Conference Talks

Listed below are confirmed talks for SciPy2013.

More details and schedule information coming soon.

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Keynotes
IPython: from the shell to a book with a single tool; the method behind the madness Fernando Perez, UC Berkeley Henry H. Wheeler Jr. Brain Imaging Center
The New Scientific Publishers William Schroeder, Kitware
Trends in Machine Learning and the SciPy community Olivier Grisel
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General
A comprehensive look at representing physical quantities in Python Bekolay, Trevor, University of Waterloo
A Portrait of One Scientist as a Graduate Student Ivanov, Paul, UC Berkeley
Analyzing IBM Watson experiments with IPython Notebook Bittner, Torsten, IBM
Breaking the diffraction limit with python and scipy Baddeley, David, Nanobiology Institute, Yale University
Bringing astronomical tools down to earth Droettboom, Michael, STScI; Dencheva, Nadia, STScI; Aldcroft, Tom, Harvard-Smithsonian Center for As
Data Agnosticism: Feature Engineering Without Domain Expertise Kridler, Nicholas, Accretive Health
DMTCP: Bringing Checkpoint-Restart to Python Arya, Kapil, Northeastern University; Cooperman, Gene, Northeastern University
Dynamics with SymPy Mechanics Moore, Jason, University of California at Davis
High Performance Reproducible Computing Zhang, Zhang, Intel Corporation; Rosenquist, Todd, Intel Corporation; Moffat, Kent, Intel Corporation
Import without a filesystem: scientific Python built-in with static linking and frozen modules Pat Marion, Kitware; Aron Ahmadia; Bradley M. Froehle, University of California, Berkeley
Julia and Python: a dynamic duo for scientific computing Bezanson, Jeff, MIT; Karpinski, Stefan, MIT
Matrix Expressions and BLAS/LAPACK Rocklin, Matthew, University of Chicago Computer Science
Modeling Complexity with Python Dr. Maksim Tsvetovat, independent; Alex Kouznesov, independent
Modeling the Earth with Fatiando a Terra Uieda, Leonardo, Observatorio Nacional; Oliveira Jr, Vanderlei C., Observatorio Nacional; Barbosa, V
Multidimensional Data Exploration with Glue Beaumont, Christopher, U. Hawaii; Robitaille, Thomas, MPIA; Borkin, Michelle, Harvard; Goodman, Alys
open('/dev/real_world') - Raspberry Pi Sensor and Actuator Control Minardi, Jack, Enthought Inc.
Opening Up Astronomy with Python and AstroML Vanderplas, Jake, University of Washington; Ivezic, Zeljko, University of Washington; Connolly, Andrew, University of Washington
Parallel Volume Rendering in yt: User Driven & User Developed Skillman, Samuel, University of Colorado at Boulder; Turk, Matthew, Columbia University
PyOP2: a Framework for Performance-Portable Unstructured Mesh-based Simulations and its Application to Finite-Element Computations Rathgeber, Florian, Imperial College London, UK; Markall, Graham R., Imperial College London, UK; Mi
Pythran: Enabling Static Optimization of Scientific Python Programs Serge Guelton, ENS; Pierrick Brunet, Télécom Bretagne; Alan Raynaud, Télécom Bretagne; Adrien Merlini, Télécom Bretagne; Mehdi Amini, SILKAN
Scientific Computing and the Materials Genome Initiative Reid, Andrew, National Institute of Standards and Technology
Scikit-Fuzzy: A New SciPy Toolkit for Fuzzy Logic Warner, Joshua, Mayo Clinic Department of Biomedical Engineering; Ottesen, Hal H., Adjunct Professor
SymPy Gamma and SymPy Live: Python and Mathematics Online Li, David, SymPy
Synoptic Atmospheric Transport of Wildfire Smoke Plume to Greenland Lavoue David, DL Modeling and Research, Milton, Ontario, Canada
The DyND Library Wiebe, Mark, Continuum Analytics
Why you should write buggy software with as few features as possible Granger, Brian, Cal Poly San Luis Obispo
XDress - Type, But Verify Scopatz, Anthony, The University of Chicago & NumFOCUS, Inc.
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Machine Learning
A Gentle Introduction To Machine Learning Kastner, Kyle, Southwest Research Institute
Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms Bergstra, James, University of Waterloo; Yamins, Dan, Massachusetts Institute of Technology; Cox, David D., Harvard University
Implicit Sentiment Mining with SnowWhite Tsvetovat, Maksim, 2042 Labs; Alex Kouznetsov,
Infer.py: Probabilistic Programming and Bayesian Inference from Python Zinkov, Rob
mystic: a framework for predictive science Michael McKerns @ California Institute of Technology, Houman Owhadi @ California Institute of Techno
Processing biggish data on commodity hardware: simple Python patterns Author: Gael Varoquaux Institution: INRIA, Parietal team
Python Tools for Coding and Feature Learning Johnson, Leif, University of Texas at Austin
Roadmap to a Sentience Stack Eric Neuman
Skdata: Data seets and algorithm evaluation protocols in Python Bergstra, James, University of Waterloo: Pinto, Nicolas, Massachusetts Institute of Technology; Cox, David D., Harvard University
Using Python for Structured Prediction Zinkov, Rob
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Reproducible Science
An efficient workflow for reproducible science Bekolay, Trevor, University of Waterloo
Complex Experiment Configuration, Control, Automation, and Analysis using Robot Operating System (ROS) Stowers, John, TU Wien; Straw, Andrew, Research Institute of Molecular Pathology
Emacs + org-mode + python in reproducible research Kitchin, John Carnegie Mellon University
Exploring Collaborative HPC Visualization Workflows using VisIt and Python Krishnan, Harinarayan, Lawrence Berkeley National Labs; Harrison, Cyrus, Lawrence Livermore National
GraphTerm: A notebook-like graphical terminal interface for collaboration and inline data visualization Ramalingam Saravanan, Texas A&M University
IPython-powered Slideshow Reveal-ed Avila, Damian, OQUANTA;
lmonade: a platform for development and distribution of scientific software Erocal, Burcin, TU Kaiserslautern
lpEdit: An editor to facilitate reproducible analysis via literate programming Richards, Adam, Duke University, CNRS France; Kosinski Andrzej, Duke University; Bonneaud, Camille,
matplotlib: past, present and future Michael Droettboom
OS deduplication with SIDUS (single-instance distributing universal system) Quemener, Emmanuel, Centre Blaise Pascal (Lyon, France); Corvellec, Marianne, McGill University (Mon
Reproducible Documents with PythonTeX Poore, Geoffrey, Union University
The advantages of a scientific IDE Cordoba, Carlos, The Spyder Project
The Open Science Framework: Improving, by Opening, Science Spies, Jeffrey, Center for Open Science; Nosek, Brian, Center for Open Science
Using IPython Notebook with IPython Cluster for Reproducibility and Portability of Atomistic Simulations Trautt, Zachary, Materials Measurement Science Division, National Institute of Standards and Technol
Using Sumatra to Manage Numerical Simulations Davison, Andrew, CNRS (principal developer); Wheeler, Daniel, NIST (speaker)
vIPer, a new tool to work with IPython notebooks Avila, Damian, OQUANTA;
Writing Reproducible Papers with Dexy Nelson, Ana