جست‌وجوی استاد و پژوهشگر

Gert-Ludwig Ingoldمشاهده پروفایل

استاد

• Casimir effect
• Dissipative quantum systems
• Mesoscopic physics
• +۲ مورد دیگر

Prof. Dr. Gert-Ludwig Ingold is a Professor of Theoretical Physics at the Institute of Physics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg. His research spans multiple areas of theoretical physics with emphasis on quantum phenomena in nanoscale systems. His work on the Casimir effect explores interactions between various geometries including spheres, plates, and dielectric materials, with applications in nanotechnology and biophysics. His research on dissipative quantum systems investigates thermodynamic anomalies and quantum Brownian motion. In mesoscopic physics, he studies charge transport through nanoscale structures and quantum interference effects. His work on quantum systems in phase space connects classical and quantum dynamics, while his semiclassical research examines quantum revival patterns and phase-space trajectories. Prof. Ingold's publications reveal a strong focus on the Casimir effect, with numerous papers examining interactions between different geometries and materials. His work demonstrates expertise in both theoretical modeling and numerical methods, as evidenced by his development of the CaPS software for Casimir effect calculations. He frequently collaborates with international researchers, particularly with Paulo A. Maia Neto, Tanja Schoger, and Benjamin Spreng. Prof. Ingold has made significant contributions to physics education through textbooks and popular science books, including Quantentheorie: Grundlagen der modernen Physik and Die 101 wichtigsten Fragen: Moderne Physik . He has co-authored the Python-based educational resource Numerische Physik mit Python and maintains extensive online teaching materials including lecture notes and video tutorials.

Muhammad Ali Gulzarمشاهده پروفایل

استادیار

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +۵ مورد دیگر

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

• Computational Chemistry
• Computer-Aided Drug Design
• Cheminformatics
• +۱۲ مورد دیگر

Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.

Koushik Senمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Formal Methods

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.

• Programming Languages
• Software Engineering
• Formal Methods
• +۳ مورد دیگر

L. Thomas van Binsbergen is a Professor in the Department of Computer Science & Computer Engineering at the University of Wisconsin-La Crosse, affiliated with the College of Science & Health. His work focuses on language design, formal methods, and policy-based systems. His research explores Executable formal specifications of programming languages Modular meta-language frameworks (e.g., iCoLa+, eFLINT) Data-dependent grammars for network protocols Purpose-based access control derived from GDPR Functional parsing algorithms (GLL, Happy-GLL) Policy enforcement in distributed systems Recent publications (2020-2025) demonstrate trends in language parametric design, security policy formalization, and exploratory programming environments. Notable collaborations include Damian Frölich, Tim Müller, and Tom M. van Engers. Van Binsbergen earned his PhD from Royal Holloway, University of London (2019) and contributes to conferences like GPCE, SLE, and workshops on programming language theory and data security.

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

Christian Winklerمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• User Experience
• +۴ مورد دیگر

Christian Winkler is Professor for AI-based UX optimization and general business administration at Nuremberg Institute of Technology since 2022. With over 25 years of experience spanning entrepreneurship, enterprise architecture, and academia, he brings substantial industry expertise to his academic role. His career includes founding multiple technology companies including an internet service provider (WWL Internet AG) that went public in 1999, Querplex GmbH through management buyout in 2003, and datanizing GmbH as an NLP SaaS provider in 2017. Professor Winkler's research focuses on practical applications of artificial intelligence in business contexts, with particular expertise in natural language processing, user experience optimization, and data-driven marketing strategies. His work bridges theoretical AI research with real-world business applications, emphasizing accessibility of complex technologies. He has published extensively on language models (including BERT and LLaMA implementations), text analysis techniques, and social media data analysis for business insights. Recent publications reveal a strong trend toward optimizing large language models for practical deployment, with significant focus on analyzing user-generated content from social platforms like Instagram and WallStreetBets. His work demonstrates how NLP can extract valuable business intelligence from unstructured data sources while making advanced AI techniques accessible to non-technical business professionals. Winkler teaches E-Commerce, International Marketing Tools - Quantitative Methods, Applied User Experience, and Communication Management, reflecting his interdisciplinary approach that combines technical AI knowledge with business administration expertise. He is an active contributor to the data science community through conference presentations at events like m3 Konferenz, MLsummit, and data2day, as well as educational content for Heise Academy on Python and NLP topics.

Andreas Thorمشاهده پروفایل

استاد

• Database Systems
• Data Integration
• Entity Resolution
• +۱۲ مورد دیگر

Andreas Thor is affiliated with the University of Applied Sciences for Telecommunications Leipzig, Germany. He has been actively publishing in computer science since 2004, with a focus on database systems, data integration, bibliometrics, and educational technology. His research spans several domains, including database systems, entity resolution, ontology matching, and more recently, e-assessment and digital learning tools. He has made significant contributions to bibliometrics through the development and application of CRExplorer for Reference Publication Year Spectroscopy (RPYS). His work also includes the design of educational tools like DMT and FeeDI for automated assessment in higher education. The recent articles (2021–2023) reflect a strong trend toward educational technology, particularly in the development of tools for e-assessment, Jupyter Notebook integration, and knowledge graphs for learning. Earlier works (2004–2012) focus on core database research such as entity resolution, data fusion, and MapReduce-based systems. The publications collectively demonstrate a transition from foundational database research to applied educational informatics. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: No information is available regarding students advised or grants received. However, his extensive publication record and leadership in workshop proceedings (e.g., GI-Workshop Grundlagen von Datenbanken) suggest involvement in academic mentoring and collaborative research initiatives. Labs and Teams: Andreas Thor has collaborated extensively with researchers at the University of Leipzig, particularly with Erhard Rahm, Toralf Kirsten, and Stefanie Scherzinger. His work on bibliometrics involves collaboration with Lutz Bornmann, Loet Leydesdorff, and Robin Haunschild. These collaborations indicate participation in research groups focused on data management and scientometrics.

Michael Coblenzمشاهده پروفایل

استادیار

• Usability of programming languages
• User-centered programming language design
• Type systems for safety
• +۶ مورد دیگر

Michael Coblenz is an Assistant Professor in the Computer Science Department at University of California, San Diego. His research focuses on integrating user-centered design into programming language development, creating safer languages for blockchain and scientific computing. He completed his Ph.D. at Carnegie Mellon University under Jonathan Aldrich and Brad A. Myers, followed by a Basili postdoctoral fellowship at University of Maryland. Specializes in usability of programming languages Created Obsidian language for safer smart contracts Developed PLIERS methodology for iterative language design Active in Rust usability and REST API quality research His work bridges programming languages and human-computer interaction, with recent projects including Kale (safer spreadsheets) and climate change-focused programming tools. He has served on program committees for SPLASH, VL/HCC, and HATRA conferences. 2025: Graduate Consortium Co-chair at VL/HCC 2024: HATRA Organizing Chair 2023: Doctoral Symposium Chair at SPLASH

Philipp Slusallekمشاهده پروفایل

استاد

• Computer Graphics
• Artificial Intelligence
• High-Performance Computing
• +۷ مورد دیگر

Philipp Slusallek is a Professor for Computer Graphics at Saarland University since 1999 and Scientific Director at the German Research Center for Artificial Intelligence (DFKI), leading the "Agents and Simulated Reality" research area since 2008. He has held leadership roles such as Director for Research at the Intel Visual Computing Institute (2009-2017) and Dean of the Faculty of Computer Science and Mathematics (2002-2004). His affiliations include being a Principal Investigator in the German Excellence Cluster "Multimodal Computing and Interaction" (2007-2019) and a Visiting Professor at NVIDIA Research (2007-2008). PhD in Computer Science from Erlangen University Diploma in Physics from Frankfurt and Tübingen University His research spans computer graphics, artificial intelligence, and high-performance computing, with contributions to real-time ray tracing, digital reality, motion synthesis, and domain-specific languages (DSLs). Recent work includes visual programming tools for image processing (2022), DSL frameworks for high-performance libraries (2018), and compiler optimizations for vectorized code (2018). Key themes include simulation technologies, programming models, and computational sciences. Scientific honors include Fellow of Eurographics, Associate Editor of Computer Graphics Forum , membership in acatech (German National Academy of Science and Engineering), and advisory roles in AI policy. He has led research groups at institutions like Stanford University (Visiting Assistant Professor, 1998-1999) and the University of Tübingen (Researcher, 1990-1992).

Jochen Merkerمشاهده پروفایل

استاد

• Mathematical Analysis
• Numerical Methods
• Partial Differential Equations
• +۵ مورد دیگر

Jochen Merker serves as Professor for Analysis and Optimization at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). His academic profile demonstrates deep expertise in mathematical analysis, numerical methods, and computational mathematics with applications across various scientific domains. Institution: Leipzig University of Applied Sciences (HTWK Leipzig) Faculty: Computer Science and Media Position: Professor for Analysis and Optimization Contact: Available by appointment via email Professor Merker's research spans multiple mathematical disciplines with particular emphasis on partial differential equations, numerical analysis, and mathematical modeling. His work bridges theoretical mathematics with practical applications in fluid mechanics, epidemiology, and machine learning. He has made significant contributions to the understanding of doubly nonlinear evolution equations, positivity preservation in numerical methods, and rate-induced tipping phenomena. His research demonstrates how advanced mathematical techniques can solve complex problems in physical systems and data science. Analysis of his publication trends reveals a consistent focus on mathematical rigor combined with practical applicability. His recent work shows increasing integration of mathematical theory with computational approaches, particularly in digital learning environments and e-assessment systems for STEM education. The interdisciplinary nature of his publications demonstrates how mathematical analysis serves as a foundation for solving problems across physics, engineering, epidemiology, and computer science. Primary research areas: Mathematical Analysis, Numerical Methods, Partial Differential Equations Application domains: Fluid Mechanics, Epidemiology, Machine Learning Methodological focus: Positivity preservation, Maximum principles, Numerical stability Educational contributions: Digital teaching in STEM fields, E-assessment systems Professor Merker actively contributes to the academic community through his research publications and educational initiatives. His work on digital teaching methods for STEM disciplines reflects his commitment to modernizing mathematical education. While specific grant information isn't available in the provided materials, his extensive publication record suggests sustained research activity across multiple projects. His laboratory or research team likely focuses on computational mathematics and numerical analysis, though specific details aren't provided in the source material.

• Educational Technology
• Learning Analytics
• Artificial Intelligence in Education
• +۳ مورد دیگر

Marcus Specht is a Professor affiliated with Delft University of Technology and Leiden University, Netherlands. His research focuses on Educational Technology, Learning Analytics, and Artificial Intelligence in Education. He has contributed to projects involving agent-based social skills training, hybrid intelligence for cognitive process analysis, and computational thinking assessment in higher education. His work spans mobile learning, collaborative learning analytics, and gamification in MOOCs. He collaborates extensively with researchers like Marco Kalz, Roland Klemke, and Hendrik Drachsler. Notable contributions include the Presentation Trainer for public speaking feedback and the DojoIBL platform for inquiry-based learning. His research emphasizes multimodal learning systems, including AR/VR applications and sensor-based training tools. He explores the integration of AI into educational platforms, as seen in projects like JELAI and the ARTES architecture for social skills training.

Michael Lyuمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• AIOps
• +۵ مورد دیگر

Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.

Iftekhar Ahmedمشاهده پروفایل

دانشیار

• Software Engineering
• Software Testing
• Machine Learning for Software Engineering
• +۳ مورد دیگر

Iftekhar Ahmed is an Associate Professor in Informatics at the Donald Bren School of Information and Computer Science, University of California, Irvine. His research focuses on software engineering, particularly combining software testing, static analysis, and machine learning to develop better tools and techniques for software quality assurance. His educational background includes: PhD in Computer Science (2018) from Oregon State University, advised by Carlos Jensen BSc in Computer Science & Engineering (2007) from Shahjalal University of Science and Technology Dr. Ahmed's research interests center on software testing, static analysis, and the application of machine learning to software engineering problems. He has made significant contributions to mutation analysis, particularly in scaling this technique for real-world software systems. His work often bridges theoretical advances with practical applications, focusing on how to make software testing more effective and efficient for developers. He leads the STAIRS (Software Engineering & Testing Using Artificial Intelligence for Reliable Software) research group at UCI, where his team explores innovative approaches to software reliability through AI and machine learning. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software engineering practices. There's a clear focus on applying machine learning to code analysis, commit message generation, and bug detection, while maintaining rigorous empirical validation through studies of real-world software projects and developer practices. His work spans multiple domains including web accessibility, quantum computing, and Jupyter notebooks, showing both depth in core software engineering topics and breadth across application areas. Dr. Ahmed has received several prestigious awards: IBM Ph.D. Fellowship for academic year 2016-2017 Graduate School tuition relief Scholarship for academic year 2016-2017 IBM Ph.D. Fellowship for academic year 2017-2018 Actively involved in the academic community, Dr. Ahmed serves on program committees for major software engineering conferences including ASE, ICSE, and ESEC/FSE. He is currently accepting PhD students into his research group and emphasizes mentorship and professional development. His research has been supported by various grants that enable his team to explore innovative approaches to software testing and analysis. Dr. Ahmed leads the STAIRS research group at UCI, which focuses on developing AI-powered techniques for software testing and reliability. The group collaborates with industry partners and academic institutions to ensure their research addresses real-world challenges in software development. Current projects include improving mutation testing scalability, analyzing code smells in emerging domains like quantum computing, and developing tools for accessibility testing.

Tomoki Nakamaruمشاهده پروفایل

استادیار

• Domain-Specific Languages
• Mining Software Repositories
• Notebook Programming
• +۵ مورد دیگر

Tomoki Nakamaru is an Assistant Professor at the Department of Multidisciplinary Systems Science, Graduate School of Arts and Sciences, The University of Tokyo. He holds a Ph.D. in Information Science and Engineering from The University of Tokyo, advised by Prof. Shigeru Chiba. Education: PhD (2021), The University of Tokyo M.Sc. (2018), The University of Tokyo B.Eng. (2016), The University of Tokyo His research focuses on Domain-Specific Languages , Mining Software Repositories , and Notebook Programming . He develops tools like Multiverse Notebook and Silverchain to enhance code exploration and API usability. He has published extensively at top venues like OOPSLA and PLDI , covering topics from Fluent Interface Generation to Runtime Monitoring . His 15 most recent works span 2017–2025. Scientific Awards: Yamashita Memorial Research Award (2023) University of Tokyo Business Reform Special Award (2021) JSST Student Encouragement Awards (2019–2020) Distinguished Artifact Award at OOPSLA (2019) Dean's Award, UTokyo (2018) Nakamaru serves on program and artifact committees for conferences like ECOOP , OOPSLA , and ‹Programming› . He teaches courses including Practical Programming and Introduction to Information Systems .