جست‌وجوی استاد و پژوهشگر

Travis J. Fuerstمشاهده پروفایل

استادیار

• Product Lifecycle Management
• Project Management
• Continuous Improvement
• +۴ مورد دیگر

Travis J. Fuerst is an Assistant Professor of Practice at the School of Engineering Technology within the Purdue Polytechnic Institute at Purdue University, West Lafayette. He has held this position since 2022, previously serving in the Department of Computer Graphics Technology from 2016 to 2022. Before returning to academia, he accumulated over 13 years of industry experience at The Boeing Company as an Engineering Workplace Coach, IT Project Manager, and Continuous Improvement Leader, complemented by 21 years of military service in the U.S. Army Reserves where he retired as a Major from USTRANSCOM in 2017. His academic credentials include: Master of Science in Technology (Product Lifecycle Management) from Purdue University (2002) Bachelor of Science in Computer Graphics Technology from Purdue University (2000) with a minor in Organizational Leadership and Supervision Professor Fuerst specializes in Product Lifecycle Management (PLM), Project Management, Continuous Improvement, and Configuration Management, integrating Lean Manufacturing and Six Sigma methodologies into both industrial applications and educational frameworks. His instruction emphasizes practical skill development for industry readiness, leveraging extensive real-world experience to bridge theoretical concepts with professional practice in engineering technology fields. His publication portfolio demonstrates a clear trajectory toward integrating Product Data Management systems into engineering education, with emphasis on digital enterprise solutions and pedagogical innovation. Key themes include parametric solid modeling applications, collaborative project-based learning frameworks, and curriculum development for PLM implementation across undergraduate programs, reflecting his dual focus on technological advancement and educational transformation. His recognition includes: Purdue Polytechnic 2013 Early Career Award Professor Fuerst actively mentors undergraduate and graduate students through project-based design learning that cultivates higher-order thinking skills, directly applying his industry expertise in risk analysis, resource allocation, and cross-functional team leadership. His curriculum development work demonstrates sustained commitment to advancing engineering education practices through practical, industry-aligned methodologies. His leadership experience spans Boeing's continuous improvement initiatives and U.S. Army cyber operations, providing a robust foundation for developing team-based project management approaches that emphasize operational efficiency and strategic problem-solving in academic settings.

Sylvain Lefebvreمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +۴ مورد دیگر

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

Lilja Øvrelidمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Lilja Øvrelid is a Professor at the Department of Informatics, University of Oslo, leading the Language Technology Research Group. Her research focuses on syntactic and semantic text processing using machine learning techniques such as dependency parsing, negation analysis, and sentiment analysis. She teaches courses including IN1140: Introduction to Language Technology , IN5550: Neural Methods in NLP , and INF5830: Natural Language Processing . Her academic interests span natural language processing, machine learning, and computational linguistics, with a particular emphasis on Norwegian language technology. Recent publications highlight work in sentiment analysis (including patient feedback), event extraction from Norwegian news, benchmarking language models, emotion analysis for under-resourced languages (Pashto, Farsi-Dari), and bias detection in multilingual models. She actively contributes to the development of Norwegian language resources such as NorBench, NorQuAD, and NoReC. Current projects include BigMed and SIRIUS , focusing on biomedical text mining and AI infrastructure. Collaborations with colleagues like Erik Velldal, David Samuel, and Vladislav Mikhailov are frequent in her work. Despite no explicit mention of scientific awards, her contributions to NLP and computational linguistics are substantial through publications, datasets, and tool development.

Maria Sydnesمشاهده پروفایل

دانشیار

• Emergency Management
• Crisis Response
• Organizational Resilience
• +۴ مورد دیگر

Maria Sydnes serves as an Associate Professor in the Department of Technology and Security at UiT The Arctic University of Norway, Tromsø. Her academic work focuses on emergency management and organizational resilience within high-risk Arctic environments, with particular emphasis on maritime safety and cross-border crisis coordination between Norway and Russia. Her research interests span interorganizational coordination during emergencies, organizational learning for resilience building, and practical applications of lean production principles in crisis contexts. She investigates Arctic-specific challenges including avalanche response in Longyearbyen, oil spill management in polar waters, and integration of unorganized volunteers into formal emergency systems, consistently addressing the intersection of climate change impacts and security vulnerabilities. Analysis of her publication record reveals a strong trend toward interdisciplinary solutions for Arctic safety, with recurring themes of Norwegian-Russian cooperation, maritime risk assessment, and adaptive governance structures. Her work bridges theoretical frameworks with real-world case studies—from Svalbard waters to the Barents Sea—demonstrating practical approaches to disaster risk reduction in extreme environments. Dr. Sydnes actively participates in multiple research initiatives including the Climate Change Adaptation and Secure Societies groups. Her current projects encompass Co-producing knowledge about risk and safety of maritime activities around Svalbard SPRICE (spray icing modelling for maritime decision support) GLOBSEC (North-South security partnerships) NoCoU (civil security for Norwegian communities) Beredt! (risk-based governance for climate-driven natural hazards) highlighting her commitment to scalable, community-focused security solutions in vulnerable Arctic regions.

Rasmus Paghمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Big Data
• +۷ مورد دیگر

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

Qi Yuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

• Educator Evaluation
• Instructional Coaching
• Education Policy
• +۴ مورد دیگر

Dr. Seth B. Hunter is an Associate Professor of Education Leadership at George Mason University ’s College of Education and Human Development . He serves as a Senior Fellow at EdPolicy Forward (Mason’s Center for Education Policy) and a Fellow with the Tennessee Education Research Alliance . His work bridges educator effectiveness, policy implementation, and human-machine collaboration in education. PhD in Education Policy from Peabody College, Vanderbilt University Dr. Hunter’s research integrates educator evaluation systems , instructional coaching , and policy impacts on educational outcomes . He employs mixed methods (econometric, psychometric, qualitative) and interdisciplinary frameworks (psychology, economics, leadership theory). Notably, he investigates how feedback mechanisms and human-machine partnerships shape instructional quality. His recent publications focus on teacher evaluation policy effects , instructional coaching distribution , and equity in rural education . Articles span topics like feedback valence, policy implementation in rural districts, and intersectional impacts of leadership demographics. Though no scientific awards are listed, his work informs state-level policies in Kentucky, Tennessee, and Virginia. Dr. Hunter previously served K-12 organizations as a teacher, union representative, and professional association president. He teaches courses on school improvement , instructional supervision , and doctoral research . Personal interests include barbecuing , ice hockey , and music .

Gregory Hillمشاهده پروفایل

استادیار

• Optometry
• Refractive Surgery
• Ophthalmology
• +۳ مورد دیگر

Dr. Gregory Hill is an Assistant Professor at the College of Optometry, Nova Southeastern University. He holds a Doctor of Optometry (O.D.) from Indiana University School of Optometry (1995). His career spans diverse practice modalities including private practice, corporate retail care, refractive surgery management, and hospital-based ophthalmology co-management. He has contributed to ecological research via a National Science Foundation study on prairie ecosystems and co-authored related journal articles. Dr. Hill is actively involved in professional organizations, including leadership roles in the South Dakota chapter of Volunteer Optometric Services to Humanity (VOSH), and has led mission trips to Mexico, Jamaica, and Native American reservations. Education: Indiana University School of Optometry (O.D., 1995), undergraduate studies with NSF-funded ecosystem research. His research interests bridge clinical optometry and ecological science, emphasizing interdisciplinary collaboration. Dr. Hill advocates for continuous learning and professional integrity, reflecting in his teaching roles in undergraduate biology and optometric lab sections. He is a certified refractive surgery specialist and has received multiple academic honors, including Dean’s List recognition throughout his undergraduate career. Awards/Honors: Advanced Clinical Certification (TLC Vision), membership in two honor societies, VOSH leadership. He emphasizes community engagement through knowledge-sharing, exemplified by media appearances on eye care topics and continuing education lectures for healthcare professionals.

• Software Testing
• Regression Testing
• Flaky Tests
• +۳ مورد دیگر

Darko Marinov is a Professor at the Siebel School of Computing and Data Science and a member of the Information Trust Institute at the University of Illinois at Urbana-Champaign. His research focuses on software testing methodologies, particularly regression testing, flaky tests, and software reliability engineering. He has contributed to frameworks like Ekstazi for regression test selection and DeFlaker for identifying flaky tests. Marinov holds an NSF CAREER Award (2008) for his work in this domain. His research interests span testing techniques for modern software systems, including configuration testing, test prioritization, and fault localization. He has pioneered studies on the characteristics of flaky tests in large-scale projects and developed tools to improve software quality assurance processes. Marinov collaborates across academic and industrial settings, addressing challenges in continuous integration, reproducibility of computational experiments, and hardware-software co-design for resilience. His work bridges theory and practice, with applications in cloud computing, AI workloads, and cybersecurity. Awards: NSF CAREER Award (2008) Key Contributions: Ekstazi, DeFlaker, FastFlip, and Ctest4J frameworks Labs/Teams: Active member of the Information Trust Institute and leads software testing research groups at UIUC

Joaquin Vanschorenمشاهده پروفایل

دانشیار

• Machine Learning
• Automated Machine Learning
• Algorithm Selection
• +۴ مورد دیگر

Joaquin Vanschoren is an Associate Professor of Machine Learning at Eindhoven University of Technology (TU/e), affiliated with the Faculty of Mathematics and Computer Science. He leads the Automated Machine Learning group and serves as Education Director for the Data Science program. His research focuses on democratizing AI, algorithm selection, and open science platforms like OpenML. He has received awards including the Dutch Data Prize and Amazon Research Award. Education: PhD in Engineering (KU Leuven, Belgium), MSc in Computer Science (KU Leuven). Research visits included IBM, Amazon Research, and universities globally. Research Interests: Machine Learning, Automated ML, Meta-learning, AI Safety, Data-centric AI. He co-founded OpenML and chairs MLCommons' AI Safety working group. Key Projects: NeurIPS Datasets and Benchmarks track, MLCommons initiatives, OpenML platform. Supervised 78 research works and authored 200+ papers. Awards: Dutch Data Prize (2016), Amazon Research Award (2019), Microsoft Azure Research Awards (2016–2017). Labs/Teams: OpenML open source team, MLCommons collaborations, Automated Machine Learning group at TU/e.

Xiajun Jiangمشاهده پروفایل

استادیار

• Adaptive AI Computing
• Physics-Informed Deep Learning
• AI for Healthcare
• +۳ مورد دیگر

Xiajun Jiang is an Assistant Professor in the Department of Computer Science at the University of Memphis, joining in Fall 2024. He holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2024), an M.S. in Computer Science from the University of Southern California (2018), and a B.S. in Electrical Engineering and Automation from Zhejiang University (2016). His research focuses on adaptive AI computing, physics-informed deep learning, and their applications in healthcare, particularly in medical imaging and cardiac simulation. Key contributions include hybrid neural state-space modeling for electrocardiographic imaging and physics-informed frameworks for bi-ventricular electrophysiological simulations. Education: PhD, Rochester Institute of Technology, 2024 M.S., University of Southern California, 2018 B.S., Zhejiang University, 2016 Research Interests: Machine learning for healthcare Adaptive computing in AI models Physics-informed deep learning His work bridges machine learning and biomedical engineering, with applications in cardiac imaging and electrophysiology. Recent articles highlight advancements in hybrid models for ECGI and meta-learning approaches for personalized cardiac simulations. He has reviewed for top conferences like ICLR, NeurIPS, and MICCAI, and contributed to projects like the Computational Biomedical Lab (CBL).

Eoin Delaneyمشاهده پروفایل

مدرس

• Explainable AI
• Trustworthy AI
• Computer Vision
• +۶ مورد دیگر

Eoin Delaney is a Lecturer in the School of Computer Science and Statistics at Trinity College Dublin (as of Spring 2025). Previously, he was a Postdoctoral Researcher at the Oxford Internet Institute (OII), University of Oxford (October 2023 – March 2025), focusing on Trustworthiness Auditing in AI. His research spans Explainable AI, algorithmic fairness, computer vision, and sustainable AI applications. He holds a PhD from University College Dublin (UCD), advised by Dr. Derek Greene and Prof. Mark T. Keane, where he developed counterfactual explanations for time series and image data. His work bridges technical AI research with societal impact, including collaborations with Accenture Labs and the VistaMilk SFI Research Centre. Key achievements include developing the OxonFair toolkit (accepted to NeurIPS 2024), winning the 2022 AI Ireland Award for Best Application of AI in a Student Project, and authoring papers in top venues like NeurIPS, AIJ, and IJCAI. He is actively involved in academic service, serving on program committees for FAccT 2025 and AAAI workshops. Eoin has supervised students at the University of Oxford and organizes workshops on AI accountability, such as the Auditing Accountability in Trustworthy Artificial Intelligence in personalized medicine. His teaching and outreach efforts include promoting STEM education through initiatives like an educational website for children, emphasizing cryptography and probability. He continues to explore fairness in AI systems, user-centric explanations, and scalable machine learning frameworks (PyTorch, TensorFlow).

• Classical Literature
• Roman History and Culture
• Literary Criticism and Commentary
• +۳ مورد دیگر

Dr. Anne Rogerson is a Senior Lecturer in Classics and Ancient History at the University of Sydney, affiliated with the Vere Gordon Childe Centre. She holds a BA and BSc from the University of Sydney and a PhD from the University of Cambridge. Her research centers on Virgil’s Aeneid , its cultural reflections, and its reception in art and literature across eras, including analyses of late antique to modern commentaries. Current projects include a book exploring how female novelists like George Eliot and Margaret Atwood engage with Latin learning, and an Australian Research Council-funded study on Vandal North African Latin literature. Education: BA and BSc, University of Sydney PhD, University of Cambridge Research Interests: Virgil’s Aeneid and its narrative dynamics Commentary traditions on Virgil Classical reception in modern literature and art Gender studies in classical contexts Grants: ARC-funded project: The Vandal Renaissance: Latin Literature in Post-Roman Africa (435-534CE) Media Engagement: Regular contributor to ABC Radio’s Self Improvement Wednesday and This Week in History , discussing topics such as ancient Roman culture, classical literature, and historical parallels. Labs/Teams: Member of the Vere Gordon Childe Centre Editor of Classicum (Classical Association of NSW journal)

Frank E. Curtisمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Numerical Analysis
• +۳ مورد دیگر

Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been since 2009. He holds a B.S. in Mathematics and Computer Science from the College of William and Mary (2003), an M.S. and Ph.D. in Industrial Engineering from Northwestern University (2004 and 2007), and completed a postdoctoral fellowship at New York University’s Courant Institute (2007–2009). His research focuses on developing numerical methods for large-scale nonlinear optimization, with applications in machine learning, operations research, and energy systems. Key achievements include the 2021 SIAM/MOS Lagrange Prize for Continuous Optimization (with Bottou and Nocedal) and the 2018 INFORMS Computing Society Prize (with Burke, Lewis, and Overton). He has secured significant funding from the NSF, DoE, and ONR, including a TRIPODS grant and ARPA-E awards. His work on the ARPA-E Grid Optimization Competition earned second place in 2020. Curtis’s research interests span mathematical optimization, numerical analysis, and algorithm design. His recent articles emphasize stochastic optimization, fairness in machine learning, and robust algorithm development for constrained systems. He serves as an Area Editor for Mathematics of Operations Research and Associate Editor for multiple top journals, including Mathematical Programming and SIAM Journal on Optimization . Notable grants include DoE ASCR Early Career Awards and NSF TRIPODS funding for collaborative projects with Northwestern, Boston University, and Cornell. His OptML @ Lehigh team develops cutting-edge optimization tools and frameworks for real-world applications.