جست‌وجوی استاد و پژوهشگر

Dan Haoمشاهده پروفایل

استاد

• software testing
• debugging
• program comprehension
• +۷ مورد دیگر

Professor Dan Hao is a distinguished faculty member at the Institute of Software, School of Computer Science, Peking University, where he has established himself as a leading researcher in software engineering. His extensive service to the academic community includes membership on the Steering Committee for The International Conference on Automated Software Engineering (ASE) since 2021, The ACM SIGSOFT International Symposium on Software Testing and Analysis since 2025, and The International Systems and Software Product Line Conference (SPLC) from 2018-2022. He has served as Program Co-Chair for multiple major conferences including ISSTA 2027, ICSME 2025, ICST 2023, SANER 2022, and ASE 2021. Professor Hao received his Bachelor's degree from Harbin Institute of Technology in 2002 and completed his Ph.D. at Peking University in 2008, followed by post-doctoral research at the same institution until 2009. His academic journey reflects a deep commitment to advancing software engineering research and education in China. Professor Hao's research primarily focuses on software testing and debugging, program comprehension, and software maintenance. His work has significantly contributed to compiler testing, fault localization, regression testing, and automated program repair. He has pioneered approaches in compiler auto-tuning, test-case prioritization, and history-guided testing techniques. His research bridges theoretical foundations with practical applications, addressing real-world challenges in large-scale software systems, particularly in online service environments. His publication record demonstrates a consistent trajectory of high-impact research in top-tier software engineering venues. Professor Hao's work shows increasing integration of machine learning techniques with traditional software engineering problems, particularly evident in his recent publications on LLM applications for code generation, neural theorem proving, and contrastive learning for vulnerability detection. His research maintains strong connections between theoretical rigor and practical applicability in industrial settings. ACM SIGSOFT Distinguished Paper Award for PDCAT: Preference-Driven Compiler Auto-Tuning at FSE 2025 Distinguished Paper Award for Formalizing, Mechanizing, and Verifying Class-Based Refinement Types at ECOOP 2024 ACM SIGSOFT Distinguished Paper Award for History-Guided Configuration Diversification for Compiler Test-Program Generation at ASE 2019 ACM SIGSOFT Distinguished Paper Award for History-driven Build Failure Fixing: How Far Are We? at ISSTA 2019 As an advisor, Professor Hao has mentored numerous graduate students, currently supervising 9 Ph.D. students and 7 Master's students. His former students have gone on to prestigious positions at institutions including King's College London, Tianjin University, Fudan University, and major technology companies like Huawei and China Construction Bank. His academic leadership extends through editorial roles as Deputy Editor-in-Chief of Software Testing, Verification and Reliability (STVR) and membership on the editorial boards of several premier journals including ACM Transactions on Software Engineering and Methodology, ACM Computing Surveys, and Empirical Software Engineering. Professor Hao leads a vibrant research group at Peking University's Institute of Software, focusing on cutting-edge problems at the intersection of traditional software engineering and artificial intelligence. His team actively collaborates with both academic institutions and industry partners to address practical challenges in software development and maintenance processes.

August Shiمشاهده پروفایل

استادیار

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

August Shi is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin. His research focuses on software testing, particularly regression testing, with emphasis on improving reliability with respect to flaky tests and increasing testing speed without compromising quality. Dr. Shi obtained his PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2020. Prior to that, he earned a B.S. in both Computer Science and Electrical and Computer Engineering from The University of Texas at Austin in 2013. Dr. Shi's research interests center on software testing and regression testing , with a particular focus on addressing challenges related to flaky tests . His work aims to make regression testing both more reliable (by tackling issues with flaky tests) and faster (without sacrificing testing quality). His research spans multiple aspects of software testing including test prioritization, test scheduling, flaky test detection and repair, and optimization of continuous development processes. Dr. Shi's publication record shows a consistent focus on flaky tests and regression testing across multiple top-tier software engineering conferences including ASE, ICSE, ISSTA, and ESEC/FSE. His research has evolved from foundational work on test suite reduction and mutant generation to more recent innovations in flaky test classification, debugging, and repair. A notable trend is his increasing application of machine learning techniques to testing problems, particularly in his 2024-2025 publications. Dr. Shi actively contributes to the software engineering research community through committee service on major conferences including ASE, ICSE, ISSTA, and ESEC/FSE, where he has served on program committees for Research Papers, NIER tracks, and Tool Demonstration tracks. Dr. Shi is currently seeking PhD students to work on projects related to his research interests in software testing. He has supervised or co-supervised multiple student projects presented at major software engineering conferences, demonstrating his commitment to mentoring the next generation of researchers.

Erin Evelyn Gabrielمشاهده پروفایل

استاد

• Causal Inference
• Biostatistical Methods Development
• Randomized Trials
• +۷ مورد دیگر

Erin Evelyn Gabriel is a Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. She holds a PhD in Biostatistics from the University of Washington awarded on August 10, 2012. She teaches Introductory Statistics and Data Analysis at both Masters and Doctoral levels. Her research focuses on biostatistical methods development with applications to infectious diseases, vaccination, cancer, and aging. Key research areas include: Causal inference methodologies Surrogate evaluation, particularly in the presence of interference Nonparametric causal bounds Designs and estimation methods for emulated and randomized clinical trials Evaluation of prediction-based decision rules Her publication record shows a strong trajectory in methodological biostatistics, with recent work (2022-2025) focusing on causal bounds, mediation analysis, age-period-cohort models, instrumental variables, and biases in randomized controlled trials. Her work appears in top-tier journals including the Journal of the American Statistical Association, Biostatistics, and Epidemiology. Dr. Gabriel has established collaborations across multiple institutions, as evidenced by her co-authorship patterns with researchers including Sachs, Sjölander, Follmann, and Ocampo. Her research impact is indicated by citations and downloads of her work, with some publications gaining attention on social media platforms and news outlets.

Weijing Tangمشاهده پروفایل

استادیار

• Network Analysis
• Machine Learning
• Survival Analysis
• +۵ مورد دیگر

Weijing Tang is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on developing statistical methodologies for network analysis, machine learning, and survival analysis, addressing challenges in large-scale interdisciplinary datasets across health and social sciences. He holds a Ph.D. in Statistics from the University of Michigan (2022) and a B.S. in Mathematics from Tsinghua University (2016). Prior to CMU, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2023). Education: Ph.D. in Statistics, University of Michigan, 2022 B.S. in Mathematics, Tsinghua University, 2016 Research Interests: Network analysis and signed network dynamics Survival analysis and Cox models Machine learning applications in biostatistics and public policy Optimization and scalable statistical methods Nonparametric methods and subgroup heterogeneity analysis Key Awards: ProQuest Distinguished Dissertation Award (2023) IMS Hannan Graduate Student Travel Award (2022) Multiple awards from ASA and ENAR (2021-2020) Grand Prize in American Heart Association's COVID-19 Data Challenge (2020) Funding: NSF Grant DMS-2412853 (sole PI) CMU Dietrich College Seed Grant (co-PI)

Lauri Ahonenمشاهده پروفایل

پژوهشگر

• Cognitive Science
• Neuroscience
• Decision-Making
• +۴ مورد دیگر

Lauri Ahonen is a Postdoctoral Researcher at the Department of Education, Faculty of Educational Sciences, University of Helsinki. His research focuses on cognitive science with applications in outdoor recreation safety, particularly in avalanche risk assessment and decision-making in dangerous environments. He is affiliated with the High Performance Cognition research group and maintains an active research profile through the University of Helsinki research portal. Dr. Ahonen's research interests span cognitive science, neuroscience, and decision-making processes, with a strong emphasis on real-world applications in outdoor recreation safety. His work combines cognitive theory with field research methodologies to understand how individuals and groups make decisions in high-risk environments like avalanche terrain. He has developed expertise in physiological measurement techniques to study cognitive processes in natural settings, bridging the gap between laboratory-based cognitive science and real-world decision contexts. His publication record shows a clear trajectory from foundational cognitive neuroscience work toward applied research in outdoor safety. Recent publications (2024-2025) focus on avalanche risk assessment, decision-making in backcountry environments, and group dynamics in high-risk recreational settings. Earlier work (2016-2019) established his expertise in psychophysiology, cognitive measurement, and collaborative learning processes. This progression demonstrates his ability to apply cognitive science principles to practical safety concerns in outdoor recreation. Dr. Ahonen is actively involved in multiple research projects, including the Niemi: DAIA 2023-2025 VETURI project funded by Business Finland Oy and the AI Personality and Cognition (AiPerCog) project funded by the Academy of Finland (2023-2027). His research has been presented at international conferences including the International Snow Science Workshop.

Yves Bergerمشاهده پروفایل

دانشیار

• Statistical Inference
• Complex Sampling Design
• Variance Estimation
• +۳ مورد دیگر

Yves Berger is an Associate Professor at the University of Southampton, specializing in statistical inference from complex sample surveys. He is affiliated with the Statistical Sciences Research Institute (S3RI) and focuses on variance estimation, non-response adjustment, and empirical likelihood methods. His research also extends to econometrics, particularly non-linear regression with endogenous covariates. He teaches courses in statistical modeling, survey theory, and demography. Currently, he supervises PhD student Atika Nashirah Hasyyati in Social Statistics and Demography. Key research projects include ESRC-funded studies on error estimation in surveys and joint statistical methodology innovations. His work has been published in top journals like the Journal of Official Statistics and Econometrics and Statistics. Berger is an Associate Editor of the Journal of the Royal Statistical Society (Series B), contributing to the advancement of statistical methodology. His research explores cutting-edge techniques for handling complex survey data, including multivariate regression estimation for temporal surveys and semi-parametric methods under endogenous selection. He emphasizes methodological rigor in both theoretical and applied contexts, with applications in auditing, inequality measurement, and multilevel data modeling.

• Nonparametric Bayesian inference
• Multiple hypothesis testing
• High-dimensional data
• +۶ مورد دیگر

Professor Subhashis Ghoshal is affiliated with North Carolina State University in the United States. His research focuses on advanced statistical methodologies for analyzing complex data structures. Nonparametric Bayesian inference Multiple hypothesis testing High-dimensional data Bayesian imaging Functional data analysis Nonparametric regression Survival analysis

Yanglei Songمشاهده پروفایل

استادیار

• Sequential Decision-Making
• Mathematical Statistics
• High-Dimensional Statistics
• +۷ مورد دیگر

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on sequential decision-making problems, including hypothesis testing, change detection, and multi-armed bandits, alongside mathematical statistics such as U-statistics, high-dimensional statistics, and cut-point analysis. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2019) M.Sc. in Mathematics, University of Illinois at Urbana-Champaign (2016) B.Eng. in Electronic Engineering, Tsinghua University (2012) Research Interests: Sequential decision-making frameworks with applications in hypothesis testing and stochastic optimization. Statistical methodologies for high-dimensional data and nonparametric modeling. Development of robust algorithms for reinforcement learning and adaptive systems. Mathematical foundations of U-statistics and their computational guarantees. Publications Trends: Recent work emphasizes theoretical advancements in bandit algorithms, robust model approximation, and statistical inference under covariate-adaptive randomization. Contributions span machine learning, computational statistics, and applied mathematics, with a focus on practical implementations and rigorous theoretical analysis. Students: J. Liu Y. Zhou Z. Wang Z. Zhao N. Li M. Zhou Labs/Teams: Maintains an active research group focused on statistical learning and sequential decision-making, with open-source projects such as Stratified incomplete local simplex tests for nonparametric regression analysis.

• Process Analytical Technology (PAT)
• Biopharmaceutical Manufacturing
• Downstream Processing
• +۳ مورد دیگر

Dr. Matthias Rüdt is an Associate Professor at HES-SO Valais-Wallis, affiliated with the School of Chemistry and Life Sciences and the Department of Life Sciences Engineering. He holds a Dr.-Ing. (Doctor of Engineering) from Karlsruhe Institute of Technology (KIT), completed with summa cum laude. Previously, he worked in industry roles at Novartis and Roche, focusing on biopharmaceutical manufacturing and downstream process development. His research interests include PAT applications in biopharmaceutical processes, machine learning for process engineering, optical spectroscopy (UV/Vis, Raman, NIR), and real-time monitoring strategies. Notable achievements include a patent in biopharmaceutical process development and multiple awards for conference publications. His recent work emphasizes data-driven process optimization, including generative data augmentation for CNN models and Kalman-filtered Raman spectroscopy. Collaborations with institutions like KIT and industry partners highlight his translational research approach. Education: PhD (Doktor der Ingenieurswissenschaften) from Karlsruhe Institute of Technology Master's in Chemistry and Biotechnology from ETH Zurich with a Stanford University exchange Professional Experience: PostDoc at Karlsruhe Institute of Technology (KIT) Process Development roles at Novartis and Roche Publications focus on improving process analytical technologies through advanced data analysis, with recent contributions in Analytical and Bioanalytical Chemistry and Biotechnology and Bioengineering .

• Machine learning
• Numerical analysis
• Numerical methods
• +۱۰ مورد دیگر

Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.

Andrew Linمشاهده پروفایل

دانشیار بالینی

• Cardiac Imaging
• Coronary Artery Disease
• Atherosclerosis
• +۷ مورد دیگر

Andrew Lin is an Adjunct Clinical Associate Professor at the Victorian Heart Institute (VHI), Monash University, where he conducts advanced research in cardiac imaging and coronary atherosclerosis. He is actively involved in clinical and translational research, particularly in the application of artificial intelligence and radiomics in computed tomography for personalized cardiovascular care. Doctor of Philosophy, Monash University (2020) Bachelor of Medicine & Bachelor of Surgery, University of Melbourne (2008) Bachelor of Medical Science, University of Melbourne (2008) Dr. Lin's research focuses on the intersection of cardiovascular disease and cutting-edge imaging technologies. His key areas include coronary artery disease, atherosclerosis, cardiac CT, radiomics, and the integration of artificial intelligence for noninvasive phenotyping and risk prediction. His work aims to improve early diagnosis, monitor treatment response, and guide personalized interventions. His recent publications (2018–2025) demonstrate a consistent trajectory in AI-enhanced cardiac imaging, with emphasis on pericoronary adipose tissue, plaque characterization, and longitudinal monitoring using CT. These studies reflect a strong commitment to innovation in cardiovascular diagnostics and precision medicine. Dr. Lin has secured multiple research grants as a Primary Chief Investigator, including ongoing and completed projects on AI-driven atherosclerosis imaging and statin effects on plaque. His collaborative network includes leading researchers in cardiology and imaging science. Artificial intelligence-enabled quantitative atherosclerosis imaging by computed tomography to guide personalised management of coronary artery disease: a randomised clinical trial (2026–2030) Effects of statins on coronary atherosclerotic plaques: longitudinal, radiomics-based precision phenotyping (2023–2024) The Association of Patterns of Dyslipidaemia with Coronary Artery Plaque on Computed Tomography Coronary Angiography (2018–2021) He is affiliated with the Victorian Heart Institute at Monash University, a hub for cardiovascular innovation and research excellence. His lab and team focus on developing AI-powered tools for CT-based cardiovascular risk assessment and plaque analysis, contributing to global efforts in reducing cardiovascular morbidity and mortality.

• Software Engineering
• Source Code Analysis
• Software Clone Detection
• +۱۲ مورد دیگر

James R. Cordy is a Professor in the School of Computing at Queen's University, Faculty of Engineering and Applied Science, Kingston, Canada. He is a leading researcher in software engineering, with a focus on source code analysis, software clone detection, model-driven engineering, and program transformation. He has been actively publishing since 1977, with a sustained record of contributions in top-tier venues such as ICSE, MoDELS, and WCRE. His research interests include software clone detection, model transformation, Simulink models, source transformation, software maintenance, and grammatical inference. He has developed and contributed to influential tools such as TXL and NiCad, and his work often involves empirical studies and tool evaluation in real-world software systems. The most recent articles highlight trends in model transformation, clone detection, verification of state machines, and migration of legacy systems. His work increasingly integrates formal methods and empirical validation, particularly in automotive and safety-critical domains. He has also explored applications in healthcare software, such as artificial pancreas systems. Most Influential Paper Award, SCAM 2001 (awarded in 2019) He has advised numerous students, including Manar H. Alalfi, Matthew Stephan, and Chanchal K. Roy, who have co-authored multiple publications with him. His research is often collaborative, involving teams from Queen's University and other institutions. He has also contributed to workshops and special issues, demonstrating leadership in the software engineering community.

Marco Rianiمشاهده پروفایل

استاد

• Robust Statistics
• Regression Analysis
• Time Series Analysis
• +۵ مورد دیگر

Marco Riani is Full Professor of Statistics at the Department of Economic and Business Sciences, Faculty of Economics, University of Parma since 2006. He directs the Interdepartmental Center Ro.S.A. (Robust Statistics Academy) and has held academic positions including Post Doctoral Fellow (1996-1998), Lecturer (1998-1999), and Associate Professor (1999-2006) at the same institution. Education: Bachelor of Science in Economics (cum laude), University of Parma (1986-1990) PhD in Statistics, University of Florence (1992-1995) His research centers on robust statistics with methodological focus on robust regression , time series , and multivariate analysis . Applied work spans risk management , quality evaluation , international trade monitoring , and medical statistics (neonatal health parameters). He pioneers outlier detection without arbitrary thresholds and robust machine learning. Recent publications highlight robust transformations for regression, explainable AI, and applications in environmental/medical fields. Key trends include automated fraud detection in trade data, soil moisture estimation through time series, and biomarker analysis for birth anomalies. Scientific awards: Best Italian PhD thesis in Statistics (1997) Best contribution at MATLAB expo 2016 (Milan) As advisor, he has supervised five PhD theses and over 100 degree theses. His grant leadership includes Horizon 2020 PrimeFish (2014-2019) and multiple Italian Ministry projects on robust methods. External expertise spans EU Automated Monitoring Tool projects (AMT3-AMT5) for trade fraud detection. Principal Investigator: 11 national/EU projects (2000-2019) Steering roles: ICORS, SIS CLADAG, Ro.S.A. center He co-developed the FSDA toolbox (MATLAB/R) with the European Commission, enabling robust multivariate analysis for institutions like the Joint Research Centre. The Ro.S.A. center drives his collaborative work on outlier detection and data mining.

Angela Carolloمشاهده پروفایل

پژوهشگر

• Demography
• Statistics
• Survival Analysis
• +۴ مورد دیگر

Angela Carollo is a Researcher at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, affiliated with the Laboratory of Fertility and Well-Being. Her work focuses on developing advanced statistical methodologies for demographic analysis, particularly in survival and event-history models with multiple time scales. Her research interests span demography, statistics, and population health, with emphasis on: Survival analysis and competing risks modeling Event-history frameworks with multidimensional time Fertility dynamics and family transitions Mortality patterns and health outcomes Statistical software development for demographic applications Carollo's publication record demonstrates consistent innovation in handling complex demographic data structures. Her recent work shows strong trends toward interdisciplinary collaboration (spanning statistics, gerontology, and public health) and methodological rigor in modeling time-dependent phenomena. Key contributions include the TwoTimeScales R package and novel approaches to hazard smoothing across multiple temporal dimensions, applied to critical demographic questions like partnership transitions and mortality prediction. No scientific awards were documented in the source material. Her collaborative research involves extensive work with international teams across Europe, though no formal student advising or grant management details were provided. Current projects include dissertation work on "Multiple Time Scales in Survival and Event-History Models" within the Laboratory of Fertility and Well-Being. Carollo operates within MPIDR's Laboratory of Fertility and Well-Being, which investigates how demographic processes like fertility and partnership transitions interact with individual well-being across the life course, leveraging advanced statistical techniques for population-level insights.

George Carrumمشاهده پروفایل

استاد

• Peripheral blood stem cell transplantation
• Bone marrow transplantation
• Hematology
• +۲ مورد دیگر

Dr. George Carrum, M.D., is a Professor at the Center for Gene Therapy of Baylor College of Medicine , Houston, Texas. His clinical and research work focuses on hematopoietic stem cell transplantation (HSCT) for hematologic malignancies and autoimmune diseases. Key affiliations : Center for Gene Therapy, Baylor College of Medicine Dan L Duncan Comprehensive Cancer Center member Houston Methodist Hospital clinic Dr. Carrum's research explores high-dose chemotherapy with stem cell rescue for multiple myeloma, lymphoma, and leukemia, alongside post-transplant immunotherapy and submyeloablative regimens in allogeneic HSCT. His work spans: Viral immune reconstitution (CMV, EBV, HHV-6) Graft-versus-host disease management Cytotoxic T-lymphocyte therapy Novel conditioning protocols Drug interactions in transplant patients Analysis of his 37+ publications (1986-2012) reveals trends in allogeneic transplantation for hematologic diseases, adoptive T-cell therapy , and viral complications post-HSCT. His 15 most recent works (2007-2012) emphasize: Immunosuppressive drug interactions CAR-T cell costimulation Viral encephalitis post-transplant Autoimmune disease applications Salvage therapies for graft failure Cytologic diagnostics in transplant complications Dr. Carrum's clinical practice at Houston Methodist Hospital (6565 Fannin St.) and collaborations with institutions like the Dan L Duncan Comprehensive Cancer Center highlight his role in translational stem cell research and clinical hematology-oncology practice.