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Wenrui LiView profile

Assistant Professor

• Statistics for network data
• Causal inference
• High-dimensional data analysis
• +3 more

Wenrui Li serves as an Assistant Professor in the Department of Statistics at the University of Connecticut, where she develops advanced statistical methodologies for complex biomedical data structures. Her work bridges theoretical statistics and practical healthcare applications, with particular emphasis on network-based modeling and high-dimensional data analysis in oncology and epidemiology. Dr. Li's research program centers on statistics for network data, causal inference under interference, and Bayesian modeling of structured high-dimensional datasets. She pioneers techniques for handling network noise in graph-guided models, with direct applications to cancer genomics, multi-omics integration, and infectious disease surveillance. Her methodological innovations address critical challenges in noisy data environments where traditional statistical approaches fail. Analysis of her 2021-2025 publications reveals three dominant research threads: (1) Digital health interventions to reduce time burden in cancer care, exemplified by her TIME text-messaging system; (2) Graph-guided Bayesian methods for multi-omics data integration with noisy network priors; and (3) Epidemiological modeling of time-varying parameters in noisy surveillance systems. These threads demonstrate consistent focus on methodological rigor applied to pressing biomedical problems. No scientific awards, student advisement records, grant funding details, or laboratory affiliations were documented in the source materials. Her collaborative work spans biostatistics, oncology, and network science, with frequent co-authorship on interdisciplinary projects addressing cancer care optimization and disease modeling.

Shirin JalaliView profile

Associate Professor

• Computational Imaging
• Machine Learning
• Information Theory
• +4 more

Shirin Jalali is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. She holds a Ph.D. and M.Sc. in Electrical Engineering and Statistics from Stanford University, with earlier degrees from Sharif University of Technology. Prior to Rutgers, she was a Research Scientist at Nokia Bell Labs and held roles at Princeton University and NYU Tandon School of Engineering. Her research focuses on computational imaging, machine learning, and information theory, addressing challenges like speckle noise mitigation and snapshot compressive imaging. She received the NSF CAREER Award in 2023 and serves as an Associate Editor for IEEE Transactions on Information Theory. Education: Ph.D. in Electrical Engineering, Stanford University, 2010 M.Sc. in Statistics, Stanford University, 2010 M.Sc. in Electrical Engineering, Sharif University of Technology, 2004 B.Sc. in Electrical Engineering, Sharif University of Technology, 2002 Research Interests: Her work spans high-dimensional inference, computational imaging, and machine learning applications in imaging systems. She develops algorithms for coherent imaging in speckle noise environments and explores structure learning through information-theoretic methods. Recent trends in her publications emphasize unrolled neural networks, Bayesian despeckling, and theoretical frameworks for compressive imaging systems. Awards and Roles: 2023 NSF CAREER Award Associate Editor, IEEE Transactions on Information Theory (since 2021) Leadership in research groups addressing imaging and machine learning challenges Grants and Labs: Her NSF CAREER grant funds theoretical work on snapshot compressive imaging systems. She collaborates across academia and industry, leading interdisciplinary projects in signal processing and AI-driven imaging solutions.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +7 more

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

Isaac M. OpperView profile

Professor

• Econometric Models
• Education Policy
• Program Evaluation
• +7 more

Isaac M. Opper is a Senior Economist at RAND and a Professor of Policy Analysis at the RAND School of Public Policy. His work bridges econometric methodology and real-world policy challenges, with a strong focus on education, labor markets, and disaster recovery. His research interests include econometric models , education policy , program evaluation , and value-added modeling in education . He investigates how teachers affect student achievement, how peer dynamics influence training outcomes, and how natural disasters erode human capital. His methodological contributions involve developing and refining statistical tools for causal inference, including Bayesian approaches to treatment effect estimation. The 15 most recent articles reflect a deep engagement with policy-relevant empirical research, spanning education reform, criminal justice, health policy, and disaster economics. A unifying theme is the use of longitudinal and experimental data to estimate causal impacts, often with attention to demographic disparities and methodological innovation. Opper holds a Ph.D. in Economics from Stanford University. His scientific contributions are widely published through RAND, though no specific awards are listed in the provided text. Ph.D. in Economics, Stanford University He has not listed any formal advisees or students. His research is conducted within the RAND Corporation, where he contributes to major policy evaluations and methodological advancements. Current projects include evaluating pretrial diversion programs, forecasting post-disaster construction costs, and analyzing long-term wage growth by initial occupation.

Tingting LiView profile

Lecturer

• AI for Cyber Security
• Automated Cyber Defence
• Diversification and Deception Strategies
• +8 more

Dr. Tingting Li is a Lecturer (Assistant Professor) in Cyber Security at Cardiff University's School of Computing and Informatics and a member of the Centre for Cyber Security Research. She also holds an Honorary Research Fellow position at Imperial College London, reflecting her ongoing research collaboration. Her work bridges artificial intelligence and cybersecurity, with a focus on protecting critical systems such as cyber-physical systems (CPS), industrial control systems (ICS/SCADA), and autonomous vehicles. BEng (Hons) in Information Security, Xidian University, China MSc in Computing, Imperial College London PhD in Artificial Intelligence, University of Bath Dr. Li’s research interests lie at the intersection of AI and cybersecurity, particularly in automated cyber defense , diversification/deception strategies , and symbolic AI for knowledge representation . She investigates how AI can enhance the resilience of critical infrastructures through intelligent, adaptive defense mechanisms. Her recent work explores quantum-inspired reinforcement learning and machine learning models for proactive threat mitigation in complex systems. Her recent publications demonstrate a strong trend in applying advanced AI techniques—especially deep reinforcement learning, LSTM networks, and Bayesian models—to cybersecurity challenges in industrial and autonomous systems. These works span domains like IoT malware suppression, network diversity for ICS resilience, and automated compliance checking, reflecting a multidisciplinary approach combining security, AI, and systems engineering. Dr. Li has secured significant research funding, including a grant from the Alan Turing Institute (2024–2025) on AI safety in autonomous cyber defense, a RITICS/NCSC-funded project on diversity-based cybersecurity (2021–2022), and an EPSRC-funded project on metric-driven cybersecurity frameworks for critical national infrastructure (2021–2023). She actively supervises PhD students in areas including cybersecurity for autonomous vehicles, moving target defense, and cyber-physical system security. Her team includes Iryna Bernyk, Sanyam Vyas, Victoria Marcinkiewicz, Ellis Doran, Stephen Morris, and Sam Braithwaite. She also leads teaching modules on databases (CM6125/CM6625) and cybersecurity (CM6224/CM6724). Dr. Li’s research is conducted within the Centre for Cyber Security Research at Cardiff University, where she collaborates with experts in AI, security, and critical infrastructure protection. Her work is highly interdisciplinary, involving partnerships with institutions like Imperial College London and funding bodies such as EPSRC, NCSC, and the Alan Turing Institute.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

Xubo YueView profile

Assistant Professor

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +5 more

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

Cristiano FanelliView profile

Professor

• Data Science
• Physics
• Machine Learning
• +12 more

Dr. Cristiano Fanelli is a Professor of Data Science and Director of Technology at William & Mary, where he leads the Dataphys Group. His research bridges data science and physics, focusing on machine learning applications in nuclear and high-energy physics, including collaborations with CERN, Jefferson Lab, and the ePIC experiment at the Electron Ion Collider. His research interests include developing advanced machine learning algorithms for physics applications such as detector optimization, particle identification, anomaly detection, and uncertainty quantification. He actively explores the use of Graph Neural Networks and Retrieval-Augmented Generation (RAG) systems for grounding Large Language Models in physical truth, minimizing hallucinations in scientific contexts. His work integrates high-performance computing with Bayesian optimization and deep learning for simulations and data analysis in experimental physics. Dr. Fanelli has supervised numerous postdoctoral researchers, PhD and master’s students, and undergraduate researchers from William & Mary and international institutions such as the University of Regina and the University of Messina. His group works on projects including AI-assisted Detector Design (AID2E), AI-Optimization of Polarization (AIOP), and deep learning for calorimeter clustering and deep inelastic scattering. Former students have gone on to positions at UC Berkeley and in industry. Dr. Fanelli received his PhD from Sapienza University and previously worked at the Massachusetts Institute of Technology and the Institute for Artificial Intelligence and Fundamental Interactions at MIT. He maintains strong collaborative ties with major experimental facilities and is contributing to next-generation physics experiments through data science innovation.

• Uncertainty Quantification
• Data Assimilation
• Machine Learning
• +4 more

Dr. Truong Vinh Hoang is a Researcher at the RWTH Aachen University , affiliated with the Chair of Mathematics for Uncertainty Quantification . His work focuses on integrating machine learning with data assimilation techniques for nonlinear dynamical systems . He has presented at multiple international conferences and seminars on these topics. Specializes in Bayesian methods and stochastic numerics Developed ML-EnCMF (Machine Learning-Ensemble Conditional Mean Filter) for non-linear data assimilation Applied techniques to Lorenz-63 and Lorenz-96 systems under chaotic regimes Contributed to localized neural network architectures for high-dimensional state tracking His research trends from 2020-2022 show increasing emphasis on deep learning-based filtering and Bayesian optimization for systems with non-Gaussian dynamics . Notably, he implemented variance reduction techniques to improve filter stability with small ensemble sizes. All publications demonstrate practical applications in computational science and stochastic modeling . Dr. Hoang is part of the MATH4UQ team at RWTH Aachen University, contributing to cutting-edge research in uncertainty quantification and nonlinear data assimilation .

Michael WilliamsView profile

Research Fellow

• Gravitational Wave Physics
• Neutron Stars
• Black Holes
• +3 more

Dr. Michael Williams is a Research Fellow at the Institute of Cosmology & Gravitation under the Faculty of Technology , University of Portsmouth. His work focuses on gravitational wave physics, neutron star dynamics, and black hole binaries through collaborations with LIGO, Virgo, and KAGRA detector networks. Active in gravitational wave detection and multi-messenger astronomy Developing AI algorithms for telescope targeting and signal processing Recipient of the IOP Trusted Reviewer status (2025) His recent research includes premerger characterization of black hole binaries , continuous wave searches from pulsars , and gravitational wave-FRB counterpart detection . He contributes to open-source tools like nessai and bayesbeat , with 25 datasets released. Williams participates in conferences like the European AI for Fundamental Physics Conference and workshops on milli-hertz gravitational wave astrophysics. Key collaborations: LIGO, Virgo, KAGRA, GEO600 Technical expertise: matched filtering , Bayesian inference , normalizing flows

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +6 more

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

Lin LinView profile

Associate Professor

• Biostatistics
• Bioinformatics
• Statistical Science
• +7 more

Lin Lin is an Associate Professor of Biostatistics & Bioinformatics at Duke University's Division of Integrative Genomics and an Associate Research Professor of Statistical Science in Trinity College of Arts & Sciences. With appointments dating from 2022 to present, Dr. Lin has established herself as a prominent researcher at the intersection of statistics, bioinformatics, and biomedical applications. Her work spans multiple departments and research centers at Duke, reflecting her interdisciplinary approach to solving complex biological problems. Ph.D. from Duke University (2012) Dr. Lin's research focuses on developing advanced statistical and machine learning methods for analyzing complex biological data, particularly in immunology and transplantation research. Her expertise in single-cell data analysis, cytometry data interpretation, and biomarker discovery has led to significant contributions in vaccine studies, HIV/AIDS research, and organ transplantation. She has pioneered methods for handling small cohort studies, longitudinal data, and multi-modal datasets, addressing critical challenges in modern biomedical research where traditional statistical approaches fall short. Analysis of Dr. Lin's publication record reveals a strong emphasis on developing interpretable computational methods that bridge the gap between complex data and biological insights. Her recent work shows increasing sophistication in handling high-dimensional single-cell data, with a particular focus on creating models that maintain interpretability while achieving high predictive accuracy. The trajectory of her research demonstrates a consistent pattern of addressing methodological challenges in biomedical data analysis, with applications spanning immunology, transplantation medicine, and infectious disease research. Dr. Lin has secured substantial research funding from multiple prestigious sources including the National Institutes of Health, National Institute of Allergy and Infectious Diseases, National Heart, Lung, and Blood Institute, and National Institute of Environmental Health Sciences. Her grants portfolio demonstrates expertise across diverse biomedical domains, from HIV/AIDS research to transplantation immunology and environmental health effects. These projects typically involve developing novel statistical methodologies while addressing pressing clinical questions, showcasing her ability to bridge theoretical statistics with practical biomedical applications. As an educator, Dr. Lin teaches advanced courses in Bayesian statistical modeling and analysis, contributing to the training of the next generation of biostatisticians and data scientists. Her research group likely focuses on developing computational tools that address real-world challenges in biomedical data analysis, with particular emphasis on making complex models interpretable and applicable to clinical settings.

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +8 more

Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.

• Power Electronics
• Energy Conversion
• Microgrids
• +4 more

Bjarte Hoff is an Associate Professor and Study Program Director for the Master of Electrical Engineering at UiT The Arctic University of Norway's Department of Electrical Technology. His core research focuses on power electronics, energy conversion systems, microgrids, and smart energy applications, particularly in Arctic environments. He leads multiple projects including RENEW (renewable energy systems), Smart Senja, and HyEkoTank (hydrogen fuel cells for maritime transport). His research interests span: Power electronics and control algorithms Low-voltage electrical installations and microgrids Distributed generation and smart grids Electric transport systems (maritime/vehicular) Renewable energy integration Capacitive wireless power transfer Publication analysis shows consistent focus on: energy conversion topologies (especially current source inverters), Arctic energy management using deep learning, capacitive wireless charging for maritime applications, and optimization of power transfer systems. His most recent works demonstrate increasing emphasis on AI-driven energy management and hydrogen fuel cell integration. He teaches courses including: ELE-3605 Electrical Engineering Project ELE-3607 Power Electronics ELE-3608 Advanced Electric Drives ETE-2801 Electrical Installations As principal investigator, he leads projects such as: RENEW - Renewable & Smart Rural Power Systems Smart Senja (Arctic microgrids) HyEkoTank (hydrogen PEM fuel cells for ships) Arctic Energy research initiative UNDERSEA capacitive charging systems He collaborates with the Electromechanical Systems research group and supervises PhD candidates in power electronics and energy systems.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +4 more

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.