جست‌وجوی استاد و پژوهشگر

Audris Mockusمشاهده پروفایل

استاد

• Digital Archaeology
• Software Engineering
• Open Source Development
• +۳ مورد دیگر

Audris Mockus is the Ericsson-Harlan D. Mills Chair Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD in Statistics from Carnegie Mellon University and has extensive experience in software engineering research, particularly in Digital Archaeology and Open Source Software analysis. His work focuses on understanding developer behavior, software evolution, and global software development challenges. Education: PhD in Statistics, Carnegie Mellon University, 1994 MS in Applied Mathematics, Moscow Institute of Physics and Technology, 1991 BS in Applied Mathematics, Moscow Institute of Physics and Technology, 1988 Research Interests: Mockus specializes in recovering and analyzing digital traces of software development to study developer behavior, software quality, and project dynamics. His research includes quantifying open-source supply chains, measuring developer expertise, and improving software defect prediction. He has pioneered methods for analyzing version control systems and global software teams. Awards and Grants: Recipient of multiple best paper awards at ICSE, FSE, and other conferences. Lead PI on NSF grants totaling over $2M for projects like the World of Code (WoC) infrastructure and forensic image curation. Patents in data visualization and software defect prediction. Advising and Labs: Mockus has advised PhD students on topics like npm ecosystem analysis and forensic image clustering. His research group collaborates with institutions like the National Institute of Justice on projects like Image Cloud Platform for forensic data curation.

Farida Cherietمشاهده پروفایل

استاد

• Biomedical technology
• Artificial intelligence
• Medical imaging
• +۴ مورد دیگر

Dr. Farida Cheriet is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . She serves as Director of the 4D Imaging and Vision Laboratory (LIV4D) and holds membership in the Institute of Biomedical Engineering and the Institute for Data Valorization (IVADO) . Her research spans medical imaging , biomedical technology , artificial vision , and 3D movement estimation . Ph.D. (Montreal), DEA (France), Ing. (Algeria) Her work focuses on interpretable AI models for retinal pathology diagnosis, 3D anatomical reconstruction from medical imaging, and automated segmentation of vascular and spinal structures. She leads innovations in deep learning applications for diabetic retinopathy screening, coronary artery analysis , and spine surgery planning . Recent publications highlight her leadership in computer vision for biomedical applications, with 15 featured articles covering ICU readmission prediction, retinal vessel analysis, dental crown design, and spinal ultrasound processing. She received CFI funding (2021) for cutting-edge facilities and collaborates on pan-Canadian ophthalmic image databases (2018). Dr. Cheriet has supervised 28 Ph.D. students and 46 Master's students in biomedical computing, including projects on scoliosis analysis, retinal imaging, and cardiac MRI. Her teaching includes digital communications and computer graphics courses.

Ayşe Başarمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Decision Making under Uncertainty
• +۷ مورد دیگر

Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.

• Software Engineering
• Software Testing
• Program Analysis
• +۴ مورد دیگر

Alessandro Orso is the Dean and Professor of the University of Georgia College of Engineering. Previously, he served as a professor at Georgia Institute of Technology, where he directed the Scientific Software Engineering Center and held leadership roles, including interim dean of the College of Computing (2023–2024). He earned his Ph.D. and master’s in computer science and electrical engineering from Politecnico di Milano, Italy. Orso’s research focuses on software testing, program analysis, and debugging, with applications in improving software reliability and security. His work includes developing automated testing tools (e.g., AutoRestTest), methodologies for scientific software engineering, and techniques for program debloating. His research has secured funding from agencies and industry partners like Google, IBM, and Microsoft, including a $11M grant from Schmidt Futures. His scientific awards include being a Distinguished Member of the ACM and IEEE Fellow. He advocates for innovative education, such as Georgia Tech’s Online Master of Science in Computer Science and his virtual software engineering course. His publications span automated testing, API analysis, and security, reflecting his commitment to advancing both academic research and practical software solutions. Orso’s leadership includes directing the Scientific Software Engineering Center, which pioneers methodologies for scientific software improvement and trains a new generation of engineers. His career blends academic excellence with impactful industry collaborations, driving advancements in software engineering practices.

• Software Engineering
• Software Testing
• Program Analysis
• +۴ مورد دیگر

Alessandro (Alex) Orso is the Dean and Professor at the University of Georgia College of Engineering , starting in 2025. Previously, he spent 25 years at Georgia Institute of Technology , where he served as a Professor in the College of Computing , Director of the Scientific Software Engineering Center , and Associate Dean for Off-Campus and Special Initiatives . Education: Ph.D. in Computer Science – Politecnico di Milano, Italy M.S. in Electrical Engineering – Politecnico di Milano, Italy B.S. in Electrical Engineering – Politecnico di Milano, Italy Research Focus: Alex Orso is a leading expert in software engineering , with a focus on software testing , program analysis , and debugging . His work aims to improve software reliability, security, and trustworthiness , and has been validated on real-world systems. He has led initiatives to integrate AI and large language models into testing workflows, particularly for REST APIs and mobile applications . Scientific Recognition: Distinguished Member of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) ISSTA Impact Paper Award (2021) Research Funding & Leadership: Orso has secured major funding from Google, IBM, Microsoft , and government agencies. In 2021, he led the acquisition of $11 million from Schmidt Futures to establish the Scientific Software Engineering Center at Georgia Tech, where he served as inaugural director. Teaching & Innovation: He was an early advocate of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) and has taught a virtual software engineering course since its launch in 2014.

Stefano Martinianiمشاهده پروفایل

استادیار

• Computational and Statistical Physics
• Materials Science
• Artificial Intelligence for Science
• +۵ مورد دیگر

Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.

• Environmental Science
• Sustainable Development
• Ecological Conservation
• +۳ مورد دیگر

Vuk Gajić is an Assistant Professor at the Faculty of Applied Ecology, Singidunum University, where he has held academic roles since 2016. His career progression includes positions as a teaching associate (2016), assistant (2019), and current role (2023). He earned a Ph.D. in Environment and Sustainable Development from Singidunum University (2019–2022), following prior studies in environmental protection and risk management at the same institution. Research interests span environmental science, sustainable development, GIS applications, and radiation technology for waste and food treatment. He has contributed to interdisciplinary studies, including soil contamination analysis in Libya, microbial decontamination via ionizing radiation, and machine learning applications for software defect prediction and agricultural weed detection. His work bridges environmental engineering with technological innovation, emphasizing sustainability and ecological conservation. Publications reflect a focus on environmental monitoring, pollution assessment, and eco-technologies. Key themes include GIS-based environmental databases, forest fire prevention through sensor networks, and agricultural waste reuse. His research often integrates quantitative methods with geospatial tools, addressing both local and global environmental challenges. Teaching responsibilities include courses on geodiversity, sustainable development, and natural hazards. He actively participates in academic conferences, contributing to peer-reviewed journals and presenting at events like Sinteza and SETI. Current projects likely explore emerging technologies in environmental management and sustainable practices.

Jonathan Yatesمشاهده پروفایل

مدرس

• Computational Electronic Structure Theory
• Magnetic Resonance
• Solid-State NMR
• +۶ مورد دیگر

Dr. Jonathan Yates is a Royal Society Research Fellow and University Lecturer in Materials Modelling at the University of Oxford, affiliated with the Department of Materials and St Edmund Hall. His research focuses on computational electronic structure theory, magnetic resonance (NMR parameters, EELS), and the development of Wannier functions for Fermi surface analysis. His research interests include: First-principles calculation of solid-state NMR parameters (chemical shifts, J-couplings, electric field gradients) Electron energy loss spectroscopy (EELS) for nanomaterials characterization Wannier functions theory and applications to anomalous Hall effect and electron-phonon interactions Development of computational tools: CASTEP-NMR, Wannier90, Optados Recent publications highlight his work on NMR crystallography of pharmaceutical compounds, hydrogen bonding analysis in carbohydrates, and structural characterization of chalcogenide glasses. His scientific awards include the prestigious Royal Society Research Fellowship. He has contributed to software development for solid-state simulations and collaborated on interdisciplinary studies involving NMR, DFT, and materials science. His academic career spans institutions including UC Berkeley, University of Cambridge, and University of Oxford.

Ole Madsenمشاهده پروفایل

استاد

• Robotics
• Automation
• Industry 4.0
• +۶ مورد دیگر

Ole Madsen is a Professor at the Department of Materials and Production within The Faculty of Engineering and Science at Aalborg University . His research focuses on Robotics and Automation , particularly in 5G Smart Production , AI for Manufacturing , and Industry 4.0 applications. He also holds a part-time position at Adding Robotics , applying his expertise in robot integration for industrial and healthcare settings. Research Interests : Robotics, Automation, AI, Sensor Systems, Modular Manufacturing, Welding Technology, Digital Twins, Human-Centered Robotics, Industry 4.0 His work spans 35+ years with over 205 publications , emphasizing smart production systems and robot-assisted processes . Key contributions include Swarm Production Architectures , 5G-Enabled Robotics , and Human-Robot Collaboration frameworks. He has supervised 10 PhD students and led major projects like RAU (Robot-Assisted Ultrasound) and GINP: Robotics & AI Innovation Network . Scientific Awards : SCAP2020 Best Presentation Award (2020) Ole's 31 projects include AP2030: Aseptic Factory 2030 (pharma production), AddSmart (robotics R&D), and 5G-Enabled Autonomous Systems . His 127 press/media mentions highlight advancements in robotic welding , industrial metaverse , and swarm production . He maintains an ORCID: 0000-0003-2133-2541 with extensive publication records across Swarm Robotics , Modular Manufacturing , and AI-Driven Production .

Michael Hiltonمشاهده پروفایل

عضو هیئت علمی

• Software Engineering
• Developer Experience
• Continuous Integration
• +۳ مورد دیگر

Michael Hilton is an Associate Teaching Professor in the Software and Societal Systems Department of the School of Computer Science at Carnegie Mellon University. He also serves as the Associate Department Head for Education and directs both the Software Engineering Minor and Software Engineering Concentration programs. His work bridges academic research with practical software engineering education. Ph.D. in Computer Science, Oregon State University (2017) M.S. in Computer Science, Cal Poly San Luis Obispo (2013) B.S. in Computer Science, San Diego State University (2002) Professor Hilton's research primarily focuses on understanding and improving the developer experience, with particular emphasis on flaky tests, continuous integration practices, and software engineering education. His work combines empirical studies of real-world development practices with educational innovations to enhance how software engineers are trained. He has conducted extensive research on test flakiness, identifying patterns, causes, and potential solutions to this pervasive problem in modern software development. His scholarly contributions reveal a consistent focus on practical software engineering challenges, particularly those affecting developer productivity and software quality. The research trajectory shows increasing attention to educational aspects of software engineering, including team-based learning, structured feedback mechanisms, and the impact of emerging technologies like AI on programming education. Professor Hilton has over 20 years of professional experience in software development, including 9 years at SPAWAR Pacific where he worked on projects for the US Navy, Coast Guard, and White House. This industry background informs his teaching approach, which emphasizes preparing students for real-world challenges they'll face after graduation. He teaches software engineering-focused courses and has developed educational approaches that integrate practical development experience with theoretical foundations. His teaching philosophy centers on providing students with both immediate practical skills and enduring principles that will serve them throughout their careers, with special attention to software engineering in startup environments.

Sorin Mitranمشاهده پروفایل

استاد

• Computational simulation of multiscale, multiphysics systems
• Data-driven constitutive relations for hyperelastic materials
• Nonlinear mode reduction
• +۴ مورد دیگر

Sorin Mitran is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research focuses on computational simulation of multiscale and multiphysics systems, data-driven constitutive relations for hyperelastic materials, and information geometry for reduced stochastic models. PhD in Aerospace Engineering from Politehnica University Bucharest (1995) Professional background includes fellowships at University of Tokyo (1993), Karlsruhe Institute of Technology (1998-1999), and University of Washington (1999-2002) His research develops numerical tools to predict macro-scale behavior from micro-scale interactions, such as plastic deformation of metals from lattice defect dynamics, microtubule mechanics from molecular dynamics, and protein folding from atomic-level simulations. Mathematical approaches include adaptive computation, machine learning for constitutive law prediction, and information geometry for stochastic process analysis. Recent publications (2023-2018) span computational biology, multiscale fluid dynamics, and medical applications of continuum mechanics. Articles frequently explore data-driven modeling, wave propagation in biological systems, and GPU-accelerated numerical methods like Lattice Boltzmann and Lattice Fokker-Planck formulations.

Julia Lawallمشاهده پروفایل

پژوهشگر

• Operating Systems
• Domain-Specific Languages
• Program Transformation
• +۴ مورد دیگر

Julia Lawall is a Senior Research Scientist (Directrice de Recherche) at Inria-Paris, where she leads research in the Whisper group. She has made significant contributions to the fields of programming languages, operating systems, and software engineering, with a particular focus on program transformation and Linux kernel development. Her work bridges theoretical computer science with practical software engineering challenges. Dr. Lawall's research primarily centers on the design and implementation of domain-specific languages for operating system problems, program transformation techniques, and automated software evolution. Her most notable contribution is the Coccinelle framework, which has been instrumental in automating the evolution of Linux device drivers for over a decade. Her work spans from theoretical foundations in optimal reduction of the lambda calculus to practical tools that address real-world software maintenance challenges in large-scale systems like the Linux kernel. Her publication record demonstrates consistent contributions across multiple domains, with recent work focusing on Android API evolution, Linux kernel bug detection, and program transformation techniques. The trajectory of her research shows a progression from theoretical programming language concepts to increasingly practical applications in system software maintenance and evolution. EuroSys Test of time award for 'Documenting and Automating Collateral Evolutions in Linux Device Drivers' at EuroSys 2008 Best paper award for 'Diagnosys: Automatic Generation of a Debugging Interface to the Linux kernel' at ASE 2012 Most Influential ICFP Paper Award for foundational work on lambda calculus Best Reviewer at GPCE 2020 and Distinguished Reviewer at ASE 2020 Dr. Lawall has been actively involved in the academic community, serving as program co-chair for numerous prestigious conferences including ASE 2019, FSE 2026, and EuroSys 2025. She has also contributed to community initiatives as the Linux kernel coordinator for Outreachy (2015-2018) and as a member of the advisory board for Software Heritage. Her leadership extends to editorial roles, including associate editor for Higher-Order and Symbolic Computation and membership on the editorial board of Science of Computer Programming. She leads the Whisper research group at Inria-Paris, which focuses on program transformation techniques and their applications to system software. The group has developed several influential tools including Coccinelle, Coccinelle4J, LiLiput, Prequel, and JMake, which have had substantial impact on both academic research and industrial practice in software maintenance and evolution.

Alessio Gambiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Software Testing
• Self-Driving Cars
• +۴ مورد دیگر

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Reid Holmesمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Code Search
• +۷ مورد دیگر

Reid Holmes is a Professor in the Software Practices Lab at the University of British Columbia's Department of Computer Science, Faculty of Science. With over 15 years of academic experience, he has progressed from Assistant Professor at the University of Waterloo (2010-2015) to Associate Professor (2015-2022) and now full Professor (2022-present) at UBC. His research spans multiple dimensions of software engineering with a strong emphasis on the human aspects of development. Dr. Holmes' research interests focus on understanding the problems software engineers encounter when creating and evolving software systems. His work examines software testing and validation, source code reuse, code search, context-sensitive example location, API understanding, speculative analysis, code review, and team awareness. He believes that by better understanding how people create, explore, evolve, and reason about software systems, we can enhance developers' effectiveness and improve software quality. His research is characterized by its practical application to real-world software development challenges. His recent publications demonstrate a consistent focus on improving developer productivity through better tools and understanding of developer behavior. The publications reveal a trajectory from traditional software engineering topics toward newer areas involving AI-assisted development, with particular attention to how generative AI affects workflow and collaboration. His work consistently bridges theoretical software engineering concepts with practical developer experience considerations. ACM SIGSOFT Distinguished Paper Award (multiple times) FSE 2024 Most Impactful Paper Award 2024 ICSE Most Influential Paper Award Winner of the 2008 MSR Mining Challenge As an educator and mentor, Dr. Holmes has supervised numerous graduate students who have gone on to successful careers in academia and industry. He has served on program committees for major software engineering conferences including ICSE, FSE, and MSR, and has been an Associate Editor for TSE since 2016. His work on developer tools like CodeShovel, Devy, and Baker demonstrates his commitment to creating practical solutions that address real developer pain points.

Patrizia Saviمشاهده پروفایل

دانشیار

• GNSS Reflectometry
• Microwave Characterization of Materials
• Graphene Nanoplatelets
• +۳ مورد دیگر

Patrizia Savi is a Tenured Associate Professor at the Polytechnic University of Turin within the Department of Electronics and Telecommunications. She actively participates in the Power Electronics Innovation Center (PEIC) and serves as a Senior Member of IEEE and the International Union of Radio Science . Teaching: She has been the Titolare del corso for 'Campi Elettromagnetici' (Electromagnetic Fields) since 2019-2021 at the Polytechnic University of Turin. Key Collaborations: Works with researchers across Italy and international institutions on projects involving GNSS-R for environmental monitoring. Research Interests: Her work focuses on GNSS Reflectometry for soil moisture retrieval, carbon-based composites (graphene, biochar, nanotubes) for microwave applications, and graphene tunable devices including biosensors. She also explores electromagnetic shielding using sustainable materials. Recent Publication Trends: Recent work emphasizes machine learning integration with GNSS-R data, biochar composite shielding in construction materials, and graphene-based biosensors for glucose and HRP detection. Key applications span climate action , environmental monitoring , and medical diagnostics . Scientific Awards: IEEE Fellow (2016-) IEEE Senior Member (2016-) International Union of Radio Science Senior Member (2024-) Advising: Supervises PhD students Simone Gaetano Ballaera and Fabio Peinetti , focusing on graphene sensors and tunable devices.