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Anirban BhattacharyaView profile

Professor

• Bayesian Statistics
• High-dimensional Data Analysis
• Bayesian Nonparametrics
• +3 more

Anirban Bhattacharya is a Professor and Patricia R. Smith and Dr. William B. Smith Faculty Fellow in the Department of Statistics at Texas A&M University, within the College of Arts & Sciences. His research focuses on Bayesian asymptotics, Bayesian nonparametrics, and high-dimensional data analysis. He holds a Ph.D. in Statistics from Duke University (2012) and Master's and Bachelor's degrees in Statistics from the Indian Statistical Institute (2008 and 2006). His academic journey includes advanced studies at prestigious institutions, blending theoretical rigor with computational innovation. Research interests emphasize robust statistical methods, scalable Bayesian computation, and applications in machine learning. Recent work explores challenges in high-dimensional inference, optimal transport, and fair clustering algorithms. Notable contributions include advancements in Gibbs posterior frameworks, constrained Gaussian processes, and variational inference techniques. He has been recognized with the prestigious Smith Fellowship, highlighting his impact in statistical methodology. His publications span foundational Bayesian theory to applied problems in physics and engineering. Bhattacharya’s work often bridges computational efficiency and statistical accuracy, addressing modern challenges in big data analysis. Ongoing research includes developing robust estimators for complex metric spaces and exploring fair machine learning methodologies.

• Computer Vision
• Medical Imaging
• Generative Models
• +4 more

Guha Balakrishnan is an Assistant Professor in the Electrical and Computer Engineering (ECE) department at Rice University, affiliated with the Digital Health Initiative. His research focuses on computer vision, medical imaging, and algorithmic fairness. He holds a BS from the University of Michigan, and MS and PhD from MIT's CSAIL, followed by a postdoc at MIT and industry experience at Amazon Web Services. Education: BS in Computer Science Engineering & Computer Engineering, University of Michigan (2011) MS in EECS, MIT (2013) PhD in EECS, MIT (2018) Postdoc: MIT (2018-2020) Applied Scientist: Amazon Web Services (2020-2021) Research Interests: His work spans generative models for visual data, medical applications (e.g., CT reconstruction, vital sign measurement), geospatial imaging, and ethical AI systems. He emphasizes fairness, accountability, and responsible deployment of vision systems. Recent projects include ALPINE (PyTorch library for implicit neural representations) and the EMACS workshop at CVPR 2025. Industry Impact: Prior experience at AWS focused on fairness in AI systems. Active in developing tools like Voxelmorph for medical image registration and frameworks like GELDA for dataset bias analysis. Labs & Teams: Leads efforts in Rice's Digital Health Initiative, focusing on low-cost medical AI solutions and cross-disciplinary collaborations.

Florian MeyerView profile

Associate Professor

• Signal Processing
• Parameter Estimation
• Probabilistic Graphical Models
• +12 more

Florian Meyer serves as an Associate Professor in the Marine Physical Laboratory at the Scripps Institution of Oceanography, University of California, San Diego, where he bridges advanced signal processing with oceanographic research. His educational foundation includes a B.S., M.S., and Ph.D. from Technische Universität Wien (TU Wien) in Austria, establishing his expertise in engineering and applied sciences. Dr. Meyer's research spans highly interdisciplinary domains, with core emphases on: Statistical and high-dimensional signal processing methodologies Probabilistic modeling and inference on graphical structures Machine learning applications in marine robotics and perception Multiobject tracking and network navigation systems Underwater acoustics and source localization techniques Applied ocean sciences instrumentation development His work integrates theoretical frameworks with practical oceanographic challenges through the Marine Physical Laboratory's experimental infrastructure.

Wicher BergsmaView profile

Professor

• Statistical Modeling
• Machine Learning
• Bayesian Statistics
• +7 more

Professor Wicher Bergsma is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical modeling techniques such as I-priors, reproducing kernel methods, and empirical Bayes approaches. Notably, he co-developed the tau-star test for independence and advanced marginal models for categorical data analysis. He has held academic positions at LSE since 2005, preceded by postdoctoral work at Eurandom and Tilburg University, and a stint as an Assistant Professor at the Central European University. His applied work includes collaborations with industry partners like GlaxoSmithKline and Flextronics on clinical trials and fault detection systems. He is currently on sabbatical until Spring 2025. Education includes a Mathematics degree from Leiden University and a PhD in Social Statistics from Tilburg University. His expertise spans theoretical and applied statistics, with contributions to graphical models, conditional independence testing, and Bayesian variable selection. He has authored influential books and R packages, including Marginal Models and the cmm package for categorical marginal modeling. Research interests emphasize bridging theory and practice, particularly in developing robust statistical tools for complex data structures. His recent work explores I-prior methodologies for regression, causal inference with stochastic confounders, and scalable Gaussian process models for environmental data analysis.

Xuewen YuView profile

Research Fellow

• Causal Inference
• Graphical Models
• Bayesian Inference
• +3 more

Xuewen Yu is a Research Fellow at the Department of Statistics, London School of Economics and Political Science (LSE). Her expertise spans causal inference, graphical models, Bayesian inference, and their applications in population health and reliability engineering. Previously, she developed hierarchical models for system failure prediction using causal algebras and worked on continuous-time g-methods for time-varying treatments. Her current research focuses on quasi-experimental design and policy evaluation. Key research interests include causal chain event graphs, Bayesian decision support systems for public health crises like the COVID-19 pandemic, and integrating causal methods into engineering and policy analysis. Her work bridges theoretical statistical frameworks with practical applications in healthcare, infrastructure maintenance, and poverty-driven policy assessment. Recent publications highlight contributions to causal analysis in reliability engineering, Bayesian modeling of pandemic countermeasures, and the development of decision support tools for resource adequacy and policy evaluation. Her interdisciplinary approach combines statistical rigor with real-world problem-solving.

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +7 more

Thomas Dyhre Nielsen is a Professor in the Department of Computer Science at Aalborg University, Faculty of IT and Design. His research centers on artificial intelligence, machine learning, and probabilistic graphical models, with applications in healthcare, environmental systems, and intelligent control. He is affiliated with the Distributed, Embedded and Intelligent Systems group and contributes to the AI for the People initiative. His research interests include Bayesian networks, decision support systems, data mining, and machine learning. He applies these to domains such as microbial ecology, clinical risk prediction, and smart water systems. His work often bridges theoretical AI with real-world engineering challenges. His recent publications (2024–2025) show a strong trend in applying explainable and probabilistic AI to healthcare (e.g., urinary tract infection risk prediction) and environmental control (e.g., stormwater management). These works emphasize safety, interpretability, and optimization under uncertainty. Scientific Awards: Best Paper Award (2018) He has been involved in PhD supervision and has collaborated on major interdisciplinary projects like DarkScience and CLAIRE , focusing on microbial dark matter and urban water control, respectively. His work integrates AI with systems engineering, environmental science, and medicine. He is associated with research labs and teams including the Distributed, Embedded and Intelligent Systems group at Aalborg University.

Nicolaj Søndberg-JeppesenView profile

Part-Time Lecturer

• Bayesian Networks
• Cyber-Physical Systems
• Healthcare Informatics
• +4 more

Nicolaj Søndberg-Jeppesen is a Part-Time Lecturer at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Bayesian networks, cyber-physical systems, and their applications in healthcare, industrial diagnostics, and decision support systems. Key research interests include: Development of Bayesian network models for predictive maintenance and health monitoring in industrial systems Integration of operational data into continuous improvement frameworks Application of probabilistic graphical models in healthcare logistics and emergency management Design of smart manufacturing architectures for diagnostics and predictive maintenance Notable projects include: Animated Computer Games (ongoing since 2010) Extending the scope of graphical models for decision problems (ongoing since 2010) His recent work emphasizes: Patient flow prediction in emergency departments Risk assessment in home care deliveries Object-oriented Bayesian networks for component-level diagnostics Publications span applications in healthcare informatics, industrial engineering, and game theory, with a focus on practical implementations of probabilistic models.

Navid AnsariView profile

Researcher

• Deep Learning
• Generative Models
• Optimization
• +4 more

Navid Ansari is a doctoral researcher at the Max Planck Institute for Informatics (MPI-INF) and Saarland University, affiliated with the Artificial Intelligence Aided Design and Manufacturing group under the Computer Graphics department. His work bridges academia and industry, with internships at Amazon AWS and the Max Planck Institute for Brain Research. PhD in Computer Science (2021–Present), Saarland University & MPI-INF MSc in Visual Computing (2018–2021), Saarland University BSc in Electrical Engineering (2013–2017), Shiraz University Navid's research focuses on deep learning, generative models, and optimization techniques. His work includes mixed-integer optimization for neural networks, Bayesian design optimization, uncertainty quantification in AI systems, and applications in computational manufacturing and molecular design. He has contributed to top-tier venues like AAAI, NeurIPS (Spotlight), and SIGGRAPH. His publications address trends in AI-aided design optimization, uncertainty-aware modeling, sparsity in large language models, and inverse molecular design aligned with molecular dynamics. This work spans applications in manufacturing, computational chemistry, and natural language processing. Navid has collaborated with institutions such as Amazon AWS (LLM sparsification) and the Max Planck Institute for Brain Research (behavioral neuroscience). He is part of the Saarland Informatics Campus, a hub for visual computing and AI research.

• Data Science
• Computational Statistics
• Bayesian Models
• +5 more

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

• International Trade
• Economic Forecasting
• Bayesian Neural Networks
• +5 more

Davide Arioldi is a Postdoctoral Researcher and Scientific Collaborator at the Institute for Economic Research (IRE) within the Faculty of Economic Sciences at the University of Italian Switzerland (USI). He holds a PhD in Economics from USI (2018) with a thesis on international trade and has prior experience as a quantitative analyst and portfolio manager in investment firms. His current research focuses on Bayesian Neural Networks, Probabilistic Programming, causal structure learning via graphical models, and text analysis. He is affiliated with the Observatory of Public Finance and Energy (O-FPE), where he develops tax revenue forecasting models and compiles comparative studies on cantonal public finances. He also contributed to the PanelCODE project and the contati.ch platform analyzing policy impacts. Research expertise includes international trade dynamics, consumer behavior, productivity-wage gaps, bargaining theory, firm production, and network analysis. His work spans economic policy evaluation, unstructured data analysis, and regional economic disparities. He is co-developer of the contati.ch platform funded by a private foundation to assess policy impacts on local populations. Key projects include annual cantonal public finance studies, tax revenue modeling at O-FPE, and business dynamics analysis using unstructured online data since 2016. He collaborates across interdisciplinary teams in Lugano, Switzerland.

• Artificial Intelligence
• Digital Signal Processing
• Machine Learning
• +7 more

Giampiero Salvi is a Professor at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU). He is affiliated with the Signal Processing research group and holds academic qualifications from La Sapienza University of Rome (Civil Engineering) and the Royal Institute of Technology (Dr.Scient). His research focuses on artificial intelligence, machine learning, speech processing, and human-machine interaction. Key contributions include advancements in speech recognition systems, neural network architectures for video prediction, and applications in healthcare analytics and cybersecurity. His work bridges theoretical foundations with practical implementations, such as developing pronunciation assessment frameworks for children, Parkinson’s disease detection via speech analysis, and real-time speaker diarization systems. He has contributed to foundational research in acoustic-to-articulatory mapping, explainable AI for clinical prediction, and multimodal dialogue systems. His research often addresses challenges in low-resource languages and clinical settings, emphasizing interdisciplinary collaboration. Salvi’s recent publications highlight trends in foundational models, generative AI for video and speech, and ethical AI applications in healthcare. He actively participates in international conferences and collaborates with institutions like KTH Royal Institute of Technology and the University of Tartu, reflecting his global academic network.

Gherardo VarandoView profile

Assistant Professor

• Uncertainty Quantification
• Probabilistic Graphical Models
• Graphical Models for Dynamical Systems
• +4 more

Gherardo Varando is an Assistant Professor at the University of Valencia's Department of Statistics and Operational Research, and a researcher in the Image and Signal Processing Group. His work focuses on causal inference, probabilistic graphical models, and their applications in Earth sciences and health data analysis. He holds a PhD and has been a faculty member since 2024. Research interests include uncertainty quantification, staged event trees, and spatio-temporal causal discovery. Varando has published extensively on topics like causal representation learning, staged tree models, and kernel methods for pairwise causal discovery. His contributions span machine learning theory, statistical methodology, and interdisciplinary applications in climate science and biomedicine. Key achievements include the development of the stagedtrees R package for structural learning of graphical models, and open-source tools like leaflet-map-builder for geospatial visualization. His work bridges theoretical advancements with practical implementations, addressing challenges in causal structure learning and interpretable AI.

Michael YipView profile

Associate Professor

• Robotics
• Medical Robotics
• Surgical Robotics
• +6 more

Michael Yip is an Associate Professor of Electrical and Computer Engineering at UC San Diego and Director of the Advanced Robotics and Controls Laboratory (ARCLab). He leads research in surgical robotics, biomimetic design, and robot learning. His work focuses on autonomous systems for medical applications, including robotic surgery and continuum robots. Dr. Yip holds affiliate faculty positions in Computer Science and Engineering, Mechanical and Aerospace Engineering, and the Contextual Robotics Institute. Education: Ph.D. in Bioengineering (Stanford University), M.Sc. in Electrical Engineering (University of British Columbia), B.Sc. in Mechatronics Engineering (University of Waterloo). Previous roles include visiting positions at Amazon Robotics and Disney Research. Research interests span surgical robotics, reinforcement learning, and robot manipulation in dynamic environments. His lab develops systems for minimally invasive surgery and medical interventions. Notable contributions include the CRANE tele-surgical system and the ARCSnake reconfigurable robot. Key awards include the NSF CAREER Award, NIH Trailblazer Award, and election to the National Academy of Inventors. He has authored over 150 peer-reviewed publications and co-founded multiple startups. His work bridges robotics, AI, and biomedical engineering, with applications in telesurgery and autonomous systems. Lab initiatives include the Medical Robotics Collaboratory and contributions to the US Congressional Robotics Roadmap. Teaching focuses on robotics control, reinforcement learning, and hands-on prototyping. Active service roles include editorial positions for IEEE Robotics and Automation Letters and leadership in robotics conferences.

Faramarz FekriView profile

Professor

• Machine Learning
• Information Theory
• Semantic Communication
• +7 more

Dr. Faramarz Fekri is the John Pippin Chair Professor and ECE-GTRI Fellow at Georgia Tech's School of Electrical and Computer Engineering. He leads the SENTINEL Research Lab, focusing on interdisciplinary research in machine learning, semantic communication, causal discovery, and biomarker sensing. His work bridges theoretical foundations with practical applications in federated learning, neuro-symbolic AI, and molecular communication. He has held editorial roles at IEEE Transactions journals and has received numerous awards including IEEE Fellow (2015) and the Sony Faculty Research Innovation Award (2018). His research spans over 150 publications, with recent emphasis on differentiable inductive logic programming, adversarial defense frameworks, and compressed sensing systems. Education: B.Sc./M.Sc. from Sharif University, Ph.D. from Georgia Tech. Affiliations include the Center for Machine Learning and the Center for Energy and Geo Processing (CeGP). Current projects include learning via inductive logic reasoning, neuro-symbolic reinforcement learning, and causal discovery from data. Notable achievements include developing BP-based trust systems, network compression frameworks using finite-field wavelets, and frameworks for analog joint source-channel coding. His SENTINEL Lab collaborates on biomarker sensing and molecular communication in biological systems.

Topias TerhoView profile

Researcher

• Operations Research
• Mathematical Optimization
• Probability and Statistics
• +3 more

Topias Terho is a Doctoral researcher at Aalto University's Department of Mathematics and Systems Analysis, School of Science. His research focuses on operations research, decision analysis, and mathematical optimization, with applications in transportation systems and financial mathematics. He contributes to the department's research groups in Mathematical Physics and Systems & Operations Research. Key research interests include influence diagrams, computational optimization, and probabilistic modeling. His recent work addresses risk-averse decision strategies, public transport optimization, and covariance matrix estimation in portfolio theory. He holds no recorded scientific awards but actively engages in academic collaborations within his field. Topias is based at Aalto's Otakaari campus in Espoo, Finland, and can be reached via email .