Knowledge Discovery and Data Mining - Seminars


  • "Just Machine Learning", Tina Eliassi-Rad, Northeastern University
  • "Symmetric tensor decomposition", Elias Tsigaridas, INRIA, Paris
  • "Homological Tools for Data", Robert Ghrist, University of Pennsylvania
  • "Topological Summaries for Machine Learning and Statistical Inference", Sayan Mukherjee, Duke University
  • "Applications of TDA to Computational Genomics and Machine Learning", Aldo Guzman Saenz & Karthikeyan Natesan Ramamurthy, IBM T. J. Watson Research Center
  • "Processing Heterogeneous Signals using Sheaf-based Filters", Michael Robinson, American University
  • "Tensorial Change Analysis using Probabilistic Tensor Regression", Tsuyoshi Ide, IBM T. J. Watson Research Center
  • "On a Contingent Labor Price Prediction Social Machine using Collaborative Cognition", Gyana R. Parija, India IBM Research Lab
  • "The CCP Selector: Best Subset Selection for Sparse Regression from Chance-Constrained Programming", Xinwei Deng, Virginia Tech


  • "Statistics: The Trajectory of our Field", David Banks, Duke University
  • "Nested Group Testing Procedures and Generalized GT Problem", Yaakov Malinovsky, University of Maryland
  • "Learning to look around", Kristen Grauman, University of Texas at Austin
  • "Combining Information from Multiple Forecasters: General Inefficiency of the Means", Ville Satopää, INSEAD
  • "Clustering from general pairwise observations -- convex and non-convex approaches", Shiau Hong Lim, IBM Research Singapore (Machine Learning for Late Lunch Seminar Series)
  • "Getting Arrays in Order with Convex Fusion Penalties", Eric Chi, North Carolina State University (Machine Learning for Lunch Series)
  • "An $l_1$-Augmented Lagrangian algorithm and why, at least sometimes, it is a very good idea", Andrew R. Conn, IBM Research (Machine Learning for Late Lunch Seminar Series)
  • "Convergence Diagnostics for Stochastic Gradient Descent", Panos Toulis, University of Chicago
  • "Predicting Solar Irradiance as a Function of Location and Time: Multiple Model Calibration, Non-Stationarity, and Non-Space-Filling Design", Ben Haaland, University of Utah
  • "Quantum Information and Many-Body Science", Ramis Movassagh, IBM Research
  • "Machine Learning Challenges in Programmatic Advertising", Claudia Perlich, Dstillery
  • "Data-driven probabilistic modeling and high-performance computing: algorithms and applications to physical and biological systems", Paris Perdikaris, Massachusetts Institute of Technology
  • "Phase Retrieval Meets Statistical Learning Theory", Sohail Bahmani, Georgia Institute of Technology
  • "Design at Large: real-world, large-scale, and sometimes disruptive", Scott Klemmer, UC San Diego
  • "Computer Vision Algorithms Inspired by Compressed Sensing", Paul Hand, Rice University
  • "Large Network Analytics: Theory and Algorithms", Pin-Yu Chen and Karthikeyan Shanmugan, IBM Research
  • "DeepStack: Expert-level artificial intelligence in heads-up no-limit poker", Matej Moravcik and Martin Schmid, Charles University, Prague
  • "Mobile Phone-based Credit Scoring in Africa", Skyler Speakman, IBM Research Africa


Yada Zhu

KDD PIC is proud to support

ML Symposium NYAS 2019

This symposium, the thirteenth in an ongoing series presented by the Machine Learning Discussion Group at the New York Academy of Sciences, will feature Keynote Presentations from leading scientists in both applied and theoretical Machine Learning and Spotlight Talks, a series of short, early career investigator presentations across a variety of topics at the frontier of Machine Learning.

CIKM 2018

The 27th ACM International Conference on Information and Knowledge Management takes place on October 22 - 26, 2018 at 'Lingotto', Turin, Italy. The theme for 2018 is "From Big Data and Big Information to Big Knowledge".


The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will take place in the Croke Park Conference Centre, Dublin, Ireland during the 10 – 14 September 2018.

COLT 2018

The 31st edition of the Conference on Learning Theory will take place at KTH Royal Institute of Technology, Stockholm, Sweden, July 5 - 9, 2018.