Dr Lin Yue is a Lecturer at the University of Adelaide , affiliated with the Faculty of Sciences, Engineering and Technology and the School of Computer and Mathematical Sciences . She earned her PhD from Jilin University, with part of her doctoral studies completed as a joint PhD candidate at the University of Queensland. Past affiliations: Northeast Normal University, University of Queensland, University of Newcastle Her research focuses on Sequential Data Analysis and its applications in Medical Data Analytics, EEG Data Analysis, Brain-Computer Interfaces, Social Media Data Analytics, and Sentiment Analysis . She collaborates with academia, government, and professional organizations, supported by internal and external research grants. Dr Yue is eligible to supervise Masters and PhD students as a Co-Supervisor and contributes to advancing data mining and machine learning techniques in healthcare and time series analysis.
Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.
Daniela De Silva is the Olin Professor of Mathematics and Chair of the Department of Mathematics at Barnard College, Columbia University. She holds a B.A. from the University of Naples Federico II and a Ph.D. from the Massachusetts Institute of Technology (MIT). Her research focuses on partial differential equations (PDEs), particularly free boundary problems and geometric analysis. She teaches advanced courses such as Calculus and Analysis at Barnard and organizes the Geometry and Analysis seminar at Columbia University. Her work explores regularity theory for free boundaries, phase transitions, and harmonic analysis. Notably, her contributions include studies on energy-minimizing free boundaries and monotonicity formulas. De Silva has also contributed to nonlinear Schrödinger equations and has held academic positions at institutions like Johns Hopkins University and MIT before joining Barnard in 2007. Her research interests span theoretical and applied aspects of PDEs, with a focus on geometric implications and singularities. While her work has been featured in prestigious journals like Comm. on Pure and Applied Math , Duke Math. J. , and Indiana Univ. Math. J. , she remains active in academic outreach, including discussions on topics like the mathematics of melting ice and the significance of π in popular media.
Rainer Gemulla is a Professor of Practical Computer Science I: Data Analytics at the University of Mannheim, heading the Data and Web Science Group within the School of Business Informatics and Mathematics. He has been a W3-Professor at the University since 2014, following positions as a senior researcher at Max-Planck-Institut für Informatik (2010-2014) and postdoctoral researcher at IBM Almaden Research Center (2008-2010). His research focuses on machine learning with structured and semi-structured data, particularly knowledge graphs, and developing efficient systems for data-intensive processing. Professor Gemulla's research spans multiple areas including machine learning with structured data (relational data), machine learning with semi-structured data (multi-relational graphs), combining these approaches with unstructured knowledge (text), and developing efficient, scalable methods for data-intensive processing. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source software projects including LibKGE, DistKGE, and AdaPM. His recent publications show a strong trend toward knowledge graph embeddings, parameter server architectures, and efficient training methods. The research demonstrates increasing focus on scalability challenges in graph learning, with particular attention to hyperparameter optimization, dynamic resource allocation, and benchmarking methodologies. His work consistently addresses the practical challenges of implementing machine learning systems at scale. Distinguished Reviewer Award at SIGMOD, 2025 Distinguished PC Member Award at EDBT, 2023 Outstanding Reviewer Award at NeurIPS, 2021 Junior-Fellow of the Gesellschaft für Informatik (GI), 2013 IBM's 2011 Pat Goldberg Memorial best paper award Best paper of NIPS 2011 Biglearn workshop Professor Gemulla actively mentors PhD students and has supervised numerous successful doctoral candidates. His leadership extends to administrative roles including Head of examination board for MSc Business Informatics since 2017, and previously serving as Study dean of the WIM faculty (2016-2019) and CIO of University of Mannheim (2022-2024). His research is supported by grants including AWS in Education Research Grant Award (2013) and Google Focused Research Award (2011). The Data and Web Science Group develops multiple open-source software projects including LibKGE (knowledge graph embedding library), DistKGE (multi-GPU training), AdaPM (adaptive parameter manager), Lapse (parameter server), and various tools for information extraction and sequence mining. The group maintains active collaborations with industry partners and academic institutions worldwide, particularly in the areas of knowledge graph research and scalable machine learning systems.
Arslan Mazitov is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the Institute of Materials (IMX) . He is part of the Computational Science and Modelling Laboratory (COSMO) , focusing on computational materials science with an emphasis on van der Waals materials, optical properties, and machine learning applications. His research explores novel materials for photonics, energy storage, and nanotechnology. Mazitov's work bridges theory and experiment, employing advanced modeling techniques to predict material behavior and design innovative solutions. Key research areas include van der Waals heterostructures , optical anisotropy engineering , and AI-driven materials discovery . He has contributed to studies on semiconductors, 2D materials, and interfacial phenomena. His computational methods address challenges in predicting material stability, optical properties, and surface behavior under various conditions. Active in collaborative projects, Mazitov's work has practical implications for photonic devices, energy storage systems, and nanoscale engineering. His research emphasizes interdisciplinary approaches, combining computational modeling with experimental validation to advance material innovation.
John Leahy is the Allen Sinai Professor of Macroeconomics and Public Policy at the University of Michigan, holding dual appointments in the Department of Economics (College of Literature, Science, and the Arts) and the Gerald R. Ford School of Public Policy. As Chair of the Economics Department, he focuses on macroeconomic theory, monetary policy, and behavioral economics, particularly rational inattention models. His research emphasizes how cognitive limitations and information processing affect economic decisions, contrasting classical economic assumptions. Leahy has held positions at Harvard, NYU, and Boston University, and served as Coeditor of the American Economic Review and Editor of the American Economic Journal: Macroeconomics. He consults with Federal Reserve Banks, advocating for data-driven, question-first research methodologies. His work bridges theoretical rigor and practical applications, influencing policy analysis and academic discourse. Education: PhD in Macroeconomics from Princeton University; MSFS from Georgetown University; BA in Math and History. His research spans macroeconomic policy, structural change, and behavioral models of decision-making, with recent focus on wishful thinking and imperfect information processing. He collaborates widely, emphasizing interdisciplinary approaches and creative problem-solving. Key contributions include modeling rational inattention, analyzing age structure impacts on monetary policy, and exploring North-South economic disparities. His editorial leadership and academic mentorship reflect his commitment to advancing innovative economic inquiry.
Dr. Yiting Xia is a tenure-track faculty member at the Max Planck Institute for Informatics (MPI-INF), leading the Network and Cloud Systems research group. She previously worked as a research scientist at Facebook and holds a PhD in Computer Science from Rice University (2018) and a B.S. in Telecommunications Engineering from Beijing University of Posts and Telecommunications and Queen Mary University of London (2011). Her research focuses on high-performance and energy-efficient networking for cloud computing, including reconfigurable data center networks, optical communications, and network protocols. Notable contributions include innovations in transport protocols, time synchronization for optical networks, and failure-resilient network design. Education: PhD in Computer Science, Rice University, 2018 M.S. in Computer Science, Rice University, 2014 B.S. in Telecommunications Engineering, BUPT & QMUL, 2011 Research Interests: Data center networking, optical communications, cloud systems, network protocols, distributed systems, and network security. Her work bridges theoretical contributions with practical implementations, addressing challenges in latency-sensitive flows, traffic engineering, and system reliability. Awards include the Ken Kennedy-Cray Fellowship and the N2Women Rising Star Award (2021). She has co-lectured courses on distributed systems and data networks at Saarland University and previously contributed to teaching at Rice University. Key projects include Aurora (for MoE inference optimization), Lighthouse (an open research framework for optical networks), and Occam (a reliable network management system). Grants & Projects: Focus on deployable optical network architectures and resilient backbone management during pandemic-driven traffic shifts. Labs/Teams: Leads the Network and Cloud Systems group at MPI-INF, collaborating with academia and industry on cutting-edge networking solutions.
Bruno Tiago da Silva Gomes is a Researcher in the Department of Electronics and Informatics at Vrije Universiteit Brussel (VUB), Belgium. His work focuses on FPGA-based hardware acceleration, biomedical signal processing, and embedded systems. He leads several high-impact projects, including ENACT (environmental health interventions) and Tech4Health (future health technologies). His research spans FPGA design, machine learning acceleration, and real-time signal processing. Education: PhD in Electronics and Informatics (2019, VUB), supervised by Professors Touhafi and Braeken. His thesis addressed streaming application acceleration on FPGAs. Research interests include Field-Programmable Gate Arrays (FPGA), biomedical sensors (e.g., photoplethysmography), beamforming, and high-level synthesis. He has co-authored over 60 publications and holds an h-index of 439. Key projects include OZR4103 (power-efficient AI for biomedical applications) and NSIS3 (decarbonisation technologies). His work integrates hardware-software co-design for edge computing and secure TinyML systems. Advising includes a Master’s thesis on PPG signal analysis. He contributes to datasets like the AMIVU Acoustic Map Imaging Dataset.
Chung-Hsing Yeh is an Associate Professor at Monash University's Faculty of Information Technology, Department of Data Science & AI. He holds a visiting professorship at National Cheng Kung University, Taiwan, and has extensive experience in academic roles including Chief Examiner and Lecturer for numerous IT and business-related courses. His research focuses on multicriteria decision analysis, applied artificial intelligence, fuzzy logic, neural networks, and sustainable operations management. He has led collaborative projects on e-waste recycling, supply chain optimization, and public health policy, funded by organizations like the Ministry of Science and Technology (Taiwan) and the Australian Research Council. Education: PhD in Operations Research/Information Systems, Monash University (1988) MSc in Management Science, National Cheng Kung University (1982) BSc (Engineering) in Industrial Design, National Cheng Kung University (1977) Research Interests: His work spans decision support systems, optimization modeling, transport research, and recycling operations. Notable contributions include algorithms for production scheduling, AI-driven solutions for healthcare, and sustainable e-waste management strategies. Awards: Listed in Marquis Who's Who in the World Listed in Who's Who in Finance and Industry Listed in Who's Who in Science and Engineering Grants & Projects: Led 6 major projects, including 'Maximizing E-waste Recycling Profitability' (2019–2020) and 'Smoke-Free Policy Effectiveness' (2007–2010). Active in grant review roles for ARC and the Netherlands Organisation for Scientific Research. Teaching: Overseeing courses such as Fundamentals of Artificial Intelligence, Business Intelligence Modelling, and Management Information Systems.
Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols
Paolo Papotti is an Associate Professor of Computer Science at EURECOM (France) since 2017, affiliated with the Data Science department. Previously, he was a senior scientist at QCRI (Qatar) and an assistant professor at Arizona State University (USA). He earned his PhD in Computer Science from the University of Roma Tre (Italy) in 2007, following an MEng in Computer Engineering from the same institution in 2003. His research focuses on scalable data management, data integration, data cleaning, and computational fact-checking. Notable contributions include work on knowledge graph rule discovery (Rudik), fact-checking frameworks (Scrutinizer), and data quality systems. His research has been supported by awards such as the 2020 Google Faculty Research Fellowship. Key publications include advancements in table representation learning, LLM-based data querying, and crowdsourced fact-checking validation. His work spans theoretical foundations and practical tools for improving data quality and information trustworthiness.
Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.
Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.
Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington. Her research focuses on real algebraic geometry, combinatorics, and convex optimization, with applications to hyperbolic polynomials, determinantal representations, and convex algebraic geometry. She collaborates extensively on projects involving numerical ranges, quasicrystals, and geometric optimization problems. Research Interests: Real algebraic geometry and its connections to combinatorics and optimization Hyperbolic and log-concave polynomials Convex geometry and spectrahedra Applications in matrix analysis and statistical mechanics Her work spans theoretical advances in algebraic geometry and computational methods, including contributions to the study of principal minors, tropical geometry, and phase retrieval problems. Recent publications highlight her focus on Fourier quasicrystals, higher-rank numerical ranges, and combinatorial structures in matroids. Publications: Over 30 peer-reviewed articles, including influential works on quartic curves, determinantal representations, and log-concave polynomials. Grants & Collaborations: Active in interdisciplinary research, with projects supported by NSF and collaborations in algebraic combinatorics and geometric optimization.
Andreas Wicenec is a Professor and Senior Principal Research Fellow at the University of Western Australia (UWA), leading the Data Intensive Astronomy Program (DIA) at the International Centre for Radio Astronomy Research (ICRAR). He specializes in data-intensive astronomy, high-performance computing, and large-scale data management systems. His work supports the Square Kilometre Array (SKA) and other major observatories. Education: PhD in Astronomy from the University of Tübingen (1994), Physics Diploma (1989). Professional roles include Archive Scientist at the European Southern Observatory (ESO) and leadership in the International Virtual Observatory Alliance (IVOA). Research focuses on petascale data flows, reproducible science workflows, and next-generation archive systems like NGAS. Current projects include the DALiuGE engine, SKA data handling, and gravitational wave detection pipelines using deep learning. Key Projects: SKA Science Data Processing (7M AUD contract), Data Activated Flow Graph Engine (DALiuGE), and NGAS archive system Awards: ACM Gordon Bell Prize 2020 finalist Grants: Includes SKA Bridging Design (2019–2021), ICRAR IV (2025–2030) Labs/Teams: Active in ICRAR's Data Intensive Astronomy group, collaborating internationally on large-scale astronomy initiatives.
