Talks

  • Building hypothesis-driven virtual screening pipelines for millions of molecules. In this talk, I will introduce a novel, hypothesis-driven filtering strategy and open-source toolkit for virtual screening, Screenlamp, which I developed and successfully applied to a identify potent inhibitors of G-protein coupled receptor-mediated signaling in vertebrates. And going beyond the mere identification of potent protein inhibitors, I will talk about techniques to integrate the computational predictions with experimental knowledge. Leveraging experimental data, supervised feature selection and extraction techniques will be introduced to identify the discriminants of biological activity using open-source machine learning libraries such as scikit-learn.
    @ ODSC West 2017, San Francisco (Nov 2017) [Slides] [Video]
  • An Introduction to Deep Learning with TensorFlow. A talk about representing mathematical functions as computation graphs, computing derivatives, and implementing complex deep neural networks conveniently and efficiently using TensorFlow.
    @ PyData Ann Arbor 2017 (Aug 2017) [Slides] [Video]
  • Hypothesis-driven virtual screening. A little teaser on our upcoming toolkit for hypothesis-based virtual screening at the SciPy 2017 Lightning talks (starting at 14:10).
    @ SciPy 2017, Austin (Jul 2017) [Slides] [Video]
  • Machine Learning and Performance Evaluation — Overcoming the Selection Bias. Every day in scientific research and business applications, we rely on statistics and machine learning as support tools for predictive modeling. To satisfy our desire to model uncertainty, to predict trends, and to predict patterns that may occur in the future, we developed a vast library of tools for decision making. In other words, we learned to take advantage of computers to replicate the real world, making intuitive decisions more quantitative, labeling unlabeled data, predicting trends, and ultimately trying to predict the future. Now, whether we are applying predictive modeling techniques to our research or business problems, we want to make “good” predictions! […]
    @ DataPhilly, Philadelphia (Nov 2016) [Slides] [Video]
  • Detecting the Native Ligand Orientation by Interfacial Rigidity. Presenting our novel novel approach to protein-ligand docking mode prediction based on graph theory.
    @ BMB Departmental Retreat at Michigan State (Oct 2016) [Slides] [Video]
  • Learning scikit-learn – An Introduction to Machine Learning in PythonAgain, I had such a great time at (not!) yet another Python & Data Science conference. Here are the tutorial materials from my PyData Chicago 2016 talk! @ PyData Chicago 2016 (Oct 2016) [GitHub Repo] [Slides] [Video]
  • Presentation of a novel approach to protein-ligand binding mode prediction by rigidity analysis using graph theory
    @ BioMolecular Sciences Gateway (Feb 2016) [Slides]

  • SeaScreen - A large-scale, hypothesis-driven virtual screening framework for structure-based inhibitor discovery
    @ GLBio, Toronto (May 2016)

  • An Introduction to Supervised Machine Learning and Pattern Classification: The Big Picture
    @ NextGen Bioinformatics [Slides]

  • MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics
    @ PSA Group [Slides]

  • […]

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