جستجوی استاد راهنمای دانشگاهی

• Materials Science
• Microstructure Design
• Grain Boundary Dynamics
• +۳ مورد دیگر

Oliver Johnson is Associate Professor of Mechanical Engineering at BYU, where he researches microstructure design, grain boundary networks, and materials characterization using computational and experimental methods. His NSF CAREER-funded work develops Bayesian inference models for grain boundary properties and spectral graph approaches for microstructure optimization. Recent publications focus on ML-driven grain growth prediction, solute effects on boundary migration, and harmonic network analysis. Awards: NSF CAREER Award National Defense Science and Engineering Graduate Fellowship TMS Young Leaders Award He mentors multiple graduate students in materials science research and teaches courses in materials kinetics and manufacturing processes.

Zhaohui Gengمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Digital Transformation
• Machine Learning
• +۴ مورد دیگر

Zhaohui Geng is an Assistant Professor in the Department of Industrial and Systems Engineering at Ohio University's Russ College of Engineering and Technology. He joined Ohio University in 2023, previously serving as an Assistant Professor at the University of Texas Rio Grande Valley and a Health Sciences Research Fellow at the University of Pittsburgh's School of Medicine. His research focuses on the intersection of data science, AI, optimization, and their applications in advanced manufacturing and healthcare systems. Educations: Ph.D. in Industrial Engineering, University of Pittsburgh, 2021 M.A. in Statistics, University of Pittsburgh, 2018 M.S. in Industrial Engineering, University of Pittsburgh, 2016 B.E. in Electronic Science and Technology, Nankai University, 2014 Research Interests: Advanced manufacturing and digital transformation Machine learning and knowledge engineering Optimization techniques for healthcare systems Reverse engineering and additive manufacturing applications His work emphasizes data-driven approaches to improve manufacturing precision and healthcare system efficiency. Recent Research Trends: Recent publications highlight advancements in reverse engineering processes, additive manufacturing optimization, and AI-driven solutions for 3D scanning and quality control. His work bridges theoretical methods like Bayesian networks with practical applications in medical devices and aerospace manufacturing. Labs & Affiliations: Center for Advanced Materials Processing Center for Advanced Systems and Transportation Logistics Engineering Russ College's Industrial and Systems Engineering research groups

Zeyun Yuمشاهده پروفایل

استاد

• Image Processing
• 3D Geometric Modeling
• Scientific Computing in Biomedicine
• +۵ مورد دیگر

Dr. Zeyun Yu is a Professor of Computer Science and Biomedical Engineering at the University of Wisconsin-Milwaukee (UWM), where he also directs the Big Data Analytics and Visualization Lab. His research focuses on integrating computational methods with biomedical applications, particularly in 3D geometric modeling, image processing, and scientific computing. Dr. Yu holds a PhD from the University of Texas at Austin (2006), an MS from the Chinese Academy of Sciences (Beijing), and a BS from Peking University (Beijing). His research interests center on advancing medical imaging techniques, including deep learning for wound segmentation, dental X-ray analysis, and porous scaffold design for 3D printing. He develops algorithms for biomedical image registration, mesh generation, and feature-preserving surface reconstruction. Key contributions include software for quality mesh generation from biomedical data and methods for analyzing calcium dynamics in cardiac cells. His work bridges engineering and medicine, with applications in diagnostics, tissue engineering, and surgical planning. Dr. Yu’s lab emphasizes data-driven solutions for complex biomedical challenges, leveraging AI and high-performance computing. Current projects include automated wound classification systems and deep learning models for medical image analysis competitions.

• Computational Design
• Architectural Optimization
• Machine Learning
• +۸ مورد دیگر

Thomas Wortmann is a Tenure Track Professor for Computing in Architecture at the Institute for Computational Design and Construction (ICD) at the University of Stuttgart. He previously taught at the National University of Singapore and Xi’an Jiaotong-Liverpool University in China. Education: Diplom-Ingenieur in Architecture (University of Kassel), M.Sc. in Design and Computation (MIT), PhD in Architecture and Sustainable Design (Singapore University of Technology and Design) His research focuses on computational methods in architectural design, including optimization, machine learning, and multi-variate visualization. He developed the award-winning tool Opossum, a machine-learning-based optimization platform with over 5,000 downloads. Recent publications highlight trends in Machine Learning , Artificial Intelligence , and Robotic Construction applied to the AEC industry, with emphasis on semantic web technologies, co-design workflows, and surrogate models for performance-informed design. Scientific Awards Best Dissertation Award, Singapore University of Technology and Design Award-winning Opossum tool (over 5,000 downloads) Thomas also contributes to open-source computational design tools and advocates for scalable, free, and platform-agnostic methodologies in architectural education.

Hua Dongمشاهده پروفایل

مدرس

• Chinese Language
• Chinese Culture
• Cultural Studies
• +۳ مورد دیگر

Hua Dong is a Principal Lecturer in Chinese and Coordinator of the Chinese Program at Northeastern University's College of Social Sciences and Humanities. She specializes in teaching Chinese language and culture, and has led the Chinese Language Dialogue of Civilizations program for over a decade. Prior to academia, she worked as a documentary film producer, creating award-winning films such as The Gate of Heavenly Peace and Morning Sun , which explored modern Chinese history and culture. Her professional background also includes collaborations on Sino-American cultural projects like the preservation of the historic Chinese house Yin Yu Tang at the Peabody Essex Museum. Education: Master of Arts from Emerson College. Research interests span Chinese cultural studies, language pedagogy, and transnational cultural production. Her work bridges academic scholarship with public humanities through film and experiential learning initiatives. Office hours are Tuesdays 11am-12pm (Zoom) and Thursdays 2-3pm (in-person) at 402 Meserve Hall.

• Artificial Intelligence
• Algorithmic Cultures
• Cultural Anthropology
• +۳ مورد دیگر

Kevin Walker serves as an Associate Professor in the Research Centre in Postdigital Cultures at Coventry University. His work bridges technical AI development with anthropological and creative research, emphasizing cross-cultural approaches and creative contexts. He leads the AI and Algorithmic Cultures research group and contributes to the university's Curatorial Research and Practice initiatives. His research explores the intersection of technology, culture, and temporality, with projects like Algorithmic Cultures of Time and Ethnographic AI . Walker's expertise spans AI ethics, cultural anthropology, and digital humanities, with a focus on how algorithmic systems shape societal temporal frameworks. He has conducted visual ethnographies of global consumption practices and developed methodologies for critical spatial practices in AI. His recent projects include ACT: Algorithmic Cultures of Time (2023-2024), exploring Silicon Valley's temporal influence, and Ethnographic AI (2023), investigating AI's role in cultural understanding. He collaborates internationally, addressing UN Sustainable Development Goals through technology's ethical implications and cultural impact. Walker's publications critique AI's societal role, analyze temporal structures in digital cultures, and explore postdigital methodologies. He actively participates in art-science collaborations and curatorial research, advocating for interdisciplinary approaches to technology's human dimensions.

Samuel Ottenمشاهده پروفایل

استاد

• Mathematics Education
• Classroom Discourse
• Secondary Mathematics Instruction
• +۳ مورد دیگر

Samuel Otten is a full professor and Lois Knowles Faculty Fellow at the University of Missouri's College of Education & Human Development, serving as Department Chair for Learning, Teaching & Curriculum. He holds a Ph.D. and M.S. in Mathematics Education and Mathematics from Michigan State University, with additional degrees from Grand Valley State University. His research focuses on secondary mathematics students' participation in mathematical practices, classroom discourse, and flipped instruction. He co-leads the Flipped Mathematics Study and hosts the Math Ed Podcast, interviewing scholars in mathematics education. Education: Ph.D. and M.S. in Mathematics Education from Michigan State University; B.S. in Mathematics and Mathematics Education from Grand Valley State University. His research projects include the Flipped Mathematics Study and investigations into Common Core standards for mathematical practice. He has authored books like Missing Letters: An Alphabet Book and contributed to professional development curricula such as Connecting the NCTM Process Standards and the CCSSM Practices . Research Interests: Secondary mathematics classrooms, student engagement in reasoning and precision, flipped instruction, and professional development for teachers. He coordinates the online Master's program in Mathematics Education and co-organized PME-NA 2019. His work emphasizes incremental teacher development and the role of discourse in learning. Awards and Recognition: Not explicitly listed, but his leadership roles (e.g., Department Chair, Faculty Fellow) and prolific publications indicate significant scholarly impact. He collaborates with institutions like the NSF and the ReSTEM Institute. Grants and Leadership: Principal Investigator on NSF-funded projects and University of Missouri grants. His work spans curriculum materials analysis, teacher education, and equity in mathematics instruction. He advises on professional development frameworks like Practice-Driven PD. Outreach: Hosts the Math Ed Podcast, featuring over 300 episodes with experts in mathematics education. Coordinates outreach initiatives like the online Master's program and the Ed Specialist program. Engages in K-12 teacher training and curriculum development.

Jianlin Chengمشاهده پروفایل

استاد

• Artificial Intelligence
• Protein Structure Prediction
• Bioinformatics
• +۲ مورد دیگر

Jianlin Cheng is a Curators' Distinguished Professor in Electrical Engineering and Computer Science at the University of Missouri, with appointments in the NextGen Precision Health Institute and Data Science Institute. He directs the Bioinformatics and Machine Learning Lab, specializing in AI methods for biomedical data analysis including protein structure prediction and 3D genome reconstruction. His research develops deep learning algorithms for protein folding, cryo-EM structure determination, and genomic data analysis. Recent work focuses on transformer networks for protein complex modeling, diffusion models for 3D molecular generation, and geometric deep learning for structural biology. Publications demonstrate consistent innovation in protein structure prediction (top performer in CASP competitions), cryo-EM enhancement, and genomic architecture analysis. Trends show increasing integration of equivariant neural networks and physics-informed AI across biological domains. AAAS Fellow (2023) Curators' Distinguished Professor (2023) AIMBE Fellow (2022) NSF CAREER Award (2012) His lab maintains MULTICOM protein prediction systems and collaborates with Danforth Plant Science Center on climate resilience genomics. Funded by NIH, NSF, and DoE, his work advances computational tools for precision health applications.

Giri Narasimhanمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Data Science

Dr. Giri Narasimhan is a Distinguished University Professor at Florida International University (FIU) in the Knight Foundation School of Computing & Information Sciences. He previously served as the Associate Dean for Research and Graduate Studies in the College of Engineering and Computing. With a B.Tech. from the Indian Institute of Technology Bombay and a Ph.D. from the University of Wisconsin-Madison, his expertise spans Algorithms, Bioinformatics, and Data Science. He leads the Bioinformatics Research Group (BioRG), focusing on interdisciplinary research with federal and state grants from NSF, NIH, and industry. His awards include Excellence in Research Awards from SCIS and FIU, and the Best Poster Award at ISMB-SCS. He holds editorial roles, contributes to international conferences, and addresses keynotes globally. Research interests emphasize algorithms for biological data analysis, environmental modeling, and education innovation. Recent work includes flood prediction systems (FIDLAR), microbiome analysis pipelines (PluMA), and machine learning for climate science. He advocates for student industry engagement through micro-internships and promotes diversity in computing via initiatives like the Software Guild workshop. Grants and collaborations include multi-omic studies on aging, HIV, and environmental health. His labs integrate computational methods with clinical and ecological data to address complex challenges in bioinformatics and data-driven decision-making.

Dimitrios Makrisمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Video Analysis
• +۳ مورد دیگر

Dimitrios Makris is a Professor in Computer Vision and Machine Learning at Kingston University's Department of Computer Science, within the Faculty of Engineering, Computing and the Environment. He holds a PhD in Computer Vision from City University London and an MEng in Electrical and Computer Engineering from Aristotle University of Thessaloniki. As Faculty Director of PGR Studies, he oversees postgraduate research programs and leads investigations into video analysis, motion tracking, and neuromorphic vision systems. His research focuses on: Multi-dimensional time-series analysis for human motion tracking Neuromorphic vision sensors for robotic grasping applications Medical video analysis using contrast-enhanced ultrasound Scene semantic modeling and multi-camera systems He has secured multiple research grants from national and international organizations to advance these areas. Publication analysis reveals consistent focus on computer vision fundamentals with growing emphasis on healthcare applications and neuromorphic computing. Recent works demonstrate increased integration of machine learning with sensor technologies for real-time robotic and medical systems. Awards and leadership roles include: Deputy Editor-in-Chief for Special Issues, IET Image Processing Journal Member of Steering Committee, IET Vision and Imaging Network He actively contributes to academic communities through editorial responsibilities and steering committee membership for major vision and imaging networks.

Shima Hajimirzaمشاهده پروفایل

دانشیار

• Thermal Fluid Sciences
• Radiation Heat Transfer
• Inverse Problems
• +۷ مورد دیگر

Shima Hajimirza is an Associate Professor in the Department of Mechanical Engineering at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She directs the Energy, Control, and Optimization (ECOlab) lab. Education : PhD in Mechanical Engineering (2013, UT Austin), MS in Bioengineering (2010, Caltech), MS in Mechanical Engineering (2009, SIUE) Research Interests span thermal fluid sciences, radiation heat transfer, inverse problems, and AI-driven modeling. Her work focuses on energy systems, nanoscale energy conversion, and digital twins for energy resilience. Recent Publications highlight machine learning integration with Monte Carlo ray tracing, radiative property prediction in porous media, and solar energy optimization techniques. Scientific Awards : NSF CAREER Award, ASME Rising Star, ASME Fellow, Harvey Davis Teaching Award Advising & Grants : Mentored students in thermal and energy systems; secured over $8 million in funding from NSF, DOD, DOE, and industry partners like Chevron and Qualcomm. Labs & Teams : Leads ECOlab, advancing computational modeling and optimization for multi-scale energy technologies.

• Evolutionary Computation
• Human-Computer Interaction
• User Experience Design
• +۴ مورد دیگر

Reza Ghanbarzadeh is a Lecturer at the Faculty of Science and Engineering, Southern Cross University, Australia, with a PhD in Information Systems from Griffith University. He holds master's and bachelor's degrees in Software Engineering. His research focuses on evolutionary computation, human-computer interaction, user experience, and innovative technology applications in healthcare and education. Reza teaches IT units including immersive interaction design and software development in the School of Business and Tourism. Education: PhD (Information Systems, Griffith University), MSc (Software Engineering), BSc (Software Engineering). Research Interests: Evolutionary algorithms, healthcare informatics, generative AI in education, and user-centric software design. He has published extensively in journals and conferences, with notable work on metaheuristic optimization algorithms and their applications in software engineering and healthcare diagnostics. Awards: Recipient of the Outstanding Teaching Award (Southern Cross University, 2024). Advising & Grants: While no specific grants or students are listed, his publications suggest active involvement in collaborative research projects. He advises on topics related to AI, healthcare IT, and educational technologies. Labs/Teams: No formal lab affiliations mentioned, though his work intersects with interdisciplinary teams in health informatics and educational technology.

Anna Soraceمشاهده پروفایل

استادیار

• Cancer Imaging
• Drug Delivery
• Tumor Microenvironment
• +۲ مورد دیگر

Anna Sorace is an Assistant Professor in Biomedical Engineering at the University of Alabama at Birmingham, jointly appointed in the School of Engineering and Heersink School of Medicine. Her research advances cancer diagnostics and treatment through imaging technologies and targeted drug delivery systems. Her work integrates quantitative imaging biomarkers for early cancer treatment response assessment and optimizes therapeutic delivery using multimodal approaches. Primary research domains include tumor microenvironment analysis and precision oncology applications. Recent publications focus on computational methods for facial reconstruction and medical imaging, demonstrating consistent innovation in neural network applications for 3D geometry processing. Trends show strong emphasis on real-time clinical translation of computer vision techniques. No awards or supervised students are documented.

Riyadh Hindiمشاهده پروفایل

استاد

• Structural Engineering
• Earthquake Engineering
• Bridge Engineering
• +۳ مورد دیگر

Dr. Riyadh Hindi is a Professor of Civil Engineering at Saint Louis University's School of Science and Engineering. He holds a Ph.D. in Structural and Earthquake Engineering from the University of British Columbia, M.Sc. in Structural Engineering from University of Baghdad, and B.Sc. in Civil Engineering (First Class Honors) from University of Baghdad. His expertise encompasses non-linear behavior of concrete structures, seismic performance of bridges, and experimental analysis of structural components. Dr. Hindi's research focuses on structural response under extreme loads, including cyclic behavior of reinforced concrete, confinement techniques for bridge columns, and damage modeling. He directs the Concrete and Materials Laboratory featuring 500,000 lbs capacity testing equipment. His work integrates experimental testing with analytical modeling to improve infrastructure resilience. Recent publications demonstrate strong focus on construction-phase structural behavior (girder rotation mitigation), advanced materials (GFRP composites, high-strength concrete), and computational methods (deep learning applications). His 2025 publications indicate emerging emphasis on AI-based structural health monitoring. Honors include Fellowship in the Structural Engineering Institute (F.SEI) and professional memberships in ACI, ASCE, and IABMAS. He chairs ACI-343A subcommittee on concrete bridge design and serves as associate dean for graduate education. Dr. Hindi has supervised over 20 graduate theses and led research projects funded by NSF, Caltrans, and state DOTs. His team addresses practical construction challenges through field monitoring and develops retrofit solutions for aging infrastructure.

Steffen Staabمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۷ مورد دیگر

Steffen Staab is a Professor at the University of Stuttgart's Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on knowledge graphs, semantic web technologies, and their applications in interdisciplinary domains like construction, healthcare, and autonomous systems. He actively contributes to advancing AI fairness, geometric relational embeddings, and explainable machine learning. Staab's work bridges theoretical advancements with real-world challenges, such as improving data interoperability in building information modeling and enhancing accessibility for deaf communities via AI-driven captioning. His recent projects span semantic industrial information modeling, blockchain-based fair exchange protocols, and large language models for ontology learning in life sciences. Staab has collaborated on initiatives like the WikiMed-DE dataset for biomedical entity linking and the SEMMA knowledge graph foundation model. He also engages in digital society discussions, addressing ethical implications of AI and societal impacts of web science. Staab's research has been presented at leading conferences including Web Science and ACM SIGKDD. His team develops tools for knowledge graph reasoning, anomaly detection in zero-shot learning, and fair algorithmic decision-making. He is affiliated with the University of Stuttgart's Analytic Computing group and maintains active participation in international academic networks.