Parallel Tempering Professors and Supervisors | ApplySharif

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Trevor CampbellView profile

Associate Professor

University of British ColumbiaCanada

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.

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Alexandre Bouchard-CôtéView profile

Professor

University of British ColumbiaCanada

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.

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Aaron SmithView profile

Associate Professor

University of OttawaCanada

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

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Maurizio ZamboniView profile

Professor

Polytechnic University of TurinItaly

Maurizio Zamboni is a Full Professor at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he also serves as Student Ombudsman. His academic career spans over three decades with continuous teaching and research contributions in electronics and computing fields. Professor Zamboni's research interests focus on cutting-edge areas including CMOS integrated circuits, computer architecture, quantum computing, semiconductor devices, and VLSI design. His work particularly emphasizes emerging nanotechnologies for digital microelectronic architectures and the design of high-performance or low-consumption processing systems. He has developed expertise in circuit architectures for probabilistic computing, logic-in-memory computing, magnetic devices, and quantum architectures. His recent publications (2021-2025) reveal a strong trend toward quantum computing applications, in-memory processing architectures, and novel approaches to overcoming the memory wall problem. These works span both theoretical algorithm development and practical hardware implementations, with significant focus on quantum annealing, FPGA-based quantum emulation, and memory-mapped processing architectures. Professor Zamboni has been actively supervising PhD students working on quantum computing algorithms, hardware AI accelerators for automotive applications, and quantum-related optimization approaches. He leads research within the VLSILAB Group at DET, focusing on the intersection of nanoelectronics, quantum computing, and advanced computer architectures. His work bridges theoretical computer science with practical electronic design, creating novel solutions for next-generation computing challenges.

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Natesh PillaiView profile

Professor

Harvard UniversityUnited States

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

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Thomas R. PegramView profile

Professor

Loyola University MarylandUnited States

Thomas R. Pegram is a Professor of History at Loyola University, specializing in American political and social history, particularly focusing on social movements and political institutions in the late 19th and early 20th centuries. He has authored works on progressive politics, Prohibition, and the Ku Klux Klan, including One Hundred Percent American and Battling Demon Rum . His current research explores state-level Prohibition enforcement as a lens for understanding federalism and state-building. Education: B.A. from Santa Clara University, Ph.D. in History of American Civilization from Brandeis University. Research interests include the intersection of social movements and governance, with a focus on Prohibition, the KKK, and labor conflicts. His work bridges historical analysis with public engagement, as seen in his contributions to Smithsonian Channel documentaries and History Channel interviews. Teaching includes courses on U.S. history, civil rights, and the Civil War era. He has advised no listed students but has mentored through seminar courses like HS 464 and HS 465. Media recognition includes a New York Times review of his KKK book and a History Channel interview on Prohibition. His interdisciplinary approach highlights the cultural and political legacies of these movements.

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Ka Ming TamView profile

Researcher

Louisiana State UniversityUnited States

Ka Ming Tam is a Researcher at Louisiana State University's Department of Physics & Astronomy, College of Science. His work spans condensed matter physics , quantum many-body systems , and machine learning applications in physics . He has contributed to advanced computational methods, including nonequilibrium dynamical mean-field theory , functional renormalization group , and parallel tempering algorithms for studying disordered systems. His research includes Quantum materials with disorder and correlation Hybrid quantum-classical algorithms for phase transitions Epidemiological modeling of social physics Tensor formulations for Anderson-Hubbard models Machine learning in critical phenomena The 15 most recent publications highlight his focus on strongly correlated systems , Anderson localization , quantum computing , machine learning in statistical mechanics , and epidemiological dynamics . Key methodologies include DMFT , RG analysis , and GPU-accelerated simulations . No awards or student advisement details are explicitly mentioned in the provided text.

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Saifuddin SyedView profile

Research Fellow

University of British ColumbiaCanada

Saifuddin Syed is a Florence Nightingale Bicentennial Fellow in computational statistics and machine learning at the University of Oxford’s Department of Statistics. He is also a member of the Algorithms and Inference Working Group for the Next Generation Event Horizon Telescope (ngEHT). His research focuses on developing robust and scalable algorithms for statistical inference and generative modeling, with applications in astrophysics, computational biology, and nuclear fusion. Syed holds a PhD in Statistics from the University of British Columbia (2022), an MSc in Mathematics (2016), and a BMath in Pure & Applied Mathematics from the University of Waterloo (2014). His research interests include parallel tempering, annealing algorithms, scalable Bayesian inference, and AI-driven scientific discovery. He has contributed to high-impact projects such as imaging black holes (e.g., Sagittarius A* and M87), modeling genetic variations in malaria, and analyzing plasma dynamics in nuclear fusion reactors. Syed’s work has been recognized with awards like the Pierre Robillard Award and the Cecil Graham Doctoral Dissertation Award. Syed’s methodologies, such as non-reversible parallel tempering (NRPT), have been implemented in software like Pigeons.jl, enabling distributed sampling for complex statistical problems. He collaborates with interdisciplinary teams, including the Event Horizon Telescope collaboration, to advance computational methods for challenging scientific problems. Awards: Pierre Robillard Award, Cecil Graham Doctoral Dissertation Award, Savage Award Honourable Mention Key Projects: Black hole imaging, malaria genetics modeling, plasma dynamics inference Software: Pigeons.jl (distributed sampling framework)

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Elmar BittnerView profile

Researcher

Heidelberg UniversityGermany

Elmar Bittner is a theoretical physicist and Researcher at the Institute for Theoretical Physics (ITP) at the University of Heidelberg. He also serves as the administrator of the institute's computer network. His academic background includes a PhD in Physics from Vienna University of Technology (2001) and a Dipl.-Ing. (equivalent to MSc) from the same institution (1998). He has held postdoctoral positions at the Institute of Atomic and Subatomic Physics in Vienna and the University of Leipzig, where he also served as Akademischer Rat (Senior Lecturer) and Head of IT. His research focuses on quantum gravity, lattice models of two- and four-dimensional systems, phase transitions in statistical mechanics, and Monte Carlo simulation techniques. Notable contributions include studies on vortex dynamics in lattice Higgs models, eigenvalue spectra in QCD, and the development of parallel tempering algorithms. Bittner has also explored applications of statistical physics to social phenomena, such as football goal distributions. His publications span topics like Ising model dynamics, critical phenomena, and random matrix theory. He has collaborated with institutions such as the University of Leipzig, Brunel University, and INFN (Italy), contributing to international conferences and workshops. Despite no listed awards, his work reflects significant contributions to theoretical physics and computational methods. Bittner’s roles include managing the ITP’s technical infrastructure, reflecting his dual expertise in theoretical physics and computational administration.

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George DeligiannidisView profile

Professor

University of OxfordUnited Kingdom

George Deligiannidis is a Professor of Statistics and Director of the MSc in Statistical Science at the University of Oxford's Department of Statistics. He is also a Hugh Price Fellow in Statistics at Jesus College. His academic journey includes degrees from the University of Warwick (MMath), Heriot-Watt University/Edinburgh (MSc in Financial Mathematics), and a PhD from the University of Nottingham. He has held roles at the University of Leicester and King's College London before returning to Oxford in 2017 as Associate Professor, promoted to full Professor in 2024. His research focuses on probability theory, statistical methodology, and their applications in computational statistics and machine learning. Key interests include Monte Carlo methods (especially MCMC), random walks, optimal transport, and diffusion models. Notable recent work explores the theoretical foundations of diffusion models under manifold hypotheses, generalization bounds in machine learning, and convergence analysis of sampling algorithms. Deligiannidis has authored influential papers in top conferences (NeurIPS, ICML, COLT) and journals (Annals of Statistics, JRSSB). He is actively involved in teaching, including Advanced Simulation Methods and Modern Statistical Theory. His work bridges theoretical probability with practical computational challenges, contributing to both methodological advances and foundational understanding in statistical inference.

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Pierre NyquistView profile

Associate Professor

KTH Royal Institute of TechnologySweden

Pierre Nyquist is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. Previously, he held a position at KTH Royal Institute of Technology. His research focuses on probability theory, mathematical statistics, and applied mathematics, with an emphasis on large deviations, stochastic numerical methods, and statistical learning theory. He is supported by grants from the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish e-Science Research Center (SeRC). Education: Ph.D. in Applied and Computational Mathematics (2014, advisor Henrik Hult), postdoc at Brown University (2014–2016), and assistant professor at TU Eindhoven (2016–2020). Research interests include large deviations theory, stochastic processes, gradient flows, and applications to machine learning and computational statistics. His work spans theoretical developments and practical methodologies, such as Monte Carlo algorithms and uncertainty quantification tools. Recent activities include organizing seminars, supervising PhD students, and collaborating with industry partners. He has been awarded membership in the Young Academy of Sweden (2024–2029) and serves as a scientific ambassador for EURANDOM. Key publications address topics like large deviations in stochastic approximations, Metropolis-Hastings algorithms, and neural entropy estimation. His teaching includes courses on statistical learning, probability, and Monte Carlo methods.

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Jonathan EssexView profile

Professor

University of SouthamptonUnited Kingdom

Professor Jonathan Essex holds the Chair in Computational Systems Chemistry at the University of Southampton, where he leads innovative research at the intersection of computational methods and biological systems. His work focuses on advancing molecular simulation techniques to enable more accurate predictions in drug discovery and medical diagnostics. Develops novel computational methodologies Applies simulations to antibody design and drug delivery Collaborates with academic and industrial partners globally His research spans fundamental challenges in simulation accuracy while addressing practical pharmaceutical needs. Key collaborations include industry partners like Johnson & Johnson and academic institutions through EPSRC and BBSRC grants. Scientific recognitions include: 2002 Marlow Medal from Royal Society of Chemistry Royal Society Wolfson Research Merit Award (2013) As an active PhD supervisor, he mentors students in computational chemistry and systems biology projects, maintaining editorial roles at Journal of Computer Aided Molecular Design and BMC Chemistry .

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Daniel AnstettView profile

Assistant Professor

Cornell UniversityUnited States

Dr. Daniel Anstett is an Assistant Professor at the Plant Biology Section of the School of Integrative Plant Science , Cornell University. His work bridges landscape genomics , rapid evolution , and conservation biology to address climate change impacts and biodiversity preservation. Rapid evolution across environmental gradients Climate change adaptation in plants Herbivory-plant defense dynamics Landscape genomics applications His research trends show strong focus on genomic responses to drought , urbanization effects , and latitudinal ecological patterns . While no formal awards are listed, his lab actively engages in genomic modeling and herbarium analysis to inform conservation strategies. Current projects examine adaptive genetic markers and plant-environment interactions through field surveys and computational simulations.

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Saifuddin SyedView profile

Research Fellow

University of OxfordUnited Kingdom

Saifuddin Syed is a Florence Nightingale Bicentenary Research Fellow at the University of Oxford's Department of Statistics, supervised by Arnaud Doucet and funded by the CoSInES project. His research focuses on computational statistics and machine learning, particularly scalable Bayesian inference, Monte Carlo methods, and non-reversible parallel tempering. He completed his PhD in Statistics at the University of British Columbia under Alexandre Bouchard-Côté. Currently, he contributes to the Next Generation Event Horizon Telescope (ngEHT) collaboration, improving algorithms for modeling and imaging supermassive black holes. Research interests include parallel tempering, sequential Monte Carlo, information geometry, and statistical physics. His work spans methodological development in MCMC schemes and applications in complex systems. He is affiliated with the Computational Statistics and Machine Learning research group and Statistical Theory and Methodology at Oxford. Key contributions include advancing non-reversible parallel tempering techniques to enhance computational efficiency in high-dimensional sampling, with applications to astrophysics and statistical mechanics. His recent work emphasizes scalable algorithms and rigorous theoretical analysis of MCMC performance.

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Dave CampbellView profile

Professor

Carleton UniversityCanada

Professor Dave Campbell is affiliated with Carleton University's School of Mathematics and Statistics and School of Computer Science. He specializes in inferential data science, Bayesian algorithms, and computational statistics. His research interests include differential equation models, uncertainty quantification, and time-series analysis. He has advised multiple graduate students in areas like statistical language models and machine learning. Recent work includes studies on climate impacts on human conflict, forensic entomology, and NMR-based metabolite quantification. Active in open data initiatives, he maintains ShinyApps for ecological and educational datasets. Teaching includes Statistical Computing and Statistical Language Models courses.

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