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Yudong ChenView profile

Assistant Professor

• Changepoint Detection
• High-dimensional Statistics
• Robust Statistics
• +4 more

Yudong Chen is an Assistant Professor in the Department of Statistics at the University of Warwick, starting September 2024. Previously, he was an LSE Fellow (2023–2024) and a postdoctoral researcher at the London School of Economics. He holds a PhD in Statistics from the University of Cambridge (2023), with a thesis on High-dimensional Online Changepoint Detection, supervised by Richard J. Samworth and Tengyao Wang. His research focuses on changepoint detection, high-dimensional statistics, robust methods, and machine learning. Education: PhD in Statistics, University of Cambridge (2023) MA & MMath in Mathematics, University of Cambridge (2018) BA in Mathematics, University of Cambridge (2018) Teaching: University of Warwick: Module leader for ST420 Statistical Learning and Big Data (2024/25) LSE: Taught ST202/6 Probability, ST447 Data Analysis, and ST449 Artificial Intelligence His research interests span statistical methodologies including online algorithms, robust statistics, and spatial models. He has published in top journals like the Journal of the American Statistical Association and presented at venues such as the IMS Annual Meeting. Awards include the LSE Class Teacher Award (2023) and the Smith–Knight Prize (2020). Grants: Worked on EPSRC-funded research on 'Change-point analysis in high dimensions' at LSE. Labs/Teams: Engaged in collaborative projects on online changepoint detection and statistical methodologies.

Aleksandar MijatovicView profile

Professor

• Probability
• Stochastic Processes
• Mathematical Finance
• +5 more

Aleksandar Mijatović is a Professor of Probability at the Department of Statistics, University of Warwick, and Deputy Head of Department for Research. He was previously Chair in Probability at King's College London and Reader in Probability at Imperial College London. His research focuses on probability theory, stochastic processes, mathematical finance, numerical stochastics, and data science. He holds a Ph.D. in low-dimensional topology from Trinity College Cambridge and worked as a quantitative analyst in foreign exchange derivatives before academia. Research interests include stochastic analysis of processes with jumps, simulation methods (e.g., Monte Carlo), stochastic control, and applications in finance. He is a Fellow of the Alan Turing Institute and maintains a YouTube channel, Prob-AM, explaining his research. His work often bridges theoretical probability with practical applications in finance and data science. Key publications explore topics like reflected Brownian motion, Lévy processes, branching processes, and stochastic gradient descent. Collaborations with institutions like King’s College London and Imperial College London highlight his academic networks. His contributions span theoretical advancements and computational methodologies, with applications in risk management, option pricing, and algorithm development.

Hankui ZhangView profile

Associate Professor

• Remote Sensing
• Environmental Monitoring
• Deep Learning
• +4 more

Hankui Zhang is an Associate Professor in the Department of Geography and Geospatial Sciences at South Dakota State University (SDSU), and a Research Scientist at the Geospatial Sciences Center of Excellence. He holds a Ph.D. from the Chinese University of Hong Kong (2013), specializing in satellite image fusion. His research focuses on developing algorithms for medium-resolution satellite data processing (e.g., Landsat and Sentinel-2), including cloud masking, BRDF correction, and compositing. He also explores AI applications in remote sensing for land cover mapping and environmental monitoring. As a Landsat Science Team member, he contributes to global remote sensing initiatives. Education: B.S. in Geographic Information Systems, Zhejiang University (2007) M.S. in Remote Sensing, Zhejiang University (2010) Ph.D. in Geography and Resource Management, Chinese University of Hong Kong (2013) Research Interests: Deep learning applications in remote sensing, land cover dynamics, analysis-ready data development, and geospatial data harmonization. His work emphasizes operationalizing satellite data for environmental decision-making. Grants & Awards: Over $2.5M in grants as PI/co-PI, including USDA and NASA-funded projects. Notable awards include the SDSU Wadsworth Research Award (2020-2021) and the Global Scholarship for Research Excellence from CUHK (2011-2012). Professional Roles: Editorial board member for Remote Sensing of Environment and Remote Sensing ; guest editor for special issues on deep learning in remote sensing. Top 20 reviewer for Remote Sensing of Environment (2020, 2022-2024). Key Contributions: Published over 70 SCI papers, developed cloud detection algorithms (e.g., LANA), and pioneered analysis-ready data workflows for global monitoring. His work bridges satellite data science with practical environmental applications.

• Biomedical Engineering
• Electrical Impedance Tomography (EIT)
• Medical Device Design
• +6 more

Professor Alistair McEwan, affiliated with the University of Sydney's School of Biomedical Engineering, specializes in biomedical devices and instrumentation for neurological and cardiovascular diagnostics. As the Cerebral Palsy Chair of Technology and Engineering and Associate Head (External Engagement), he focuses on low-cost medical solutions for resource-limited settings, including neonatal care and stroke detection. His research bridges biophysics and clinical translation, with applications in electrical impedance tomography, wearable sensors, and neuroprosthetics. PhD from University of Oxford Member of the Sydney Nano Institute, Brain and Mind Centre, and Charles Perkins Centre His work explores how tissue electrical properties can enhance understanding of neural signaling and implant design. Clinical collaborators include the Bill & Melinda Gates Foundation for newborn nutrition monitoring. Recent projects involve AI-assisted critical care modeling, bionic devices for stress measurement, and adaptive control systems for neonatal resuscitation equipment.

Edoardo SerraView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +5 more

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.

Samer M. KhanafsehView profile

Research Professor

• Navigation Systems
• Aerospace Engineering
• Sensor Integration
• +5 more

Samer M. Khanafseh is a Research Associate Professor in the Department of Mechanical, Materials, and Aerospace Engineering at Illinois Institute of Technology (IIT), affiliated with the CARNATIONS research group. He holds a Ph.D. in Mechanical and Aerospace Engineering from IIT (2008), an M.S. from IIT (2002), and a B.S. in Mechanical Engineering from Jordan University of Science and Technology (2000). His research focuses on high-accuracy navigation algorithms, cycle ambiguity resolution, fault monitoring, and robust estimation techniques. Key areas include GNSS spoofing detection, integrity risk bounding, and sensor integration for aerospace applications. He is a member of the Institute of Navigation (ION) and the American Institute of Aeronautics and Astronautics (AIAA). His publications span navigation integrity, fault-tolerant systems, and GNSS applications, with notable work on Bayesian fault-tolerant estimators and GNSS spoofing attack detection using aircraft autopilot responses. His work bridges theoretical modeling with experimental validation, such as testing ground-based augmentation systems (GBAS). Awards: Best-of-Session Paper Award, Institute of Navigation (2006) Institute of Navigation 2011 Early Achievement Award Grants/Advising: Active involvement in CARNATIONS research initiatives, though no formal advisee list is provided. Labs/Teams: CARNATIONS (Context-Aware Navigation and Optimal Sensing) research group at IIT.

• Time series analysis
• Changepoint analysis
• Signal processing
• +6 more

Xueheng Shi is an Assistant Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL), with a joint appointment in Biological System Engineering . He joined UNL in August 2022 after postdoctoral positions at UC Santa Cruz (2020–2021) and UC Davis (2021–2022) under mentors Robert Lund, Alexander Aue, and Thomas Lee. Dr. Shi teaches graduate courses including mathematical statistics, asymptotic theory, probability, stochastic processes, and time series analysis. Research Interests: Dr. Shi's work focuses on developing statistical methodologies for time series analysis , changepoint detection , and high-dimensional data . Key applications include climate science, signal processing, and machine learning. His research emphasizes computational algorithm development and theoretical foundations across domains like climate modeling and stochastic optimization. Publications: Recent articles (2019–2024) concentrate on climate change detection , particularly global warming trends and temperature series analysis. Methodological innovations include autocovariance estimation around changepoints, comparative studies of detection algorithms, and reviews of best practices in climatological statistics. Dominant themes are changepoint techniques, environmental data diagnostics, and statistical computing. Advising: Dr. Shi actively mentors graduate students in statistics and invites prospective applicants interested in his research areas to contact him directly.

• Data Quality
• Database Systems
• Data Profiling
• +7 more

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

• Computer Vision
• Robotics
• Computer Graphics
• +5 more

Ioannis Stamos is a Professor of Computer Science at Hunter College, City University of New York (CUNY), within the School of Arts and Sciences. His research focuses on Computer Vision, Robotics, Computer Graphics, and 3D Visualization, with emphasis on 3D modeling using range and image data. He earned his Ph.D. in Computer Science from Columbia University (2001), followed by an M.S. and M.Phil. from Columbia's Computer Science Department, and a Diploma of Engineering from the University of Patras, Greece. Dr. Stamos has received prestigious awards including the NSF CAREER Award (2003) and Google Research Awards (2014, 2017). His work integrates 2D images and 3D range data for urban scene modeling, sensor fusion, and real-time object detection. Notable contributions include advancements in 6DoF pose estimation, LiDAR-based curb detection, and Kronecker product models for repeated patterns in urban imagery. He leads the Computer Vision & Robotics Lab and teaches graduate courses in 3D Computer Vision and Photorealistic Modeling. His research is supported by NSF grants, including MRI awards for mobile robotics and large-scale 3D modeling. He serves as Area Editor for the Journal of Computer Vision and Image Understanding and has co-chaired conferences like 3DV 2013. His lab collaborates on projects involving procedural modeling of urban environments and online classification of 3D point clouds.

Jean PonceView profile

Professor

• Computer Vision
• Machine Learning
• Robotics
• +3 more

Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.

IMRAN BashirView profile

Senior Lecturer

• Acoustic Metamaterials
• Aeroengine Acoustics
• Condition Monitoring
• +3 more

Dr. IMRAN Bashir is a Senior Lecturer in Instrumentation & Control Engineering at Teesside University's SCEDT Engineering Centre for Sustainable Engineering. He holds a PhD in Acoustics Metamaterials from an EU-FP7 funded project and has extensive postdoctoral experience across the University of Bath, Southampton, and Exeter. His research focuses on acoustic metamaterials, aeroengine acoustics, and condition monitoring. Education: PhD in Acoustics Metamaterials (EU-FP7 HOSANNA Project) MSc in Data Communication (University of Sheffield) BEng in Electrical Engineering (COMSATS University, Pakistan) His research interests span acoustic metamaterials, aeroengine acoustics, vibration control, and signal processing. He leads projects like the EU Horizon 2020 AERIALIST initiative for noise reduction in aeronautics. Current work includes smart bearing systems for aeroengines and condition monitoring for wave energy devices. Collaborations include multi-university projects on hydrogen production and sustainable engineering. He has published extensively in peer-reviewed journals and conferences, with a focus on battery diagnostics, acoustic modeling, and smart systems. As a Co-Investigator in the Hydrogen Production Life-Cycle project (2024–2026), he contributes to knowledge transfer for emerging energy markets. His interdisciplinary approach bridges engineering disciplines, emphasizing practical applications in aerospace, energy, and environmental acoustics.

• Health Services Research
• Narrative Interventions
• Hypertension Control
• +3 more

Dr. Jeroan Allison is Chair and Professor at UMass Chan Medical School's Department of Population and Quantitative Health Sciences. Trained as a primary care physician and epidemiologist (Harvard SPH), he leads research in narrative interventions for health behavior change, with continuous funding from NIH, AHRQ, and RWJF. His work spans hypertension control in Vietnam, AI-driven health communication, and implementation science for non-communicable diseases. Harvard School of Public Health: Master of Science in Epidemiology University of Alabama at Birmingham: MD Samford University: BS in Chemistry Research interests include narrative medicine , health disparities , mHealth , and health services research . He has published extensively on hypertension interventions in Vietnam, digital health tools, and tobacco cessation strategies using peer engagement. Recent publications focus on AI applications , global implementation science , and social determinants of health . His team has developed culturally adapted interventions for HPV vaccination and hypertension management using community storytelling methods. Scientific contributions include: Co-Editor-in-Chief of Medical Care Developing simulation-based training for research assistants Designing peer recruitment models for smoking cessation Dr. Allison holds multiple grants and leads the UMMS-Vietnam research collaboration , focusing on late-stage translation research in low-resource settings.

Jingchao NiView profile

Assistant Professor

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +9 more

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

• Statistical Inference
• Measurement Error Models
• Boundary Crossing Problems
• +3 more

Liqun Wang is a Professor of Statistics at the University of Manitoba, within the Faculty of Science. His research focuses on statistical inference in complex models, measurement error correction, boundary crossing problems in stochastic processes, and Monte Carlo simulation methods. He holds a prominent role in advancing methodologies for nonlinear time series analysis and Bayesian inference. His work integrates theoretical rigor with practical applications, addressing challenges in econometrics, environmental science, and public health. Notable contributions include advancements in instrumental variable estimation, second-order least squares methods, and high-dimensional covariance estimation. He actively mentors graduate students in these areas and has published extensively in top-tier statistical journals. Recent research highlights include Bayesian bias correction techniques, sparse covariance matrix estimation, and modeling SARS-CoV-2 dynamics via wastewater data. His methodologies often bridge computational efficiency with statistical accuracy, making them applicable to diverse fields such as finance, biostatistics, and environmental monitoring. Despite prolific output (over 70 publications since 1990), Dr. Wang has yet to be explicitly noted for formal scientific awards. His academic profile emphasizes methodological innovation, with a strong focus on real-world data challenges and interdisciplinary collaboration.

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.