جست‌وجوی استاد و پژوهشگر

Longbing Caoمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Professor Longbing Cao serves as a Professor at University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology, specifically within the Data Science Institute. He also holds the position of Distinguished Chair Professor in AI at Macquarie University and is an ARC Future Fellow (Level 3). As a pioneering researcher in AI, data science, and advanced analytics, Professor Cao founded the UTS Advanced Analytics Institute, the first data science initiative in Australia. Professor Cao's research interests span across data science fundamentals, machine/deep learning, behavior informatics, and enterprise data science applications. His work bridges the gap between theoretical research and practical implementation, focusing on non-IID learning, actionable knowledge discovery, and complex behavior modeling. His research has addressed real-world challenges in government services, finance, insurance, banking, telecommunications, transport, and e-commerce sectors. His 15 most recent publications reveal a strong trend toward addressing distributional challenges in machine learning, developing transdisciplinary AI approaches, modeling complex temporal dependencies, and advancing negative sequence analysis. His work consistently integrates theoretical foundations with practical applications, particularly in financial analytics, healthcare, and disaster resilience. Scientific Awards: Eureka Prize for Excellence in Data Science (2019) ACM Distinguished Scientist Professor Cao has made significant contributions to academic leadership and research supervision. He established world-first research degrees at UTS (Master of Analytics and PhD Thesis: Analytics), founded the IEEE Task Force on Data Science and Advanced Analytics, and served as general chair of the KDD conference in 2015 (the first edition in Australia). His funded research portfolio includes multiple ARC grants and industry collaborations focusing on complex data analytics, behavior informatics, and AI applications. He leads the Data Science Lab at UTS, which has become a prominent center for data science research in Australia. The lab focuses on developing fundamental theories and practical applications of data science, with strong industry partnerships that translate research into real-world impact. Professor Cao's leadership in establishing the annual Big Data Summit in 2012 created Australia's first platform for bridging academic research with industry and government applications.

George Slotaمشاهده پروفایل

دانشیار

• Graph Mining
• Network Analysis
• Big Data Analytics
• +۵ مورد دیگر

George Slota serves as an Associate Professor in the Computer Science Department within the School of Science at Rensselaer Polytechnic Institute (RPI). Previously, he held positions as a Computer Scientist at Sandia National Labs New Mexico and as a Computer Engineer at the Naval Undersea Warfare Center. His research spans graph and network mining, big data analytics, machine learning, bioinformatics , and their intersections with parallel computing, scientific computing, and high performance computing . His work focuses on scalable algorithms for trillion-edge graphs, graph partitioning, community detection, and GPU-accelerated graph processing. His recent publications reveal strong emphasis on graph coarsening and spectral methods distributed graph algorithms for biconnectivity and coloring random graph generation and validation GPU acceleration for graph neural networks His research has been recognized with a prestigious NSF CAREER award supporting his project "Scalable Graph Processing to a Quadrillion Edges and Beyond." Award: National Science Foundation Faculty Early Career Development (CAREER) Award His work demonstrates consistent funding and collaboration with major national laboratories including Sandia National Labs and involvement in ExaGraph projects targeting exascale computing challenges. He maintains an active research group focused on extreme-scale graph analytics.

Minge Xieمشاهده پروفایل

استاد

• Statistical Inference
• Data Science Foundations
• Meta-Analysis
• +۶ مورد دیگر

Minge Xie is a Distinguished Professor and Director of the Office of Statistical Consulting at Rutgers, The State University of New Jersey . His research focuses on foundational aspects of statistical inference, data science, and interdisciplinary applications. He serves as a leader in the field of statistics, contributing to methodologies in meta-analysis, reproducibility, and computational inference. Education : Ph.D. from the University of Illinois at Urbana-Champaign Xie’s research spans statistical theory, computational methods, and practical applications in data science. He has pioneered approaches in confidence distributions , fusion learning , and high-dimensional modeling , with notable work on zero-inflated data, conformal prediction, and reproducibility frameworks. His recent publications emphasize the integration of Bayesian, frequentist, and fiducial paradigms, advancements in meta-analysis for rare events, and scalable methods for big data analysis. Xie’s work bridges theoretical rigor with real-world applications, including microbiome studies, nuclear detection, and maritime threat modeling. Scientific Awards : ASA Fellow IMS Fellow ISI Fellow Xie actively contributes to statistical education and interdisciplinary collaboration, leading the Office of Statistical Consulting at Rutgers. His methodological innovations continue to influence fields requiring robust and interpretable statistical inference.

• Autonomous Systems
• Bayesian Statistics
• Big Data
• +۷ مورد دیگر

Professor Simon Maskell is a Professor of Autonomous Systems at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science and the Faculty of Science and Engineering. He holds degrees including MEng, MA, PhD, and is a Fellow of the Institution of Engineering and Technology (FIET) and Chartered Engineer (CEng). His research focuses on algorithmic solutions at the intersection of Computer Science, Engineering, and Statistics. Key areas include Bayesian statistics for decision-making under uncertainty, particle filters for high-dimensional parameter estimation, and applications in autonomous systems, big data analytics, and data fusion for sectors like defense, healthcare, and maritime surveillance. He leads the Liverpool Big Data Network and contributes to the Stone Soup open-source tracking framework. Maskell has secured grants from the Home Office, European Commission, and EPSRC, addressing challenges like combating cyber threats, improving drug interaction detection, and enhancing sensor technologies. His notable award includes an Honorary Research Fellow title from Imperial College (2010). In teaching, he oversees modules such as Instrumentation & Control and supervises research on topics like Bayesian methods in proteomics, traffic signal control, and antimicrobial resistance. He serves as an Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Signal Processing Letters. Professional activities include editorial roles and administrative leadership in interdisciplinary initiatives, emphasizing ethical AI deployment and data-driven healthcare solutions.

Juan Carlos Pichel Camposمشاهده پروفایل

دانشیار

• Parallel Computing
• Distributed Computing
• Big Data Technologies
• +۶ مورد دیگر

Juan Carlos Pichel is an Associate Professor at the Center for Research in Intelligent Technologies (CITIUS) within the University of Santiago de Compostela (Spain). He holds a B.Sc. in Physics and a Ph.D. in Computer Science from the same university (2006). His research focuses on parallel and distributed computing, Big Data technologies, quantum computing, and optimization for emerging architectures. He has held visiting positions at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked at the Galicia Supercomputing Center. Education: B.Sc. Physics (USC, Spain), Ph.D. Computer Science (USC, 2006). Research interests include: HPC frameworks (IgnisHPC), quantum computing tools (NetQIR), bioinformatics software (BigSeqKit/VeryFastTree), and misinformation detection systems. His work emphasizes interdisciplinary applications of parallel computing in genomics, health informatics, and social media analysis. Notable contributions: MPI4All (universal MPI bindings), IgnisHPC (HPC-Big Data framework), and VeryFastTree (phylogeny tool). Active in projects like C3HS (health search systems) and HYBRIDS (AI for democratic practices). Labs/Teams: Leads the ARQCOMP research group in Computer Architecture. Collaborates with multidisciplinary teams in bioinformatics, quantum computing, and social media analytics.

• Big Data Analytics
• Text Mining
• Machine Learning
• +۶ مورد دیگر

Dr. Wei Zhang is a Senior Lecturer and Associate Head of People and Culture at the University of Adelaide's School of Computer and Mathematical Sciences, with a secondary appointment as Honorary Lecturer at Macquarie University. She holds a PhD in Computer Science from the University of Adelaide (2017) and leads research in big data analytics, text mining, machine learning, and IoT. Her work has resulted in over 100 peer-reviewed publications, including top-tier journals like ACM Computing Surveys and IEEE Communications Surveys. Dr. Zhang currently holds an ARC Early Career Industry Fellowship (IE230100119) and leads an ARC Discovery Project (DP240103070), with total funding exceeding AUD $3M from ARC, Google, and DST over five years. Her research focuses on text summarization, AIoT systems, adversarial attacks on neural networks, and medical information extraction. Professional roles include Program Coordinator, Course Coordinator, and outreach contact at her school. She actively contributes to academic governance as Associate Head of People & Culture and serves on conference committees (ACL, KDD, WWW) and journal editorial boards. Industrial collaborations span six years with roles in project leadership and technical advisory roles across multiple sectors. Dr. Zhang is a member of ACM, IEEE, and ACS, and her work has been recognized by prestigious awards such as top 100 global researchers in Network Security (SciVal). Her lab develops AI-driven solutions for IoT-enabled agriculture (e.g., mushroom cultivation monitoring) and smart construction systems, with applications in energy optimization, healthcare, and environmental monitoring.

Sazia Mahfuzمشاهده پروفایل

استادیار

• Streaming IoT data analytics
• Lifelong machine learning
• Medical image analytics

Dr. Sazia Mahfuz is an Assistant Professor at the Jodrey School of Computer Science, Acadia University. She holds a Ph.D. from Queen’s University, an M.Sc. from Acadia University, and a B.Sc. from the University of Dhaka, Bangladesh. Her research focuses on Streaming IoT Data Analytics, Lifelong Machine Learning, and Medical Image Analytics, addressing challenges in real-time data processing, adaptive learning systems, and healthcare technology. Dr. Mahfuz’s work spans interdisciplinary applications including human activity recognition, vehicular sensor data classification, and diabetic retinopathy detection using deep learning. Her recent contributions emphasize hybrid architectures (CNN-RNN), transformer-based models, and frameworks for multi-level data analytics. She has published extensively on topics ranging from wearable sensor optimization to distributed data stream processing. Her research trends highlight a strong emphasis on IoT-enabled healthcare solutions and lifelong machine learning algorithms that generate synthetic examples to improve model adaptability. While no awards are listed, her articles reflect significant engagement with both theoretical and applied computer science domains. No advising or grant information is provided in the text.

• Causal Machine Learning
• Healthcare Informatics
• Medical Data Analysis
• +۳ مورد دیگر

Dr. Vinod Kumar Chauhan is a Research Fellow at the Institute of Biomedical Engineering, University of Oxford. His work focuses on causal machine learning and healthcare applications, including individualized treatment effects estimation and addressing sample selection bias in medical data. Previously, he held a postdoctoral position at the University of Cambridge and earned his PhD from Panjab University, Chandigarh, India. Education: BSc, MCA, PhD (Panjab University, India). Research interests include causal inference, healthcare informatics, and graph neural networks for EHR analysis. Recent projects involve postoperative atrial fibrillation prediction and optimizing treatment effects for composite outcomes. Key Awards: 2021 Institute for Manufacturing Postdoctoral Award (Cambridge), Travel Grant for Asian Conference on Machine Learning 2017. Selected Talks: Keynote on HyperNetworks (2023 Panjab University), invited talks on treatment effects estimation (IISER Pune, 2023). Labs/Teams: Computational Health Informatics group under Prof. David Clifton. Active in departmental EDI committee roles.

• Geographic Information Systems (GIS)
• Parallel Computing
• GPU Acceleration
• +۳ مورد دیگر

Jianting Zhang is an Assistant Professor of Computer Science at the City College of New York (CUNY). His research focuses on geospatial data management, parallel computing, and GPU acceleration techniques for handling large-scale spatial datasets. He specializes in optimizing spatial query processing, indexing methods for moving objects, and high-performance computing frameworks for web-based GIS applications. His work bridges theoretical algorithm design with practical implementations in distributed and cloud environments. Key research interests include parallel indexing of geospatial rasters and trajectories, efficient spatial join algorithms, and GPU-based acceleration of data compression and visualization. He has explored applications in biodiversity analysis, urban mobility (e.g., NYC taxi data), and query-driven web-GIS systems. His contributions emphasize scalable solutions for big data challenges in geographic information systems. His publications highlight advancements in GPU-accelerated spatial processing, including novel approaches to quadtree construction, sparse matrix operations, and top-K trajectory similarity queries. While no scientific awards are explicitly mentioned, his active publication record reflects sustained contributions to the field of geospatial computing.

Zhe Shandianمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning (Theory and Modeling)
• Physics-informed Machine Learning
• +۷ مورد دیگر

Zhe Shandian is an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on advancing machine learning methodologies, particularly in physics-informed machine learning, operator learning, and Bayesian methods. He explores applications in differential equations, multi-omics data, and engineering simulations. His work emphasizes scalable algorithms and data efficiency in complex systems. Education details are not explicitly provided in the text, but his professional trajectory aligns with a strong computational and mathematical background. Research interests include developing novel frameworks for neural operators, symbolic regression, and functional Bayesian decomposition. Key research trends from his articles highlight advancements in physics-guided AI models, multi-fidelity simulation, and scalable kernel-based methods. His contributions span domains like material science, cancer prediction, and traffic flow modeling. No scientific awards are mentioned in the provided text. Zhe Shandian has not listed any advisees, and specific grants or labs are not detailed here. His work often involves collaboration across disciplines, leveraging both theoretical and applied machine learning techniques.

Cong Gaoمشاهده پروفایل

استاد

• Data Science
• Geospatial data management
• Spatio-temporal data mining
• +۷ مورد دیگر

Cong Gao is a Professor and Head of the Division of Data Science at Nanyang Technological University's College of Computing & Data Science. He also holds a courtesy appointment with the School of Physical & Mathematical Sciences. Previously, he served as an Assistant Professor at Aalborg University, Denmark, and worked as a researcher at Microsoft Research Asia. He co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU). His educational background includes: Ph.D. in Computer Science from National University of Singapore (2004) Master of Engineering from Tianjin University, China (1999) Bachelor of Engineering from Tianjin University, China (1996) Professor Gao's research focuses on Data Science, with particular expertise in geospatial data management, spatio-temporal data mining, recommendation systems, and social media data analysis. His work has significantly impacted areas like spatial-textual indexing, point of interest recommendation, and mining social networks. He has published extensively in top venues including VLDB, SIGMOD, ICDE, KDD, and WSDM, with over 14,000 citations and an H-index of 61. His recent publications demonstrate strong trends in applying machine learning to database systems, with particular focus on spatial and trajectory data management. Key research directions include learned indexing techniques, trajectory data analysis, and integrating large language models with database systems for improved query optimization. Professor Gao has received notable scientific recognition including: Best paper runner-up award at WSDM'22 Best paper award runner-up at WSDM 2020 He has advised numerous students who have become significant contributors in their own right, including Xin Cao, Lisi Chen, Kaiyu Feng, and Kaiqi Zhao. His research has been supported by substantial grants from Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft, including a S$42.4 million funding over 5 years for the SCALE@NTU lab. Professor Gao leads the Data Management Research Group (DANTE) and co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU), which develops market-leading AI and data science technologies.

• Labour Economics
• Health Economics
• Education Economics
• +۵ مورد دیگر

Dr. Zhang Wei is a Grant-Funded Researcher (C) at the University of Adelaide's School of Economics and Public Policy, affiliated with the Future of Employment and Skills Research Centre. He holds a PhD in economics and has held roles including Associate Lecturer at the University of Adelaide and Senior Research Fellow at Flinders University's National Institute of Labour Studies. His research focuses on labour economics, health economics, education economics, international trade, and applied game theory. Current projects include evaluations of the National Disability Insurance Scheme, impacts of ageing populations on productivity, job mismatches, and education's role in labour markets. Education: PhD in economics (thesis on multilateral vs. bilateral trade liberalisation) Research interests span policy-relevant areas such as workforce dynamics, healthcare economics, and trade agreements. He has contributed to projects funded by ARC/NHMRC, NCVER, and government departments, addressing topics like retirement policy, VET completers' job mobility, and economic security. Supervision eligibility includes co-supervision for Masters/PhD candidates.

• Mechanical Design
• Industrial Engineering
• Railway Engineering
• +۷ مورد دیگر

Nicolo' Zampieri is an Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) of Politecnico di Torino, specializing in railway dynamics, digital twins, and mechanical design. His research focuses on computational modeling of wheel-rail interactions, multibody systems, and sustainable transportation technologies. Academic Affiliation: Politecnico di Torino (Polytechnic University of Turin) Research Groups: Design and experimentation of industrial and railway systems (DIMEAS) Research Interests Zampieri's work bridges artificial intelligence and multibody dynamics to advance railway engineering. Key areas include: Digital Twins for freight train braking systems Machine Learning applications in longitudinal train dynamics Thermal Modeling of tread braked wheels Experimental Prototyping of twin-disc wear testing devices Article Trends His recent publications emphasize AI-driven railway simulations , multibody dynamics , and computational wear modeling . Notable projects include digital twin applications for emergency braking analysis and innovative twin-disc testing methodologies for wheel-rail interactions. Advising Zampieri supervises Rosario Pagano , a PhD student in Mechanical Engineering focusing on railway wear simulations. Teaching Roles Course Lecturer for Multibody Systems Applications (PhD in Mechanical Engineering, 2024-2025) Course Owner for Materials and Production Process Simulation (Master's in Materials Engineering for Industry 4.0) Collaborator for Costruzione di Macchine (Master's in Mechanical Engineering)

Emily Cohenمشاهده پروفایل

استاد

• Bankruptcy Law
• Legal Writing and Advocacy
• Cognitive Psychology
• +۳ مورد دیگر

Professor Emily Cohen is a faculty member at Washington University School of Law specializing in legal writing, analysis, and advocacy. Her professional background includes eight years in private practice handling business bankruptcy and creditors' rights, followed by fourteen years as career law clerk to Judge Barry S. Schermer in the U.S. Bankruptcy Court for the Eastern District of Missouri. Her educational credentials include: J.D. from Washington University School of Law (2000) B.A. from the University of Michigan (1997) Professor Cohen's research bridges bankruptcy law and cognitive psychology. Her legal work focuses on consumer bankruptcies, municipal reorganizations, and Chapter 11 proceedings, while her psychological research investigates cognitive aging mechanisms, Stroop effect variations, and semantic priming phenomena. She examines how cognitive control systems operate across the lifespan and how educational interventions can counteract cognitive biases in legal reasoning contexts. Her publication record (2010-2025) demonstrates an interdisciplinary trajectory where cognitive psychology methodologies inform legal education research. Key patterns include experimental studies on cognitive bias mitigation in educational settings, lifespan analyses of executive function in legal decision-making contexts, and methodological innovations in data visualization for cognitive aging research. Her work consistently applies cognitive science frameworks to practical legal education challenges.

• Accounting Information Systems
• Financial Reporting
• Debt Contracting
• +۷ مورد دیگر

Andrew J. Leone is the Keith I. DeLashmutt Chair of Accounting Information & Management at the Kellogg School of Management, Northwestern University (2018–present). He previously served as the Warren Johnson Chair of Accounting at the University of Miami (2008–2018) and held academic appointments at the University of Rochester and Penn State University . His research focuses on the role of accounting information in contracting and capital markets , addressing topics like textual analysis , financial statement complexity , and audit quality . Ph.D., University of Pittsburgh (1997) MBA, University of New Hampshire (1991) B.S., Quinnipiac University, Magna Cum Laude (1984) Leone's work explores how accounting disclosures affect regulatory compliance , investor trust , and corporate governance . His recent publications examine textual analysis tools , credibility in unregulated markets , and methodological rigor in empirical accounting research. His findings have been published in leading journals such as the Journal of Accounting and Economics and The Accounting Review . Leone has received numerous honors, including the American Accounting Association's Distinguished Contribution to the Literature Award (2019) and the Emerald Management Reviews' Citation of Excellence (2012) . He has also served on editorial boards for Contemporary Accounting Research and The Accounting Review , and his teaching includes Empirical Research in Accounting III , emphasizing research design in debt contracting and banking .