جست‌وجوی استاد و پژوهشگر

Ruzica Piskacمشاهده پروفایل

استاد

• Software Verification
• Security
• Applied Cryptography
• +۳ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University, where she joined the Department of Computer Science in 2013 and leads the Rigorous Software Engineering (ROSE) research group. Her work focuses on formal techniques to improve software reliability and trustworthiness through verification, security, and synthesis approaches. Education: PhD in Computer Science from the Swiss Federal Institute of Technology (EPFL) in 2011 Thesis title: Decision Procedures for Program Synthesis and Verification Previously led an independent research group at the Max Planck Institute for Software Systems in Germany (2012-2013) Research Interests: Piskac's research spans software verification, security and applied cryptography, automated reasoning, and code synthesis. Her work combines formal methods with practical applications in programming languages, particularly focusing on making software systems more reliable and trustworthy. She has developed tools for symbolic execution in Haskell, privacy-preserving formal methods, and verification techniques for configuration files and software updates. Publication Trends: Her recent work shows a strong focus on applying formal verification techniques to emerging challenges including large language models, zero-knowledge proofs, quantum computing security, and legal accountability for AI systems. There's a clear trajectory toward bridging formal methods with practical security and verification challenges across diverse domains. Awards: Multiple Amazon Research Awards Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation Patrick Denantes Prize for her doctoral dissertation Advising and Service: Piskac has graduated five PhD students, four of whom are now assistant professors of computer science. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and is on the Steering Committee of the Formal Methods in Computer-Aided Design conference. Her service includes numerous program committee roles across major programming languages and verification conferences. Research Group: She leads the Rigorous Software Engineering (ROSE) group at Yale, which focuses on developing formal techniques for software reliability and trustworthiness, with projects spanning symbolic execution, privacy-preserving verification, reactive synthesis, and configuration file verification.

Norbert Siegmundمشاهده پروفایل

استاد

• Software Engineering
• Machine Learning Systems
• Performance Analysis
• +۳ مورد دیگر

Norbert Siegmund is Professor and Chair of Software Systems at Leipzig University's Institute of Computer Science in Germany. His research bridges software engineering and artificial intelligence, focusing on configurable software systems, performance analysis, and energy consumption optimization. He has established himself as a leading researcher in the field of configurable systems through extensive publications in top-tier software engineering venues. His research interests span multiple interconnected domains: Configurable, complex systems that evolve over time Non-functional properties, particularly performance and energy consumption Sampling, learning, and optimization methods from AI applied to software engineering Contemporary empirical methods in software engineering MLOps and DevOps practices Social aspects of AI-production teams His publication record reveals a consistent focus on creating accurate performance models for configurable software systems, with increasing attention to energy consumption aspects in recent years. The research trajectory shows evolution from basic performance modeling to incorporating machine learning techniques, transfer learning across environments, and addressing energy efficiency concerns. His scientific achievements have been recognized through several prestigious awards: ACM SIGSOFT Distinguished Paper Award at ICSE 2015 SPLC Best Paper Award in 2012 Multiple keynote invitations including at QAVS 2021 Open-Source Award from the state of Thuringia (2020) Best PhD award from University of Magdeburg (2013) Prof. Siegmund has secured significant research funding including DFG projects like 'Green Configuration' (€590,000) and 'Pervolution' (€290,000), as well as BMBF projects related to AI and software engineering. He has mentored numerous students through research projects and has served on program committees for major conferences including ICSE, ASE, ESEC/FSE, and SPLC. His teaching portfolio includes courses on Software Engineering for AI-Enabled Systems, Configurable Software Systems, and Automated Software Engineering.

Aymeric Blotمشاهده پروفایل

مدرس ارشد

• Genetic Improvement
• Search-Based Software Engineering
• Algorithm Configuration
• +۵ مورد دیگر

Aymeric Blot is a Senior Lecturer at the University of Rennes, France, where he is affiliated with the ISTIC faculty. He is also a member of the IRISA research centre within the joint Inria/IRISA DiverSE team. His work focuses on advancing software improvement techniques through genetic algorithms and automated optimization approaches, with significant contributions to the field of Genetic Improvement. Dr. Blot received his PhD in Computer Science from Université de Lille (2015-2018), following a Magistere in Computer Science and Telecommunications from ENS Rennes (2011-2015). His educational background also includes an MSc from ENS Cachan and a BSc from Université Rennes 1, establishing a strong foundation in both theoretical and applied computer science. Dr. Blot's research primarily centers on Genetic Improvement and Search-Based Software Engineering . His work explores how software can be automatically evolved and optimized through techniques like evolutionary computation, automated machine learning, and algorithm configuration. A key contribution to this field is his development and maintenance of Magpie , an automated software improvement framework. His research has practical applications in software performance optimization, program repair, and multi-objective combinatorial optimization problems. Dr. Blot has made significant contributions to understanding mutation operator efficacy, fitness function design, and the combination of different software improvement approaches. Analysis of Dr. Blot's recent publications shows a consistent focus on empirical evaluation of software improvement techniques. His work often compares different approaches like genetic improvement, parameter tuning, and their combinations to determine optimal strategies for software optimization. There's a clear trajectory toward more comprehensive surveys and frameworks that integrate multiple improvement techniques, as evidenced by his recent survey on benchmarks for software's non-functional properties published in ACM Computing Surveys. Dr. Blot has been actively involved in the academic community, serving on program committees for major conferences including ASE, ICSE, ISSTA, and GECCO. He has organized workshops on Genetic Improvement and contributed to community resources like the genetic improvement website. His service includes roles as Program Committee member, track chair, and keynote speaker at numerous international conferences. At the University of Rennes, Dr. Blot teaches Software Engineering and Operational Research courses. He previously served as a Research Associate at University College London (2018-2022) and at Université du Littoral Côte d'Opale (2022-2023) before joining his current tenured position in 2023. His international experience includes research visits to Leiden University in the Netherlands and Shinshu University in Japan.

Karin Melzerمشاهده پروفایل

استاد

• Data Science
• Statistics
• Quantitative Methods
• +۳ مورد دیگر

Prof. Karin Melzer serves as a Professor at Esslingen University of Applied Sciences within the School of Computer Science and Engineering. Her institutional roles include: Equal Opportunity Officer overseeing university diversity initiatives Member of the International Committee facilitating global partnerships Liaison Professor (Vertrauensdozentin) for the German Academic Scholarship Foundation She teaches core subjects in Mathematics, Statistics, Quantitative Methods, and Data Science, with consultation hours scheduled for Winterterm 2024/25. Her research interests focus on: Data Science applications in automotive and educational contexts Statistical methodology for learning analytics Innovative Educational Technology including flipped classroom models Ethical AI integration in project management systems Quantitative assessment design for higher education Mathematics pedagogy through digital tools Analysis of her 2020-2024 publications reveals a consistent trajectory toward technology-enhanced education, particularly in online assessment configuration and AI-driven instructional methods. Her work bridges theoretical statistics with practical applications in automotive R&D project management, emphasizing ethical considerations and efficiency metrics while developing video-free flipped classroom approaches. During the 2021/22 academic year, she conducted applied research at DAIMLER Trucks North America's Data Intelligence Hub in Portland, Oregon, focusing on industrial data science implementations.

• Robotics
• Motion Planning
• Multi-robot Systems
• +۳ مورد دیگر

Liding Zhang is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich (TUM), supervised by Prof. Alois Knoll. His work focuses on advanced robotics with emphasis on motion planning and multi-robot systems. His educational background includes: Master’s degree in Mechanical Engineering and Automation Technology from Technical University of Clausthal (2022) Bachelor’s degree in Mechanical Engineering from Rhine-Waal University of Applied Sciences (2020) Zhang's research centers on sampling-based asymptotically optimal motion planning, high-DOF multi-robot manipulation, coordinated control of mobile robot fleets, and real-time performance optimization. His work explores geometric reasoning in complex robotic systems, addressing challenges in manipulation, navigation, and control through innovative algorithmic approaches that balance computational efficiency with solution quality. Key contributions include adaptive sampling techniques and optimization frameworks for dynamic environments. His publication record (2023-2025) demonstrates consistent contributions to top robotics venues including ICRA, IROS, and IEEE Transactions. The body of work reveals a cohesive research trajectory advancing sampling-based planning through innovations in informed search strategies, adaptive batch processing, and physics-inspired optimization. Recurring themes include handling high-dimensional configuration spaces, ensuring real-time performance, and developing robust solutions for multi-robot coordination and deformable object manipulation. Zhang actively contributes to the robotics community as a reviewer for major conferences (ICRA, IROS, Humanoids) and journals (RA-L, T-ASE, T-Mech), and chaired the Robot Motion Planning IV session at IROS 2024. As a researcher, he participates in nationally and EU-funded projects including Bavarian State Project KI.FABRIK. He mentors bachelor's and master's students on topics spanning LLM-based risk mapping, task and motion planning, and multi-robot coordination, with several theses resulting in peer-reviewed publications. He operates within TUM's Robotics, AI and Real-Time Systems group, which pioneers real-time control systems and advanced planning algorithms for industrial and service robotics applications, maintaining strong industry collaborations and state-of-the-art experimental facilities.

• Artificial Intelligence
• Automated Planning
• Symbolic Reasoning
• +۶ مورد دیگر

Dr. Peter Kissmann is a researcher in the Computer Science Department at Universität des Saarlandes, Germany, affiliated with the Foundations of Artificial Intelligence (FAI) Group. His work focuses on automated planning systems, particularly in optimal and net-benefit planning domains. Research Interests: His primary research areas include Artificial Intelligence, Automated Planning, Symbolic Reasoning, Heuristic Search, and Knowledge Representation. He specializes in BDD-based planning, variable ordering, and symbolic search algorithms for optimal planning. The 15 most recent publications reflect his sustained contributions to the International Planning Competitions (IPC) from 2008 to 2014, showcasing evolution in planner design, including support for conditional effects, bidirectional search, improved variable ordering, and integration of modern BDD libraries like CUDD. These works demonstrate deep technical innovation in planner efficiency, robustness, and scalability. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information about students or grant funding in the provided material. However, his leadership in developing and releasing competitive planning systems suggests involvement in research projects, possibly with student contributors or collaboration within the FAI group. Labs and Teams: He is a member of the Foundations of Artificial Intelligence (FAI) Group at Universität des Saarlandes, which develops advanced planning systems and participates in international competitions like the IPC.

Christian Dietrichمشاهده پروفایل

استاد

• Operating Systems
• Real-Time Systems
• Embedded Systems
• +۳ مورد دیگر

Christian Dietrich is a Professor at the Technische Universität Hamburg (TUHH) in the Operating System Group (OSG) . His research focuses on Operating Systems , Real-Time Systems , and Embedded Systems , with specific interests in Software Fault Tolerance , Memory Management , and Software Variability . Projects: ParPerOS (Parallel Persistency OS), ATLAS (Adaptable Thread-Level Address Spaces), CLASSY-FI (Cross-Layer Fault Injection), AHA (Automated Hardware Abstraction), CADOS (Configurability-Aware OS) Supervised Theses: 10+ completed theses on topics like io_uring Integration , Heterogeneous Multi-Core , Virtual Memory Primitives , and Fault Injection His recent publications (2022-2024) address Heterogeneous Computing , Virtual Memory Interfaces , and Crash-Consistent Systems . He received awards including USENIX ATC 2017 Best Paper , RTAS 2015 Best Paper , and ISORC 2022 Outstanding Paper .

Emmanuel Baccelliمشاهده پروفایل

استاد

• Distributed Algorithms
• Computer Networks
• Internet of Things
• +۷ مورد دیگر

Emmanuel Baccelli is a Professor for 'Open and Secure IoT Ecosystem' at Freie Universität Berlin's Department of Mathematics and Computer Science, holding a dual appointment with Inria (French national research institute for digital sciences) and the Einstein Center Digital Future (ECDF). His research focuses on the intersection of low-power protocols, deeply embedded open source software, and IoT security, addressing the critical trade-off between energy efficiency and security in resource-constrained devices. Professor Baccelli's research interests span Distributed Algorithms, Computer Networks, Internet of Things, Open Source Software Development, Wireless Networks, Internet Architecture, Network Protocol Design & Analysis, Cybersecurity, TinyML, and Standardisation . His work emphasizes Privacy-by-Design principles, advocating for open specifications and open source solutions to enhance user control, privacy, and sovereignty in IoT ecosystems. He investigates how deeply embedded open source software can improve both functionality and security of IoT devices, with particular attention to the technical challenges of maintaining security while preserving energy efficiency. Baccelli's publication record demonstrates consistent contributions to networking standards, particularly in low-power and lossy networks. His research has significantly influenced IoT protocols and standards, with numerous RFC publications that form foundational elements of modern IoT networking infrastructure. His work bridges theoretical networking concepts with practical implementations in constrained environments. As an educator and mentor, Professor Baccelli has advised multiple PhD students through completion, including Koen Zandberg (defended June 2025), Oliver Hahm (defended December 2016), and Juan Antonio Cordero (defended September 2011), with Zhaolan Huang currently working on Ultra-Low Memory Footprint Machine Learning. He actively seeks new students interested in programming, cybersecurity, applied mathematics, communication protocols, and tinyML. Professor Baccelli co-founded and coordinates the RIOT open source community, an operating system for IoT devices based on microcontrollers. His work extends beyond technical domains to include engagement with humanities scholars, lawyers, and designers on open source philosophy and practice. He maintains active involvement in standardization efforts through the IETF, contributing to multiple working groups focused on IoT and networking protocols.

Pascal Kerschkeمشاهده پروفایل

استاد

• Exploratory Landscape Analysis
• Multi-Objective Optimization
• Automated Algorithm Selection
• +۳ مورد دیگر

Pascal Kerschke is a Professor at the Chair of Big Data Analytics in Transportation at Technische Universität Dresden (TU Dresden). His research focuses on exploratory landscape analysis, multi-objective optimization, and automated algorithm selection for complex computational problems. Research Areas: Exploratory Landscape Analysis, Multi-Objective Optimization, Traveling Salesperson Problem (TSP), Machine Learning, Continuous Optimization Affiliation: Chair of Big Data Analytics in Transportation, TU Dresden Kerschke has pioneered methodologies for characterizing optimization problem landscapes using machine learning and statistical features, bridging gaps between algorithm design and practical applications in transportation analytics. His work includes developing frameworks like FLACCO for fitness landscape analysis and advancing visualizations for multi-objective optimization. Recent publications highlight his contributions to deep learning integration for landscape analysis (e.g., Deep-ELA), multiobjectivization techniques, and benchmarking challenges. He has also explored adversarial robustness in neural networks and parameter tuning methodologies. Scientific Awards: PPSN 2016 Best Paper Award Kerschke's collaborations with optimization heuristic developers and his role in creating benchmarking libraries (e.g., ASlib, OpenML) underscore his interdisciplinary impact in computational optimization and data-driven decision-making.

Rubi Debnathمشاهده پروفایل

پژوهشگر

• Time-Sensitive Networking
• Machine Learning
• Optimization
• +۳ مورد دیگر

Rubi Debnath is a researcher at the Technische Universität München (TUM) affiliated with the Department of Embedded Systems and Internet of Things . They focus on Time-Sensitive Networking (TSN) , Machine Learning for TSN , and Optimization of TSN Scheduling Algorithms using heuristics, ILP, and DRL. Specializes in TSN Scheduling , Runtime Reconfiguration , and Wireless-TSN Actively teaches IoT Security , Software Architecture for Distributed Embedded Systems , and System Design for IoT since Winter Semester 2018/2019 Supervised over 15 Master's and Bachelor's theses on TSN, ML, and 5G-TSN integration Developed simulation frameworks like CyclicSim and 5GTQ Research Trends : Recent publications address TSN scheduling optimization , ML-assisted traffic classification , and 5G-TSN integration , with a focus on low-latency communication and industrial automation . Scientific Awards : IEEE ComSoc Four Minute PhD Thesis Competition - Third Prize Winner (Round 2), Round 1 Winner Global Fellows Program - Imperial College London, TUM, and NTU Singapore Advising and Grants : Supervised 15+ theses on TSN, ML, and 5G-TSN. Involved in the 6G Research Hub "6G-Life" and nIoVe cybersecurity framework for IoT.

• Computer Architecture
• High-Performance Computing (HPC)
• Internet of Things (IoT)
• +۷ مورد دیگر

Vincent Bode is a researcher and teaching assistant at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich (TUM) . His work focuses on benchmarking and optimizing Data Distribution Service (DDS) middleware for Industrial IoT applications, particularly through the DDS-Perf project in collaboration with Siemens .

Celso Gustavo Stall Sikoraمشاهده پروفایل

پژوهشگر

• Operations Research
• Logistics Optimization
• Manufacturing Systems
• +۵ مورد دیگر

Dr. Celso Gustavo Stall Sikora is a Researcher at the Institute for Operations Research within the University of Hamburg Business School at the University of Hamburg. His work focuses on advanced manufacturing systems, particularly in assembly line balancing, logistics optimization, and stochastic modeling. He holds an office at Moorweidenstr. 18, Room 4001, and can be reached via email celso.sikora@uni-hamburg.de or phone +49 40 42838 5518 . His research emphasizes optimizing assembly line operations under uncertainty, integrating robotics, buffer management, and decomposition algorithms. Key areas include mixed-model production planning, last-mile logistics via innovative infrastructure (e.g., cargo tunnels), and real-time resequencing strategies. He has contributed to both theoretical advancements, such as chance-constrained stochastic balancing, and practical case studies involving robotic assembly line design and industrial collaboration. Publications span 2015–2025, reflecting sustained engagement with topics like Benders’ decomposition for balancing problems, cyclic scheduling, and maintenance-integrated production systems. His work bridges academic rigor and industrial application, addressing challenges in automotive manufacturing, renewable energy systems, and buffer-aware line configurations. No academic awards or formal student advisees are listed, but his contributions to operations research and manufacturing efficiency are evident through his prolific publication record. His office hours are by appointment, indicating a focus on project-based or collaborative research activities.

Frank Petzoldمشاهده پروفایل

استاد

• Architectural Informatics
• Building Information Modeling (BIM)
• Computer-Aided Design
• +۳ مورد دیگر

Prof. Frank Petzold is a Full Professor at the Chair of Architectural Informatics within the Department of Architecture at the Technical University of Munich (TUM). His academic career includes roles as Vice Dean of the Department of Architecture (2010–2012) and Dean of the Faculty of Architecture at Bauhaus-University Weimar (2008–2009). He holds a Doctorate in Computer Science from Bauhaus-University Weimar (2001) and has been a core member of the Munich Data Science Institute (MDSI) since 2021. His research focuses on digital methods in architectural design, BIM integration, AI-driven generative design, and sustainable urban planning. Key affiliations include the Leonhard Obermeyer Center for Digital Methods (co-founder, 2014) and leadership roles in DARL (Deans' Association for Architecture, 2015–present). Research interests span computational design tools, digital twin technologies, and AI applications in construction. Notable contributions include the TUM2TWIN benchmark dataset and frameworks for automated design decision support. He serves on editorial boards for journals like the International Journal of Architectural Computing and chairs Bavaria’s 'Digital Construction and Planning' platform. His work bridges academic research with industry applications, emphasizing interdisciplinary collaboration and digital innovation in built environment disciplines. Education: Doctorate in Computer Science, Bauhaus-University Weimar (2001) Affiliations: Munich Data Science Institute, TUM Georg Nemetschek Institute, German Association of Computing in Civil Engineering Committees: DARL, Task Group AKIM (Construction Industry), Bavarian Digitalization Center (Z.DB) Publications emphasize BIM interoperability, generative design systems, and urban sustainability. Current projects explore AI for building lifecycle assessments, digital twin applications, and crowd-safety simulations in early design stages.

• Dependable, reliable, and secure computing
• Stochastic behavioural modelling and verification
• Concurrency theory
• +۱۲ مورد دیگر

Holger Hermanns is a Full Professor of Computer Science at Saarland University, Germany, where he leads the Dependable Systems and Software group. He is co-spokesperson of the Center for Perspicuous Computing (SFB/TRR 248) and has been a scientific director at Schloss Dagstuhl – Leibniz Center for Informatics since 2025. He is a member of Academia Europaea and holds an ERC Advanced Grant (POWVER) and a Proof-of-Concept Grant (LEOpowver) . His leadership roles include former Dean of the Faculty of Mathematics and Computer Science and active service in major academic organizations such as the ETAPS Association (Vice President) and Friends of Dagstuhl (President, 2013–2023). Education: Researcher and academic positions at Universität Erlangen-Nürnberg (1993–1998), Universiteit Twente (1998–2006), and INRIA Grenoble Rhône-Alpes (2007–2010). His research focuses on modeling and verification of concurrent systems , stochastic behavioral modeling , energy informatics , and proactive algorithmic accountability . He is a leading figure in probabilistic model checking, formal methods for cyber-physical systems, and software dependability. His work bridges theory and practical applications in satellite systems, AI safety, and environmental monitoring. The recent publications reflect a strong trend toward AI accountability , software doping detection , formal verification of machine learning systems , and dependable satellite network design . His team develops tools like RTLola and Momba, integrating formal methods into real-world systems. There is a growing emphasis on interdisciplinary work, particularly in human oversight of AI and ethical algorithmic design. Scientific Awards and Recognition: ERC Advanced Grant POWVER ERC Proof-of-Concept Grant LEOpowver Member, Academia Europaea (2013) Dutch Vernieuwingsimpuls (2001) Preis des Fakultätentages Informatik (2013) ERC Advanced Investigators Grant (2016) Otto Mønsted-Guest Professorship (2013) Hermanns has advised numerous students and researchers, though specific names are not listed. He leads major research grants, including ERC projects and collaborative efforts within SFB/TRR 248. His leadership extends to program committees of top conferences such as CAV, CONCUR, TACAS, and QEST, and he has chaired LICS and ICALP. He is affiliated with the Dependable Systems and Software lab at Saarland Informatics Campus and contributes to the Schloss Dagstuhl ecosystem as scientific director and former president of its support association. His recent work is increasingly centered on the AI Act , algorithmic transparency, and the formal foundations of trustworthy AI.

Stefanie Rinderle-Maمشاهده پروفایل

استاد

• Process-Oriented Information Systems
• Flexible and Distributed Process Technologies
• Compliance Management
• +۶ مورد دیگر

Prof. Dr. Stefanie Rinderle-Ma is a full professor for Information Systems and Business Process Management at the Department of Informatics, Technical University of Munich (TUM). Prior to TUM, she held a full professorship at the University of Vienna, leading the Research Group Workflow Systems and Technology. Her research focuses on process-oriented information systems, flexible/distributed process technologies, compliance management, production intelligence, and process mining. She has authored numerous publications in top journals and conferences, including Information Systems and BPM. Education and Career: Studied Business Mathematics at the University of Augsburg. Completed her doctorate and habilitation at the University of Ulm. Postdoctoral stays in the Netherlands and Canada. Headed the Workflow Systems and Technology research group at the University of Vienna before joining TUM in 2021. Research Interests: Her work emphasizes advancing digitalization and automation through processes while supporting human-centric decision-making. Key domains include production, logistics, and healthcare. Awards and Recognition: Best Paper Awards (CAiSE/IEEE EDOC, 2018). BPM Test of Time Award (2013). Merckle Research Prize (2008). Dissertation Prize (Ulm University Society, 2005). IFIP TC2 Manfred Paul Award (2005). Grants and Projects: Principal investigator for AdONE (Advanced Optimization in a Networked Economy, DFG, 2021–2025). Principal investigator for TRPro (Taming Regulatory Document Changes, DFG, 2023–2026). Principal investigator for BPAIS (SAP Signavio-funded project on process modeling, 2023–2027). Labs and Teams: Leads the KrcmarLab and Wittges Lab, focusing on process intelligence, generative AI in process modeling, and IoT integration in process systems.