جست‌وجوی استاد و پژوهشگر

S. Murty Godduمشاهده پروفایل

استاد

• Radiation Oncology
• Medical Physics
• Quality Assurance (QA)
• +۶ مورد دیگر

S. Murty Goddu, PhD is a Professor of Radiation Oncology at Washington University School of Medicine. He earned his BS, MS, and PhD in Physics and Nuclear Physics from Andhra University, India, and completed a residency in medical physics at Washington University. His work focuses on quality assurance (QA) for radiation therapy systems, including linear accelerators, helical tomotherapy, and proton therapy. He has contributed to developing QA tools for 3D-conformal therapy, IMRT, Gamma-Knife, brachytherapy, and radiopharmaceutical treatments. Education: BS, Physics: Andhra University (1983) MS, Nuclear Physics: Andhra University (1985) PhD, Nuclear Physics: Andhra University (1991) Residency, Medical Physics: Washington University School of Medicine (1999) Certifications: American Board of Radiology, Therapeutic Medical Physics (2001) His research emphasizes improving patient safety through QA innovations, such as automated commissioning of radiotherapy systems, electronic chart checks, and deformable image registration. He has published extensively on topics like CBCT volumetric coverage extension , metal artifact reduction , and monitor unit calculations . Selected publications highlight advancements in: Proton therapy commissioning Helical tomotherapy QA Y-90 microsphere delivery protocols Bone marrow radiotoxicity studies Deformable image registration software (DIRART) Multimodality image registration He has collaborated with institutions on clinical trials and protocols for adaptive radiotherapy, dose calculation validation, and patient-specific QA. His work bridges physics, engineering, and clinical oncology to enhance treatment accuracy and safety.

Thomas W. Priceمشاهده پروفایل

استادیار

• Computing Education
• Educational Data Mining
• Advanced Learning Technologies
• +۵ مورد دیگر

Thomas W. Price is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. He directs the Help through INTelligent Support (HINTS) Lab and is affiliated with NCSU's Center for Educational Informatics. His work bridges computer science, education, and artificial intelligence to create intelligent learning environments. Dr. Price earned his M.S. and Ph.D. in Computer Science from NC State University in 2015 and 2018 respectively, and was named the College of Engineering Doctoral Scholar of the Year in 2018. His research focuses on computing education with an emphasis on automatically generating programming hints and feedback using student data. He has evaluated innovative programming environments including block-based and frame-based systems, and designs intelligent support tools that integrate with these technologies. His work examines how students seek and use help in both classroom and online settings, with particular interest in open-ended programming projects, data-driven feedback systems, and understanding student programming behaviors. His recent publications reveal a strong trend toward integrating AI and educational data mining techniques to support programming education. His work spans from fundamental research on student help-seeking behaviors to practical tools that provide immediate feedback, detect student struggles, and offer personalized support. A significant portion of his recent work addresses emerging challenges like student use of generative AI tools and detecting AI-generated code submissions. College of Engineering Doctoral Scholar of the Year (2018) Exemplary Paper Award from International Conference on Educational Data Mining Exemplary Paper Award from ACM Technical Symposium on Computer Science Education Recognition by STARS Computing Corps for leadership in computing outreach Dr. Price mentors several Ph.D. students including James Skripchuk, John Thomas Bacher, and Keith Tran. He has secured over $4 million in research funding from the National Science Foundation for projects including infrastructure for sustainable innovation in computer science education, data-driven technologies for STEM instruction, and intelligent support for creative programming projects. His research has practical applications in classroom settings and aims to create scalable solutions that don't place additional burden on instructors. As director of the HINTS Lab, Dr. Price leads a team investigating how to re-imagine educational programming environments as adaptive, data-driven systems. The lab focuses on supporting students working in creative, open-ended contexts through automated tools for planning, hint generation, and progress monitoring. Their research emphasizes practical methods that can scale to new classrooms and contexts while providing personalized support tailored to individual student needs.

John Paparrizosمشاهده پروفایل

استادیار

• Databases
• Data Science
• Machine Learning
• +۵ مورد دیگر

John Paparrizos is an Assistant Professor of Computer Science and Engineering at The Ohio State University's College of Engineering, where he directs The DATUM Lab (Data Analytics, Understanding, Mining, and Management Lab). He maintains an adjunct affiliation with the School of Informatics at Aristotle University of Thessaloniki. His research spans databases, data science, machine learning, and artificial intelligence , with focus areas including: Time-series analysis (clustering, anomaly detection) Scalable data mining for structured/unstructured data Adaptive algorithms for resource-constrained environments Foundational technologies for data-intensive applications His work addresses real-world challenges across relational, time-series, multimedia, text, graph, web, and IoT data domains. Notable recognition includes: 2025 ACM SIGMOD Test-of-Time Award for k-Shape time-series clustering 2023 IEEE TCDE Rising Star Award ACM SIGMOD Research Highlight Award NetApp Faculty Award His research has been featured in New York Times (front page), Washington Post , Forbes , and adopted by Fortune 500 companies (Exelon, Nokia) and the European Space Agency. He actively serves on program committees for premier conferences including ACM SIGMOD, VLDB, IEEE ICDE, ACM SIGKDD, and NeurIPS. His open-source tools have exceeded 100,000 downloads and are integrated into academic curricula at Brown, Columbia, Purdue, and University of Chicago.

Bengt Jonssonمشاهده پروفایل

استاد

• Formal Methods
• Real-Time Systems
• Distributed Systems
• +۶ مورد دیگر

Bengt Jonsson is a Professor at the Division of Computer Systems, Department of Information Technology, Uppsala University. His research focuses on formal methods, real-time and distributed systems, semantics and verification of concurrent systems, and IoT security. Current Projects: UPMARC (Software Technology for Multicore Programming), aSSIsT (Secure Software for IoT), and Designed for UPDATE (Safe Embedded Software Updates) Past Projects: CoDeR-MP (Multicore Real-Time Applications), ProFun (Wireless Sensor Networks), CONNECT (Networked Component Synthesis) His work includes automated verification, model checking, and symbolic execution for concurrent systems. Recent publications address dynamic partial order reduction, IoT protocol testing, and lock-free data structures. Scientific Awards : CAV Award 2017 He advises PhD students and teaches courses like Model-Based Development of Embedded Systems and graduate-level symbolic execution. Personal interests include piano playing and orienteering.

Julie Caillerمشاهده پروفایل

دانشیار

• Automated Deduction
• Theoretical Computer Science
• Formal Methods
• +۶ مورد دیگر

Julie Cailler is an Associate Professor at the University of Lorraine, affiliated with CNRS, Inria, and LORIA in Nancy, France. She is a member of the VeriDis Team and focuses on automated deduction, interactive proof systems, and logic in computer science. PhD in Theoretical Computer Science from the University of Montpellier (2023) Former Associate Professor at the University of Regensburg Her research interests include: Designing concurrent and parallel decision procedures for first-order logic Integration of automated and interactive theorem proving Proof system interoperability and formal verification Recent publications highlight her work on: SC-TPTP format extensions for sequent-based proofs (CADE-30, 2025) Graph Neural Networks for word equation solving (ATVA 2024, 2025) Deskolemization strategies in first-order logic (LPAR 25, 2024) Scientific recognition: Woody Bledsoe Award (2022) Best Newcomer Prover (CASCJ11, 2022) She supervises students in projects related to: Automated theorem prover development Logic programming and deduction modulo theory Formal verification and proof tool integration Julie is actively involved in academic service as: Co-head of GT LVP (Languages and Program Verification) working group PC member for major conferences like FMCAD, FSTTCS, and ITP

• Declarative Process Modeling
• Process Mining
• Formal Verification
• +۴ مورد دیگر

Paul Cosma is a Postdoctoral Researcher at the Department of Computer Science (DIKU), University of Copenhagen, specializing in the Software, Data, People & Society (SDPS) section. His work focuses on declarative process modeling, formal verification, and explainable AI systems, with strong connections to process mining and Petri net theory. He completed his PhD at the University of Copenhagen's Faculty of Science in 2024 with a thesis on declarative process models as verifiable AI. His research interests center on declarative process modeling and formal verification of complex systems. Cosma develops techniques for improving model simplicity through nested group discovery and creates frameworks like BERMUDA for participatory mapping of domain activities to event data. His work bridges theoretical computer science with practical applications in business process management and AI explainability, emphasizing human-centered design principles where software systems are developed with societal impact in mind. Cosma's publication record shows a clear trajectory in process modeling research, with recent work focusing on transforming Dynamic Condition Response Graphs to Safe Petri Nets (2023) and improving declarative model simplicity (2024). His research demonstrates strong interdisciplinary connections between formal methods, AI verification, and human-computer interaction, particularly in making complex process models accessible and verifiable. Cosma actively collaborates with researchers including Thomas Hildebrandt, Tijs Slaats, and Axel Christfort, primarily within the SDPS section at DIKU. His work receives consistent citations in process mining literature, with his 2023 PETRI NETS paper accumulating 1 citation and his CAiSE 2024 paper gaining 2 Scopus citations. He maintains an ORCID profile (0000-0001-8022-6402) and contributes to open-access research through the university's Pure repository. Based in Sigurdsgade 41, Copenhagen N, Cosma operates within DIKU's collaborative research environment that emphasizes industry partnerships and interdisciplinary work. His recent PhD defense (June 13, 2024) marks his transition from doctoral candidate to postdoctoral researcher, positioning him to expand his contributions to process-aware information systems and verifiable AI.

Giovanni Squilleroمشاهده پروفایل

استاد

• Artificial Intelligence
• Soft Computing
• Evolutionary Algorithms
• +۷ مورد دیگر

Giovanni Squillero is a Full Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He leads the CAD group (Electronic CAD & Reliability) and serves on Politecnico's Joint Committee for Teaching and Ph.D. Steering Committee (Pure and Applied Mathematics).

Wen Liمشاهده پروفایل

استادیار

• Software Security
• Software Engineering
• Network Security

Wen Li is an Assistant Professor at the School of Computing , Utah State University. Their research focuses on software security , software engineering , and network security , with significant contributions to analyzing multilingual software systems. Recent work highlights include: Characterization of multilingual software and language selection patterns Security analysis of cross-language interactions and runtime vulnerabilities Development of fuzzing frameworks like Pyrtfuzz and POLYFUZZ Investigation of dynamic information flow and memory management in complex systems Research trends demonstrate expertise in software testing, language interoperability, and security verification across programming environments.

• Information Systems Engineering
• Privacy Threat Modeling
• IoT Security
• +۴ مورد دیگر

Dimitri Van Landuyt serves as an Associate Professor in the Department of Computer Science at KU Leuven , affiliated with the Information Systems Engineering Research Group (LIRIS) . He leads and co-promotes multiple high-impact research projects focused on security and privacy engineering, including initiatives on model-driven security risk analysis , privacy by design , and IoT security . His work spans GDPR compliance, synthetic data management, and threat modeling innovations. Academic Leadership : Member of the Council of FEB and Campus Council Leuven/Kortrijk Research Pillars : Privacy threat modeling, security automation, IoT systems, GDPR technical implementation His publications demonstrate expertise in privacy-enhancing technologies, with recent work analyzing LLM applications in threat modeling, developing tree-based privacy analysis frameworks, and creating adaptive trust management architectures. He explores serious games for security training, synthetic data quantification standards, and runtime threat assessment mechanisms. Dimitri contributes to educational programs through courses in ICT Service Management , Security & Privacy by Design , and Research Methodologies . He supervises student research while collaborating with industry and academia on data protection challenges.

John T. Evans IVمشاهده پروفایل

استادیار

• Machine Systems Engineering
• Precision Agriculture
• Agricultural Automation
• +۳ مورد دیگر

John T. Evans IV is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University. He specializes in Machine Systems and Automation, focusing on agricultural machine design, precision agriculture technologies, and automated systems modeling. His research bridges robotics, digital agriculture, and sustainable farming practices, contributing to advancements in autonomous navigation and machine learning applications for crop management. Bachelor's in Biosystems Engineering, University of Kentucky Master's in Biosystems Engineering, University of Kentucky PhD in Biological Systems Engineering, University of Nebraska–Lincoln Evans' work centers on optimizing agricultural machinery through synthetic vision systems, digital twin environments, and deep learning algorithms. Key areas include autonomous roadside mowing , corn row identification , and transfer performance optimization for robotic systems. His publications reveal interdisciplinary trends combining robotics, precision agriculture, and computational modeling. He actively mentors Purdue's Quarter Scale Tractor Team and contributes to the American Society of Agricultural and Biological Engineers. His research involves collaboration with Purdue's ADM Agricultural Innovation Center and Discovery Park facilities, focusing on automation, data science, and machine learning in agricultural contexts.

John J. Lucidoمشاهده پروفایل

عضو هیئت علمی

• Treatment planning for radiation therapy
• Special procedures in radiation therapy
• Quality assurance in radiation therapy
• +۵ مورد دیگر

John J. Lucido, III, Ph.D., is a Medical Physicist at the Mayo Clinic in Rochester, Minnesota, specializing in Radiation Oncology . He is affiliated with the Mayo Clinic College of Medicine and Science and actively involved in clinical research, education, and global medical physics initiatives. Education: Ph.D. in Medical Physics (2013) - University of British Columbia M.S. in Mathematical Physics (2008) - Rutgers University B.S.E. in Engineering Physics - Electrical Engineering (2008) - University of Michigan Dr. Lucido's research focuses on advanced radiation therapy techniques , including: Automated treatment planning systems Dosimetric characterization of novel radiation devices Machine learning applications in organ-at-risk segmentation Clinical decision tools for spine stereotactic body radiation therapy (SBRT) Quality assurance protocols for radiation delivery His publications highlight the integration of Monte Carlo simulations , deep learning , and radiobiological modeling to optimize radiation therapy for complex cases such as pregnant patients, ultracentral lung cancers, and skin electron beam therapy. Scientific awards and honors include: Teacher of the Year - Radiation Physics (Mayo Clinic, 2022) International Graduate Student Fellowship (University of British Columbia, 2009) Graduate Aid in Areas of National Need Fellowship (Rutgers University, 2005) Certified Therapeutic Medical Physicist (American Board of Radiology, 2016) Dr. Lucido actively contributes to professional societies as a Voting Member of the Society of Directors of Academic Medical Physics Programs (SDAMPP) , Chair of the Eclipse Scripting and Automation Group , and International Consultant for C/CAN - Kumasi City Radiotherapy Project . He also serves on multiple Mayo Clinic committees related to education and quality assurance. His work emphasizes global radiation therapy standardization and educational infrastructure development , particularly in underserved regions like Ghana. Recent projects include automated testing platforms for treatment planning scripts and customizable radiation shields for skin therapy.

• Thermal Systems
• Battery and Vehicle Energy Management
• Model Predictive Control
• +۶ مورد دیگر

Professor Peter Fussey holds a dual role at the University of Sussex as a Professor in the School of Engineering and Informatics and Associate Dean for Global and Civic Engagement at the Faculty of Science, Engineering and Medicine. He leads the Energy and Materials Engineering Research Centre, focusing on Thermal Systems , Battery and Vehicle Energy Management , Model Predictive Control , and Data Analytics . Previously, he spent over 25 years at Ricardo UK, leading the Control Department and developing advanced control systems for hybrid/electric vehicles and thermal management. His career also includes rail acoustics research at British Rail Research and SNCF. Education: DPhil in Engineering from the University of Oxford MA in Engineering from the University of Cambridge Research Interests: Professor Fussey’s work spans automotive and mechanical engineering , with a focus on low-emission powertrains , energy management systems , and advanced control algorithms . His recent projects include optimizing EV cabin climate control for extended range, geofencing for smart mobility, and neuro-fuzzy modeling for thermal systems. He has also pioneered applications of model predictive control (MPC) in selective catalytic reduction (SCR) systems and engine combustion optimization. Teaching: Courses include Low Emission Vehicle Propulsion , Vehicle Dynamics , and supervising Formula Student projects. His teaching emphasizes practical applications of control theory and sustainability. Labs/Teams: Leads the Energy and Materials Engineering Research Centre , fostering interdisciplinary research in sustainable energy and advanced propulsion systems.

• Systems Engineering
• Control Systems Design
• Thermal Management Systems
• +۵ مورد دیگر

James Allison is an Associate Professor in both the Industrial and Enterprise Systems Engineering and Aerospace Engineering departments at the University of Illinois at Urbana-Champaign. He is also affiliated with the Carl R. Woese Institute for Genomic Biology. His research focuses on systems engineering, control systems design, thermal management systems, and optimization methodologies. Dr. Allison has contributed to advancements in fluid-based thermal management systems, floating offshore wind turbine control co-design, and AI-driven design optimization. His work emphasizes interdisciplinary approaches, blending mechanical, aerospace, and computational engineering principles. Key research areas include design automation, graph neural networks for system architecture exploration, and reliability-based co-design of complex systems. He has pioneered methodologies for extracting design knowledge from optimization data and advancing multifunctional structures for attitude control in aerospace systems. Awards: NSF CAREER Award (2017) Labs/Groups: Involved in the development of tools like LGR-MPC and SS-MPC for Model Predictive Control, and the WEIS toolset for offshore wind turbine analysis. Dr. Allison’s recent publications highlight contributions to thermal management system configurations, control strategies for offshore renewable energy systems, and topology optimization techniques. His research bridges theoretical advancements with practical applications in aerospace, energy, and manufacturing sectors.

Shan Luمشاهده پروفایل

استاد

• Computer Systems
• Software Reliability
• Program Analysis
• +۳ مورد دیگر

Shan Lu is a Professor in the Department of Computer Science at the University of Chicago. He is affiliated with the UChicago Systems Group and holds a faculty position at the Crerar Library. His research focuses on software systems, reliability, and program analysis, with an emphasis on improving software correctness and efficiency through automated tools and methodologies. Lu earned his Ph.D. from the University of Illinois, Urbana-Champaign in 2008, under the guidance of Yuanyuan Zhou. His work spans multiple areas, including concurrency bug detection, performance optimization, and machine learning integration in software systems. His research interests include developing automated tools for detecting and fixing software bugs, optimizing database-backed web applications, and enhancing the reliability of distributed systems. Recent work highlights include innovations in large language model serving, retry bug detection, and hybrid data plane optimizations. Lu has been recognized with prestigious awards, including the SOSP Best Paper Award (2019), OSDI Best Paper Award (2022), and ACM Distinguished Member status. He actively contributes to program committees, including roles such as Vice Chair of the ACM Publications Board and Chair of ACM SIGOPS. His advising spans over 20 students, many of whom have secured notable positions at top institutions and companies like Google, Facebook, and LinkedIn. Lu’s research has also led to impactful tools like SkyWay, Yak, and DCatch, addressing critical challenges in software systems.

Mariana Silvaمشاهده پروفایل

عضو هیئت علمی

• Educational Technology
• Computer Science Education
• Large Language Models (LLMs) in Assessment
• +۳ مورد دیگر

Mariana Silva is a Teaching Associate Professor at the Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign, and CEO of PrairieLearn Inc. She holds a Ph.D. in Theoretical and Applied Mechanics from UIUC (2009). Her research focuses on leveraging educational technologies, such as Large Language Models (LLMs), to enhance computer-based assessments and scalable teaching practices. Silva has taught over 11 courses to 9,000+ students, emphasizing innovations in STEM education. She has pioneered adaptive testing tools and randomized question generators to improve equity and accessibility in large-scale courses. Education : Ph.D., Theoretical and Applied Mechanics, UIUC (2009) M.S., Mechanical Engineering, Federal University of Rio de Janeiro (2003) B.S., Mechanical Engineering, Federal University of Rio de Janeiro (2001) Research Interests : Silva’s work centers on technology-driven education , including automated grading using LLMs, design of adaptive assessment systems, and fostering collaborative learning. She has developed tools like PrairieLearn to streamline teaching workflows while maintaining educational rigor. Awards & Recognition : Scott H. Fisher Computer Science Teaching Award (2022) Rose Award for Teaching Excellence (2022) Multiple “List of Teachers Ranked as Excellent” (2009–2017) Engineering Council Outstanding Advising Award (2014–2019) Labs & Teams : Co-founder of PrairieLearn Inc., which provides open-source platforms for STEM education. She collaborates with interdisciplinary teams to advance educational technologies and testing infrastructure.