As the Chief Science Officer at Continuum Analytics, I lead a data science team creating tools for big data applications in the Python ecosystem. I am also the President of the NumFOCUS foundation, where I help open source science projects sustain.

Ben is a data scientist and developer at Continuum Analytics. He has several years of experience with Python and is passionate about any and all forms of data. Part of his duties at Continuum include exploring a vast array of data (social networks, climate, astronomy, biology, finance, etc.). in addition to building tools to help others in the data munging world

Mr. Van de Ven received undergraduate degrees in Computer Science and Mathematics from UT Austin, and a Master's degree in physics from UCLA. He has worked at the Applied Research Labs, developing software for sonar feature detection and classification systems on US Naval submarine platforms. He also spent time at Enthought, where he worked on problems in financial risk modeling and fluid mixing simulation, and also contributed to the Chaco visualization library. He has also worked on an assortment of iOS projects as an independent consultant.

I am a Data Scientist at Axial where I work on information retrieval and recommender systems. I received B.S from Bilkent University and M.Sc from New York University focusing signal processing and machine learning. I also do consulting in NLP and machine learning on a project basis; document classification, topic modeling and time-series signal analysis.

Chengkai Li is an Associate Professor and Director of the Innovative Database and Information Systems Research Laboratory (IDIR) in the Department of Computer Science and Engineering at UT-Arlington. His research interests are in several areas related to big data and data science, including database, data mining, Web data management, and information retrieval. His current research focuses on building large-scale human-assisting and human-assisted data and information systems with high usability, low cost and applications for social good. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2007.

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Chou-han is principal engineer at BloomReach. At BloomReach, he has developed job management systems for web crawling and data processing pipelines. Previously, Chou-han worked for VMware on their Workstation team. He holds a Master's degree from Stanford University specializing in Artificial Intelligence.

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Data Scientist

Cliff Click is the CTO and Co-Founder of H2O, makers of H2O, the opensource math and machine learning engine for Big Data. Cliff wrote his first compiler when he was 15 (Pascal to TRS Z-80!), although Cliff’s most famous compiler is the HotSpot Server Compiler (the Sea of Nodes IR). Cliff helped Azul Systems build an 864 core pure-Java mainframe that keeps GC pauses on 500Gb heaps to under 10ms, and worked on all aspects of that JVM. Before that he worked on HotSpot at Sun Microsystems, and was at least partially responsible for bringing Java into the mainstream. Cliff is invited to speak regularly at industry and academic conferences and has published many papers about HotSpot technology. He holds a PhD in Computer Science from Rice University and about 15 patents.

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The Dallas Morning News

David Saroff is a graduate student examining radio telescope data for indications of pulsing radio sources, especially pulsars, outside our milky way galaxy.

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James Powell is a NYC-based Python programmer with experience in quantitative finance and data science. He's also very active in the Python community, where he organizes NYC Python which is the world's largest and most active Python meetup group. He also works with the numeric & scientific computing non-profit NumFOCUS to help organize the PyData conference series. In addition, he's a frequent speaker at Python conferences, and has been invited to speak at events such as PyData New York, PyData London, PyGotham, the conference ‘For Python Quants,’ and PyCon Spain.

As Chief Technology Officer at RealMassive, Jason Vertrees, Ph.D, leads the company’s team of scientists and engineers to ensure that RealMassive’s commercial real estate customers are receiving best-of-breed technology solutions customized to their specific business needs. Prior to joining RealMassive, Dr. Vertrees was Director of Core Modeling Products at Schrodinger. He is focused on leveraging his expertise in product strategy and user experience to best serve each of RealMassive’s customers. Dr. Vertrees graduated from the University of Texas with two Bachelor of Arts degrees: one in Computer Science and one in Japanese. He received his Ph.D. in Structural and Computational Biology and Theoretical and Computational Biophysics from the University of Texas Medical Branch.

Big Data Engineer at Institute for Data Analytics, UT Dallas

Jonathan (Jon) Riehl has been using Python to help satisfy his curiosity for almost 20 years. Jon is a software researcher and developer, having developed production code for NASA, Lucent, and the NIH. He is also an author of and contributor to relevant open source Python projects such as Basil, Mython, and Numba. He received his BS in Computer Science from Texas A&M University, and earned his MS and PhD in Computer Science from the University of Chicago under the direction of David Beazley and John Reppy. Jon's research pursuits include software engineering tools, compiler design, and embedded domain-specific languages.

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As a senior malware researcher, Kyle Maxwell leads efforts in addressing threats related to hacktivism and cyber crime, as well as cyber espionage. He works globally to drive collection and knowledge surrounding network attacks, exploited vulnerabilities, and adversary tactics. Previously, he led the incident response team at Heartland Payment Systems and performed digital forensics for clients across the United States at several private investigation firms. As a researcher at Verizon, he contributed to the company’s annual Verizon Data Breach Investigations Report. Mr. Maxwell holds a degree in Mathematics from the University of Texas at Dallas.

Meltem is a data analyst, algorithm developer and neuroscientist. She has established labs, worked on academic project, published articles and written grants. She loves startups, especially university spin ups. Meltem Ballan earned a PhD in Complex Systems and Brain Sciences from Florida Atlantic University. She has been invited to speak at Harvard, Yale and has consulted with many healthcare startups. She is a well travelled scholar, has lived and worked in several world regions, and has a superb mastery of turning the most complex technical ideas into business plans.

Oliver worked in the telecommunications industry, has a research background, and is currently employed by a startup to optimise radio networks. Aside from sport and Python programming he likes to dabble in signal processing, control, and (game) physics.

Geophysicist with computer science habits; founder of PyLadies-HTX. Paige is passionate about public transportation, sustainable energy, scientific computation, STEM education reform, adventures -- and how Python integrates with all of the above. She is currently an Earth Sciences graduate student at Rice University, and is employed full-time by Chevron in upstream technical computing. http://www.paige-bailey.com

Paul Ingram, Principal Data Scientist at HUGEdata, LLC Paul is a graduate of the University of Texas at Dallas with an MS in Applied Economics. As a Principal Data Scientist at HUGEdata, he helps companies identify opportunities for analytical innovation through the creative application of machine learning and distributed algorithms. Paul is an accomplished speaker and instructor. Upcoming engagements include “Data Science: No longer a luxury, a necessity for competitive advantage” at Dallas Innotech on April 16th. Paul is regarded as an expert in his field, a cross disciplinarian that brings the creative to the analytical for game changing business results. His experience includes: • Architecting big data customer preference models for leading companies • Refactoring statistical algorithms for use in distributed environments. • Leading analytics teach-out sessions to educate external analytics teams on new statistical techniques and best practices for deriving value out of data analysis. Guest lectures on the R language and implementing statistical methods. • Creating an iPad app for grade school math teachers to teach students concepts using interactive, customizable lessons with over 10,000 users.

OAfter completing a degree in Chemistry at UCLA, I taught for several years in such widely separated places as Ethiopia, England, rural Iowa and western NY state. In the late 90s I was bitten by the programming bug first learning and loving C++. I jumped off the academic ship and into the "real world" and spent almost 10 years as and embedded programmer in the Accelerator Division at Fermi National Accelerator Lab, using C/C++, Python, LabVIEW and MATLAB. In 2011, I moved from the large corporate environment of Fermilab to a small startup working on innovative uses of wearable sensors and predictive analytics to improve health care. Initially, I was tasked with translating MATLAB algorithms into an undetermined high level language. Once my supervisor gave me the go ahead to attempt the implementation in Python, after an initial project using Java, I never looked back. I use Python regularly to develop algorithms, tests systems and analyze data, and enjoy the language and the extensive availability of high quality libraries like numpy, scipy and pandas, immensely.

Peter holds a B.A. in Physics from Cornell University and has been developing applications professionally using Python since 2001. Before co-founding Continuum Analytics in 2011, Peter spent seven years at Enthought designing and developing applications for a variety of companies, including investment bankers, high-frequency trading firms, oil companies, and others. In 2007, Peter was named Director of Technical Architecture and served as client liaison on high-profile projects. Peter also developed Chaco, an open-source, Python-based toolkit for interactive data visualization.

Phillip enjoys building Python based tools that make others’ experience with data analysis a pleasure. He graduated with a BS and MA in psychology from CUNY City College where he worked on quantifying the activity of spontaneously firing neurons in vivo. In addition to being a Python evangelist he is also a core developer on the pandas data analysis library.

I have been working with Artificial Intelligence and Machine Learning applications for over 20 years and defended a Masters Thesis in Machine Learning. With recent realization of Big Data platforms, I am now able to put AI applications at the forefront to solve complex business problems. I have helped organizations adopt emerging technologies successfully. I believe in using fundamental Computer Science principles to tackle challenging business problems.

After a dozen years of building scalable systems in Microsoft, AddThis, AWS, and Dato, Rajat recently started working closely with data scientists, helping them build intelligent applications using the Dato platform.

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Vivian S Zhang, Founder and CTO of SupStat Inc / NYC Data Science Academy Vivian began her career at Brown University, where she provided statistical modeling and implementation to Ivy league professors and researchers. Later at Memorial Sloan-Kettering Cancer Center, ranked as a “best hospital” for cancer care, Vivian launched new, data-enabled predictive modeling approach. More recently, Vivian learned first-hand about the challenges of doing Data Science business across borders while at SupStat, an international data science consulting firm. She is an organizer of NYC Open-Data meetup and is teaching Data Science courses at NYU and Stony Brook University. Vivian is a graduate of Applied Mathematics and Statistics, Stony Brook University, and Computer Science, San Jose State University.

After finishing his degree in Probability Theories at Kiev National University in 2004, Volodymyr (Vlad) spent next 6 years programming Graphics and Visual effects, contributing to winning three Oscars for Visual Effects on such projects as Avatar, Dark Knight and etc. After briefly working at King in London as product manager, Vlad has joined Product Madness in 2013, another social gaming company (specialising in Social Casino apps) as a Head of Data Science. In this role, Vlad has built a Data Science department, replaced Excel and Tableau with Python-based analytics stack and built Data Warehouse system capable of storing and processing 200M events per day. Vlad also has an MBA from London Business School and has been using Python since 2001.

Yucheng Low is a co-founder and Chief Architect at Dato Inc. He completed his PhD in Machine Learning in 2013 from Carnegie Mellon University advised by Prof. Carlos Guestrin where he worked on parallel and distributed systems for large scale Machine Learning. As part of his thesis work he also co-developed the open source PowerGraph system for distributed Machine Learning.