Enthought Python Training Instructors

Alexandre Chabot-Leclerc

Alexandre Chabot-Leclerc holds a Ph.D. in Electrical Engineering from the Technical University of Denmark. His graduate research was in the field of hearing research, where he developed models of human speech perception. Alexandre's interests include teaching, psychoacoustics, and rock climbing.

Michael Connell

Mike is a Learning Scientist with a background in Electrical Engineering (Robotics), Computer Science (AI and Software Engineering), and Applied Cognitive Science (Education).  He has worked as a Software Design Engineer at Microsoft and several other companies; a faculty member at Harvard University, Dartmouth College, and the University of Texas; co-founder and CEO of an educational technology startup (Native Brain, Inc.); and a consultant to schools, non-profit organizations, the federal government, and corporations spanning a broad range of industries. Much of his research and consulting has involved practical applications of data science and its component technologies (computer science, statistics, machine learning, etc.) to generate actionable insights and solve practical problems.  Representative projects include predictive modeling of human learning, design of adaptive learning systems, assessment design and validation, and data mining of large text corpora to discover actionable business insights in the financial services industry. Mike holds B.S. and M.S. degrees in Electrical Engineering and Computer Science from MIT and a doctorate in Education from Harvard University.

David Cournapeau

David graduated with a MsC in EE from Telecom Paristech, Paris in 2004, and obtained his PhD in Computer Science at Kyoto University, Japan, in the domain of speech recognition. He is a long-time contributor to NumPy and SciPy, and also started what would become the scikit-learn package for machine learning during summer 2007. He joined Enthought during the summer 2011, after having worked for Silveregg, a SaaS Japanese company delivering recommendation systems for some of the biggest Japanese online retailers.

Mark Dickinson

Mark received a B.A. in pure and applied mathematics from the University of Cambridge and a Ph.D. in pure mathematics from Harvard. He has held teaching and research positions at the University of Michigan, the University of Pittsburgh, and the National University of Ireland, Galway. Mark is a member of the Python core development team, and has worked on much of Python's numeric code.

Tim Diller

Tim holds a Ph.D. in mechanical engineering, specializing in thermal and fluid sciences. He has worked in the automotive industry with emissions measurement, modeling, and control in addition to vehicle dynamics modeling and simulation. He comes to us from a post-doctoral research position at the University of Texas, where he developed a numerical model and simulation of thermal transport processes in the laser sintering rapid-prototyping process at the LFF

Chris Farrow

Chris has a background in computational physics, which he has applied to various problems in materials science. Chris earned his Ph.D. in physics at Michigan State University, where he investigated the dynamics of correlated percolation and methods for combining complimentary structural information to determine the atomic structure of materials. At Michgan State, and later Columbia University, Chris was a member of the DANSE diffraction team and helped develop the DiffPy package for diffraction analysis.

Michael McKerns

Mike McKerns has over fifteen years of classroom teaching experience in Python, physics, applied math, computing, and finance, and has given hundreds of workshops and seminars.He has been developing parallel and distributed computing software infrastructure for over ten years, large-scale optimization and risk analysis software frameworks for over five years, and has served as manager and lead developer for multi-million dollar software projects on predictive science and large-scale computing. Mike has also worked as a software architect in financial credit risk, and has consulted on scientific applications of HPC in various fields.

Mike is co-founder of the UQ Foundation, a non-profit for the advancement of predictive science, and co-creator of OUQ theory, a rigorous mathematical framework for uncertainty quantification. Mike is also the author several open source software Python packages, including mystic (highly-constrained non-convex optimization and uncertainty quantification), pathos (parallel graph management and execution in heterogeneous computing), and klepto (persistent caching to memory, disk, or database), and has been a research scientist at Caltech since 2002.

Mike has a B.S. in Applied Physics from Notre Dame, and a Ph.D. in Physics from the University of Alabama at Birmingham.

Dillon Niederhut

Dillon Niederhut holds a Ph.D. in Anthropology from the University of California at Berkeley. His graduate research was in computational semantics and advanced neuroimaging applications, and he taught graduate-level classes in R and Python. Prior to joining Enthought, Dillon developed heterogeneous processing and analytics pipelines for Berkeley's Data Lab. Outside of the office, he contributes to several open-source initiatives, including Mozilla Science Lab, Bayes Impact, and Open Austin.

Didrik Pinte

Didrik Pinte brings us his broad experience in data management and software development. Prior to working with Enthought, Didrik ran his own company providing data management solutions in the environmental sector. He also worked as a research assistant during 4 years at Catholic University of Louvain (UCL) in Belgium. His research there focused on the development of integrated water resource management applications with Python. Didrik holds a M.S. degree from UCL in Agricultural Engineering as well as a M.S degree from UCL in Management.

Joris Vankerschaver

Joris has a Ph.D. in applied mathematics from Ghent University in Belgium and has held research positions at Caltech, the University of California, San Diego, and Imperial College before joining Enthought. He has published extensively about dynamical systems and hydrodynamics and has developed fast numerical algorithms for curve matching and for the geometric integration of hydrodynamical systems.

Corran Webster

Corran obtained his B.S. from the University of New South Wales and his Ph.D. in pure mathematics from UCLA. He has held teaching positions at the University of Nevada, Las Vegas as well as Texas A & M. His academic areas of concentration included functional analysis and operator algebras. As Chief Scientist at Compudigm International, Corran worked on enterprise data visualization and redictive modeling using self-organizing maps. Corran has been programming in Python since 1995, when he was a teaching assistant in UCLA's Program in Computing courses.