Maarten Sap is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute with a courtesy appointment in the Human-Computer Interaction Institute. He also holds a part-time research scientist position at the Allen Institute for AI (AI2) as an AI safety lead. Current affiliations: CMU (2022–present), AI2 (2022–present) Prior: Postdoctoral Researcher at AI2 (2021–2022), Research Intern at AI2 (2018–2019) and Microsoft (2019) His research focuses on enhancing AI systems with social intelligence and addressing social biases in language technology. Key themes include: Ethical AI and Human-Centric Design Narrative Dynamics and Social Context Analysis AI Agents and Social Intelligence Toxic Language Detection and Cultural Bias Mitigation Recent publications examine: AI safety frameworks like HAICOSYSTEM Clinical reasoning alignment (ALFA) Multilingual moderation (PolyGuard) Cultural sensitivity in non-verbal AI (Mind the Gesture) Personality shaping in LLMs (BIG5-CHAT) Scientific Recognition: 2025 Okawa Research Grant Best Paper Runner Up - NAACL 2025 Outstanding Paper - EMNLP 2023 Best Paper - FAccT 2023 Best Paper - WeCNLP 2020 He advises a diverse group of PhD students across CMU and MIT, and has served on multiple program committees including ACL, EMNLP, and FAccT. His work appears in top venues like Nature Machine Intelligence, PNAS, and ACL.
Mohammad Pirani is an Assistant Professor in the Department of Mechanical Engineering at the University of Ottawa, with a joint appointment at the School of Electrical Engineering and Computer Science. Previously, he held postdoctoral and research assistant professor roles at the University of Waterloo, University of Toronto, and KTH Royal Institute of Technology. Education: Ph.D., Mechanical and Mechatronics Engineering, University of Waterloo (2017) MASc., Electrical and Computer Engineering, University of Waterloo (2014) BASc., Mechanical Engineering, Amirkabir University of Technology (2011) His research focuses on resilient and fault-tolerant control in complex systems, including networked control systems and multi-agent systems . He explores intersections with network science , cybersecurity , and machine learning , addressing vulnerabilities in cyber-physical systems like automotive networks. Notable contributions include publications in IEEE Transactions on Control of Network Systems and Automatica , with recent work on network critical slowing down and graph-theoretic resilience strategies. His research trends emphasize reliable learning , security in distributed systems , and data-driven detection of critical transitions . Scientific Awards: Senior Member, IEEE Mohammad Pirani holds a dual appointment at the University of Ottawa and an adjunct professor position at the University of Waterloo. His work bridges mechatronics , robotics and automation , and networked systems , with future directions targeting cybersecurity in cyber-physical systems.
Matthieu Sozeau is a prominent researcher at Inria in the Gallinette team in Nantes, France, and a key contributor and coordinator of the Coq/Rocq proof assistant project. His work bridges theoretical computer science and practical software development, focusing on creating reliable formal verification tools. His research interests span Type Theory, Proof Assistants, Functional Programming, and Unification. He has made significant contributions to the development of Coq (recently renamed to Rocq Prover), particularly through the MetaCoq project which aims to verify Coq's kernel within Coq itself, the Equations plugin for dependent pattern matching, and CertiCoq, a verified compiler from Coq to assembly. His work enables stronger guarantees about formalized mathematics and verified software. Sozeau's publications reveal a consistent focus on foundational aspects of proof assistants. His recent work includes verified type checking ('Coq Coq Correct!'), verified extraction from Coq to OCaml, and sort polymorphism for proof assistants. These contributions advance both theoretical understanding and practical implementation of dependently-typed programming languages. Distinguished paper award for Verified Compilation from Coq to OCaml at PLDI'24 As an academic mentor, Sozeau has supervised PhD students including Théo Winterhalter and Antoine Allioux. He regularly teaches courses on proof assistants, notably at MPRI (Master Parisien de Recherche en Informatique), and actively participates in the academic community through program committees, invited talks, and workshops. His work has significantly influenced both the theoretical foundations and practical applications of interactive theorem proving.
Helmholtz Centre Potsdam - German Research Centre for GeosciencesGermany
Yuyu Zhou is a Professor in the Department of Geography at The University of Hong Kong. With an extensive publication record of 301 papers and over 18,000 citations, Dr. Zhou is a leading researcher in urban environmental studies, climate change, and sustainability science. Dr. Zhou received their PhD in Environmental Science from the University of Rhode Island (2004-2008) and previously worked as a Research Scientist at Pacific Northwest National Laboratory's Joint Global Change Research Institute (2010-2015). They currently serve as Chief Editor for Earth System Science Data (Copernicus Publications), Associate Editor for Ecological Processes, and Section Editor for All Earth. Dr. Zhou's research focuses on the intersection of urbanization, climate change, and environmental sustainability. Their work spans several key areas including urban heat island effects, vegetation phenology in urban environments, energy modeling, and sustainable urban development. Through innovative remote sensing approaches and spatial analysis, Dr. Zhou investigates how urban environments respond to and influence global environmental change. Analysis of Dr. Zhou's recent publications reveals a strong emphasis on urban environmental challenges, with particular attention to urban heat islands, vegetation dynamics, and climate change impacts in cities. Their work combines remote sensing data with ground observations to develop high-resolution models of urban environmental processes. Recent research has focused on urban greening effects, building energy use under climate change, and environmental justice issues related to urban heat exposure. Dr. Zhou has received significant recognition for their work, as evidenced by the high citation count of their publications. Their research has important implications for urban planning, climate adaptation strategies, and sustainable development policies worldwide. As an educator and mentor, Dr. Zhou advises numerous graduate students and collaborates with researchers globally. Their work with international teams has resulted in significant contributions to understanding urban environmental systems across different geographical contexts.
Jonathan Miles Robker is a Lecturer (Privatdozent) at the University of Münster's Faculty of Protestant Theology. He holds a Habilitation in Old Testament (2018), Doctor of Theology (2011), Master of Theological Studies (2006), and dual Bachelor's degrees in History and Philosophy (2003). Since 2020, he serves as Editor for Biblical Studies and has held research positions at the university since 2013. His research focuses on textual criticism, ancient Near Eastern epigraphy, Deuteronomistic history, and literary analysis of biblical texts. Primary interests include the Book of Kings, history of Israel, and comparative ancient Near Eastern traditions. Robker's publications demonstrate consistent engagement with textual variants across ancient manuscripts, particularly examining the Deuteronomistic History through Septuagint and Masoretic textual traditions. His works frequently analyze political theology, prophetic literature, and the development of biblical canons.
Associate Professor Hu Yunfei is affiliated with the School of New Materials and New Energy at Shenzhen University of Technology , where she leads the New Energy Systems and Smart Microgrids Laboratory . She is a member of the China Renewable Energy Society and Guangdong Solar Energy Association . PhD in Materials Processing Engineering (2005), South China University of Technology Bachelor of Engineering (2000), South China University of Technology Her research focuses on new energy systems , solar-storage direct-flexible systems , and high-efficiency photovoltaic devices , including perovskite solar cells , tandem solar cells , and transparent conductive oxides . Her work spans fundamental materials science and applied energy systems. The 15 most recent publications highlight her expertise in polycrystalline silicon thin films , transparent conductive oxides , perovskite solar cells , and optoelectronic materials . These works reflect trends in improving solar cell efficiency, stability, and manufacturing scalability. She has led projects such as the development of consumer solar power optimizers , optical performance testing for bifacial solar panels , and industrial collaborations on silicon ribbon substrates . Her projects are funded by institutions like the Norwegian Science Foundation and National Natural Science Foundation of China . At Shenzhen University of Technology, she oversees the New Energy Systems and Smart Microgrids Laboratory , integrating advanced materials and system design for renewable energy applications.
Sible Andringa is Professor of Second Language Pedagogy at the University of Amsterdam's Faculty of Humanities, officially inaugurated on June 16, 2023. Dr. Andringa serves as Academic Director of the Institute for Dutch Language Education (INTT), Coordinator of the Language Learning, Literacy and Multilingualism research group, and Coordinator of the Master's program in Dutch as a Second Language and Multilingualism. Dr. Andringa's research focuses on second language acquisition and bilingualism, specifically investigating the added value of explicit instruction, how input distribution affects language learning outcomes, and the role of awareness in language learning trajectories. Key ongoing projects include the Meta-LLL project examining how literacy shapes language learning, the SLA4All initiative for reproducing SLA research with non-academic samples, and the OASIS project creating accessible research summaries for practitioners. Previously, Dr. Andringa led Project MIND studying bilingual daycare effects and contributed to the Stilis project on listening proficiency. As General Editor of the Dutch Journal of Applied Linguistics (DuJAL), Dr. Andringa promotes open science principles in language research. Recent publications demonstrate a focus on addressing sampling biases in SLA research, open access publishing ethics, and practical applications of language acquisition research for educational settings. Academic Director, Institute for Dutch Language Education (INTT) Coordinator, Language Learning, Literacy and Multilingualism research group Coordinator, Master's program Dutch as a Second Language and Multilingualism General Editor, Dutch Journal of Applied Linguistics (DuJAL) Member, Mastery Team for Modern Foreign Languages Member, OASIS project team Member, IRIS database advisory group Dr. Andringa supervises PhD candidates including Kyra Hanekamp and Darlene Keydeniers, particularly in research related to bilingual daycare environments and language development. The research program has received funding from the Dutch ministry of Social Affairs for Project MIND and continues to secure support for ongoing projects examining language learning mechanisms. Dr. Andringa leads the Language Learning, Literacy, and Multilingualism research group which investigates language and literacy acquisition across the lifespan, with emphasis on how language skills are learned, maintained, and used in educational contexts. The group meets weekly to discuss projects, plans, funding opportunities, and research topics while promoting collaboration, methodological innovation, and open science principles.
Olli Seppänen serves as Associate Professor in Civil Engineering at Aalto University's School of Engineering, specializing in operations management for construction productivity improvement. He coordinates the Vision 2030 consortium—comprising 13 Finnish construction and design firms—to develop industrialized building methods for 2030, while leading multiple Business Finland-funded research initiatives focused on digital construction workflows and real-time monitoring. His research centers on lean construction principles, location-based management systems, and digital transformation through IoT, AI, and robotic vision. Key focus areas include prefabrication optimization, construction logistics, and shifting work off-site to industrialize processes. He aims to solve industry-wide productivity challenges by creating real-time situational awareness and implementing takt production systems for workflow stability. Recent publications (2024-2025) reveal strong emphasis on digital twin frameworks, semantic modeling for quality assurance, and AI applications in risk management. His work bridges theoretical lean construction concepts with practical implementations, particularly in real-time resource tracking, waste reduction in MEP work, and cross-sector learning from high-performing teams. Seppänen has received significant recognition including: School of Engineering doctoral dissertation award (2024) Best paper at IEEE Wireless Sensors Conference (2019) Nordic Conference best paper award for PhD research (2019) DSc dissertation award (2010) As principal investigator, he manages: Vision 2030 consortium projects (2-3 annually; PI for two current projects) iCONS: Real-time resource flow monitoring via indoor positioning RECAP: Deep learning analysis of progress/quality from images/point clouds DiCtion: Integrated data systems for real-time stakeholder situation pictures He actively contributes to the "Performance in Building Design and Construction" research group and leverages the Vision 2030 consortium as a collaborative platform for industry transformation, driving adoption of digitalized, industrialized construction methods through academic-industry partnerships.
Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.
Zhidan Zheng is a researcher at the Technical University of Munich (TUM), working within the Chair of Electronic Design Automation led by Prof. Ulf Schlichtmann. His office is located in room 0509.05.911 at Arcisstr. 21, 80333 Munich, with direct contact available via email zhidan.zheng@tum.de and phone +49 (89) 289 - 23692. Zheng holds a Master of Science degree as indicated by his academic title M.Sc. and has been actively contributing to the field of optical interconnects and network-on-chip design. Zheng's research focuses on wavelength-routed optical networks-on-chip, with particular expertise in network topology optimization, fault tolerance mechanisms, waveguide routing algorithms, and bandwidth allocation strategies. His work addresses critical challenges in photonic integrated circuit design, including thermal variation effects, crosstalk mitigation, and lifetime extension for communication-intensive systems. Zheng has developed several innovative methodologies including ToPro+ for topology projection, LightR for fault-tolerant architectures, and WROXIM for network-level simulation. Analysis of Zheng's publication trends from 2021-2025 reveals a consistent focus on practical implementation challenges of optical networks-on-chip. His research has evolved from foundational topology design (Light, 2021) to increasingly sophisticated solutions addressing reliability (LightR, 2023) and comprehensive system integration (ToPro+, 2025). The work demonstrates strong collaboration with researchers including Mengchu Li, Tsun-Ming Tseng, and Ulf Schlichtmann across multiple high-impact venues including DAC, DATE, ICCAD, and ASP-DAC. Zheng actively contributes to the Electronic Design Automation research group at TUM, participating in projects related to analog EDA, emerging technologies, and optical networks. His research is situated within TUM's broader initiatives in photonic integration and high-performance computing architectures, working closely with Prof. Schlichtmann's team on funded projects in the optical NoC domain.
Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.
Professor Hossein Rahmani serves at the School of Computing and Communications , Lancaster University , with a focus on Computer Vision and Machine Learning . His career spans institutions like the University of Western Australia (PhD), Shahid Beheshti University (MSc), and Isfahan University of Technology (BSc). Research Interests : Computer Vision, Machine Learning, Video Analysis, Action Recognition/Detection, Object/Human Pose Estimation, 3D Reconstruction, Diffusion Models, Human-Object Interaction Editorial Roles : Associate Editor for IEEE Transactions on Neural Networks and Learning Systems , Pattern Recognition , ACM Computing Surveys ; Area Chair for CVPR 2025, ICLR 2025, ECCV 2024, IJCAI 2024 His recent work leverages diffusion models for domain-generalized object pose estimation, 3D scene editing, and human mesh recovery, published in top venues like TPAMI , CVPR , ICCV , and ECCV . He received the Best Scientific Paper Award from the International Conference on Pattern Recognition and actively supervises 5 PhD students with interdisciplinary projects in digital health and data science.
Johan Liu is a Full Professor in Electronics Production at Chalmers University of Technology, Sweden, and leads the Electronics Materials and Systems Laboratory within the Department of Microtechnology and Nanoscience. He is a member of the Royal Swedish Academy of Engineering Sciences and an IEEE Fellow, with over 500 publications and 75 patents in nanoelectronics and thermal management. Education: Master's and Ph.D. in Materials Science from the Royal Institute of Technology (KTH), Sweden His research focuses on graphene-based thermal interface materials, carbon nanotubes for 3D integration, and advanced packaging solutions. Recent work includes laser-induced graphene films, nano-soldering techniques, and biomedical nanoscaffolds. His publications span high-impact journals like Nature Communications , Advanced Materials , and IEEE Transactions , with recent trends emphasizing thermal conductivity enhancement, composite materials, and nanofluids. Johan has received prestigious awards including the IEEE Exceptional Technical Achievement Award and IEEE CPMT Best Paper Award. He has secured funding from the National Science Foundation (NSF), Swedish Board for Strategic Research (SSF), Vinnova, and EU Horizon 2020 programs. His lab specializes in scalable graphene synthesis, CNT array engineering, and reliability testing of nanomaterials in electronics.
Ola Carlson is a Professor in Sustainable Electric Power Production at Chalmers University of Technology. He specializes in electrical systems for renewable power production and hybrid electric vehicles. Since 2022, he serves as a senior advisor to the Swedish Wind Centre, focusing on island operation with Chalmers wind turbine and battery systems. Research Interests His research spans renewable power systems, wind energy integration, grid stability, and microgrid optimization. Key projects include modeling Nordic transmission systems, analyzing wind turbine bearing currents, and developing maintenance schedules for aging components. Article Trends Recent publications emphasize wind turbine design, microgrid stochastic optimization, and dynamic state estimation for transmission protection. Topics cover machine learning applications in forecasting, fault handling, and battery degradation impacts on energy systems. Projects & Collaborations RESIST - Energy islanding for resilient systems (2026–2027) COSPACT - Nordic-Baltic co-simulation platform (2020–2023) Fossil Free Energy Districts (2016–2019) Collaborations with ABB, Swedish Energy Agency, and European Commission Labs & Teams Works with Power Grids and Components at Chalmers, leading projects like 'Detecting and eliminating bearing currents' (2018–2023) funded by the Swedish Energy Agency. Involved in Chalmers Campus as a testbed for intelligent grids.
Marina Petrova is a Professor at RWTH Aachen University, holding positions in both the Teaching and Research Area of Mobile Communications and Computing and the Chair and Institute for Networked Systems. She is also a member of the Steering Committee for the Mobility & Transport Engineering (MTE) profile area at the university. Her office is located at Kackertstraße 9, 52072 Aachen, Germany. Professor Petrova's research focuses on cutting-edge wireless communication technologies, with particular emphasis on next-generation mobile networks. Her work spans multiple dimensions of wireless systems including: 5G and 6G network architectures and protocols Cell-Free Massive MIMO systems Millimeter-wave communications Resource allocation and scheduling in wireless networks Wi-Fi sensing and coexistence analysis Integration of distributed learning services in wireless networks Beamforming and beam management techniques Ultra-Reliable Low-Latency Communications (URLLC) Her recent publications demonstrate a strong trend toward the integration of artificial intelligence and machine learning techniques in wireless network design and optimization. She has been particularly active in exploring the convergence of communication and sensing functionalities (ISAC - Integrated Sensing and Communication), which is considered a key enabler for future 6G networks. Professor Petrova's research also addresses practical implementation challenges in next-generation wireless systems, with several publications focusing on ns-3 implementations and experimental validations. Professor Petrova has received recognition for her contributions to the field through numerous publications in top-tier venues, though specific awards are not mentioned in the available information. Her work shows strong industry relevance with applications in smart industries, autonomous systems, and future communication networks.
