Find Academic Supervisors

Stefano ErmonView profile

Associate Professor

• Machine Learning
• Generative AI
• Computational Sustainability
• +7 more

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

Pascal FrossardView profile

Professor

• Machine Learning
• Data Science
• Graph Signal Processing
• +6 more

Pascal Frossard is a Full Professor at the Department of Electrical Engineering in the School of Engineering (STI) at EPFL, with a courtesy appointment in the School of Computer and Communication Sciences. He founded and directs the LTS4 laboratory since 2003, co-leads the EPFL AI Center and Swiss Data Science Center, and serves as Associate Dean for Research at STI. Research Focus: Machine Learning, Graph Signal Processing, AI Applications in Healthcare, Computer Vision Academic Leadership: IEEE Fellow, ELLIS Fellow, Conference Chair roles Key Projects: Digital Pathology for Oncology, Cardiac Digital Twins, Robust Machine Learning Research Interests: His work bridges signal processing, machine learning, and applied mathematics, emphasizing biomedical applications. Recent research includes adversarial robustness in classifiers, network representation learning, and 360-degree video analysis. Scientific Awards: IEEE Fellow ELLIS Fellow Leadership in IEEE technical committees Advising & Grants: Supervised 20+ PhD students and postdocs. Secured major grants from PHRT, Hasler Foundation, FNS-Sinergia, Armasuisse, Google, and Cisco.

• Bioinformatics
• Machine Learning
• Deep Learning
• +5 more

Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).

Harris H. WangView profile

Associate Professor

• Systems Biology
• Synthetic Biology
• Genome Engineering
• +17 more

Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.

Romain LopezView profile

Assistant Professor

• Single-cell omics
• Spatial transcriptomics
• Deep generative models
• +5 more

Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.

Hauke SmidtView profile

Professor

• Microbiology
• Environmental Microbiology
• Gut Microbiota
• +4 more

Hauke Smidt is a Personal Professor at Wageningen University & Research, affiliated with the Molecular Ecology group within the Faculty of Veterinary Medicine and Animal Sciences. His research focuses on microbial ecology, particularly gut microbiota dynamics, antibiotic resistance, environmental microbiology, and the One Health framework. Smidt has supervised over 50 PhD students and contributed to 519 publications, including studies on microbiome resilience, fermented food impacts, and bioremediation strategies. Education and affiliations include leadership roles in projects addressing antimicrobial resistance, pig health, and drinking water microbiology. His work spans collaborations across Europe and Asia, with notable contributions to understanding microbial community responses to environmental stressors and dietary interventions. Smidt is actively involved in public outreach, as seen in media contributions discussing microbiome research and health benefits. Key projects include aquifer treatment for drinking water, microbiome simulation models, and combating plant pathogens. His awards and recognition are not explicitly listed, but his extensive publication record and project leadership highlight significant academic impact.

• Indigeneity
• Settler Colonialism
• Race Studies
• +6 more

Maile Arvin is a Kanaka Maoli feminist scholar and Assistant Professor at the Department of Ethnic Studies, University of Hawaiʻi at Mānoa. Her research and teaching focus on Indigenous relationships to land, histories of race and settler colonialism in Hawaiʻi, Oceania, and the United States. Previously, she was faculty in History and Gender Studies at the University of Utah and founding director of the Center for Pasifika Indigenous Knowledges. PhD in Ethnic Studies, University of California San Diego (2013) MA in Ethnic Studies, University of California San Diego (2009) BA in English, Swarthmore College (2005) Her research examines: Colonial production of racial categories, particularly Polynesians as an 'almost white race' in historical social science History of incarceration of Native Hawaiian children in government institutions (1865–1960s) Intersection of settler colonialism and Indigenous feminisms Critique of eugenics and genomic studies in the Pacific Community-engaged memorialization projects in Hawaiʻi Recent scholarly trends include analyses of Indigenous sovereignty, critiques of settler colonial whiteness, and interdisciplinary studies of race-gender dynamics. Arvin's publications explore Polynesian racialization, decolonial feminism, and the socioeconomic impacts of colonial institutions. NEH Chair's Grant for Nā Lei Poina ʻOle ACLS Digital Justice Seed Grant Arvin leads Nā Lei Poina ʻOle (Beloved Children Never Forgotten), a community history project about reformatories and industrial schools in Hawaiʻi. In collaboration with Kawela Farrant and the North Shore Community Land Trust, this project uses archival and oral histories to confront the legacy of child incarceration. She also critiques the nonprofit-industrial complex's role in limiting Indigenous sovereignty.

Han LiuView profile

Professor

• Artificial Intelligence
• Computer Systems
• Statistical Machine Learning
• +4 more

Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).

Kian-Lee TanView profile

Professor

• Query Processing
• Big Data Analytics
• Database Systems
• +5 more

Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +5 more

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

Daniel CooneyView profile

Assistant Professor

• Mathematical Biology
• Applied Dynamical Systems
• Differential Equations
• +4 more

Daniel Cooney is an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign. He holds additional affiliations as an Affiliate at the Carl R. Woese Institute for Genomic Biology. His research focuses on applying partial differential equations (PDEs), dynamical systems, and stochastic processes to study evolutionary dynamics, particularly in biological and social systems. Key themes include multilevel selection, evolutionary game theory, and the emergence of cooperative behavior. Cooney earned his PhD in Applied and Computational Mathematics from Princeton University, advised by Simon Levin, and completed a postdoc as a Simons Fellow in Mathematical Biology at the University of Pennsylvania. His work bridges theoretical mathematics with applications in ecology, epidemiology, and social sciences. Recent publications explore topics like altruistic punishment in cultural systems, classroom-turnover dynamics, and protocell evolution. He actively participates in academic outreach, co-organizing conferences such as the AMS Special Session on Mathematics of Infectious Disease and the SIAM Minisymposium on Social-Ecological Systems. His research has been published in high-impact journals including *Proceedings of the National Academy of Sciences* and *Bulletin of Mathematical Biology*.

Alexis BattleView profile

Associate Professor

• Genomics
• Machine Learning
• Computational Biology
• +3 more

Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .

• Fibrosis Research
• Liver Fibrosis
• Kidney Fibrosis
• +9 more

Karen Piper Hanley is Professor of Molecular Medicine and Head of Division in the Division of Diabetes, Endocrinology & Gastroenterology at the University of Manchester. She serves as the fibrosis lead in the Wellcome Trust Centre for Cell-Matrix Research and previously served as Director of Core Research Technologies (2019-2022). She is also an editorial board member for Scientific Reports. Her research focuses on fibrosis (scarring) mechanisms across multiple organs, particularly liver, kidney, and lung fibrosis. Her lab investigates the cellular and molecular basis of fibrotic disease, with emphasis on how pathological scar components are produced and how they signal to surrounding cells. Key areas include the role of SOX9 transcription factor, integrin beta-1 signaling, Group 1 PAKs, and YAP1 in fibrosis progression. Her recent publications reveal trends toward increased clinical translation of fibrosis research, with studies on health inequalities in liver cancer care, post-COVID diabetes incidence, and innovative approaches to liver disease detection. Her work increasingly integrates spatial transcriptomics and advanced omics technologies to understand cellular transitions in liver disease. Career-track postdoctoral fellowship from the University of Southampton (2006) As senior postgraduate tutor, she has mentored numerous PhD students who have presented at key conferences and authored high-quality manuscripts. All her PhD students have submitted and been awarded their degrees within 4 years. She has successfully guided previous Academic Clinical Fellows to win MRC clinical training fellowships. Her teaching extends to undergraduate Personal & Professional Development and supervision of MRes and BSc students, many of whom have achieved first-class degrees and prestigious awards. Her lab maintains active collaborations with Professor Neil Hanley's group on human development, stem cells, and regenerative medicine, bridging developmental biology with fibrosis research to understand why regeneration is impaired in chronic diseases.

• Synthetic Biology
• Systems Biology
• Quantitative Biology
• +5 more

Ting Lu is an Associate Professor at the University of Illinois at Urbana-Champaign in the School of Biomedical and Translational Sciences, focusing on microbial synthetic biology and systems biology. Their research bridges biology, engineering, and physics to reprogram cellular functionalities through gene regulatory networks. Ph.D. in Biophysics, University of California at San Diego (2007) B.S. in Physics, Zhejiang University (2002) Ting Lu's work explores microbial ecosystems, synthetic gene circuits, and their applications in biotechnology and medicine. By combining experimental approaches with mathematical modeling, they investigate bacterial communication networks, metabolic pathways, and spatial dynamics in microbial communities. Selected research trends include microbial consortia engineering for bioremediation and bioproduction, complexity reduction in microbiomes, and predictive modeling of synthetic gene networks. Their publications span high-impact journals such as Nature Communications , Nature Chemical Biology , and eLife . Fellow, American Institute for Medical and Biological Engineering (2022) Future Insight Prize (2021) Donald Biggar Willett Faculty Scholar (UIUC) (2020) NIH Maximizing Investigators' Research Award (2019) NSF CAREER Award (2015) AHA National Scientist Development Grant (2012) Ting Lu's lab has received grants from NIH, NSF, ONR, and industry partners. They offer undergraduate research opportunities in synthetic and systems biology, and teach advanced courses such as BIOE 430 - Intro Synthetic Biology and BIOE 432 - Systems Biology .

Alexandre Bouchard-CôtéView profile

Professor

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +4 more

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.