جست‌وجوی استاد و پژوهشگر

• Computational Materials Design
• Ab Initio Calculations
• Phase Diagrams
• +۷ مورد دیگر

Dr. Tilmann Hickel is a researcher at the Max Planck Institute for Sustainable Materials, leading the Computational Phase Studies group. He holds a PhD in physics from Humboldt-University Berlin (2005) and has been a scientific co-worker and group leader in the CM department of MPIE since 2005. His research focuses on computational materials design, defect phase diagrams, and ab initio methods to study materials thermodynamics and magnetism. Key areas include hydrogen embrittlement, grain boundary engineering, and materials informatics. Education & Career: Bachelor/Master (Dipl.-Phys.): Physics at Humboldt-University Berlin (1995-2001) PhD in Theoretical Physics: Study of many-body effects in Kondo-lattice models (2001-2005) Group Leader roles since 2006 (CM department) and 2008 (ASG Modelling at ICAMS) Research Interests: Ab initio calculations, phase diagrams for defects, magnetism in alloys, and materials data spaces. His work bridges computational methods with industrial applications like steel design and corrosion science. Publications: Recent work emphasizes workflow engineering for materials design, hydrogen effects in metals, and high-throughput studies of segregation at grain boundaries. He contributes to initiatives like NFDI-MatWerk and pyiron IDE development. Grants & Teams: Leads interdisciplinary projects on materials digitalization and defect-driven materials design. Collaborates with industry and academic partners through platforms like MaterialDigital.

Wilhelm Kleppmannمشاهده پروفایل

استاد

• Design of Experiments (DoE)
• Statistical Process Control
• Reliability Engineering
• +۳ مورد دیگر

Prof. Wilhelm Kleppmann is a Professor at the Faculty of Electronics & Computer Science at Hochschule Aalen, Germany. His primary research focuses on Design of Experiments (DoE), statistical process control, and reliability engineering. He has authored influential textbooks on optimization methodologies and contributed to Six Sigma integration in industrial processes. His work bridges theoretical statistical methods with practical applications in manufacturing and engineering systems. Key contributions include developing experimental design frameworks for machine learning integration, optimizing cooling circuits in automotive systems, and statistical evaluation of electromagnetic interference. He maintains active engagement with industry through software recommendations for DoE, emphasizing tools like Minitab and JMP. Kleppmann's educational efforts include curriculum development for engineering courses and promoting hands-on learning through simulation tools. His research spans decades with notable publications from 1980s semiconductor reliability studies to modern machine learning-driven process optimization. While no specific awards are listed, his extensive publication record and textbook revisions (e.g., 10th edition of 'Versuchsplanung') highlight sustained scholarly impact.

• Cybersecurity
• Authentication Systems
• Blockchain Technology
• +۶ مورد دیگر

Jun-Ho Huh is a Professor at Sungkyunkwan University's College of Information and Communication Engineering, Department of Computer Science and Engineering. His extensive research portfolio demonstrates leadership in cybersecurity, authentication systems, and AI applications across multiple domains including financial technology, maritime security, and agricultural technology. Dr. Huh's research interests span cybersecurity, authentication systems, blockchain technology, artificial intelligence, edge computing, and IoT security. His work bridges theoretical computer science with practical applications in smart systems engineering. His research has significant implications for improving security in emerging technologies like DeFi, autonomous systems, and smart infrastructure. Analysis of Dr. Huh's recent publications reveals a strong focus on practical applications of AI and security technologies. His work covers diverse areas including cybersecurity for maritime autonomous systems, authentication mechanisms, financial technology applications, and agricultural technology solutions. The publications demonstrate consistent productivity and relevance in addressing contemporary challenges in computer science and engineering. Dr. Huh has made significant contributions to authentication systems research, particularly in biometric and behavioral authentication methods. His work on keystroke dynamics, voice spoofing detection, and wearable device authentication has advanced the field of usable security. His research extends to practical applications in smart factories, agricultural technology (AgTech), and financial technology. The integration of blockchain with AI for various applications represents a significant thread throughout his recent work, demonstrating interdisciplinary approaches to solving complex technological challenges.

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Xiaoming Wang is a Professor at Shaanxi Normal University's School of Computer Science, specializing in computer vision, machine learning, and artificial intelligence. His research spans multiple domains including object detection, federated learning, and industrial process modeling. He maintains active collaborations with researchers across China and internationally, particularly with Yongmeng Liu, Chuanzhi Sun, and Shitong Wang. Dr. Wang's research interests focus on developing advanced AI models for practical applications. His work in computer vision includes improving object detection systems like YOLO variants for security and industrial applications. In machine learning, he has made significant contributions to federated learning with privacy preservation techniques. His industrial applications research addresses real-world problems in manufacturing, aerospace, and process control systems. Analysis of Dr. Wang's recent publications reveals a strong emphasis on practical AI applications with industrial relevance. His work shows consistent innovation in adapting established AI techniques like LSTM networks, transformers, and attention mechanisms to solve specific domain challenges. The publications demonstrate a clear trajectory toward more efficient, privacy-preserving, and real-time AI systems applicable across multiple sectors. Dr. Wang actively mentors students and junior researchers, with multiple co-authored publications featuring individuals likely to be his advisees. His collaborative approach is evident through numerous multi-institutional projects and consistent research partnerships. While specific grant information isn't detailed in the publication record, the scope and consistency of his work suggest substantial research funding support.

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۴ مورد دیگر

Feng Jiang is a Professor at Nanjing University's School of Computer Science and Technology, Department of Computer Science and Technology. With an extensive publication record spanning from 1999 to 2025, Professor Jiang has established himself as a prominent researcher in multiple computer science domains. His research interests include Machine Learning, Artificial Intelligence, Data Mining, Software Engineering, and Computer Vision. Professor Jiang's work demonstrates significant interdisciplinary applications, ranging from healthcare and bioinformatics to industrial manufacturing and agricultural technology. His research approach often combines theoretical advancements with practical implementations, addressing real-world challenges across diverse sectors. Analysis of his recent publications reveals a strong focus on deep learning applications, graph neural networks, and ensemble methods. His work shows consistent contributions to both theoretical frameworks and practical implementations, with particular emphasis on software engineering applications, computer vision systems, and biomedical informatics. The breadth of his research indicates active collaborations across multiple institutions and disciplines. Professor Jiang has supervised numerous research projects resulting in publications across top-tier journals including IEEE Access, Expert Systems with Applications, and Knowledge-Based Systems. His work has been consistently published in high-impact venues, demonstrating sustained research productivity and relevance in the field.

Hussam Amrouchمشاهده پروفایل

استاد

• AI Hardware Acceleration
• Emerging Nanoelectronics
• Cryogenic Circuits
• +۴ مورد دیگر

Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.

Robert Willeمشاهده پروفایل

استاد

• Design Automation
• Quantum Computing
• Computer Science
• +۷ مورد دیگر

Prof. Dr. Robert Wille is a Full Professor at the Technical University of Munich (TUM) in the School of Computation, Information and Technology and Chief Scientific Officer at the Software Competence Center Hagenberg GmbH . He leads the Chair for Design Automation , focusing on automatic methods for complex system design in conventional and future technologies. Studied Computer Science (Diploma) at the University of Bremen (2002-2006) Doctorate (summa cum laude) from the University of Bremen (2009) His research spans quantum computing , microfluidic biochips , field-coupled nanotechnologies , and reversible circuits , with applications in machine learning , artificial intelligence , and cyber-physical systems . Recent work includes quantum circuit verification, radar-camera fusion, and silicon dangling bond logic optimization. Robert Wille has received prestigious awards such as the ERC Consolidator Grant , Google Research Award , and Distinguished Professor appointment . He serves as Associate Editor for journals like IEEE TCAD and Springer LNCS, and has chaired conferences including DATE and ICCAD.

• Artificial Intelligence
• Machine Learning
• Automotive Production
• +۸ مورد دیگر

Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.

• Empirical Software Engineering
• Code Quality Analysis
• Software Maintenance
• +۷ مورد دیگر

Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.

• Knowledge Representation and Reasoning
• Geometry Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Stavros Nousias is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich , focusing on applications of Artificial Intelligence in the Built Environment . His work bridges Knowledge Representation and Reasoning , Geometry Processing , and Machine Learning to advance construction informatics and digital twinning. Research Interests: AI for building evacuation prediction, technical drawing segmentation, BIM optimization, and respiratory disease modeling. Publications: 15+ peer-reviewed articles on topics including graph neural networks for construction simulations, pulmonary airflow analysis, and heritage site monitoring. Supervised Theses: Guided projects on AI-based BIM command prediction and robotized construction simulation . Labs: Active in the BIM-Lab and Robotic Fabrication Lab . Teaching: Co-instructor for courses like Artificial Intelligence in Engineering and Computation in Engineering 1 .

Yasutaka Kameiمشاهده پروفایل

استاد

• Empirical Software Engineering
• Software Reliability
• Software Testing
• +۸ مورد دیگر

Yasutaka Kamei is a Full Professor at Kyushu University's Graduate School and Faculty of Information Science and Electrical Engineering, where he leads the POSL Lab (Process-Oriented Software Laboratory). He was promoted from Associate Professor to Full Professor in January 2024 after serving as Associate Professor from March 2015 to December 2023. He is also an InaRIS Fellow (2023-2033), receiving 10 million yen annually for his research on 'New paradigm for software development styles based on machine-human interaction.' Dr. Kamei's research focuses on Empirical Software Engineering (ESE) and Open Source Software Engineering (OSSE), with particular expertise in software reliability, testing, defect prediction, code review analysis, and mining software repositories. His work bridges empirical methods with practical software engineering challenges, emphasizing how data-driven approaches can improve software quality assurance processes. His recent publications demonstrate a strong trend toward understanding human factors in software development processes, analyzing modern code review practices, investigating technical debt, and exploring the application of large language models in software engineering tasks. His research spans both traditional software engineering challenges and emerging AI-driven approaches to software development. IPSJ/ACM Award for Early Career Contribution to Global Research (2019) Best industry paper award at ESEM 2018 Distinguished Paper Award at MSR 2014 InaRIS Fellow (2023-2033) Dr. Kamei actively serves the software engineering community as Tutorials Chair for ASE 2025 and has been a program committee member for numerous top-tier conferences including ICSE, FSE, ASE, ESEC/FSE, SANER, and MSR. He has secured multiple competitive grants from MEXT (Ministry of Education, Culture, Sports, Science and Technology) to support his research on test case generation, automated software testing for deep learning systems, technical debt engineering, and mining software repositories. His POSL Lab at Kyushu University serves as a hub for empirical software engineering research, focusing on data-driven approaches to improve software development processes and outcomes through rigorous analysis of software repositories and developer activities.

Muhammad Ali Babarمشاهده پروفایل

استاد

• Software Security & Privacy
• Big Data Platforms & Architectures
• Empirical Software Engineering
• +۶ مورد دیگر

Muhammad Ali Babar is a Professor in the School of Computer Science at the University of Adelaide. He leads a theme on architecture and platform for security as service in the CyberSecurity Cooperative Research Centre (CSCRC), which has an estimated budget of A$140 Millions over 7 years with A$50 Millions provided by the Australian government. Prof Babar has established CREST (Centre for Research on Engineering Software Technologies), directing the research, education, and engineering activities of more than 25 researchers and engineers. He has attracted more than $12 Millions in funding from industry and government since 2017. Prof Babar's research focuses on Secure Software Systems and Services for emerging technologies including Cloud Computing, Edge Computing, Internet of Things (IoT), and Big Data. His work sits at the intersection of Software Engineering, Artificial Intelligence (ML/NLP), and Cyber Security, employing empirical research methods both qualitative and quantitative. His recent publications demonstrate a strong emphasis on vulnerability prediction, secure microservices management, and applying large language models to security challenges. With over 320 peer-reviewed scientific papers and 17,763 citations (h-index 66 as of January 2025), Prof Babar ranks among the leading Software Engineering researchers in Australia and New Zealand. His research has been recognized with the most influential paper award for the Australasian Software Engineering Conference in 2014 and multiple best paper awards at international conferences. Most influential paper for Australasian Software Engineering Conference (2014) Multiple best paper awards at international conferences Prof Babar obtained his Ph.D. in Computer Science and Engineering from the University of New South Wales, Australia. His research is conducted in collaboration with industry and government partners including DST, ActewAGL, TSS, ATO, Jemena, Cisco, DST Group, Health SA, and Defence SA, as well as key players in the Australian Cyber Security ecosystem such as AustCyber, Data61, DST Group, Oceania Cyber Security Centre, and Australian Cyber Collaboration Centre (AC3). Prof Babar is eligible to supervise Masters and PhD students and has led high-performing R&D teams that have successfully carried out high-quality research in close collaboration with industry. His leadership in the Cyber Security Cooperative Research Centre represents a significant contribution to Australia's cyber security research capabilities and industry engagement.

Amiangshu Bosuمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Code Reviews
• Software Security
• +۸ مورد دیگر

Amiangshu Bosu is a tenured Associate Professor in the Department of Computer Science at Wayne State University's College of Engineering. Previously, he served as a tenure-track Assistant Professor at Southern Illinois University Carbondale from 2016-2018. His academic journey includes completing his Ph.D. at the University of Alabama in 2015 under Professor Jeffrey Carver, followed by postdoctoral research with Dr. Danfeng Yao at Virginia Tech. His educational background reflects a strong foundation in software engineering research, with his doctoral work focusing on empirical approaches to software development practices. This foundation has enabled his current research trajectory examining human aspects of software engineering. Bosu's research program centers on empirical software engineering with particular emphasis on code review dynamics, toxicity detection in developer communications, and diversity issues in open source communities. His work bridges technical analysis with social science perspectives, investigating how communication patterns affect participation, especially regarding gender dynamics in OSS projects. Recent work increasingly incorporates natural language processing and machine learning techniques to build tools like ToxiCR for automated toxicity detection in code reviews. Analysis of his publication record reveals a clear evolution toward examining social dynamics in software development, with growing emphasis on gender bias, toxicity, and diversity issues. His research consistently applies empirical methods to understand real-world developer experiences while developing practical tools to improve code review processes and community health. His scientific recognition includes the prestigious NSF CAREER award (2024), NSF CRII award (2019), and Wayne State University's College of Engineering Research Excellence award (2025). He maintains senior membership status in both ACM and IEEE, reflecting his standing in the computing community. NSF CRII award (2019) NSF CAREER award (2024) College of Engineering Research Excellence award (2025) Senior Member of ACM Senior Member of IEEE Bosu actively contributes to the software engineering research community through service on program committees for major conferences including ICSE, ASE, ESEC/FSE, and ESEM. His GitHub presence (amiangshu.me) demonstrates active engagement with research tools and datasets, particularly those related to code review analysis and toxicity detection. While specific lab structure isn't detailed in the provided materials, his research group appears focused on empirical studies of software development practices with strong connections to industry through collaborations on projects like Chromium OS security analysis.

Gül Calikliمشاهده پروفایل

دانشیار

• Human aspects in software engineering
• Empirical software engineering
• Cognitive psychology in software development
• +۶ مورد دیگر

Gül Calikli is an Associate Professor (Senior Lecturer) in Software Engineering at the School of Computing Science, University of Glasgow, United Kingdom. She has held academic positions at several prestigious institutions including the University of Zurich as a senior researcher, Chalmers | University of Gothenburg as a lecturer, and postdoctoral fellowships at The Open University (UK) and Ryerson University (Canada). Dr. Calikli earned her Ph.D. in Computer Engineering from Boğaziçi University in Istanbul. Her academic journey reflects a strong commitment to advancing empirical software engineering with a focus on human aspects. Dr. Calikli's research centers on the intersection of software engineering and cognitive psychology, with a particular emphasis on understanding and mitigating cognitive biases in software development practices. Her work explores how human cognitive limitations impact program comprehension, code review, and vulnerability detection. She investigates how to present information effectively to software practitioners considering human cognitive constraints, and develops tools and techniques based on cognitive psychology to enhance decision-making in software development. Her research also incorporates machine learning systems with "human in the loop" approaches, creating joint cognitive systems that extend human intelligence. Analysis of Dr. Calikli's recent publications reveals a consistent focus on human aspects in software engineering, particularly examining cognitive biases like confirmation bias and their impact on software quality. Her work spans multiple domains including code review practices, vulnerability detection, program comprehension, and privacy-aware software development. A notable trend is her methodological approach combining controlled experiments, field studies, and quantitative analysis of system logs to investigate human factors in software engineering. Best Paper Award at ESEM2013 (Industry Track) Chalmers Area of Advance SEED Funding in 2018 ACM SIGSOFT Distinguished Artifact Award at ICSE 2020 ACM Distinguished Paper Award at ICSE 2021 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'22 Distinguished Reviewer Award at ICSME'23 Distinguished Reviewer Award at ICPC'22 Dr. Calikli actively supervises PhD students working on diverse topics including team dynamics in agile development, eye-tracking for human-AI pair programming, leveraging LLMs for software development/testing, and sustainability in software engineering teams. She has served on numerous program committees for major software engineering conferences including ASE, ICSE, FSE, and ESEC/FSE. Her research has been supported by various funding mechanisms, including the Chalmers Area of Advance SEED Funding. As an active member of the software engineering community, Dr. Calikli contributes to the advancement of the field through her service on editorial boards (including ACM Transactions on Software Engineering and Methodology), participation in the EPSRC Peer Review College, and organization of conference tracks such as the ICPC 2024 ERA Track which she co-chaired.

Zhen Dongمشاهده پروفایل

دانشیار

• Software Reliability
• Software Security
• Mobile Computing
• +۴ مورد دیگر

Zhen Dong is an Associate Professor at Fudan University, China, specializing in software engineering with a focus on software reliability and security, particularly in mobile computing. Previously, he was a PostDoc and Senior Research Fellow at the National University of Singapore under the guidance of Abhik Roychoudhury. His educational background includes: PhD in Computer Science from Heidelberg University (2017), advised by Prof. Artur Andrzejak Dr. Dong's research centers on developing techniques and tools for improving software reliability and security. His work spans mobile application testing, Android security, flaky test detection, and vulnerability localization. He has made significant contributions to the field of software testing and analysis, with a particular emphasis on practical applications for mobile systems. His research bridges theoretical foundations with real-world software engineering challenges. Analysis of Dr. Dong's recent publications reveals a strong focus on leveraging AI/ML techniques for software engineering tasks, particularly using LLMs for test automation and program analysis. His work consistently addresses critical challenges in mobile computing, especially Android application reliability and security. There's a clear trajectory toward more sophisticated analysis techniques, from traditional testing methods to AI-driven approaches. His notable scientific achievements include: ACM Distinguished Paper Award at ICSE'20 Best Paper Award at AsiaCCS'21 (1/370 submissions) ASE'22 Distinguished Reviewer Award Dr. Dong serves on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology and has been an active member of numerous program committees for top software engineering conferences including ICSE, ASE, and ISSTA. His service to the academic community extends to reviewing for prestigious journals such as IEEE Transactions on Software Engineering and Methodology and ACM Transactions on Software Engineering and Methodology. His research has been supported through various academic channels, enabling him to maintain an active lab focused on software testing and analysis, particularly for mobile platforms. The lab has produced numerous tools and techniques that have influenced both academic research and industrial practice in software reliability.