جست‌وجوی استاد و پژوهشگر

• Econometrics
• Bayesian Statistics
• Quantitative Marketing
• +۲ مورد دیگر

Kenichi Shimizu is an Assistant Professor in Econometrics (tenure-track) at the Department of Economics, University of Alberta. He holds a PhD in Economics from Brown University (2021) and previously worked at the Adam Smith Business School, University of Glasgow. His research focuses on Bayesian econometrics, quantitative marketing, industrial organization, and time-series analysis. He teaches courses such as Introductory Econometrics (ECON 399) and Applied Econometrics (ECON 599). Education: PhD in Economics from Brown University (2021). Professional affiliations include roles at the University of Alberta and University of Glasgow. His work emphasizes methodological advancements in econometrics with applications to marketing and industrial organization. Research trends in his publications highlight Bayesian methodologies for dynamic modeling, structural breaks, and high-dimensional data. Key topics include semiparametric estimation, sparse models, and policy evaluation frameworks. Grants: Recipient of SSHRC Insight Development Grant (2024-2026) for research on Bayesian econometric methods in industrial organization and marketing. Active presenter at major conferences including the NBER-NSF Seminar, Canadian Economic Association meetings, and the World Congress of the Econometric Society. Teaching responsibilities include undergraduate and graduate econometrics courses with emphasis on applied regression methods and model specification.

Farhad Malekiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Farhad Maleki is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. He holds a PhD in Computer Science from the University of Saskatchewan (2019). His postdoctoral research at McGill University’s Augmented Intelligence & Precision Health Laboratory focused on machine learning for medical image analysis. He has held leadership roles, including President of the Association of Postdoctoral Fellows at McGill and President of the Computer Science Graduate Council at the University of Saskatchewan. Currently, he serves on the Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine and as a guest editor for journals in medical data analysis. Dr. Maleki’s research spans Artificial Intelligence , Machine Learning , Biomedical Data Analysis , and Computer Vision . His work emphasizes medical applications, including tumor segmentation, clinical outcome prediction, and AI-driven diagnostics in oncology and cardiology. He also explores agricultural challenges, such as wheat head segmentation using generative models and domain adaptation. Key contributions include developing robust medical imaging tools (e.g., Rel-UNet for tumor segmentation) and frameworks for evaluating AI model reliability ( RIDGE ). His work bridges clinical needs with computational innovation, addressing issues like reproducibility, generalizability, and low-annotation learning across healthcare and agriculture domains. Dr. Maleki’s articles focus on advancing AI methods for precision health and agriculture. His recent work highlights interdisciplinary applications, such as integrating clinical and pathology data for cancer survival prediction, optimizing radiation therapy using Bayesian methods, and leveraging synthetic data for crop phenotyping. These studies emphasize practical deployment and ethical considerations in AI adoption.

Chen Wei Wayneمشاهده پروفایل

استادیار

• Generative Design AI
• Machine Learning
• Uncertainty Quantification
• +۳ مورد دیگر

Chen Wei Wayne is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research focuses on generative design AI, machine learning, uncertainty quantification, and advanced manufacturing. He leads the DIGIT Lab, which develops AI methods for design innovation, automation, and manufacturing integration. Education: Ph.D., Mechanical Engineering, University of Maryland, College Park (2019) M.S., Mechanical Engineering, Chongqing University, China (2015) B.S., Mechanical Engineering, Chongqing University, China (2012) Research Interests: Generative adversarial networks (GANs) for design synthesis Data-driven metamaterials and multiscale systems Uncertainty quantification in engineering design AI-driven design automation Awards & Honors: ASME Journal of Mechanical Design Reviewer of the Year Award (2023) ASME DAC Best Paper Award (2022) Journal of Mechanical Design Editors’ Choice Honorable Mention (2021) Lab Activities: Recent lab milestones include successful completion of TAMUQ Summer Research Programs (2024) Hosts undergraduate researchers like Wisam Gadam and Eddie Guerrero

Valen Johnsonمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Medical Imaging
• +۷ مورد دیگر

Valen E. Johnson is a University Distinguished Professor and Dean Emeritus of the College of Science at Texas A&M University, where he has been a faculty member since 2012. He previously held professorships at the University of Texas M. D. Anderson Cancer Center (2004–2012), the University of Michigan (2002–2004), and Duke University (1989–2001). He also served as a Technical Staff Member at Los Alamos National Laboratory (2001–2002). Johnson earned his Ph.D. in Statistics from the University of Chicago (1989), M.A. in Applied Mathematics from the University of Texas at Austin (1985), and B.S. in Mathematics from Rensselaer Polytechnic Institute (1981). His research focuses on Bayesian methodology, including hypothesis testing, variable selection in high-dimensional spaces, latent variable models, and applications in medical imaging, clinical trials, and educational assessment. He has contributed to the development of non-local prior densities and Bayesian diagnostics for MCMC convergence. His work bridges Bayesian and classical statistical approaches, emphasizing reproducibility and rigorous evidence assessment in scientific research. Johnson has held editorial roles, including Co-Editor of Bayesian Analysis (2010–2014) and Associate Editor of the Journal of the American Statistical Association (2011–present). He is a Fellow of the American Statistical Association and the Royal Statistical Society and has served on the Board of Directors of the International Society for Bayesian Analysis. His advocacy for revised statistical significance standards has sparked major debates in scientific methodology. Johnson has supervised numerous doctoral students, including those whose theses won prestigious awards like the Savage Award. He has collaborated on grants addressing medical imaging, system reliability, and cancer symptom management, reflecting his interdisciplinary impact in biostatistics and public health.

Liu Yongمشاهده پروفایل

استادیار

• Applied Econometrics
• Bayesian Econometrics
• Machine Learning
• +۴ مورد دیگر

Yong Liu is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. His research focuses on applied econometrics, Bayesian methodologies, machine learning, and agricultural policy with emphasis on crop insurance and risk management. He holds a Ph.D. in Agricultural Economics from the University of Guelph (2016), preceded by dual M.S. degrees in Economics and Agricultural Economics from Yunnan University and the University of Guelph, respectively, and a B.S. in Chemistry from Yunnan University. His research explores econometric techniques for agricultural policy evaluation, including causal inference methods and behavioral economics applications. Recent work analyzes climate change impacts on crop yields, the efficacy of insurance subsidies, and the integration of weather data into risk assessment models. Liu’s publications demonstrate a strong focus on improving crop insurance mechanisms through statistical innovations like Bayesian model averaging and spatial-temporal forecasting. His academic contributions span theoretical advancements in density estimation and practical applications in agricultural finance. While no specific awards or grants are listed, his active publication record indicates sustained research engagement in agricultural economics and quantitative methods.

Chris Rogersمشاهده پروفایل

استاد

• Financial mathematics
• probability theory
• stochastic analysis
• +۲ مورد دیگر

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.

Amrita Basakمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Metal 3D Printing
• Laser Powder Bed Fusion
• +۶ مورد دیگر

Amrita Basak serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at Pennsylvania State University. Her research focuses on advancing metal additive manufacturing technologies, particularly for gas turbine applications. She maintains her laboratory in 233 Reber Building at University Park, PA. Her primary research interests center on laser-based additive manufacturing processes including Laser Powder Bed Fusion (L-PBF) and Laser Directed Energy Deposition (LDED). Specific expertise spans nickel-based superalloys, melt pool dynamics, microstructure-property relationships, fatigue behavior of additively manufactured components, and AI-driven process optimization. Her work addresses critical challenges in thermal distortion control, surface roughness effects, and high-temperature performance of turbine components. Analysis of her recent publications reveals strong emphasis on integrating machine learning with experimental methods to optimize additive manufacturing processes. Key trends include Gaussian process regression for melt pool modeling, Bayesian optimization for thermal management, reinforcement learning for parameter control, and multi-fidelity modeling approaches. Her research bridges fundamental materials science with practical engineering applications in aerospace and energy sectors. Scientific Awards: NSF CAREER Award (2024) for gas turbine research DARPA Young Faculty Award (2022) for multi-laser additive manufacturing Materials Research Institute Roy Award (2023) Professor Basak actively mentors graduate students including R. Pal, N. Menon, and A. Kushwaha who appear as first authors on multiple publications. Her research is supported by significant grants including NSF CAREER funding, Office of Naval Research grants (2024), and DARPA funding. Current projects include 'On-Demand 3D Printing of Food-Grade Biopolymer-Encapsulated Ferrate(VI) for Individualized and Equitable Access to Drinking Water' and metal additive manufacturing research for gas turbine hot section components.

Zeda Liمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Time Series Analysis
• +۵ مورد دیگر

Zeda Li is an Assistant Professor of Statistics at the Paul H. Chook Department of Information Systems and Statistics in the Zicklin School of Business at Baruch College, CUNY. She holds a PhD in Statistics from Temple University (2018) and advanced degrees in Biostatistics and Electrical Engineering.

Rodrigo Gonzálezمشاهده پروفایل

استادیار

• Data-driven modeling
• System identification
• Control systems engineering
• +۴ مورد دیگر

Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)

Weifeng Liمشاهده پروفایل

دانشیار

• Artificial Intelligence Security
• Cybersecurity Applications
• Machine Learning
• +۴ مورد دیگر

Weifeng Li serves as an Associate Professor in the Department of Management Information Systems at the University of Georgia's Terry College of Business. His academic foundation includes a Ph.D. in Management Information Systems from the University of Arizona (2017) and a B.S. from Shanghai Jiao Tong University (2012). Ph.D., Management Information Systems, University of Arizona (2017) B.S., Management Information Systems, Shanghai Jiao Tong University (2012) Dr. Li's research centers on AI security and cybersecurity applications , with methodological expertise in machine learning, natural language processing, and Bayesian modeling. His work spans critical domains including adversarial robustness in AI systems, dark web threat intelligence, disinformation detection, and phishing defense mechanisms. He develops frameworks for proactive cyber defense through generative adversarial learning and interpretable multi-modal models. His publication portfolio reveals a strong trajectory in top-tier venues, with recent work focusing on adversarial robustness (RADAR framework), dark web community analysis, and interpretable AI for security applications. Research consistently bridges theoretical machine learning advances with practical cybersecurity implementations, particularly in financial technology and social media contexts. Dr. Li's research has received funding from the National Science Foundation's Secure and Trustworthy Cyberspace (SaTC) program, supporting his work on AI security frameworks. His collaborations span multiple institutions and research groups focused on cyber threat intelligence. He contributes to cybersecurity infrastructure through systems like the AZSecure text mining platform for dark web monitoring and hacker community analysis. His work enables proactive threat detection through nonparametric topic modeling and generative adversarial approaches to counter cybercriminal tactics.

Seongjin Choiمشاهده پروفایل

استادیار

• Urban Mobility Data Analytics
• Spatiotemporal Data Modeling
• Deep Learning & Artificial Intelligence
• +۳ مورد دیگر

Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.

Alexandros Kontogiannisمشاهده پروفایل

پژوهشگر ارشد

• Fluid Dynamics
• Applied Mathematics
• Bayesian Inference
• +۱۰ مورد دیگر

Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
• +۳ مورد دیگر

Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

Allen Caldwellمشاهده پروفایل

استاد

• Particle Physics
• Accelerator Physics
• Neutrino Physics
• +۳ مورد دیگر

Prof. Dr. Allen Caldwell is an Honorary Professor at the Technical University of Munich (TUM) School of Natural Sciences since 2006. He is also a Director at the Max Planck Institute for Physics (since 2002) and has held leadership roles in major international collaborations like the AWAKE Collaboration at CERN and the ZEUS Collaboration at DESY. Education : Ph.D. in Physics from the University of Wisconsin (1987), preceded by studies at Rice University (Texas, USA). His research spans fundamental Particle Physics , focusing on plasma wakefield acceleration , neutrino properties , and dark matter candidates like axions. Notably, he leads the AWAKE experiment at CERN, which explores proton-driven plasma acceleration. Recent publications highlight work on plasma wave-based particle acceleration , Bayesian neutrino mass analysis , and dielectric haloscopes for axion detection . These reflect his expertise in experimental and theoretical frameworks for solving high-energy physics challenges. Scientific awards include the Distinguished Alumnus Award (University of Wisconsin, 2012), Fellow of the American Physical Society (2000), and Alexander von Humboldt Fellowship (1995-1997). He contributes to international collaborations such as the Belle II Experiment (KEK, Japan), MADMAX (searching for axions), and LEGEND (neutrino properties). He also teaches probability, statistics, and Monte Carlo methods at TUM.

• Macroeconomics
• Macro-Finance
• Econometrics
• +۷ مورد دیگر

Prof. Dr. Alexander Meyer-Gohde is a Professor of Financial Markets and Macroeconomics at Goethe University Frankfurt’s Faculty of Economics and Business, and a key figure at the Institute for Monetary and Financial Stability (IMFS). His research spans macroeconomic theory, macro-finance, numerical methods, and econometrics, focusing on DSGE models, nonlinear dynamics, and the impact of risk and uncertainty on monetary policy. Education : PhD in Economics (Technische Universität Berlin), MA in Economics and Management (Humboldt-Universität zu Berlin), BA in Language, Literature & Culture (Colorado State University). Research Interests : Macroeconomics, macro-finance, numerical methods, recursive preferences, stochastic volatility, and model uncertainty. Grants : DFG Individual Research Grant (2021-2024) and MatlabMakro DigiTeLL Grant (2022-2023). Publications : Focus on DSGE model solution methods, numerical stability, term premia, and nonlinear dynamics in macroeconomics. Students : Supervises job market candidates Johanna Saecker and Mary Tzaawa-Krenzler. Leadership : Chair of Financial Markets and Macroeconomics at Goethe University (2018–present) and coimplementation of the IMFS “Project Monetary and Financial Stability”.