Professor Ben Liang is a faculty member at the Department of Electrical and Computer Engineering , University of Toronto, holding the L. Lau Chair . He has served on editorial boards of IEEE Transactions on Mobile Computing , IEEE Transactions on Wireless Communications , and Wiley Security and Communication Networks . His research focuses on networked systems , mobile communications , and distributed machine learning , with applications in wireless network virtualization, edge computing, and resource optimization. He explores stochastic scheduling , computation-communication co-design , and multi-resource fair allocation . Key publication trends: Wireless federated learning (2024-2025) Network virtualization and MIMO systems (2022-2025) Online distributed optimization (2023-2025) Stochastic resource management (2020-2024) Scientific Awards: Fellow of IEEE Best Paper Award, ACM MSWiM 2013 INFOCOM 2010 Finalist Ontario ERA Award 2007 IFIP Networking 2005 Best Paper Intel Foundation Graduate Fellowship 2000 Polytechnic University valedictorian 1997 Affiliated with IEEE , ACM , and Tau Beta Pi , he teaches courses like ECE368: Probabilistic Reasoning and ECE421: Machine Learning , emphasizing stochastic networks and random processes .
Dr. Gary K. Owens is a Professor in the Department of Molecular Physiology and Biological Physics at the University of Virginia , with a secondary appointment in the Department of Medicine, Division of Cardiology. He serves as Director of the Robert M. Berne Cardiovascular Research Center and leads groundbreaking research on vascular smooth muscle cell (SMC) dynamics in atherosclerosis. His work challenges conventional paradigms about SMC roles in plaque stability and thromboembolic events. Primary Affiliation: University of Virginia Key Titles: Robert M. Beirne Professor of Cardiovascular Research, Professor, Director of CVRC Research Themes include: Cardiovascular Biology Atherosclerosis Pathogenesis Smooth Muscle Cell Plasticity Epigenetic Regulation Inflammatory Signaling Tumor Metastasis Mechanisms Publication Trends (2015–2024) reveal expertise in Nature Medicine and Circulation studies, focusing on SMC gene regulation (Klf4/Oct4), plaque stability, and cross-talk between vascular cells and tumors. His work demonstrates that SMCs account for >80% of advanced lesion cells and that Klf4 inhibition reduces metastasis by >70%. Lab Leadership includes mentoring PhD students Anita Salamon and Victoria Milosek , alongside senior scientists and postdocs. Collaborations span with Dr. Gwen Randolph (Washington University) and Dr. Rosey Kaplan (NIH) on IL1β signaling and metastasis.
Daniel Schiller is a Professor of Economic and Social Geography at the University of Greifswald, where he has been employed since April 2016. He currently serves as Vice Rector for Research and Transfer (since April 2025) and has held several significant administrative positions including Managing Director of the Institute of Geography and Geology (2020-2024) and Vice Dean of the Faculty of Mathematics and Natural Sciences (2020-2023). His academic affiliations include leadership roles at the Steinbeis Research Center for Regional Economics and membership in the Northeast Regional Working Group at the Academy for Spatial Development. Professor Schiller's research focuses on knowledge-based and sustainable regional development, transformation of regional economies, spatial justice, and futures of globalization, with regional expertise in Germany, the Baltic Sea region, and East and Southeast Asia. His work bridges theoretical economic geography with practical regional development challenges, particularly examining innovation systems, bioeconomy transitions, and the spatial dimensions of sustainability. His publications reveal a strong emphasis on how regions navigate economic transformation, with particular attention to the role of institutions, networks, and governance in shaping regional development trajectories. His recent publications demonstrate a clear trend toward examining sustainability transitions in regional contexts, with increasing focus on bioeconomy development, One Health approaches, and the spatial dimensions of innovation. The geographical scope of his work spans from local German regions to East Asian contexts, particularly China's innovation systems. Methodologically, his work combines qualitative case studies with spatial analysis and theoretical frameworks from economic geography and innovation studies. Scientific Prize 'Human Geography' of the Prof. Dr. Frithjof Voss Foundation for Geography (2013) Professor Schiller has secured substantial research funding from multiple sources including BMBF, DFG, EU, and regional programs, leading major projects such as Plant³ RIIS, RegioTransformOH, and ChiKUBIG. His research portfolio demonstrates strong international collaboration, particularly with Chinese institutions. As Chair of Examination Boards for M.Sc. programs in Regional Development and Tourism and Bioeconomy, he plays a significant role in academic program development and student education. His leadership extends to the WIR! innovation alliance Plant³, where he serves as spokesperson since 2019. Through his work at the Steinbeis Research Center for Regional Economics and various research projects, Professor Schiller leads teams examining regional innovation systems, economic transformation, and sustainable development pathways. His current research agenda focuses on the intersection of bioeconomy development, spatial justice, and regional transformation, with particular attention to how regions can navigate the complex challenges of sustainability transitions while addressing socio-spatial inequalities.
Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.
Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.
Duong Nguyen serves as an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. His research integrates operations research, artificial intelligence, economics, and engineering to develop mathematical models for decision-making in large-scale networked systems including cloud/edge computing, smart grids, and crowdsourcing. He directs the NEMO research group focused on building intelligent multi-agent platforms through optimization and market design. His educational credentials include: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2020) M.Sc. in Telecommunications from INRS, University of Quebec (2014) B.Sc. in Electronic and Telecommunications from Hanoi University of Science and Technology (2011) Dr. Nguyen's research spans Operations Research, Artificial Intelligence, Decision-Making, Market Design, and Optimization with applications in edge computing, power systems, and network economics. His work emphasizes robust algorithms for uncertain environments and secure multi-agent platforms, recently expanding into quantum machine learning and privacy-preserving distributed systems. Current projects address decentralized federated learning, dynamic pricing, and EV charging network design. Analysis of his publication record reveals consistent focus on distributed optimization techniques for edge/cloud systems, with increasing integration of game theory and quantum computing. His work demonstrates strong methodological innovation in handling spatio-temporal uncertainty while addressing practical challenges in energy flexibility and secure genomic computation. His scientific recognition includes: Finalist for Best Student Paper Award at American Control Conference (ACC) 2024 Finalist for Best Paper Award at International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) 2023 Dr. Nguyen actively mentors Ph.D. students including Jiaming Cheng and Long Vu, with student-led research achieving significant recognition. His NEMO group collaborates with institutions including ETH Zurich on projects spanning autonomous driving, edge AI, and quantum optimization. Current research directions emphasize fair resource allocation, privacy-preserving learning, and dynamic pricing frameworks for next-generation networked systems.
Guimu Guo is an Assistant Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His research focuses on parallel and distributed computing techniques for large-scale graph mining problems, with applications in bioinformatics and transportation engineering. Ph.D. in Computer Science from University of Alabama at Birmingham M.Sc. in Computer Science from Tongji University Dr. Guo has published extensively in top-tier venues like VLDB, ICDE, and IEEE BigData. His work spans graph mining algorithms, parallel computing, and interdisciplinary applications in transportation and genomics. He actively mentors PhD and Master's students, offering fully funded positions. Key research trends include: Advancing GPU-accelerated graph decomposition techniques Developing distributed frameworks for subgraph querying and task concurrency Exploring parallel algorithms for frequent pattern mining and clique-like subgraphs Scientific Recognition: NSF CRII Award UAB Outstanding PhD Student Award Alabama GRSP Awards (Rounds 15 & 16) Teaching spans from foundational object-oriented programming to advanced graduate courses in parallel programming. His lab group has produced significant contributions to subgraph mining, transportation simulation, and genome assembly systems.
Lauren Weiss, PhD is a Professor of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a faculty member at the UCSF Weill Institute for Neurosciences. Her research focuses on understanding the genetic architecture of autism spectrum disorder through genome-wide genetic data analysis and human induced pluripotent stem cell (iPSC) models. Dr. Weiss's laboratory investigates the genetic mechanisms by which DNA variants influence autism risk, examining questions about copy number vs. SNP variation, rare vs. common variation, gene-sex interaction, gene-gene interaction, and gene-environment interaction. Her team uses rich genetic datasets to identify susceptibility loci and the physiological pathways these risk loci implicate. Additionally, they employ iPSC models to study known mutations or copy number variants predisposing to autism, first identifying the effects of genetic risk variants and then determining whether these effects can be modified at the cellular level by environmental or pharmacological agents. Analysis of Dr. Weiss's recent publications reveals a strong focus on sex differences in autism genetics, the role of specific copy number variants (particularly 16p11.2 and 22q11.2), maternal environmental factors during pregnancy, and the integration of multi-omics data to understand neurodevelopmental pathways. Her work bridges basic genetic research with potential clinical applications for improving understanding, prevention, diagnosis, and treatment of autism and related traits. Dr. Weiss has secured significant research funding as Principal Investigator on multiple NIH grants, including R01MH114924 (Decoding the Genetics of Sexual Dimorphism in Autism Spectrum Disorders), R01MH107467 (Utilizing eQTL networks to gain biological insight into multigenic CNVs), and DP2OD007449 (Dissecting Epistasis and Pleiotropy in Autism towards Personalized Medicine). Her laboratory offers research opportunities for students interested in analytical genetics projects related to gene-environment effects, gene-sex effects, gene-gene effects, and the relationship between ASD and brain size. Dr. Weiss actively collaborates with numerous researchers across institutions, particularly on large-scale genomic studies of autism and other neurodevelopmental disorders. Her work has contributed significantly to our understanding of the complex genetic architecture underlying autism spectrum disorder and related conditions.
Jeremy Douthit is an Associate Professor of Accounting at the Eller College of Management, University of Arizona, where he joined in 2014. He holds a PhD in Accounting from Florida State University (2014), an MAcc from The Ohio State University, and a BA from Troy University. His research centers on Management Accounting with emphasis on Budgeting, Control Systems, Behavioral Economics, and Corporate Social Responsibility. He investigates how social norms, firm CSR initiatives, and control system designs influence managerial decision-making processes and employee behavioral outcomes through experimental methodologies. Dr. Douthit's publication trend (2012-2024) reveals consistent contributions to top accounting journals including The Accounting Review and Journal of Management Accounting Research. His work demonstrates a progression from foundational agency theory investigations toward contemporary explorations of CSR's internal organizational impacts, with recurring themes of honesty in reporting, control system design, and behavioral responses to incentives. Scientific awards: Eller Fellow While no formal advisees are listed in the provided materials, his active research agenda suggests ongoing mentorship activities. His working papers on relative performance incentives and team uncertainty indicate current grant-supported projects, though specific funding sources aren't detailed in the source text. Dr. Douthit maintains affiliations with the Dhaliwal-Reidy School of Accountancy and the Center for Trust Studies, reflecting his integration into specialized research communities focused on accounting ethics and organizational trust dynamics.
Dr. Dragan Doder is an Assistant Professor in the Intelligent Systems group within the Faculty of Science at Utrecht University. His office is located in the Buys Ballot Building at Princetonplein 5, Room 5.20, 3584 CC Utrecht, Netherlands. He is actively engaged in research and teaching within the domain of Artificial Intelligence, with a specific focus on logical frameworks for AI systems. Dr. Doder's research expertise spans several interconnected areas of theoretical and applied AI. His primary interests include: Artificial Intelligence with emphasis on logical foundations Argumentation theory and frameworks Probabilistic reasoning and temporal logic Deontic logic for normative reasoning Human-centered AI approaches His work bridges theoretical computer science with practical applications in multi-agent systems and decision-making frameworks. Analysis of Dr. Doder's recent publications (2020-2025) reveals a strong focus on the intersection of logic, probability, and AI. His research trajectory shows increasing sophistication in handling uncertainty through probabilistic temporal logics, while maintaining strong connections to practical applications in multi-agent systems and argumentation frameworks. A notable trend is his work on integrating causal reasoning with probabilistic models, particularly in multi-agent contexts where group responsibility and risk assessment become critical concerns. His publications demonstrate consistent contributions to top AI conferences including IJCAI, AAAI, and ECAI. Dr. Doder actively collaborates with researchers across multiple institutions. His collaborative network includes prominent researchers in AI and logic from institutions worldwide. His research has practical implications for developing AI systems that can reason under uncertainty, handle complex normative constraints, and make responsible decisions in multi-agent environments. Dr. Doder is affiliated with the Intelligent Systems research group at Utrecht University, which focuses on developing theoretically sound approaches to AI that can be applied to real-world problems. The group emphasizes human-centered AI approaches that consider ethical implications and practical usability alongside technical excellence.
Michihiro Yasunaga is an Assistant Professor in the Department of Computer Science at Stanford University's School of Engineering. He received his PhD in Computer Science from Stanford, advised by Percy Liang, Jure Leskovec, and Chris Manning. Prior to his faculty position, he worked as a researcher at Google DeepMind and Meta. His research focuses on building LLMs and agents that assist humans in diverse tasks, with particular expertise in post-training techniques (RL, reward models, and evaluation), reasoning systems (AnalogicalReasoner), retrieval and tool use for LLMs (LinkBERT, QAGNN, DRAGON, REPLUG, HippoRAG), and multimodality (RA-CM3, Med-Flamingo, Transfusion). His work spans both theoretical foundations and practical applications of large language models. Yasunaga's publication record demonstrates significant contributions to the field of AI, with 15 recent articles (2023-2025) covering diverse aspects of language model development, evaluation, and application. His research shows a clear trajectory toward building more capable, efficient, and reliable multimodal AI systems, with particular emphasis on knowledge integration and robust evaluation frameworks. Among his notable achievements is the Best Paper Award at AAAI 2023 Deep Learning on Graphs Workshop for the DRAGON paper. He has also been deeply involved in major benchmarking efforts including HELM and HEIM, which provide comprehensive evaluation frameworks for language and vision-language models. Yasunaga actively contributes to the research community through service roles including Organizing Committee for the Workshop on Knowledge-Augmented Methods for NLP (ACL 2024), Workshop on Structured and Unstructured Knowledge Integration (NAACL 2022), and the Workshop on Scientific Document Summarization (SIGIR 2017-2020). He has also served on program committees for top conferences including NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, and ICCV from 2020-2025.
Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications
Lorenz Dörschel is an Adjunct Professor (Lehrbeauftragter) at the Institute of Automatic Control at RWTH Aachen University. He holds the academic title PD Dr.-Ing. habil, signifying post-doctoral research qualifications. His position is part-time, focusing on advanced control theory and applications. His primary research interests include: Control of distributed parameter systems (e.g., fluid dynamics, thermal processes) Model predictive control for industrial and automotive systems Parameter space methods for robust controller design Model reduction techniques for complex nonlinear systems Dörschel's recent publications (2018-2024) demonstrate broad applications across biomedical engineering, renewable energy, automotive systems, and industrial automation. His work consistently integrates mathematical rigor with practical implementations, emphasizing advanced control methodologies like nonlinear MPC, Lyapunov-based design, and Bayesian optimization. A recurring theme is the development of computationally efficient control strategies for distributed parameter systems. No scientific awards, student advising relationships, or research grants are documented in the available information.
Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .
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.
