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Shin YooView profile

Professor

• Software Testing
• Debugging
• Search-based Software Engineering
• +7 more

Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +4 more

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

Xinyu JiaView profile

Research Fellow

• Uncertainty Quantification
• Structural Reliability
• Risk Assessment
• +3 more

Xinyu Jia is currently a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich since June 2024, and concurrently serves as Associate Professor in the Department of Mechanical Engineering at Hebei University of Technology, China since October 2022. Her research focuses on advancing uncertainty quantification, structural reliability, and risk assessment methodologies for engineering systems. Her academic background includes: PhD in Mechanical Engineering, University of Thessaly, Greece (2018-2021) Bachelor of Engineering and Master of Science in Mechanical Engineering, Hunan University, China (2011-2018) Dr. Jia specializes in Bayesian learning frameworks for physics-based models, with particular expertise in uncertainty propagation in structural dynamics and industrial robotics applications. Her work develops hierarchical Bayesian approaches that integrate multi-level data to enhance predictive accuracy for complex engineering systems, addressing critical challenges in structural health monitoring and risk-informed decision making. Analysis of her 2022-2023 publications reveals a concentrated research trajectory in applying Bayesian inference to structural dynamics, with emphasis on hierarchical modeling techniques, variational inference schemes, and nonlinear model updating. These contributions predominantly appear in top-tier mechanical engineering journals, demonstrating methodological innovations that bridge theoretical statistics with practical engineering reliability problems. Her scientific recognition includes: Humboldt Research Fellowship (2023) Marie Curie Early Stage Researcher Fellowship (2018) No specific student advisement records are documented, though her Associate Professor role implies teaching responsibilities. Her fellowship awards represent significant research funding supporting her work in uncertainty quantification. As an active member of TUM's Engineering Risk Analysis Group, she contributes to high-impact projects including digital twins for ships, S3UQDyn, Navigating Risk, and infrastructure resilience initiatives like BIG-ROHU and INFRA.RELEARN, focusing on probabilistic risk modeling across civil and mechanical engineering domains.

• Database Systems
• Stream Processing
• Distributed Data Management
• +5 more

Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.

• Semiconductor Materials
• Nanotechnology
• Condensed Matter Physics
• +3 more

Dr. Emmanouil Dimakis is a Researcher and Group Leader in the Spectroscopy department at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). His research focuses on semiconductor nanowires, plasmonics, and optoelectronics, with emphasis on strain engineering, heterostructure design, and advanced spectroscopic techniques such as THz and infrared nanospectroscopy. His work explores electronic and optical properties of nanomaterials, including III-V semiconductor systems, InGaN/GaN quantum wells, and core-shell nanostructures. Key themes include understanding strain effects on material performance, developing high-electron-mobility nanostructures, and optimizing optoelectronic devices for near-infrared applications. Dimakis' publications highlight innovations in nanowire growth methodologies, nonlinear plasmonic responses, and time-resolved analysis of carrier dynamics under extreme conditions. His group employs cutting-edge techniques like probe-corrected HRSTEM and THz pump-probe spectroscopy to uncover fundamental material behaviors. Despite no listed awards or grants in the provided text, his contributions to nanomaterials research and spectroscopic characterization are evident through his prolific publication record spanning over two decades. His research group at HZDR continues to advance the frontiers of semiconductor physics and nanotechnology.

Iason PapaioannouView profile

Adjunct Professor

• Uncertainty Quantification
• Structural Reliability
• Reliability-Based Optimization
• +5 more

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

Nele MentensView profile

Professor

• Applied Cryptography
• Hardware Security
• Secure Coprocessor Design
• +12 more

Nele Mentens is a full professor at both KU Leuven and Leiden University, where she leads cutting-edge research in applied cryptography, hardware security, and secure embedded systems. At KU Leuven, she is affiliated with the Faculty of Engineering Technology and the Electrical Engineering Department (ESAT), leading the Emerging Technologies, Systems & Security (ES&S) research group at the Diepenbeek campus. Simultaneously, she holds a full professorship at Leiden University’s Leiden Institute of Advanced Computer Science (LIACS), focusing on applied cryptography and security. She has been instrumental in numerous national and international research initiatives, including Horizon Europe and NWO-funded projects. Full Professor, KU Leuven (since 2023) Full Professor, Leiden University (since 2020) Associate Professor, KU Leuven (2014–2023) Post-doctoral Researcher & Lecturer, KHLim / KU Leuven (2007–2014) Ph.D. in Engineering Science, KU Leuven (2007) M.Sc. in Electrical Engineering, KU Leuven (2003) Her research focuses on secure and efficient hardware design, particularly for cryptographic applications on FPGAs, reconfigurable architectures, IoT security, and neuromorphic computing. She explores physical attack resistance, side-channel analysis protection, and trusted computing architectures, with applications in healthcare, industrial monitoring, and endpoint AI. Her work bridges theoretical cryptography with practical hardware implementations, emphasizing energy efficiency and real-time performance. The 15 most recent publications reflect a strong trend toward secure, energy-efficient, and intelligent embedded systems. Topics include neuromorphic AI accelerators, trusted IoT architectures, dynamic reconfiguration for side-channel protection, and secure medical data processing. These works span disciplines such as computer architecture, cybersecurity, digital design, and embedded systems, with a focus on hardware-software co-design and real-world deployment. Nele Mentens has received recognition for her contributions, including: Best Paper Award, DATE'16 Best Paper Nomination, AsianHOST'17 Best Paper Award, CHES'19 She has supervised over 15 Ph.D. students and post-docs, both current and former, and has served as principal investigator in approximately 25 funded research projects. Her work has attracted significant grants from Horizon Europe, NWO, FWO, and national innovation programs. She actively contributes to the academic community through editorial roles in top journals and leadership in major conferences. Nele Mentens leads the ES&S research group at KU Leuven and collaborates closely with LIACS at Leiden University. Her team includes Ph.D. students, post-docs, and research experts working on projects like NimbleAI, NeuroSoC, and TrustedIoT. She has also established secure electronics labs through infrastructure grants and maintains strong international ties with institutions such as EPFL, Ruhr University Bochum, and ETH Zurich.

Anne RemkeView profile

Professor

• Dependability
• Security
• Critical Infrastructures
• +5 more

Professor Anne Remke leads the safety-critical systems group at the Faculty of Mathematics and Computer Science at Westfälische Wilhelms-Universität Münster since October 2014. She is also affiliated with the Design and Analysis of Communication Systems group at the University of Twente, where she served as assistant professor from June 2010 and became associate professor in March 2016. Her research focuses on dependability and security in critical infrastructures, particularly electrical power systems and telecommunication networks. Her educational background includes a PhD (2008) and MSc (2004) in Computer Science from the University of Twente and RWTH Aachen respectively. Her doctoral research focused on 'Model Checking Structured Infinite Markov Chains,' for which she publicly defended her thesis in June 2008. Professor Remke's research interests center on cyber-physical systems, with particular focus on evaluation of charging strategies for local energy storage in smart homes and security of control networks (SCADA) in smart grids. Her work bridges theoretical model checking techniques with practical applications in critical infrastructure protection. She has made significant contributions to the analysis of hybrid Petri nets, stochastic models, and the development of tools for dependability evaluation. Her recent publications demonstrate a strong trend toward integrating machine learning with formal verification methods for cyber-physical systems. The research spans stochastic hybrid systems, reachability analysis, and security evaluation of smart grid infrastructures, showing consistent growth in both theoretical foundations and practical applications of dependability analysis. Veni award from Dutch Science foundation (NWO) for 'Counting on a reliable water supply' GI/ITG MMB prize for best diploma thesis in computer and communication systems Best Paper Award at Valuetools 2023 conference Best Repeatability and Artifact Evaluation Award at QEST21 Teaching award from Fachschaft FB10 (2019) Professor Remke has successfully secured multiple research grants including the DFG project 'RealyST: Reachability Analysis for Stochastic Hybrid Systems' in collaboration with RWTH Aachen. She has supervised numerous students including Katharina Sichma, Pauline Blohm, Joanna Delicaris, Verena Menzel, Mathis Niehage, Jonas Stübbe, and Lisa Willemsen. Her research group actively participates in international collaborations and standardization efforts in critical infrastructure security. The safety-critical systems group maintains several research tools including HYPEG (for simulation and analysis of hybrid Petri nets), TimeNET (a GUI for modeling hybrid Petri nets), and a Smart Neighbourhood Simulation Tool for community energy storage and trading. These tools support their research in modeling and evaluating complex critical infrastructures through both analytical methods and simulation techniques.

Thomas WöhlingView profile

Researcher

• Integrated environmental systems modeling
• Surface water-groundwater interactions
• Braided river systems
• +9 more

PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.

Christian HaubeltView profile

Professor

• Embedded Systems
• Cyber-Physical Systems
• Smart Sensors
• +9 more

Christian Haubelt is a Professor at the Institute of Computer and Network Engineering, School of Engineering, University of Rostock, Germany. He is actively engaged in research and teaching in the areas of embedded and cyber-physical systems, smart implants, and IoT. His work is supported by multiple national and international projects including ELAINE (SFB 1270), SmILE (EU), 6G-Health (BMBF), and GenerIoT (BMBF). His research interests include: Embedded and Cyber-Physical Systems Smart Sensors and Smart Implants System-Level Design Methodologies SystemC-based Modeling and Verification Design Space Exploration and Multi-Objective Optimization Industrial Internet of Things and 6G for Healthcare His recent publications focus on real-time communication protocols, 5G/6G localization, smart implants, and secure IoT systems. Trends show a strong emphasis on integrating embedded systems with medical and industrial applications, particularly leveraging TSN, MQTT-SN, and OPC UA for reliable and secure communication. His work bridges theoretical modeling with practical implementation in safety-critical domains. Christian Haubelt has supervised multiple researchers including Michael Nast, Benjamin Rother, Nico Kalis, and Nico Graumüller. He leads several funded research projects such as ELAINE, SmILE, 6G-Health, and SUSTAIN, which focus on smart implants, secure IoT, and next-generation medical systems. These projects involve collaboration with DFG, EU, and BMBF. He is involved in the following research labs and teams: Embedded Systems and Cyber-Physical Systems Group Smart Implants Research Team (SmILE, ELAINE) 6G-Health Localization Team Industrial IoT Security (SUSTAIN, CargoAssist)

• Fluid Power Engineering
• Hydraulic Systems
• Tribology
• +4 more

Prof. Dr.-Ing. Katharina Schmitz serves as Institute Director and Vice Dean at the Institute for Fluid Power Drives and Systems, RWTH Aachen University. Her leadership within the Production Technology Cluster and extensive contributions to fluid power engineering establish her as a leading authority in mechanical engineering research and education. Her research spans fluid power systems, hydraulic component design, tribology, and physics-informed machine learning applications. She pioneers sustainable propulsion solutions through bio-hybrid fuels research while addressing fundamental challenges in polymer material behavior under hydraulic stresses. Current work focuses on carbon-neutral heavy-duty transportation, physics-based neural networks for lubrication modeling, and advanced control systems for electro-hydraulic actuators. Analysis of her 15 most recent publications reveals a dominant trend toward integrating physics-based modeling with deep learning to solve complex engineering problems. Her team consistently develops novel frameworks for cavitation prediction, flow rate determination, and material compatibility assessment - significantly advancing fluid power system reliability, efficiency, and digitalization. Scientific recognition includes: GfT Förderpreis 2023 for experimental and simulative investigation of partially hydrostatic relieved contacts in variable speed axial piston machines As head of the Institute for Fluid Power Drives and Systems, she leads cutting-edge research in sustainable fluid power technologies. The institute maintains strong industry partnerships while driving innovation in hydraulic component design, digital twins for condition monitoring, and next-generation propulsion systems through its position within RWTH Aachen's Production Technology Cluster.

Hamid ZargariaslView profile

Researcher

• Computer Networks
• IoT and Sensor Technology
• RFID Systems
• +3 more

Dr. Hamid Zargariasl is a Researcher in the Computer Engineering Department at BTU Cottbus-Senftenberg. His work focuses on IoT systems, RFID technology, social network analysis, and distributed computing. He leads the UBICO Team and contributes to the Computer Engineering Group, exploring interdisciplinary applications of telecommunications and sensor networks. His research integrates theoretical frameworks with practical implementations, addressing challenges in smart city infrastructure, healthcare systems, and educational technology. Key research areas include optimizing RFID and sensor performance, analyzing social object interactions in IoT networks, and developing protocols for distributed computing. His experimental studies bridge academic performance metrics with mobile social network dynamics, and he has pioneered methodologies for network integration and centrality analysis in evolving systems. Publications span 15 years (2009–2024), emphasizing real-world data analysis and system optimization. Notable work includes enhancing digital thread functionality in aerospace engineering and evaluating smart parking sensor technologies. His contributions inform both technical systems and socio-economic policy recommendations. Dr. Zargariasl collaborates within interdisciplinary teams, contributing to the BTU's research initiatives while maintaining active engagement in the academic community through lectures and student mentorship.

Sven ApelView profile

Professor

• Software Engineering
• Software Variability and Configuration
• AI-based Program Generation and Optimization
• +7 more

Sven Apel is Professor of Computer Science at Saarland University, where he holds the Chair of Software Engineering and directs the Saarbrücken Graduate School of Computer Science within the Saarland Informatics Campus. His research aims to advance software engineering into an era of intensive automation by developing methods, tools, and theories for building efficient, reliable, and maintainable software systems, with a strong emphasis on the human factor and interdisciplinary inquiry. His primary research interests include software variability and configuration, AI-based program generation and optimization, socio-technical software analysis, and the application of empirical and neurophysiological methods to study program comprehension. He actively collaborates with industry partners such as Siemens AG, Bosch Engineering, and Airbus Helicopters to apply his research in real-world contexts. His recent publications demonstrate a strong trend towards integrating artificial intelligence and neurocognitive methods into software engineering, focusing on configurable systems, performance modeling, debugging processes, and the scientific validity of empirical studies. His work spans top venues like ICSE, FSE, ASE, and IEEE TSE. ERC Advanced Grant “Brains On Code” ASE Fellow ACM Distinguished Member Hugo Junkers Award for Research and Innovation Heisenberg Professorship (DFG) Emmy-Noether Fellowship (DFG) Best Doctoral Dissertation Awards (University of Magdeburg, Ernst-Denert Foundation, 2007) Most Influential Paper Awards (SPLC'19, ICPC'22, GPCE'23) ACM SIGSOFT Distinguished Paper Awards (ICSE'15, ICSE'21) Best Paper Awards (SPLC'11, Modularity'15, Academy of Management'18) Distinguished Reviewer Awards (ASE'18, ICSE'24, FSE'24) Sven Apel has secured significant research funding, including an ERC Advanced Grant (€2.5M) and multiple DFG grants as Principal Investigator and Project Leader. He has advised numerous PhD and Master’s students and is actively involved in the academic community through program committees for major conferences like ICSE, FSE, and ASE. His work is conducted within a collaborative environment that includes close partnerships with researchers at the Max Planck Institute for Informatics and other institutions within the Saarland Informatics Campus.

Christina RothView profile

Professor

• Materials Science
• Electrochemical Engineering
• Energy Systems
• +5 more

Christina Roth is a Professor and Chair of Materials Process Engineering at the University of Bayreuth's Faculty of Engineering. Her research focuses on materials science and electrochemical energy systems, including fuel cells, redox flow batteries, CO₂ electro-reduction, and advanced materials synthesis via templating and electrospinning. She leads projects on operando methods to study catalytic processes and material degradation mechanisms. Her work bridges fundamental electrochemistry with applied energy technologies. Key research areas include developing high-performance electrocatalysts, optimizing electrode structures, and understanding reaction mechanisms under industrial conditions. Recent studies emphasize operando techniques to monitor catalyst behavior during operation, enhancing reliability in high-current-density electrolysis systems. Collaborations span academia and industry, addressing challenges in sustainable energy storage and conversion. Dr. Roth's expertise is reflected in over 150 publications across top journals like Nature Materials , ACS Catalysis , and Journal of the Electrochemical Society . She actively contributes to conferences such as the livMatS Colloquium series and serves on the Scientific Advisory Board. Her lab focuses on innovative materials for next-generation energy systems, emphasizing scalability and environmental impact.

Brigitte PientkaView profile

Professor

• Programming Languages
• Verification
• Type Theory
• +6 more

Brigitte Pientka is a Professor at McGill University's School of Computer Science , where she leads the Computation and Logic group . She earned her PhD from Carnegie Mellon University in 2003 and previously studied at the University of Edinburgh and Technical University of Darmstadt. Education PhD in Computer Science, Carnegie Mellon University (2003) University of Edinburgh Technical University of Darmstadt Research Interests Her work focuses on the theoretical and practical foundations for building reliable software systems, combining logic, type theory, and verification with system-building. Key areas include: Type Theory and Dependent Types Logical Frameworks and Mechanized Metatheory Session-Typed Concurrency and Linear Logic Metaprogramming and Contextual Type Systems Theorem Proving and Formal Verification Functional Programming and Language-Based Security Professional Roles She has served as: PC Chair for ICFP'24 and CPP'24 General Chair for POPL'20 Executive Editor of Logical Methods in Computer Science Steering Committee Member for LICS, POPL, and ESOP Scientific Awards Dr. Pientka has received: Test of Time Award at PPDP'18 Humboldt Fellowship for research at MPI-SWS, Germany Labs & Teams She actively develops the Beluga programming language , a tool for mechanizing meta-theory proofs and type-driven program manipulation.