlur R. Khan Distinguished Lecture Series and the journal Structure and Infrastructure Engineering .

Yang Zhangمشاهده پروفایل

عضو هیئت علمی

• Aerospace Engineering
• Fluid Dynamics
• Noise Reduction
• +۳ مورد دیگر

Yang Zhang is a Research Assistant Professor at the Illinois Institute of Technology (Illinois Tech), affiliated with the Department of Mechanical, Materials, and Aerospace Engineering within the Armour College of Engineering. His research focuses on experimental and computational aerodynamics, particularly in noise reduction mechanisms, flow control, and advanced measurement techniques such as particle image velocimetry (PIV). Key research interests include slat noise mitigation in aircraft systems, turbulent flow dynamics, and the application of smart materials and spectral analysis methods. Zhang’s work addresses challenges in high-lift systems, supersonic cavity flows, and aerodynamic efficiency optimization. His publications highlight contributions to noise prediction, flow visualization techniques, and passive/active control strategies for aerodynamic systems. Recent studies emphasize the integration of porous materials and dielectric elastomers for acoustic liner design, alongside advancements in data assimilation frameworks for turbulent boundary layer analysis.

• Machine Learning
• Cybersecurity
• Biometric Authentication
• +۳ مورد دیگر

Dr Wencheng Yang is a Senior Lecturer in Computing at the School of Mathematics, Physics and Computing , University of Southern Queensland. He holds a PhD in Computing from UNSW, an MSc from Korea, and a BMgmt from Wuhan University of Technology. His research spans multiple domains including machine learning security , biometric authentication , privacy-preserving systems , and IoT applications . Education : BMgmt (Wuhan University of Technology), MSc (Korea), PhD (UNSW) Yang’s work emphasizes privacy and security in AI systems, particularly in biometric authentication (e.g., fingerprint, face, ECG) and IoT security. His recent publications focus on model inversion attacks , homomorphic encryption , and federated learning for healthcare applications. Key trends include secure face-swapping , Alzheimer’s disease prediction , and anti-forensic detection . His research has been cited 3378 times, with 1458 total downloads and 31 monthly views. While no explicit scientific awards are listed, his interdisciplinary work bridges computer science , healthcare , and cryptography . Current projects include privacy-preserving frameworks for implantable medical devices and military health systems .

• Probabilistic Forecasting
• Statistical Evaluation
• Climate Science
• +۳ مورد دیگر

Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.

• Soil Organic Carbon
• Digital Soil Mapping
• Soil Data Standardization
• +۷ مورد دیگر

Niels Batjes is a leading researcher at ISRIC - World Soil Information , specializing in soil data standardization, digital soil mapping, and carbon sequestration modeling. His work focuses on creating harmonized global soil databases like WoSIS and SoilGrids to support climate mitigation and environmental conservation. Key Research Areas : Soil Organic Carbon, Digital Soil Mapping, Pedotransfer Functions, Measurement Error Analysis Projects : Development of soil databases with quantified uncertainties, HoliSoils initiative for European forest soils Recent publications highlight his contributions to global soil water retention modeling, interoperable soil data exchange frameworks, and machine learning applications for carbon stock assessment. He plays a central role in international soil data infrastructure development.

• Empirical Asset Pricing
• Derivative Markets
• Real Options
• +۳ مورد دیگر

Anastasios Kagkadis is a Senior Lecturer (Associate Professor) in Finance at the University of Liverpool Management School and an Honorary Researcher at Lancaster University Management School. His research focuses on empirical asset pricing, derivative markets, and real options. He teaches modules such as Empirical Asset Pricing (ULMR804) and Theory of Finance II (ACFI234), serving as Module Coordinator for both in the 2024-25 academic year. Research interests include analyzing hedge fund performance through bear factor dynamics, evaluating political risk pricing in equity options, and exploring stock-level investment anomalies related to real uncertainty. Recent work examines factor timing using portfolio characteristics and the predictive power of dispersion in investor versus analyst expectations. No scientific awards or grants are listed in available records. He maintains affiliations with both Liverpool and Lancaster universities without documented part-time status. No advisee or lab-team details are provided in the source materials.

Danial Faghihiمشاهده پروفایل

استادیار

• Multiscale Computational Modeling
• Data-Driven Predictive Modeling
• Uncertainty Quantification
• +۴ مورد دیگر

Danial Faghihi is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo. His research focuses on multiscale computational modeling, data-driven predictive methods, and uncertainty quantification with applications in advanced manufacturing, thermal insulation materials, and biomedical systems. He holds a PhD from Louisiana State University and completed a postdoctoral fellowship at the University of Texas at Austin. Education: Postdoc, Computational Engineering, University of Texas at Austin (2015) PhD, Civil Engineering, Louisiana State University (2012) MS, Civil Engineering, Sharif University of Technology (2008) BS, Civil Engineering, K. N. Toosi University of Technology (2008) His research interests integrate advanced computational frameworks with experimental data to address challenges in materials science and biomedical engineering. Notable areas include predictive modeling of silica aerogels, stochastic surrogate models for uncertainty quantification, and tumor growth simulations using Bayesian methods. He has received prestigious awards such as the NSF CAREER Award (2022) and the SES Travel Award (2013). Recent work emphasizes dynamic data-driven approaches for real-time material damage prediction and scalable algorithms for multi-material design under uncertainty. His contributions span interdisciplinary collaborations in thermal management systems, carbon sequestration materials, and personalized oncology through computational oncology models. Key Honors: NSF CAREER Award (2022) SES Travel Award (2013) NSF Summer Institute Fellowship (2013) His research narrative includes development of novel surrogate models for high-dimensional systems and validation frameworks that bridge atomistic to continuum scales. While specific grants or lab affiliations are not detailed in the provided text, his work reflects sustained innovation in computational science and engineering applications.

• Business Process Management
• Process Mining
• Business Process Simulation
• +۴ مورد دیگر

Marlon Dumas is a leading researcher in business process management and process mining at the University of Tartu, Estonia. With over 467 publications spanning from 1997 to 2025, his work has significantly advanced methodologies in business process analysis, simulation, and optimization. His research bridges theoretical foundations with practical applications, developing tools and frameworks that enable organizations to analyze and optimize operational processes. Dumas's primary research interests include business process management, process mining, business process simulation, prescriptive process monitoring, and data-aware business processes. He has pioneered methods for modeling resource availability, activity delays, and waiting times in business processes. His work on prescriptive process monitoring addresses critical challenges such as resource constraints, uncertainty in predictions, and causal effect estimation for interventions. Recent publications reveal a strong trend toward integrating artificial intelligence with business process management, particularly exploring the application of large language models to process optimization, monitoring, and redesign tasks. His research demonstrates consistent innovation, with publications appearing in top venues including Information Systems, Data & Knowledge Engineering, and the International Conference on Business Process Management. Dumas has developed several influential tools including SIMOD for automated discovery of business process simulation models, Optimos for simulation-driven process optimization, and Kairos for prescriptive monitoring. His collaborative network is extensive, featuring frequent co-authorship with prominent researchers including Marcello La Rosa, Luciano García-Bañuelos, Fabrizio Maria Maggi, and Wil M. P. van der Aalst. His work on privacy-preserving process mining, particularly regarding differentially private release of event logs, addresses critical challenges in applying process mining techniques while maintaining data privacy and compliance with regulations like GDPR. Dumas's research continues to push boundaries, with recent work exploring the integration of large language models with business process management systems, suggesting an ongoing commitment to advancing the field through innovative applications of emerging technologies.

William Kleiberمشاهده پروفایل

دانشیار

• Spatial Statistics
• Geostatistics
• Uncertainty Quantification
• +۵ مورد دیگر

William Kleiber is an Associate Professor in the Department of Applied Mathematics at the University of Colorado Boulder, specializing in spatial statistics for geophysical applications and renewable energy systems. His work bridges advanced statistical theory with practical environmental challenges, particularly in climate modeling and uncertainty quantification. Education: Ph.D. in Statistics, University of Washington (2010) Dr. Kleiber's research centers on spatial statistics and geostatistics for large-scale nonstationary datasets, with significant contributions to statistical climatology , stochastic weather generators , and uncertainty quantification in geophysical models. His methodologies address critical energy science challenges, including solar irradiance forecasting and renewable resource assessment. Recent work integrates machine learning with spatial statistics to handle massive multivariate environmental data, advancing predictive capabilities for climate extremes and renewable energy systems. His 2023-2025 publications reveal a strong trend toward hybrid statistical-machine learning approaches for environmental data, emphasizing uncertainty-aware predictions in snow hydrology, sea surface temperature reconstruction, and extreme weather events. This work consistently tackles the computational and theoretical challenges of high-dimensional spatial processes. Scientific Awards: American Statistical Association's Section on Statistics and the Environment 2016 Young Investigator Award Dr. Kleiber has served on the Board of Directors for The International Environmetrics Society and the American Statistical Association's Section on Statistics and the Environment, and contributed to editorial boards of leading statistical journals. His research has been supported by collaborative grants including 'Collaborative Research: Theory and Methods for Highly Multivariate Spatial Processes with Applications to Climate Data Science' (2018) and 'Scalable Statistical Validation for Large Spatio-Temporal Datasets' (2014). Current projects focus on integrating deep learning with spatial statistics for renewable energy forecasting and climate risk assessment. He maintains active international collaborations, evidenced by his 2016 Lebesgue Chair visiting professorship at University of Rennes 1, France, and partnerships with the National Center for Atmospheric Research where he previously conducted postdoctoral research.

Leonidas Guibasمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Machine Learning
• +۵ مورد دیگر

Leonidas Guibas is the Paul Pigott Professor of Computer Science at Stanford University, leading the Geometric Computation group. He is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM), associated with the Visual Computing Focus Group under Prof. Matthias Nießner. His research bridges computer vision, graphics, and machine learning, focusing on 3D data processing, geometric modeling, and sensor networks. Guibas holds prestigious awards including the Vannevar Bush Fellowship and ACM Allen Newell Award, and is a member of the US National Academy of Engineering and American Academy of Arts and Sciences. Education: Ph.D. from Stanford University under Donald Knuth, with prior appointments at Xerox PARC, MIT, and international institutions. His work emphasizes algorithms for sensing, reasoning, and acting in physical environments, with contributions to robotics, computational geometry, and discrete algorithms. Research interests span 3D vision (e.g., generative models for shape synthesis), deep learning architectures for spatiotemporal data, and multimodal sensor integration. Recent projects include object pose estimation, functional relationship learning in scenes, and deformation-aware 3D model retrieval. Publications highlight advancements in 3D reconstruction, point cloud analysis, and geometric deep learning. His TUM fellowship supported work on digital twins and visual computing, emphasizing high-fidelity environmental modeling. Awards also include IEEE and ACM Fellowships, underscoring his impact on theoretical and applied computer science.

Musa EŞİTمشاهده پروفایل

دانشیار

• Hydrology
• Water Resources Engineering
• Drought Analysis
• +۴ مورد دیگر

Musa EŞİT is an Associate Professor at the Department of Civil Engineering, Faculty of Engineering, Adiyaman University. He completed his B.S. in Civil Engineering from Gaziantep University in 2012, followed by a Master's degree in Hydraulics in 2015, and ongoing doctoral research in hydrological studies. His professional career includes technical roles in private companies and academic positions since 2014 at Adiyaman University. He conducted a research project on long-term drought analysis at Auburn University (USA) from 2019 to 2020 under Dr. Kumar's leadership. His research focuses on hydrological drought analysis, hydraulic geometry, climate change impacts, and water resource management. Key areas include drought severity-duration-frequency analysis, copula-based statistical modeling, and machine learning applications in environmental science. He has published extensively in peer-reviewed journals and conference proceedings, with topics ranging from Ceyhan Basin studies to Van Province climatic trends. EŞİT has served as a reviewer for journals like Water Resources Management and as Deputy Editor for Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi . His work integrates hydrology, climatology, and engineering to address water scarcity and environmental challenges, contributing to both theoretical and applied research in Türkiye's water resources.