Chris Fonnesbeck

Keynote speaker and PyMC BDFL

Twitter: @fonnesbeck

GitHub: fonnesbeck

Chris is a Senior Quantitative Analyst in Baseball Operations for the New York Yankees. He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.

Viola Priesemann

Keynote speaker

Twitter: @violapriesemann

GitHub: vpriesem

Personal website: www.viola-priesemann.de/

Viola heads a research team at the Max Planck Institute for Dynamics and Self-Organization. She investigates the self-organization of spreading dynamics in the brain to understand the emergence of living computation. With the outbreak of COVID-19, she adapted these mathematical approaches to infer and predict the spread of SARS-CoV-2, and to investigate mitigation strategies. Viola is board member of the Campus Institute for Data Science and Fellow of the Schiemann Kolleg.

Aki Vehtari

Keynote speaker

Twitter: @avehtari

GitHub: avehtari

Personal website: users.aalto.fi/~ave/

Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. His numerous research interests are Bayesian probability theory and methodology, especially probabilistic programming, inference methods, model assessment and selection, non-parametric models such as Gaussian processes, dynamic models, and hierarchical models. Aki is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and the brand new « Regression and other stories ». He is also a core-developer of the seminal probabilistic programming framework Stan. An enthusiast of open-source software, Aki has been involved in many free software projects such as GPstuff for Gaussian processes and ELFI for likelihood inference.

Mo Akhshik

Speaker

Twitter: @MoAkhshik

GitHub: makhshik

Mo is a grad student of (astro)physics by day, a matheux and a Bayesian enthusiast all along. Broadly interested in cosmology and probability too.

Alex Andorra

Speaker

Twitter: @alex_andorra

GitHub: AlexAndorra

Personal website: learnbayesstats.anvil.app/

By day, Alex is a data scientist and modeler at the brand new PyMC | Labs consultancy. By night, he doesn’t (yet) fight crime, but he’s an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. Alex is also the creator and host of the only podcast dedicated to Bayesian statistics, “Learning Bayesian Statistics”. Every fortnight, he interviews practitioners of all fields about why and how they use Bayesian statistics. He also loves Nutella a bit too much, but he doesn’t like talking about it -- he prefers eating it.

Agustina Arroyuelo

Speaker

I am a PhD candidate in Biology. In my research, I apply Bayesian statistics to biomolecular structure determination and validation, i.e. finding the 3-dimensional shape of biomolecules and evaluating if that shape is a good model. I enjoy contributing to open source software. I have participated in the Google Summer of Code program with PyMC3 and ArviZ and I have recently incorporated to ArviZ core developers team.

Matthijs Brouns

Speaker

Twitter: @MatthijsBrs

GitHub: mbrouns

Personal website: www.linkedin.com/in/mbrouns/

I’m a data scientist, active in Amsterdam, The Netherlands. My current work involves training junior data scientists at Xccelerated.io. This means I divide my time between building new training materials and exercises, giving live trainings and acting as a sparring partner for the Xccelerators at our partner firms, as well as doing some consulting work on the side. I spent a fair amount of time contributing to our open scientific computing ecosystem through various means. I maintain open source packages (scikit-lego, seers) as well as co-chair the PyData Amsterdam conference and meetup and vice-chair the PyData Global conference. In my spare time I like to go mountain biking, bouldering, do some woodworking or go scuba diving.

Dario Caramelli

Speaker

Twitter: @dariocaramelli

GitHub:

Personal website: linkedin.com/in/dario-caramelli-705250150

Dario Caramelli is a research associate in the Cronin group at the University of Glasgow. His research involves building and programming of autonomous robots for reaction discovery as well as the development of algorithms for chemical space modelling and data processing. Dario obtained a Master degree in Organic chemistry in Rome (2015) and a PhD in the Cronin group (2019).

Tushar Chandra

Speaker

Twitter: @threeshar

GitHub: tuchandra

Personal website: tusharc.dev

Tushar is a senior data scientist at Nielsen Global Media in Chicago. At Nielsen, he works on developing Bayesian models for next-generation audience measurement. He loves cats (living with two, Luna and Ruby), chai, and college football. This is his first conference talk!

Cameron Davidson-Pilon

Speaker

Twitter: @cmrn_dp

GitHub:

Cameron Davidson-Pilon has worked in many areas of applied statistics, from the evolutionary dynamics of genes to modeling of financial prices. His contributions to the community include lifelines, an implementation of survival analysis in Python, lifetimes, and Bayesian Methods for Hackers, an open source book & printed book on Bayesian analysis. Formally Director of Data Science at Shopify, Cameron is now applying data science to food microbiology.

Tim Dodwell

Speaker

Twitter: @proftimdodwell

GitHub:

Personal website: www.datacentricengineering.co.uk/team.html

Prof. Tim Dodwell has a personal chair in Computational Mechanics at the University of Exeter, is the Romberg Visiting at Heidelberg in Scientific Computing and holds a 5 year Turing AI Fellowship at the Alan Turing Institute where he is also an academic lead.

Allen Downey

Speaker

Twitter: @allendowney

GitHub: AllenDowney

Personal website: allendowney.com

Allen Downey is a professor of Computer Science at Olin College and Visiting Lecturer at Ashesi University in Ghana. He is the author of a series of open-source textbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. in computer science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.

Dan Foreman-Mackey

Speaker

Twitter: @exoplaneteer

GitHub: dfm

Personal website: dfm.io/

Dan is an Associate Research Scientist at the Flatiron Institute's Center for Computational Astrophysics studying the application of probabilistic data analysis techniques to solve fundamental problems in astrophysics.

Laura Helleckes

Speaker

Twitter: @laurahelleckes

GitHub: lhelleckes

A biotechnologist by training, Laura transitioned to Data Science in the past years and is now a Bayesian enthusiast. In her Master’s thesis, she _actually_ collected the data Michael was using for his fancy Bayesian models. During her wet lab experience, Laura gained valuable knowledge on microorganisms and biological processes that she is now applying to implement mechanistic process models. Her experimental work also gave her the motivation to focus on lab automation for bioprocess development in her PhD at Forschungszentrum Jülich.

Michael Johns

Speaker

Michael Johns is a data scientist at HelloFresh US. His work focuses on building statistical models for business applications, such as optimizing marketing strategy, customer acquisition forecasting and customer retention.

Sayam Kumar

Speaker

Twitter: @sayamkumar753

GitHub: Sayam753

Personal website: www.codingpaths.com/

Sayam Kumar is a Computer Science undergraduate student at IIIT Sri City, India. He loves to travel and study maths in his free time. He also finds Bayesian statistics super awesome. He was a Google Summer of Code student with NumFOCUS community and contributed towards adding Variational Inference methods to PyMC4.

Junpeng Lao

Speaker

Twitter: @junpenglao

GitHub:

Junpeng Lao is a PyMC developer and currently a data scientist at Google. He also contribute to Tensorflow Probability and varies other Open source libraries.

Mikkel Lykkegaard

Speaker

Personal website: emps.exeter.ac.uk/engineering/staff/ml624

Mikkel Lykkegaard is a PhD student with the Data Centric Engineering Group and Centre for Water Systems (CWS) at University of Exeter. His research is mainly concerned with Uncertainty Quantification (UQ) for computationally intensive forward models.

Ruben Mak

Speaker

Back in 2012, Ruben introduced data science at Greenhouse, a digital advertising agency in the Netherlands. He is currently principal data scientist and cluster lead. He’s given several talks at PyData conferences and is one of the founders of PyData Eindhoven.

Osvaldo Martin

Speaker

Twitter: @aloctavodia

GitHub: aloctavodia

Osvaldo is a researcher at the National Scientific and Technical Research Council in Argentina and is notably the author of the book Bayesian Analysis with Python, whose second edition was published in December 2018. He also teaches bioinformatics, data science and Bayesian data analysis, and is a core developer of PyMC3 and ArviZ, and recently started contributing to Bambi. Originally a biologist and physicist, Osvaldo trained himself to python and Bayesian methods – and what he's doing with it is pretty amazing!

Hessam Mehr

Speaker

Twitter: @hessammehr

GitHub: hessammehr

Personal website: hessammehr.github.io

Hessam Mehr is a research associate in the Cronin group at the University of Glasgow's School of Chemistry. He works with an interdisciplinary group of scientists and engineers to build robots and teach them how to do chemistry. Since he joined the group in 2018, Hessam's main focus has been the integration of probabilistic reasoning with chemical robotics and discovery.

Grigorios Mingas

Speaker

Dr. Grigorios Mingas is a Senior Research Data Scientist at The Alan Turing Institute. He received his PhD from Imperial College London, where he co-designed MCMC algorithms and hardware to accelerate Bayesian inference. He has experience in a wide range of projects as a data scientist.

Quan Nguyen

Speaker

Personal website: krisnguyen135.github.io/

Quan is a Bayesian statistics enthusiast (and a programmer at heart). He is the author of several programming books on Python and scientific programming. Quan is currently pursuing a Ph.D. in computer science at Washington University in St. Louis, researching Bayesian methods in machine learning.

Michael Osthege

Speaker

Twitter: @theCake

GitHub: michaelosthege

Michael Osthege is a biotech Bayesian by choice. He likes to work with robots, bacteria and models as much as he loves to work in enthusiastic teams. As a PhD student in laboratory automation for bioprocess development at Forschungszentrum Jülich, he writes software to make robots generate his data. Since he unit-tests his code, he always blames the robots if the data doesn't agree with his Bayesian models.

Luciano Paz

Speaker

Personal website: lucianopaz.github.io

I got into Bayesian stats during my PhD in cognitive neuroscience. During my postdoc I got more involved with machine learning, and discovered PyMC3. I became a core contributor of PyMC, learnt a lot in the process and made up my mind to pursue a career outside of academia. I am now a machine learning engineer at Innova SpA in Italy.

Pedro German Ramirez

Speaker

In the year 2014 I completed my Bs. in Molecular Biology at the National University of San Luis, Argentina and in 2020 I finished my PhD in the Instute of Applied Mathematics (IMASL) while working within the Structural Bioinformatics Group (BIOS). My PhD thesis was centered around the use of a statiscal mechanics model to simulate biologically relevant systems of peptide-lipid interactions. Currently I'm doing my postdoc alongside Dr. Osvaldo Martin on probabilistic modeling and Sequential Monte Carlo.

Elizaveta Semenova

Speaker

Twitter: @liza_p_semenova

GitHub: elizavetasemenova

Elizaveta is currently a postdoc in Bayesian Machine Learning at a pharmaceutical company. Her interests span Gaussian Processes, Bayesian Neural Networks, compartmental models and differential equations with applications in epidemiology and toxicology. She is tool agnostic and builds probabilistic models in either Stan, PyMC3 or Turing.

Ali Akbar Septiandri

Speaker

Twitter: @aliakbars

GitHub:

A data scientist and a lecturer. Learning/teaching data science, machine learning, and artificial intelligence.

Max Sklar

Speaker

Twitter: @maxsklar

GitHub: maxsklar

Personal website: www.localmaxradio.com

Max Sklar is a machine learning engineer and a member of the innovation labs team at Foursquare. He hosts a weekly podcast called The Local Maximum which covers a broad range of current issues, including a focus on Bayesian Inference.

Nicoleta Spinu

Speaker

Twitter: @nicospinu

GitHub:

Nicoleta Spînu is a PhD candidate in Computational Toxicology with a background in pharmaceutical sciences and regulatory affairs looking to have her own impact on the protection of human health while promoting animal welfare (Replacement, Reduction and Refinement of animal testing; "the 3Rs"). Research interests include the science of network and causal inference, computational modelling of chemical toxicity, and regulatory toxicology and policy making.

Evdoxia Taka

Speaker

Twitter: @evdoxiataka

GitHub: evdoxiataka

Evdoxia is a PhD student at the School of Computing Science of University of Glasgow since 2019. Her research focuses on the creation of novel representations of probabilistic models that incorporate animation and interaction for a more intuitive communication of the uncertainty in the variables of probabilistic models. She became a Python and Bayesian enthusiast ever since she started her PhD and she got a foot in the door of a whole new-to-her, but very charming world. Evdoxia completed her undergraduate and master studies in the Aristotle University of Thessaloniki, Greece as Electrical and Computer Engineer. She worked as a Research Assistant at the Centre for Research & Technology Hellas in Thessaloniki contributing to various national- and EU-funded research projects in areas such as computer vision, 3D reconstruction and simulation, machine learning. She has also worked as a Research Database Engineer for the HCV Research UK project at the Centre for Virus Research of the University of Glasgow.

Vincent D. Warmerdam

Speaker

Twitter: @fishnets88

GitHub: koaning

Personal website: koaning.io/calmcode.io

Vincent likes to spend his days debunking hype in ML. He started a few open source packages (whatlies, scikit-lego, clumper and evol) and is also known as co-founding chair of PyData Amsterdam. He currently works at Rasa as a Research Advocate where he tries to make NLP algorithms more accessible.

Zhenyu Wang

Speaker

Zhenyu Wang is a Senior Business Intelligence Analyst at HelloFresh International. He works on developing and implementing methods to measure the effectiveness of advertising campaigns using analytic and statistical methods.

Thomas Wiecki

Speaker

Twitter: @twiecki

GitHub: twiecki

Thomas is the founder of [PyMC Labs](https://pymc-labs.github.io), a Bayesian consulting firm.

Ivan Yashchuk

Speaker

Ivan Yashchuk has 3 years’ experience in computational mechanics and scientific computing with occasional contributions to OSS projects. He received his M.Sc. in Computational Mechanics from Aalto University, Finland and is currently doing PhD research in Probabilistic Machine Learning group at Aalto.

Rob Zinkov

Speaker

Twitter: @zaxtax

GitHub: zaxtax

Personal website: zinkov.com

Rob Zinkov is a PhD student at University of Oxford. My research covers how to more efficiently specify and train deep generative models as well as how to more effectively discover a good statistical model for your data. Previously I was a research scientist at Indiana University where I was the lead developer of the Hakaru probabilistic programming language.

Ravin Kumar

Executive Chair

Twitter: @canyon289

GitHub: canyon289

Personal website: canyon289.github.io

Ravin Kumar is a senior engineer that uses data and statistics to inform humans decision making from C Suite long term strategy to ground floor “in the moment” choices. Ravin is (likely) a big proponent of Bayesian statistics and is Core Contributor to PyMC3 and ArviZ. Both are Open Source projects that strive to make Bayesian methods accessible and easy to use and for anybody. Ravin also helps plan community conferences such as PyDataLA, and enjoys building people up as much as he enjoys building code.

Thomas Wiecki

Executive Chair

Twitter: @twiecki

GitHub: twiecki

Personal website: twiecki.io

Thomas discovered the power of Bayesian statistics during his PhD at Brown University where it provided an invaluable tool in building computational models of the human brain to deepen our understanding of psychiatric illnesses. After his PhD he spent several years at Quantopian as the VP of Data Science to lead their research efforts in building the world\'s first crowd-sourced hedge fund. These days he is building a PyMC consultancy as well as developing an intuitions-first course on Bayesian statistics.

Oriol Abril-Pla

Diversity Chair

Twitter: @OriolAbril

GitHub: OriolAbril

Personal website: oriolabril.github.io

Oriol is a research assistant on Bayesian model selection on generalized linear models at Universitat Pompeu Fabra (Barcelona). His work there with David Rossell is available as part of the R/C++ package mombf. He is passionate about Bayesian statistics, open science and reproducibility. He is an ArviZ core contributor and tries to contribute to the PyData ecosystem and the greater Bayesian community. He has just started a blog on Bayesian statistics and open source software at oriolabril.github.io

Sid Ravinutala

Marketing Chair

Twitter: @sid_ravinutala

GitHub: sidravi1

Personal website: sidravi1.github.io

As Director, Sid provides technical and thought leadership within the data science team. He is responsible for growing the data science capability within IDinsight, and oversees the design and development of all data science projects. Prior to joining IDinsight, Sid worked as a data scientist at QuantumBlack, McKinsey’s advanced analytics arm; Coles, a supermarket chain in Australia; and the Center for International Development at Harvard. He worked in public health for the Clinton Health Access Initiative where he helped countries scale access to diagnostics, and developed global forecasts to inform pricing negotiations for sub-Saharan Africa. Sid enjoys using a wide range of statistical methods to inform policy, and recently discovered that he is a closet Bayesian. His previous projects have involved large-scale predictive analytics, optimisation, and Bayesian inference. Sid holds a bachelor’s degree in Computer Science and Electrical Engineering from the University of Melbourne, and an MPA in International Development (MPA/ID) from Harvard Kennedy School. He speaks English, Hindi, and Telugu. Sid was born in New Delhi and did a large share of his growing up in Melbourne. He has lived and worked in a number of developing countries, including Uganda, Ghana, India, and Papua New Guinea. He now lives in Boston with his wife and spends his evenings chasing after his two toddlers.

Hector Muñoz

Program Chair

Twitter: @hectoremunoz

GitHub: hectormz

Hector is a recent Bioengineering PhD from UCLA. He seeks to solve complex biomedical problems with reproducible statistics and machine learning. He is a Python and Bayesian enthusiast, who tries to think harder about his priors, and seeks to quantify uncertainty and confidence in life.

Quan Nguyen

Program Chair

Personal website: krisnguyen135.github.io

Quan is a programming enthusiast with a background in mathematics and statistics. His past projects include Python programming books such as The Statistics and Calculus Workshop and Hands-on Application Development with PyCharm. Quan is currently a Ph.D. student in computer science at Washington University in St. Louis, researching Bayesian methods in machine learning.

Chris Krapu

Program Committee

Chris is a recently graduate PhD student specializing in applied spatiotemporal modeling for environmental modeling at Duke University. His past collaborations cover a range of subjects including spatial ecology, econometrics, hydrology and machine learning. He is also very excited about the intersection of statistical physics and practical statistical modeling!

Cem Tabakci

Volunteer Chair

Twitter: @cemtabakci

GitHub: ctabakci

Cem is currently pursuing a cognitive neuroscience master’s degree at Sapienza - University of Rome (Rome). His studies mainly focused on neuroimaging techniques. He is interested in computational neuroscience and Bayesian statistics. He finished his bachelor’s degree in psychology at Yeditepe University (Istanbul).

Colin Carroll

Marketing Committee

Twitter: @colindcarroll

GitHub: ColCarroll

Personal website: colindcarroll.com/

Colin is a software engineer at Google Research Cambridge. He is interested in statistical computing and visualization, particularly as related to Bayesian methods. He is heavily involved in open source - a core contributor to PyMC3, a Python library for Bayesian modelling and inference, as well as ArviZ, a Bayesian visualization and diagnostic library. He received his PhD in mathematics from Rice University, where he researched geometric measure theory.

Alex Andorra

Diversity Chair

Twitter: @alex_andorra

GitHub: AlexAndorra

Personal website: learnbayesstats.anvil.app

By day, Alex is a data scientist and modeler at the brand new PyMC | Labs consultancy. By night, he doesn’t (yet) fight crime, but he’s an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. Alex is also the creator and host of the only podcast dedicated to Bayesian statistics, “Learning Bayesian Statistics”. Every fortnight, he interviews practitioners of all fields about why and how they use Bayesian statistics. He also loves Nutella a bit too much, but he doesn’t like talking about it – he prefers eating it.

Maria Gorinova

Reviewer

Twitter: @migorinova

GitHub: mgorinova

Personal website: homepages.inf.ed.ac.uk/s1207807

Probabilistic Programming PhD student at University of Edinburgh

Vianey Leos Barajas

Reviewer

Twitter: @vianey_lb

GitHub: vianeylb

Personal website: vleosbarajas.com

I am a statistician primarily interested in working in the areas of statistical ecology, time series modeling, Bayesian statistics and, more recently, spatial modeling of environmental data. Currently, I am an Assistant Professor in the Department of Statistical Sciences and School of the Environment at the University of Toronto.

Abhay Goyal

Committee Member

Personal website: abhaygoyal.github.io

Abhay Goyal is currently pursuing his Master's in Computer Science at Stony Brook University. His current work entails the use of network science to understand the transmission of information in social networks. He is interested in Data Science and Machine Learning among other things. He also loves to collaborate with people, build/write code, and using his CS knowledge in other areas.

Christian Luhmann

Technology Committee

Twitter: @1010is10

GitHub: cluhmann

Personal website: cluhmann.github.io

Christian Luhmann holds a BS in computer science and a PhD in psychology. He is faculty at Stony Brook University, where his research involves modeling behavior, often with applications to behavioral economics and health-related data. His interests draw him to problems in which computation/statistics/tech and human beings bump into one another. Most recently this has meant forays into the world of interpretable machine learning.

Corrie Bartelheimer

Planning Committee

Twitter: @corrieaar

GitHub: corriebar

Personal website: www.samples-of-thoughts.com/

Corrie is a Data Scientist soon starting a new position at Dalia Research where she'll be using Bayesian methods such as MRP for brand tracking. She also co-organizes the Berlin Bayesian meetup group though she recently moved to Brussels which definitely has the better waffles. Apart from Bayesian Statistics and waffles, she's also enthusiastic about data visualization and about using data science to help with social issues such as disinformation.