Hussein T. Mouftah is a Professor at the University of Ottawa , affiliated with the College of Engineering and Computer Science and the Department of Electrical and Computer Engineering . He is a leading figure in Intelligent Transportation Systems , Wireless Networks , and Smart Cities research. His research spans vehicular ad hoc networks (VANETs) , autonomous electric vehicles (CAEV) , edge computing , IoT security , and 5G/6G-enabled infrastructure . He pioneers dynamic wireless charging , deep reinforcement learning , and federated learning for mobility solutions. The 15 most recent articles highlight his work in edge computing (4 entries), vehicular networks (5 entries), machine learning (3 entries), and blockchain (2 entries), with sub-fields including UAV energy management , real-time task offloading , multi-modal fusion , and smart grid integration . His methodology combines deep RL for autonomous driving , federated learning for decentralized energy trading , and blockchain to secure ITS systems . He collaborates extensively with researchers like Parisa Fard Moshiri , Burak Kantarci , and Binod Vaidya on smart city infrastructure and vehicular security projects.
Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.
Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .
Yi-Chi Liao is a postdoc researcher at ETH Zürich under the SIP Lab, funded by the ETH Zürich Postdoc Fellowship Programme. He holds a PhD from Aalto University (supervised by Prof. Antti Oulasvirta) and Master's/Bachelor's degrees from National Taiwan University. His research focuses on computational interaction, human-in-the-loop optimization, and biomechanical simulation. He has contributed to over 14 top-tier publications in venues like CHI, UIST, and TiiS. Education: PhD in Computer Science, Aalto University (Finland) Master's in Computer Science, National Taiwan University Bachelor's in Computer Science, National Taiwan University Research Interests: Advances in HILO frameworks for adaptive systems, human-AI co-design, and biomechanical motion simulation. His work integrates reinforcement learning, Bayesian optimization, and computational modeling to enhance interactive systems. Publications: Over 14 papers in top HCI venues, emphasizing optimization techniques, tactile interfaces, and 3D interaction. Recent work includes meta-Bayesian optimization for wrist-based interactions and real-time target inference via biomechanical simulation. Awards: CHI 2022 Honorable Mention, multiple outstanding review recognitions, and the ETH Postdoc Fellowship. He serves on program committees for CHI, UIST, and TEI. Teaching: Delivered lectures on Bayesian statistics, deep learning, and computational design at Aalto and NTU. Served as a teaching assistant for HCI and computer architecture courses. Labs: Active in the Human-Computer Interaction Lab (Saarland University), SIP Lab (ETH Zürich), and collaborations with Meta Reality Labs.
Adlen Ksentini is a Professor at EURECOM, a leading graduate school and research center in Sophia Antipolis, France, specializing in digital science and communication systems. His extensive research focuses on next-generation mobile networks (5G/6G), network management, and the integration of artificial intelligence with telecommunications infrastructure. Dr. Ksentini actively contributes to major EU research initiatives including 6G-BRICKS and AC3, serving as a key researcher and project leader in the development of future network architectures. Dr. Ksentini's research interests center around network slicing, intent-based networking, edge computing, and the application of machine learning to network management problems. His work bridges theoretical advancements with practical implementations in 5G/6G systems, with particular emphasis on zero-touch network management, energy efficiency optimization, quality of service assurance, and the integration of large language models with network operations. His research has significantly contributed to the development of O-RAN (Open Radio Access Network) frameworks and the evolution of network automation. His recent publication trends reveal a strategic shift toward AI-native network architectures, with increasing focus on integrating large language models (LLMs) with network management systems. His work demonstrates a clear progression from traditional network management approaches to more autonomous, AI-powered systems capable of intent-based configuration, self-optimization, and predictive maintenance. The publications show strong emphasis on practical implementations within the 6G research ecosystem, addressing critical challenges in network slicing, resource allocation, and energy efficiency. As a research supervisor, Dr. Ksentini mentors several PhD students including Abdelkader Mekrache, Karim Boutiba, Bouziane Brik, and Houda Hafi, who frequently appear as co-authors on his publications. His research is primarily funded through major EU research projects such as 6G-BRICKS (Building Reusable Testbed Infrastructures for Cloud-to-Device Breakthrough Technologies) and AC3 (which focuses on Cloud Edge Continuum). Dr. Ksentini is actively involved with the 6G-BRICKS project consortium and the AC3 project team, where he contributes to developing next-generation network architectures that integrate communication, computing, and sensing capabilities. His work within these projects focuses on creating reusable testbed infrastructures and addressing security and trust management challenges in the cloud-edge continuum.
Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.
Prof. Dr. Julia Rieck is a Full Professor of Business Administration at the University of Hildesheim , leading the Department of Business Administration and Operations Research within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. As Dean of the Faculty , she oversees academic programs, quality management, and research initiatives. Her roles include academic advising for the Business Information Systems (B.Sc./M.Sc.) programs and active participation in examination boards and quality committees. Education: PhD in Political Science (Dr. rer. pol.) with summa cum laude (2008), Habilitation at Clausthal University of Technology (2014), and studies in Business Mathematics (Diploma, University of Hamburg, 2003) and Mathematics (Georg-August-University Göttingen, 2000). Research: Focuses on Operations Research , Supply Chain Management , Project Planning , and Logistics . Her work integrates mathematical modeling , machine learning , and real-world applications , particularly in disaster response , dynamic transportation , and sustainable e-commerce . Projects: Leads third-party funded initiatives like "IT für die sorgende Gesellschaft" (AI in healthcare/social sectors) and contributes to the HULLS real-lab (AI in aging societies). Collaborates with regional companies (e.g., Youco, ADITUS) and institutions (HAWK, University of Hannover). Teaching: Emphasizes practical application through case studies, industry partnerships, and the IT-Speed Dating event for student-company connections. Her courses cover project resource planning , logistics , and digital transformation . Labs & Teams: Active in the Institute of Business Administration & Business Information Systems , contributing to the KET Kompetenzwerkstatt (entrepreneurship support) and interdisciplinary teams in AI and sustainability research.
Dr. Sander Leemans is a Professor at RWTH Aachen University leading the Business Process Management Foundations and Engineering research group. His work focuses on advancing process mining theory and practice with emphasis on stochastic modeling and conformance verification. Leemans' research centers on process mining, business process management, and stochastic process modeling. He investigates conformance checking techniques for probabilistic models, process discovery algorithms, and the integration of exogenous data into process analysis. His work bridges theoretical foundations with practical applications in healthcare, robotic process automation, and inter-organizational systems. Recent publications reveal a concentrated research trajectory in stochastic conformance checking, where Leemans develops methods for matching observed traces to stochastic process models using alignment techniques, entropy metrics, and partial-order reasoning. He also pioneers object-centric process mining frameworks and explores silent transitions in labeled Petri nets, significantly enhancing the precision and applicability of process mining in real-world scenarios. The Business Process Management Foundations and Engineering group under Leemans' leadership drives innovation in process mining through rigorous theoretical development and open-source tooling, maintaining RWTH Aachen's position at the forefront of business process intelligence research.
Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.
Professor Yann Disser is a faculty member in the Department of Mathematics at TU Darmstadt since 2021. He previously held an Assistant Professor (tenure-track) position at TU Darmstadt (2016-2021), a PostDoc position at TU Berlin (2012-2016), and a Visiting Professor role at Augsburg University (2015). His research spans Combinatorial Optimization , Online Algorithms , Graph Exploration , Computational Complexity , and Robust Optimization . Current position: Professor (W2), TU Darmstadt Previous: Assistant Professor (2016-2021), TU Darmstadt PostDoc & Habilitation: TU Berlin (2012-2016) His research focuses on algorithmic approaches to optimization problems, including: Combinatorial Optimization for problems like Steiner trees and knapsack variants Online Algorithms with applications to scheduling and transportation Graph Exploration by mobile agents and complexity bounds Computational Complexity of linear programming pivot rules Network Flows and geometric reconstruction Recent publications analyze incremental maximization with greedy methods, lower bounds for active-set methods , and universal circuit designs . His work appears in top venues like IPCO , ESA , and SODA . He advises a team of researchers including David Weckbecker, Farehe Soheil, and Alexander Birx. Current and past advisees often focus on algorithmic theory, with placements at institutions like HPI Potsdam and Merck.
Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.
Muhammad Waqas is a researcher affiliated with COMSATS University Islamabad , where he holds a position in the Department of Meteorology under the School of Applied Sciences and Humanities . His academic collaborations span institutions like Bahria University, National University of Technology, and University of Bahrain, indicating a multidisciplinary approach. Research interests include Mechanisms for integrating fuzzy logic and machine learning in health monitoring Application of deep learning to medical imaging and clinical diagnostics Development of smart sensors for wearable technology in biomechanics Analysis of social media data for public health surveillance and sentiment analysis Investigation of digital citizenship and ICT leadership in educational contexts Trends in his 15 most recent publications (2025-2024) reveal a focus on medical diagnostics (e.g., monkeypox, breast cancer), smart infrastructure (e.g., sensor placement, structural health monitoring), and social media analytics for health and behavioral insights. These works leverage machine learning , fuzzy systems , and multi-objective optimization .
Oliver Deussen is a Professor of Visual Computing at the University of Konstanz, recognized by the German Informatics Society (GSI) as a Fellow for his contributions to computer science. His research focuses on visualization, robotics, and environmental modeling, particularly in plant and landscape representation. He has pioneered methods in image manipulation and robotic painting, emphasizing digitalization's societal impacts. His work spans computational biology (e.g., schooling fish behavior) and AI-driven creative technologies. Research Interests: Visualization techniques, swarm behavior analysis, robotic creativity, and interdisciplinary applications of computer science. He explores how computational methods can model natural systems and enhance human-machine interaction. Awards: Fellow of the German Informatics Society (GSI) His research often bridges theory and practice, with contributions to SLAM frameworks, style transfer algorithms, and uncertainty visualization tools. Collaborations in robotics and biology reflect his commitment to applied computational research.
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Professor Karl Bernhard Friedrich serves as Universitätsprofessor and Chair of Metallurgical Process Engineering and Metal Recycling at RWTH Aachen University's Faculty of Georesources and Materials Engineering. He leads the Institute for Metallurgical Science and Electrometallurgy (IME), with his office located in Building 1401, Room 108 at Intzestraße 3 in Aachen. His leadership extends to numerous EU-funded research initiatives focused on sustainable metallurgy and circular economy principles. Professor Friedrich's research interests span metallurgical process engineering, metal recycling, extractive metallurgy (pyrometallurgy and hydrometallurgy), battery recycling technologies, lithium recovery processes, and vacuum metallurgy. His work emphasizes resource efficiency and sustainable practices in metal production and recycling, with particular focus on developing environmentally sound processes for electronic waste and battery recycling. The institute under his leadership has pioneered several innovative recycling concepts for lithium-ion batteries and electronic scrap. Analysis of his recent publications reveals a strong focus on battery recycling technologies, particularly lithium recovery from spent batteries through both pyrometallurgical and hydrometallurgical routes. His research group has developed advanced processes for black mass treatment, slag valorization, and metal recovery optimization. The work demonstrates increasing integration of digital technologies and multi-objective modeling in metallurgical process development. Honorary doctorate from Montanuniversität Leoben (2025) recognizing outstanding scientific achievements and commitment to promoting young scientists Long-standing collaboration with Montanuniversität Leoben through the Aachen-Leoben Workshop for non-ferrous metallurgists Professor Friedrich actively supervises numerous doctoral students and has established several collaborative research programs including the DFG priority program 'engineered artificial minerals,' and doctoral programs 'circular electronics' and 'circular e-cars.' His institute maintains strong industry connections, with growing demand for TBRC and VAR trial campaigns. The institute recently commissioned a new large-scale arc furnace and is preparing for leadership transition in early 2027. The IME under Professor Friedrich's leadership operates extensive research facilities focused on vacuum metallurgy, pyrometallurgy, and hydrometallurgy. The institute collaborates with industry partners through the GDMB Zinc & Lead Technical Committee and participates in numerous European research networks. Current research activities focus on developing sustainable processes for battery recycling, electronic waste treatment, and critical metal recovery with emphasis on circular economy principles.
