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 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 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
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 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 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 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 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 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.
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.
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 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 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 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.