جست‌وجوی استاد و پژوهشگر

• Computational Materials Science
• 2D Materials
• High-Entropy Ceramics
• +۵ مورد دیگر

Dr. Rico Friedrich is a computational materials scientist leading the "Autonomous Materials Thermodynamics - AutoMaT" research group, jointly operated by the Chair of Theoretical Chemistry at Technische Universität Dresden and the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). His work focuses on data-driven computational design of advanced materials for information technology and energy applications through the DRESDEN-concept research alliance. His research spans several cutting-edge areas: Discovery and design of 2D non-van der Waals materials with novel electronic and magnetic properties Data-driven modeling of high-entropy ceramics based on entropy maximization principles Development of computational methods for accurate thermodynamic stability prediction, particularly the coordination corrected enthalpies (CCE) method Applications of artificial intelligence in materials design Dr. Friedrich's publication record shows a strong trend toward computational materials discovery, with significant contributions to understanding non-van der Waals 2D materials and high-entropy ceramics. His work bridges theoretical developments with practical applications, resulting in publications in high-impact journals including Nature, Nano Letters, and Advanced Electronic Materials. His key scientific contributions include: Development of the coordination corrected enthalpies (CCE) method for accurate formation enthalpy calculations Creation of the AFLOW-CCE module implemented in the AFLOW software ecosystem Discovery of novel 2D non-van der Waals materials with ultra-low exfoliation energies Formulation of the disordered enthalpy-entropy descriptor (DEED) for high-entropy ceramics Dr. Friedrich actively mentors the next generation of materials scientists, currently supervising PhD students and postdoctoral researchers in his AutoMaT lab. His research group collaborates extensively within the DRESDEN-concept research alliance, leveraging computational resources and expertise across multiple institutions to advance materials science and engineering.

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +۵ مورد دیگر

Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.

Andrew Logsdailمشاهده پروفایل

استاد

• Computational Chemistry
• Catalysis
• Materials Science
• +۴ مورد دیگر

Dr. Andrew Logsdail is a Reader in Catalytic and Computational Chemistry at Cardiff University’s School of Chemistry, part of the Cardiff Catalysis Institute (CCI). He holds a PhD in Chemistry (University of Birmingham), an MRes in Materials and Nanochemistry, and a BSc in Natural Sciences. His research focuses on computational modeling of catalytic materials, software development (e.g., ChemShell), and heterogeneous catalysis with applications in energy and sustainability. He is a Fellow of the Higher Education Authority and a Chartered Chemist with the Royal Society of Chemistry. Key roles include UKRI Future Leaders Fellow (2020–2024) and leadership in international organizations like the IUPAC Division II. His work is funded by UKRI, EPSRC, and industry partners like BP and Johnson Matthey. Research interests span computational catalysis, nanomaterials, and data-driven materials discovery. Notable projects include QM/MM simulations for catalytic systems, development of the ChemShell software, and studies on zeolites, palladium catalysts, and CO₂ reduction. He supervises PhD students and contributes to teaching at undergraduate and postgraduate levels. Dr. Logsdail’s achievements include over 100 peer-reviewed publications and significant contributions to software development in computational chemistry. His awards include the UKRI Future Leaders Fellowship and leadership roles in national and international scientific committees. He actively engages in outreach, promoting chemistry education and catalysis research.

Karim Aliمشاهده پروفایل

دانشیار

• Programming Languages
• Static Analysis
• Software Security
• +۸ مورد دیگر

Karim Ali is an Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), where he leads research in programming languages, static analysis, security, and compilers. He is affiliated with the Department of Computer Science within the College of Arts and Science. Prior to joining NYUAD, he served as an Associate Professor at the University of Alberta. His academic training includes a BSc from The American University in Cairo, and MMath and PhD degrees from the University of Waterloo, completed in 2014. BSc: The American University in Cairo MMath: University of Waterloo PhD: University of Waterloo (2014) His research focuses on making static analysis tools more practical by enhancing their scalability, precision, and usability. He investigates program analysis techniques applicable to real-world software, with applications in security, just-in-time compilation, and mobile app development. His work spans theoretical foundations and tool development, including the SWAN framework for Swift and contributions to secure cryptographic API usage through CogniCrypt. The recent publications reflect a strong trend in developer-centric static analysis, secure coding, energy efficiency in mobile apps, and compiler optimization. His work combines empirical studies with tool-building, emphasizing usability and integration into developer workflows. Scientific Awards: Dahl-Naygaard Junior Prize (2021) ACM SIGSOFT Distinguished Paper Award ACM SIGPLAN Distinguished Paper Award Distinguished Artifact Award, ECOOP 2014 Karim Ali has mentored numerous students and collaborated extensively with researchers worldwide. His lab has contributed tools adopted by major static analysis frameworks like Soot, WALA, and DOOP. He has secured research recognition through awards and industrial impact, including helping Symantec fix a security vulnerability. He teaches core courses such as Computer Systems Organization and supervises capstone projects, guiding students in original research. His lab conducts research on programming languages and static analysis, with projects including SWAN for Swift analysis, usability studies of static analysis tools, and development of precise pointer analysis techniques. The team works on both academic research and practical tooling for developers.

Scott T. Doyleمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Digital Pathology
• Bioinformatics
• +۷ مورد دیگر

Scott T. Doyle is an Associate Professor in the Department of Pathology and Anatomical Sciences at the Jacobs School of Medicine & Biomedical Sciences, University at Buffalo. His research integrates biomedical imaging, artificial intelligence, and computational pathology to develop quantitative tools for clinical diagnostics and anatomical modeling. Education: PhD in Biomedical Engineering, Rutgers, The State University of New Jersey (2011) BS in Biomedical Engineering, Rutgers, The State University of New Jersey (2006) Optical Microscopy & Imaging in the Biomedical Sciences, Marine Biological Laboratory (2014) hES Stem Cell Culture Training, WNYSTEM (2014) R Bioconductor Training, Roswell Park Cancer Institute (2016) Dr. Doyle’s research focuses on developing AI-driven algorithms for biomedical image analysis, particularly in digital pathology and 3D anatomical modeling. His work spans tumor segmentation, risk prediction in oral and thyroid cancers, and integration of virtual and physical anatomy in medical education. He applies machine learning, deep learning, and computational modeling to enhance diagnostic accuracy and patient outcomes. His recent publications reflect a strong trend in applying artificial intelligence to histopathology, with emphasis on active learning, 3D reconstruction, and multi-institutional data fusion. Key areas include oral cavity cancer recurrence prediction, thyroid cancer subtyping, and computational modeling of surgical margins and anatomical structures. Scientific Service and Recognition: Reviewer for NIH SPORE grants Peer reviewer for journals including Medical Image Analysis , BMC Bioinformatics , IEEE Transactions on Biomedical Engineering Program Committee and Session Chair, SPIE Medical Imaging: Digital Pathology (2016–present) Member, Graduate Program Steering Committee, Pathology & Anatomical Sciences Mentor, McNair Scholarship and CSTEP programs for underrepresented students Dr. Doyle has secured significant research funding as Principal Investigator on NIH and CTSI grants, including a $2M+ NIH grant for predicting oral cancer recurrence. He has also contributed to educational innovation through hybrid anatomy curriculum development and AI training for pathologists. He leads the 'Atoms to Anatomy' research initiative and is active in strategic planning at the Jacobs School. Laboratories and Collaborative Teams: Dr. Doyle collaborates with the Center for Computational Research (CCR) and is involved in the Structural Sciences Learning Center (SSLC). He has led projects with teams at Ibris, Inc., Veterans Affairs Hospital, and Mount Sinai School of Medicine.

Jens Palsbergمشاهده پروفایل

استاد

• Programming Languages
• Quantum Computing
• Software Engineering
• +۳ مورد دیگر

Jens Palsberg is a Professor and former Department Chair of Computer Science at the University of California, Los Angeles (UCLA), where he currently serves as Director of the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence and co-director of UCLA's quantum research center. He chairs ACM SIGPLAN and is a member of the ACM Council. His research spans programming languages, software engineering, quantum computing, compilers, embedded systems, and information security. Palsberg has authored over 80 technical papers, co-authored the book Object-Oriented Type Systems , and revised Appel's textbook on Modern Compiler Implementation in Java . His recent work shows a significant shift toward quantum computing, including compiler techniques and program analysis for quantum systems. Analysis of his recent publications reveals a clear transition from traditional programming language research to quantum computing, with nearly half of his 2022-2024 publications focusing on quantum topics while maintaining strong work in software engineering and programming languages. His quantum research particularly emphasizes compiler optimization, abstract interpretation, and circuit analysis. ACM SIGPLAN Distinguished Service Award (2012) UCLA teaching award for quantum computing courses (2023) National Science Foundation CAREER and ITR awards Purdue University Faculty Scholar award IBM Faculty Award Okawa Foundation research award Palsberg has served in numerous leadership roles including general chair of POPL, conference chair of LICS, and vice chair of ACM SIGBED. His research has been supported by DARPA, Intel, British Telecom, and the National Science Foundation. He was instrumental in establishing UCLA's Masters degree in quantum science and has mentored numerous students through his legendary proof sessions. He leads a research group of over 30 professors in UCLA's quantum research center and maintains active collaborations across academia and industry, particularly with Amazon through the UCLA-Amazon Science Hub.

Qiongshi Luمشاهده پروفایل

دانشیار

• Biostatistics
• Genetic Epidemiology
• Human Genetics
• +۳ مورد دیگر

Qiongshi Lu is an Associate Professor at the University of Wisconsin–Madison, affiliated with the Department of Biostatistics and Medical Informatics within the School of Medicine and Public Health. He also holds affiliate faculty positions in the Department of Statistics, Computer, Data & Information Sciences at the College of Letters and Science. His research focuses on developing statistical methods for human genetics, including genome-wide association studies (GWAS), gene-environment interactions, and genetic risk prediction. Lu’s work bridges computational biology, epidemiology, and clinical genetics, with a particular emphasis on understanding complex trait variability and resilience mechanisms in neurodegenerative diseases like Alzheimer’s. His lab (qlu-lab.org) explores innovative statistical frameworks such as PIGEON for gene-environment interaction analysis and has contributed to tools like the R package 'ipd' for predictive data inference. Recent studies highlight his interdisciplinary approach, addressing topics from congenital heart disease genetics to socioeconomic health gradients using large-scale genomic datasets. While no formal advisees are listed, his research collaborations span multiple institutions and disciplines.

• Software Engineering
• Software Testing
• Automated Testing
• +۳ مورد دیگر

Rudolf Ramler is an External Lecturer at TU Wien's Faculty of Informatics, Department of Information Systems Engineering. He specializes in software testing methodologies, automated testing frameworks, and software quality assurance. His research focuses on improving testing practices for legacy systems, industrial automation software, and defect prediction in software projects. He teaches the Software Testing course (VU 188.280) in 2025S. His work spans empirical investigations, tool-supported testing, and systematic literature reviews. Ramler has contributed to projects like CDL-SQI (2018–2024), exploring practical approaches for testing industrial automation systems. Research interests include test code readability, automated testing strategies, and value-driven testing frameworks. His publications address challenges in retrofitting tests for legacy code, comparing manual and automated testing efficacy, and developing context-specific defect prediction models. He has collaborated with industry partners to apply academic research to real-world software engineering problems.

• mHealth Application Development
• Ontology
• Machine Learning
• +۳ مورد دیگر

Daniel Campbell is a Lecturer in Web Development & Web AI at the Computer Science department of Edge Hill University. His work contributes to UN Sustainable Development Goals related to health and innovation. He is affiliated with the Centre for Intelligent Visual Computing and the Data and Complex Systems Research Centre. Education: He completed his Doctoral Thesis in 2018 titled 'An Ontology-Driven Approach To Personalised mHealth Application Development' under supervisors E. Pereira, G. McDowell, and C. Balakrishna. Research focuses on mHealth applications, ontology-driven frameworks, machine learning for health monitoring, and software engineering practices like bug prediction and open-source repository analysis. Recent projects include a Knowledge Exchange initiative with the water industry (2024-2026) as a Co-Investigator. His articles explore topics ranging from accelerometer-based elderly activity prediction to automated classification of software repository messages. Collaborations span institutions globally, with active engagement in topics like healthcare technology and user-centric design.

Yoseph Barashمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Yoseph Barash is an Associate Professor at the University of Pennsylvania, jointly appointed in the Perelman School of Medicine's Department of Genetics and the School of Engineering and Applied Science's Department of Computer and Information Science. He leads the BioCiphers Lab, integrating machine learning with experimental biology to decode RNA biogenesis and splicing regulation in human disease. Education: B.Sc. in Physics and Computer Science, Hebrew University Ph.D. in Machine Learning, Hebrew University (2006) His research spans Machine Learning , Computational Biology , and Bioinformatics , focusing on predictive models for RNA splicing and its role in diseases like cancer and neurological disorders. He pioneered the splicing code (Barash et al., Nature 2010) and extended it to genetic variations (Xiong et al., Science 2015). Recent publications emphasize RNA splicing variations in cancer, tool development (e.g., MAJIQ V3, MAJIQ-CLIN), and machine learning for drug discovery (e.g., trametinib sensitivity in AML). His work bridges computational innovation with wet-lab validation, enabling novel high-throughput assays. Scientific Awards: Lap-Chee Tsui Publication Award (2010) NSERC EWR Steacie Fellowship Canadian Institute for Advanced Research Fellowship He advises companies in RNA therapeutics and has licensed splicing quantification tools to Pfizer, GSK, and Biogen. His lab collaborates extensively, training students and postdocs in interdisciplinary approaches to RNA biology.

Beyza EKENمشاهده پروفایل

استادیار

• Software Defect Prediction
• Natural Language Processing
• Machine Learning in Software Engineering
• +۲ مورد دیگر

Beyza Eken serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences, teaching core courses including Software Project Management, Natural Language Processing, and Graduation Projects while maintaining active research in software engineering and AI applications. Her academic credentials include: Doctorate in Computer Engineering from Istanbul Technical University (2015), thesis: "Software Defect Prediction" Master's in Computer Engineering from Istanbul Technical University (2011-2015), thesis: "Entity Name Recognition in Short Texts" Bachelor's in Computer Engineering from Sakarya University (2007-2011) Dr. Eken's research integrates machine learning with software engineering, specializing in defect prediction models that incorporate personalized developer factors and industrial deployment challenges. Her work bridges natural language processing for Turkish social media analysis with software quality assurance, demonstrating expertise in both theoretical modeling and practical implementation in industrial settings. Recent expansions include neuro-symbolic AI for test oracle generation and MLOps frameworks. Publication trends reveal consistent focus on empirical software engineering from 2018-2021 (defect prediction, community smells, industrial deployment), evolving into cutting-edge domains by 2023-2025 (neuro-symbolic testing, employee feedback analysis, MLOps). Her work shows strong industry-academia collaboration patterns with increasing methodological sophistication. Dr. Eken actively contributes to academic service as reviewer for ACM Transactions on Software Engineering and Methodology (2024) and IEEE Transactions on Software Engineering (2023). She leads research projects including "Developer-specific error prediction modeling" (2020) and the Mevlana exchange project with Ryerson University on data mining for defect prediction (2018), while supervising graduation projects and research area courses that develop student expertise in software engineering practices. Her international research engagement includes participation in the ASTERIx project at Università della Svizzera Italiana's Software Testing and Analysis Research Group (2023), demonstrating ongoing commitment to global collaboration in software engineering advancements.

David A. Egolfمشاهده پروفایل

دانشیار

• computational physics
• nonequilibrium systems
• spatiotemporal chaos
• +۶ مورد دیگر

David A. Egolf is an Associate Professor in the Department of Physics at Georgetown University, specializing in computational physics with a focus on systems maintained far-from-equilibrium. His research spans fluid dynamics, granular materials, biophysics, and statistical mechanics, employing nonlinear dynamics and large-scale computation to understand complex phenomena. His educational background includes undergraduate and doctoral studies at Duke University, where he earned his PhD in Physics with a thesis on Characterizations of Extensively Chaotic States and Transitions . Prior to Georgetown, he held postdoctoral positions at Cornell University's Cornell Theory Center and Los Alamos National Laboratory's Center for Nonlinear Studies. Egolf's research interests center on spatiotemporal chaos and nonequilibrium systems. He investigates how localized events determine the evolution of complex systems, with applications ranging from fluid convection to fibrillating heart tissue. His work reveals that seemingly chaotic systems often contain predictable elements around specific critical events, providing pathways to develop a statistical mechanics for nonequilibrium phenomena. His publication record shows consistent contributions to understanding dynamical systems, with recent work focusing on granular jamming transitions, biopolymer networks, and QCD calculations. The research demonstrates recurring themes of identifying fundamental building blocks within chaotic systems and establishing connections between nonequilibrium behavior and equilibrium statistical mechanics. Alfred P. Sloan Research Fellow Software of the Year Award (1984) for AtariLab Science Series Egolf has successfully mentored numerous undergraduate researchers at Georgetown, supervising over a dozen senior theses with several students receiving departmental awards and honors. His research has been supported by major funding agencies including the National Science Foundation, Research Corporation, NASA, and the Alfred P. Sloan Foundation. He maintains an active collaboration with experimental physicist Jeffrey Urbach, combining theoretical and experimental approaches to study driven granular systems and biophysics. His laboratory work focuses on computational modeling of nonequilibrium systems, utilizing large computer clusters to simulate complex phenomena across multiple scales. Current projects include studying granular systems driven by both shaking and shearing to introduce multiple time-scales, and investigating biopolymer networks relevant to cellular mechanisms.

Chanchal K. Royمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Code Clone Detection
• +۳ مورد دیگر

Dr. Chanchal K. Roy is a Professor of Software Engineering/Computer Science at the University of Saskatchewan (USask), Canada, and Director of the NSERC CREATE SOAR program. He leads the Software Research Lab (SRLab) and is renowned for his work on code clone detection (NiCad tool) and software maintenance. His research spans software evolution, big data analytics, and quantum computing applications in software engineering. Dr. Roy holds a Ph.D. from Queen’s University, an M.Sc. from RWTH Aachen University, and a B.Sc. from Khulna University. Research interests include software clone detection, maintenance, and evolution, with emphasis on semantic analysis and cross-language clones. He has published over 240 papers (h-index 52) and attracted $6M+ in funding, including NSERC grants and CFI-JELF support. Awards include the GSA Advising Excellence Award, Outstanding Young Computer Science Researcher Award, and multiple Most Influential Paper awards. Key contributions include developing NiCad, advancing Stack Overflow search techniques, and leading collaborative projects in software analytics. His work has been featured in ACM Tech News, TechRepublic, and Stack Overflow blogs. Dr. Roy actively engages in keynotes at conferences like WCRE, IWSC, and BIM.

Kevin Schneiderمشاهده پروفایل

استاد

• Software Architecture and Design
• Software Evolution and Maintenance
• Software Analysis and Navigation
• +۶ مورد دیگر

Dr. Kevin Schneider is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on software architecture, evolution, analysis, and visualization, with notable work in quantum computing applications and machine learning. He also explores collaborative software teams and domain-specific languages to enhance development processes. Education: Ph.D., Computing and Information Science, Queen’s University (2000) Research Associate, Computing and Information Science, Queen’s University (1991–94) M.Sc., Computing and Information Science, Queen’s University (1990) B.Sc.(Hon), Computational Science, University of Saskatchewan (1980) Research interests include software design principles, maintenance strategies, and the integration of AI/quantum computing into software engineering. He emphasizes reproducibility in scientific workflows and tools like VizSciFlow. His work often bridges theory and practice, addressing challenges in code clone stability, user feedback management, and healthcare-related machine learning frameworks. Scientific Awards: Most Influential Paper at SCAM 2001 for his work on software engineering via source transformation. Advising and grants: While no specific advisees are listed, his research involves large-scale projects such as the Nutrient App and automated polyp segmentation tools. He collaborates on grants related to quantum computing applications and cloud-based software systems. Labs and teams: His work centers on collaborative scientific data analysis groups and developing tools for real-time groupware systems in complex workflows. He contributes to projects like CloneCognition and FSECAM, aiming to improve software design and maintenance through advanced analytics.

• Structural Integrity
• Multiaxial Fatigue
• Additive Manufacturing
• +۷ مورد دیگر

Luca Susmel is a Professor of Structural Integrity and Transforming Lives Fellow at Sheffield Hallam University. His expertise spans static, dynamic, and fatigue assessment of engineering materials and structures, with a focus on notched, welded, and 3D-printed components. He has authored over 350 publications, including 165+ peer-reviewed articles and a seminal book on multiaxial fatigue. His research has earned a high h-index and top 2% global scientist recognition. He serves as Editor-in-Chief of Theoretical and Applied Fracture Mechanics and has developed multiaxial fatigue-assessment software. Education and Career: Susmel has held academic roles at institutions including the University of Padova, Trinity College Dublin, and the University of Sheffield. His work bridges theoretical and experimental investigations, validated through systematic experiments. Notable projects include fatigue assessment of cast iron water pipes and hydrogen effects on metallic materials. Research Interests : Multiaxial fatigue, additive manufacturing materials, fracture mechanics, welded joints, notch fatigue, and fatigue life prediction. His methodologies include critical distance theory and advanced stress analysis approaches. Grants and Projects : Led numerous projects funded by EC, trusts, and private companies. Key areas include HyDeploy (hydrogen pipeline safety) and FRAMED (fatigue assessment of welded joints). Awards : Top 2% global scientist (Clarivate Analytics), Transforming Lives Fellow, high h-index recognition.