جست‌وجوی استاد و پژوهشگر

Thomas Mazurمشاهده پروفایل

دانشیار

• Radiation Oncology
• Medical Physics
• Image-Guided Radiation Therapy
• +۳ مورد دیگر

Thomas Mazur , PhD, serves as Associate Professor of Radiation Oncology at Washington University School of Medicine . He joined the faculty in 2016 after completing a medical physics residency at the same institution. His academic journey began with a BS in Physics and Mathematics (2007) from Union College, followed by a PhD in Experimental Atomic Physics (2014) at The University of Texas at Austin. Key Research Areas: Image-Guided Radiation Therapy (MR Guidance) Adaptive Treatment Delivery Systems Radiomics and Belief Function Theory Optically Stimulated Luminescence Dosimetry Stable Isotope Enrichment for Radioisotopes Selected Scientific Awards: RO-ILS Physics Safety Star Award (2024) Mentor of the Year – Radiation Oncology (2020) Springer Theses Prize (2015) UT Austin Outstanding Dissertation Award (2014) Dr. Mazur's publications (55 total) demonstrate expertise in tumor motion prediction, radiomics-based outcome modeling, and automated treatment planning systems. Collaborative projects include copper complex anticancer studies and storage phosphor imaging development.

Luis N Amaralمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Biomedical Informatics
• +۵ مورد دیگر

Luis N Amaral is a Professor at both the Department of Medicine (Pulmonary and Critical Care) and the McCormick School of Engineering at Northwestern University . His interdisciplinary research bridges computational sciences with biomedical applications. PhD in computational biology from Boston University (1996) Key research areas include machine learning , biomedical informatics , scientific integrity , and network science . Recent publications focus on clinical state extraction using AI, reproducibility in materials science, and foundational issues in artificial intelligence. Publications from 2025-2024 reveal trends in AI ethics , medical data analysis , and scientific methodology . His work combines complex systems research with healthcare applications , including pneumonia subtyping algorithms and tools for critical care diagnostics. Affiliated with the Institute for Artificial Intelligence in Medicine and Center for Computational and Social Sciences in Health , Amaral contributes to interdisciplinary research initiatives like NUCATS. His studies explore social network effects on health outcomes and system-level scientific challenges.

• Materials Science
• Shape Memory Materials
• Biomaterials
• +۴ مورد دیگر

Ken Gall is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University with cross-appointments in Orthopaedic Surgery and Biomedical Engineering. His career integrates fundamental materials science with biomedical device commercialization, focusing on processing-structure-property relationships in metals and polymers for orthopedic applications. Education: B.S., University of Illinois at Urbana-Champaign (1995) M.S., University of Illinois at Urbana-Champaign (1996) Ph.D., University of Illinois at Urbana-Champaign (1998) Gall specializes in shape memory materials (metals and polymers), biomaterials, and 3D printing for medical devices. His current work targets 3D printed metals/polymers with engineered porous networks, soft synthetic biomaterials for tissue integration, and biopolymer surface structures. This bridges materials mechanics with clinical orthopedics and pelvic organ prolapse repair. Recent publications (2023-2025) show a clear shift toward computational methods (neural networks, finite element modeling) for predicting mechanical behavior of porous 3D printed structures. Key trends include clinical translation of gyroid lattices, in vivo validation of titanium implants, and material optimization for pelvic tissue applications—reflecting his entrepreneurial focus on commercializing university research. Scientific awards: ASEE Curtis McGraw Award (2012) TMS Robert Lansing Hardy Award (2008) ASM Bradley Stoughton Award (2005) ASME Gold Medal (2004) Presidential Early Career Award for Scientists and Engineering (PECASE) (2002) Gall actively consults for industry, the US Military, and US Intelligence Community while serving as an expert witness in patent litigation. His entrepreneurship includes co-founding MedShape and Vertera to commercialize orthopedic technologies. He teaches mechanical engineering courses (ME 592, 591, 555, 492, 491, 392) and engineering projects (EGR 393). The Gall Group research team develops porous metallic/polymeric implants using additive manufacturing, with emphasis on osseointegration, load-sharing, and fatigue behavior—particularly for foot/ankle and spinal applications.

Pierre-François Gimenezمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Machine Learning
• Network Security
• +۳ مورد دیگر

Pierre-François Gimenez is a cybersecurity and machine learning researcher affiliated with the Laboratory Confidentiality, Integrity, Availability, Distribution . His work focuses on intrusion detection systems, malware analysis, and secure software design. Research Interests : Secure programming languages resistant to injection attacks Anomaly detection in network environments Machine learning applications for cybersecurity Malware dataset validation and bias mitigation Recent Publication Trends : His articles from 2025 demonstrate expertise in graph-based detection of advanced threats, synthetic network traffic generation, and robust malware detection frameworks. He also explores preference learning for recommendation systems in interactive configuration.

Dan Knightsمشاهده پروفایل

دانشیار

• Microbiome Research
• Computational Genomics
• Data Mining
• +۴ مورد دیگر

Dan Knights is an Associate Professor in the Department of Computer Science and Engineering and the Biotechnology Institute at the University of Minnesota. His research bridges computational science and microbiome studies, developing advanced data mining methods for high-dimensional biological data analysis. He leads the Knights Lab, which focuses on computational genomics applications in human health and sustainability modeling. Dr. Knights' research interests center on microbiome analysis, computational genomics, and data science applications in biological systems. His work has significantly advanced our understanding of how the human gut microbiome changes with industrialization, immigration, and dietary patterns. He has developed innovative computational approaches including contributions to the widely used QIIME platform for microbiome analysis. His recent work extends into sustainability modeling, applying data science to identify practices that promote long-term civilizational resilience. Analysis of Dr. Knights' publication record reveals a strong focus on microbiome research with emphasis on human health applications. His work spans methodological development, clinical studies, and population-level investigations of microbiome dynamics. Key themes include the impact of Westernization on gut microbial communities, personalized diet-microbiome relationships, and computational approaches for analyzing complex microbial ecosystems. His research bridges computer science, genomics, and clinical medicine. 2023 Charles E. Bowers Faculty Teaching Award 2020 Highly Cited Researchers designation Dr. Knights has successfully mentored multiple PhD students to completion, including Benjamin Hillmann (2022), Pajau Vangay (2018), and Gabe Al-Ghalith (2018). His research has attracted significant funding, leading to the creation of CoreBiome, a University of Minnesota startup that was acquired by OraSure Technologies. His work has been recognized with multiple innovation awards, including the OVPR Innovation Award. Dr. Knights' research program demonstrates strong translational impact, moving from basic computational methods to commercial applications that accelerate microbiome discovery across industries. The Knights Lab at the University of Minnesota is a multidisciplinary research group focused on data science applications in microbiome research and sustainability modeling. The lab has developed widely used computational tools like QIIME 2 for microbiome analysis and has conducted groundbreaking research on how human migration and lifestyle changes affect gut microbial communities. Current projects include AI applications for predicting environmental impacts and developing methods for analyzing incomplete and high-dimensional biological datasets. Although not currently accepting new students, the lab has established itself as a leader in computational approaches to microbiome research.

• Software Product Lines
• Software Variability
• Feature Modeling
• +۳ مورد دیگر

Sebastian Krieter is a researcher in software engineering and product-line systems, affiliated with Otto-von-Guericke University Magdeburg, Germany. He completed his PhD in 2022 with a dissertation focused on efficient interactive and automated product-line configuration. His research spans software variability, feature modeling, configuration management, and automated software configuration. Research Interests: Software Product Lines and Variability Management Feature Modeling and Analysis Automated Configuration and Sampling Algorithms Combinatorial Testing and Feature Interaction Knowledge Compilation and SAT-based Analysis His recent work includes developing methods for T-wise interaction coverage, improving sampling efficiency in configurable systems, and integrating AI techniques into software variability management. He has contributed to the development of tools like FeatureIDE and UVL (Universal Variability Language). Scientific Contributions: Over 70 peer-reviewed publications in top-tier venues such as ICSE, SPLC, GPCE, and ASE. Key organizer of workshops including the 1st International Workshop on Reverse Variability Engineering (Re: Volution). Active contributor to open-source tools and datasets for benchmarking feature model analyses. Collaborations: He has collaborated extensively with Thomas Thüm, Gunter Saake, Thomas Leich, and a network of international researchers in software engineering and AI.

• Data Science Systems
• Machine Learning Systems
• Database Systems
• +۷ مورد دیگر

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science and the Data and Information Systems (DAIS) Research Lab. He co-founded and serves as Chief Scientist at Keebo, Inc., a startup developing automated data warehouse optimization platforms. Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (2017) M.S. in Computer Science, University of Michigan, Ann Arbor (2013) B.S. in Electrical Engineering, Seoul National University (2009) His research focuses on building intelligent data-intensive systems that integrate statistical and AI techniques for improved reliability, scalability, and usability in data science workflows. Key projects include Kishu (undoable Jupyter notebooks), AirIndex (automated index optimization), and CARE (causal-reasoning data systems). His work bridges database systems and machine learning, emphasizing end-to-end optimization for structured/unstructured data processing. Recent publications highlight trends in computational notebook checkpointing, index tuning through data-aware storage, and AI-driven database learning. His projects have been recognized at top venues including SIGMOD, VLDB, and CHI, with a focus on practical implementations for real-world data challenges. Scientific Awards: NSF CAREER Award (2025) Best Demo Award at SIGMOD 2025 ACM SIGMOD Jim Gray Dissertation Award Runner-up (2018) Engineering Council Outstanding Advising Award (2021) Teaching Excellence Awards (2023, 2024) Yongjoo advises multiple PhD and MS students, including Supawit Chockchowwat (joining CMKL University in Thailand) and Nikhil Sheoran (now at Databricks). His research group maintains strong industry collaborations and open-sources systems like Kishu and VerdictDB through their GitHub organization .

Fabrizio Fornariمشاهده پروفایل

استادیار

• Internet of Things (IoT)
• Digital Twins
• Business Process Management (BPMN)
• +۶ مورد دیگر

Fabrizio Fornari is an Assistant Professor in Computer Science at the University of Camerino (Italy), focusing on cutting-edge technologies like Internet of Things (IoT), Digital Twins, and Business Process Management (BPMN). His work bridges theoretical modeling with practical implementations, emphasizing formal verification and modern software development practices. Research Pillars: IoT programming paradigms (Fluidware, FloWare), Digital Twin applications for business processes, and formal BPMN verification. Technical Contributions: Development of model-driven frameworks (X-IoT, FloBP), 3D simulation tools for IoT platforms, and BPMN analysis utilities (BPMN Inspector, BProVe). Domain Applications: Earthquake response systems (SAFE), intelligent transportation data flows, and educational IoT test environments. Methodological Focus: Community detection algorithms for routine extraction, CI/CD optimization for databases, and systematic literature reviews on IoT-aware BPMN modeling. Collaborative Efforts: Active involvement in MIDas4CS workshops and repository platforms for process model sharing (RePROSitory).

Stefano Zacchiroliمشاهده پروفایل

استاد

• Digital Commons
• Open Source Software Engineering
• Computer Security
• +۵ مورد دیگر

Stefano Zacchiroli is a full professor at Télécom Paris , part of the Polytechnic Institute of Paris , affiliated with the Computer Sciences and Networks (Infres) department and the Autonomous Critical Embedded Systems (ACES) research team within the Information Processing and Communication Laboratory (LTCI) . His research focuses on digital commons , open source software engineering , computer security , and software supply chain integrity .

• Data Provenance
• Intelligent Systems
• Knowledge Graphs
• +۴ مورد دیگر

Prof. Dr. Paul Groth is a faculty member at the Informatics Institute within the Faculty of Science at the University of Amsterdam . He leads the Intelligent Data Engineering Lab (INDElab) and collaborates extensively with institutions like the Vrije Universiteit Amsterdam and Elsevier Labs . His research focuses on data provenance , knowledge graphs , and machine learning systems with emphasis on reproducibility and semantic integration. Research Interests include Data Provenance and Traceability Knowledge Graph Construction & Evolution Neurosymbolic AI Systems Graph Learning & Semantic Web Technologies Digital Twins & Ontology Design Article Trends reveal a 2025 focus on LLM-knowledge graph intersections , emergent data patterns , and hyperbolic embedding architectures . His work addresses challenges in data distribution shifts , entity matching , and graph neural network explanations , while maintaining a strong foundation in FAIR data principles and provenance tracking . Secondary Activities include co-founding longform.ai , serving as co-editor for Morgan & Claypool , and contributing to international conferences like ESWC and ISWC . His research receives funding from European Union , NSF , NWO , and industry partners such as Weaviate and Ahold Delhaize .

Edward J. Auerbachمشاهده پروفایل

دانشیار

• MRI Physics
• Neuroimaging
• Ultra-high Field MRI
• +۹ مورد دیگر

Edward J. Auerbach is an Associate Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research (CMRR). He has been instrumental in advancing ultra-high field (UHF) MRI technology since joining CMRR in 1998. Ph.D. in Biomedical Sciences and Medical Physics (University of Minnesota, 2003) B.S. and B.A. in Chemistry and Criminology & Law (University of Florida, 1997) His research focuses on optimizing MRI hardware/software for improved image quality, resolution, and acquisition speed at ultrahigh magnetic fields (7T/10.5T). Current projects include: 10.5T deuterium-based MRS/I for brain metabolism Full-stack automation for reproducible MRI of brain cancer Informatics tools for MRS optimization Recent publications highlight advancements in: 10.5T brain imaging with enhanced SNR arrays Fourier aggregation for motion-robust MRI Ghost correction in 7T diffusion MRI He collaborates extensively with NIH-funded teams exploring human brain mapping and connectome projects.

Daniel Friedمشاهده پروفایل

استادیار

• Natural Language Processing
• Code Generation
• Grounded Language Interaction
• +۳ مورد دیگر

Daniel Fried is an Assistant Professor at the Language Technologies Institute within the School of Computer Science at Carnegie Mellon University. His research bridges natural language processing, pragmatics, and human-computer interaction, with a focus on making programming more communicative through language models. He previously held postdoctoral positions at FAIR Seattle and the University of Washington, earned a PhD from UC Berkeley, M.Phil from Cambridge, and B.S from the University of Arizona. University: Carnegie Mellon University Department: Language Technologies Institute Ranks: Assistant Professor Research Interests: Daniel Fried's work centers on grounded interaction, where language models perform tasks in real-world contexts. His key domains include code generation (e.g., InCoder, StarCoder), multi-agent pragmatic modeling, and multimodal dialogue systems. He explores how agents can infer user intent through language, enhance code synthesis via reinforcement learning, and improve factuality in embeddings. Recent Article Trends: His 2025 publications emphasize multi-agent coordination, programmatic skill induction, and execution-based code benchmarks. Work like RepoST and Agent Workflow Memory highlights scalable repository-level environments and memory mechanisms for agentic tasks. Papers such as CRScore and Dynamic Coalition Structure Detection reflect his focus on pragmatic evaluation and strategic language modeling. Scientific Awards: Okawa Research Grant (2023), Best Paper Award at Deep Learning for Code Workshop (2023), Best Paper Award at CVPR (2012) Advising: He mentors PhD students at CMU, including Andre He, JY Koh, and Zora Wang. His alumni advisees, like Alex Xie, have contributed to machine learning and education projects.

• Medical Physics
• Breast Cancer Imaging
• Magnetic Resonance Imaging
• +۳ مورد دیگر

Dibash Basukala is a Research Fellow at the Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center, affiliated with the Medical Physics Department . His work focuses on advanced MRI techniques for breast cancer assessment. Research Focus : Functional MRI, hypoxia imaging, diffusion-weighted imaging, and multisite reproducibility studies Key Collaborations : Katja Pinker-Domenig, Sunitha Thakur, Arka Bhowmik, Panagiotis Kapetas Publications : 2 journal articles and 1 review (2024–2025) in Journal of Magnetic Resonance Imaging and Frontiers in Oncology His research addresses technical validation of MRI biomarkers and clinical translation of functional imaging techniques for breast tumor characterization.

Cecilia Äijäläمشاهده پروفایل

پژوهشگر

• Physical Sciences
• Computer and Information Sciences
• Atmospheric Sciences
• +۳ مورد دیگر

Cecilia Äijälä is a Researcher at the Institute for Atmospheric and Earth System Research (INAR) , affiliated with the Doctoral Programme in Atmospheric Sciences at the University of Helsinki. Her work focuses on polar oceanography, climate modeling, and sea ice dynamics. Her research spans Antarctic and Arctic systems, utilizing regional models like MetROMS and CICE to analyze ocean-atmosphere interactions, sea ice drift, and Lagrangian transport pathways. She has contributed to datasets and model evaluations critical for understanding polar climate processes. Key Collaborations: Finnish Meteorological Institute (Arctic Space Centre), European Commission (PolarRES project). Methodologies: Climate modeling, Lagrangian pathway analysis, albedo validation, coupled atmosphere-ocean simulations. Recent Work: 2025 studies on Antarctic ocean models, 2024 contributions to sea ice albedo analysis, and 2023-2022 presentations on ocean connectivity and Arctic ice dynamics. Her projects, including PolarRES, aim to improve Earth system models for polar regions. She actively engages in scientific discourse through oral presentations and invited talks.

• Computer Science
• Fundamental Components
• Physical Computing
• +۳ مورد دیگر

Florian Wörister is a researcher in the Faculty of Computer Science, actively contributing to computer science education through the Research Group Education, Didactics and Entertainment Computing. His work focuses on developing innovative block-based programming environments to teach complex low-level computing concepts, making them accessible to learners through visual interfaces. His educational background includes a Bachelor of Science (BSc) and a Diplom-Ingenieur (Dipl.-Ing.), a German engineering degree equivalent to a Master of Science. This foundation supports his research in bridging theoretical computer science with practical educational tools. Wörister's research centers on fundamental components of computer science, with specific expertise in physical computing, JavaScript libraries, and programming education. He investigates how block-based interfaces can demystify low-level computing concepts like assembler, effectively connecting visual programming paradigms with traditional machine-level languages. His work extends to data management challenges, particularly in persistent identification of dynamic data subsets for research reproducibility. Recent publications reveal a strong emphasis on educational technology for computer science pedagogy, demonstrating how block-based environments lower barriers to learning low-level computing. His 2024 Koli Calling publication shows significant research impact with 3 citations and 5 reader captures, while his 2021 data management work addresses critical infrastructure needs for dynamic scientific datasets. No scientific awards were documented in the provided materials. Information regarding student advising, grant funding, or formal mentorship roles is not specified in available sources. As a core member of the Research Group Education, Didactics and Entertainment Computing, Wörister contributes to advancing computational thinking pedagogy and exploring intersections between educational methodologies and entertainment computing applications.