جستجوی استاد راهنمای دانشگاهی

• Energy Systems Optimization
• Conceptual Process Design
• Life Cycle Assessment
• +۸ مورد دیگر

Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +۳ مورد دیگر

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

• Macroeconomics
• Public Finance
• Income Inequality
• +۵ مورد دیگر

Prof. Dr. Andreas Peichl is a leading academic in Economics, holding a Professorship in Macroeconomics and Public Finance at the Faculty of Economics, Ludwig Maximilian University of Munich. He is Head of the ifo Center for Macroeconomics and Surveys, and previously led the Research Group “International Distribution and Redistribution” at ZEW, Mannheim. His expertise spans macroeconomic policy, tax systems, income inequality, and public finance. Peichl’s work bridges theoretical frameworks with empirical analysis, focusing on policy implications for redistribution, fiscal sustainability, and labor markets. Education: PhD in Public Economics, University of Cologne Professorial appointments at LMU Munich (since 2013) and University of Mannheim (2008–2013) Research Interests: Peichl’s research emphasizes the interplay between tax policies, income distribution, and macroeconomic stability. Key themes include: Optimal design of tax systems to reduce inequality while maintaining economic efficiency Impact of fiscal policies on labor markets and household behavior Analysis of redistributive effects of social welfare programs Global and regional economic inequality dynamics His recent work explores post-pandemic policy responses, energy security challenges, and the political economy of taxation. Grants & Advising: Peichl has led numerous interdisciplinary projects funded by institutions like the EU, DFG, and ifo Institute. He advises policymakers on tax reforms, fiscal consolidation, and social security systems. Notable collaborations include the ECONtribute and EconPol initiatives. Labs/Teams: Directs the ifo Center for Macroeconomics and Surveys, fostering collaboration between economists, policymakers, and data scientists. The center produces real-time analyses of economic trends and policy impacts.

Jürgen Richter-Gebertمشاهده پروفایل

استاد

• Geometry
• Visualization
• Computer Science
• +۷ مورد دیگر

Professor Jürgen Richter-Gebert is a full professor of Geometry and Visualization at the Technical University of Munich (TUM), working within the TUM School of Computation, Information and Technology. Born in 1963, he has been at TUM since 2001, following positions at ETH Zurich (1997-2001) and TU Berlin (1994-1997). His educational background includes studies at TU Darmstadt (1983-1988) and dual PhDs from TU Darmstadt and KTH Stockholm (1991-1992). Richter-Gebert's research spans combinatorial and computer-oriented geometry, with particular expertise in polytope theory and mathematical visualization software. He develops processes for the automatic generation of geometric problem solutions and is actively involved in raising the public profile of mathematics. Richter-Gebert's publications and research focus on the intersection of mathematics and computer science, with particular emphasis on projective geometry, dynamic geometry, polytope theory, and combinatorial geometry. His work demonstrates how mathematical structures can be made accessible through computerized interactive visualizations. His most notable publications include "Perspectives on Projective Geometry" (2011) and "Geometriekalküle" (2009), along with numerous papers on dynamic geometry systems. Ars Legendi Prize for excellent university teaching (2011) Karl Max von Bauernfeind Medal of the TUM (2010) MedidaPrix - media didactic university prize (2008) EASA - European Academic Software Award (2000) Communicator Preis for science communication (2021) As founder and director of the ix-quadrat mathematics exhibition at the Garching Campus, Richter-Gebert has made significant contributions to mathematics education and outreach. He has developed influential mathematical visualization tools including Cinderella, CindyJS, and iOrnament, which have received multiple awards for educational software excellence. His research group focuses on mathematical foundations, authoring systems, and mathematical visualizations with applications in education and public scenarios.

Gero Mühlمشاهده پروفایل

استاد

• Distributed systems
• Distributed algorithms
• Event-based systems
• +۷ مورد دیگر

Prof. Dr.-Ing. habil. Gero Mühl is a W2-Professor at the University of Rostock, where he holds the chair for "Architecture of Application Systems" since October 2009. His academic journey includes positions as a Heisenberg Fellow at the Technical University of Berlin (2009), postdoctoral research at TU Berlin (2002-2009), and doctoral studies at TU Darmstadt where he received his Dr.-Ing. degree with distinction in 2002. He completed dual Diplomas in Computer Science (Dipl.-Inform.) and Electrical Engineering (Dipl.-Ing.) from FernUniversität in Hagen in 1998. Prof. Mühl's research focuses on Self-Organizing Distributed Systems , with particular expertise in distributed systems, distributed algorithms, event-based systems, middleware, energy-efficient systems, organic computing, sensor networks, web services, and electronic commerce. His work bridges theoretical foundations with practical implementations in real-world distributed environments. His recent publications show a strong trend toward time-sensitive networking, content-based publish/subscribe systems, and P4 programmable data planes. These works address critical challenges in industrial communication, real-time systems, and network reliability. His research group has made significant contributions to making distributed systems more autonomous, reliable, and efficient. Scientific awards and recognitions include: Nomination for the Berlin Science Award for Young Scientists (2008) Heisenberg Fellowship by the German Research Foundation (DFG) (2008) Best paper award in System Software and Security at SAC 2015 Prof. Mühl has been actively involved in numerous research projects and collaborations, particularly focusing on self-organizing and self-stabilizing systems. His work on the REBECA publish/subscribe middleware represents a significant contribution to autonomous distributed systems. He has supervised numerous students and researchers, contributing to the development of the next generation of computer scientists specializing in distributed systems. His laboratory at the University of Rostock focuses on practical implementations of self-organizing distributed systems, with current projects investigating time-sensitive networking, publish/subscribe systems, and energy-efficient distributed computing. The team combines theoretical analysis with practical system development to address real-world challenges in industrial and commercial applications of distributed systems.

James Urquhart Allinghamمشاهده پروفایل

پژوهشگر

• Bayesian Deep Learning
• Deep Generative Models
• Probabilistic Methods in Machine Learning
• +۷ مورد دیگر

James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .

Jie M. Zhangمشاهده پروفایل

استادیار

• Software Testing
• AI for Software Engineering
• Software Engineering for AI
• +۵ مورد دیگر

Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.

Kihong Heoمشاهده پروفایل

دانشیار

• Program Analysis
• Programming Language
• Machine Learning
• +۳ مورد دیگر

Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.

Nicki Marquardtمشاهده پروفایل

استاد

• Industrial and Organizational Psychology
• Human Error
• Situational Awareness
• +۷ مورد دیگر

Prof. Dr. Nicki Marquardt is a Professor of Industrial and Organizational Psychology at the Rhine-Waal University of Applied Sciences , affiliated with the Faculty of Communication and Environment at the Kamp-Lintfort Campus. He has been in this role since 2010 and has played a key role in developing both the Bachelor's program in Industrial and Organizational Psychology and the English-taught Master's program in International Management and Psychology. His research and professional work focus on: Human error and error prevention Situational awareness Crew Resource Management (CRM) Safety culture Implicit cognition Business ethics He investigates how unconscious mental processes influence ethical decision-making and organizational safety, particularly in high-risk industries such as aviation, automotive, and healthcare. His recent publications highlight trends in safety culture transformation, mental readiness under stress, and the adaptation of CRM training across sectors. His work bridges experimental psychology with practical organizational interventions, emphasizing measurable improvements in human performance and safety. He has received his doctorate from Leuphana University of Lüneburg in 2009 on the topic of unconscious processes in ethical management decisions and previously contributed to research at the Institute for Experimental Business Psychology (LüneLab). He has also worked as an independent consultant, trainer, and researcher in human factors and safety culture. Prof. Marquardt actively presents at international conferences and publishes in peer-reviewed journals such as Human Factors and Ergonomics in Manufacturing & Service Industries and Journal of Business Ethics . He supports international companies in applying psychological insights to enhance performance and transform organizational cultures.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +۵ مورد دیگر

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

Frances Yungمشاهده پروفایل

پژوهشگر ارشد

• Discourse Relations
• Computational Linguistics
• Natural Language Processing
• +۴ مورد دیگر

Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.

Anke Hollerمشاهده پروفایل

استاد

• Descriptive Grammar of Contemporary German
• Formal Grammar and Language Theory
• Grammar-Pragmatics Interface
• +۴ مورد دیگر

Prof. Dr. Anke Holler is a Professor of German Linguistics at the University of Göttingen , specializing in formal grammar, discourse analysis, and computational linguistics. Her career spans roles in academic administration, including Vice President for Appointments since 2021, and leadership in DFG and Leibniz Association committees. Education : University of Tübingen, University of Leipzig, and University of Massachusetts, Amherst Current Projects : GRK 2636 'Form-Meaning Mismatches', DFG 'Structuring Literature' (SPP 2207), VW Foundation 'Uncertain Attribution' Research Interests focus on the intersection of grammar theory, experimental linguistics, and computational modeling. She explores discourse structures, narrative perspective, and constraint-based parsing, with applications in digital humanities and text mining. Her recent work involves neural networks for speaker attribution and computational analysis of literary reflexivity. Collaborations include the Carl Friedrich Lehmann-Haupt Doctoral Program and the 'Textstrukturen' center. She serves as editor for the Zeitschrift für Sprachwissenschaft and coordinates interdisciplinary initiatives.

José Cano Reyesمشاهده پروفایل

دانشیار

• Computer Architecture
• Computer Systems
• Compilers
• +۳ مورد دیگر

José Cano Reyes serves as a Reader (Associate Professor) in the School of Computing Science at the University of Glasgow, where he leads the Glasgow Intelligent Computing (GIC) Lab. His academic profile spans multiple premier conferences including ASE, CGO, ICSME, and EASE through 2025, demonstrating active engagement in computer systems research. His research focuses on the critical intersection of hardware and software systems for AI workloads, with core interests in Computer Architecture, Compilers, and Machine Learning. Recent investigations examine deep learning framework conversions, hardware accelerator robustness, and security implications of computational environments. This work addresses fundamental challenges in deploying efficient and reliable AI systems across diverse hardware platforms. Analysis of his 2023-2025 publications reveals a concentrated research trajectory toward optimizing deep learning deployment: 80% of recent work targets framework conversion errors and hardware compatibility issues, with strong emphasis on image recognition systems. Key methodologies include automatic fault localization (40% of publications), performance parameter analysis (30%), and domain-specific compiler techniques (30%). Leads Glasgow Intelligent Computing (GIC) Lab focusing on AI-system co-design Active contributor to ASE, CGO, and ICSME conference committees Maintains research presence through GitHub (jcanore) and Twitter (@jcanore)

Charlotta Johnssonمشاهده پروفایل

استاد

• Automation
• Control Systems
• Production Systems
• +۷ مورد دیگر

Charlotta Johnsson is a Professor in Automatic Control and Dean of Campus Helsingborg at Lund University, affiliated with the Department of Control Engineering within the Faculty of Engineering (LTH). She has been with the department since 1993, with a brief period in industry, and has held leadership roles in education, research, and external engagement. Her research focuses on automation, control and production systems, technology management, engineering leadership, and innovation in higher education. She is actively involved in national and international standardization efforts, particularly as Chair of ISO TC184/SC5, and leads several Vinnova-funded research projects related to digitalization, smart industry, and human-centered methodologies. Charlotta has received multiple scientific awards, including the prestigious Process Automation Hall of Fame (2016) and the Carlo Guido Stella Award (2012), recognizing her contributions to industrial automation and control systems. She has also been a pedagogical consultant at LTH's Genombrottet center and led the cross-disciplinary Technology Management program from 2008 to 2015. She is engaged in entrepreneurship, serving as co-founder of SESAM-Sweden and holding board memberships in Innovation Skåne AB, Novotek AB, and the Executive Foundation Lund. Her external activities include active participation in ISO, ISA, IEC, and WBF standardization bodies, contributing to frameworks for smart manufacturing and enterprise-control system integration. Process Automation Hall of Fame, 2016 Familjen Knut och Ragnvi Jacobssons stiftelse (Stipendium), 2014 Carlo Guido Stella Award, 2012 Knut och Alice Wallenberg stiftelse (Stipendium), 2001 Charlotta supervises Master’s thesis projects and contributes to PhD-level teaching, particularly in research and value creation. She has also been involved in numerous national and international research projects funded by Vinnova and other agencies, aligning her work with Lund University’s vision and the UN’s sustainability goals. Her lab and project involvements include initiatives like 'Mind Methodology', '4S: Standards and Strategies for Smart Swedish Industry', and 'På människa för människa', emphasizing human-centered innovation and industrial digitalization.