Keynotes

Valerie Daggett (University of Washington) -  Dynameomics: From Atomistic Simulations of All Protein Folds to the Discovery of a New Protein Structure to the Design of a Diagnostic Test for Alzheimer’s Disease

Maria Girone (European Organization for Nuclear Research) -   Big Data Challenges at the CERN HL-LHC

Lisa Schurer Lambert (Oklahoma State University) -  Research Methods as a Lens: How We Know What We Know

Courses

Paolo Addesso (introductory/intermediate) [introductory]  Data Fusion for Remotely Sensed Data

Thomas Bäck & Hao Wang (Leiden University) [introductory/intermediate]  Data Driven Modeling and Optimization for Industrial Applications

Marcelo Bertalmío (Spanish National Research Council) [intermediate]  Novel Vision Models in Image Processing, and How They Can Improve Artificial Neural Networks

Gianluca Bontempi (Université Libre de Bruxelles) [intermediate/advanced]  Machine Learning against Credit-card Fraud: Lessons Learned from a Real Case

Altan Cakir (Istanbul Technical University) [intermediate]  Big Data Analytics with Apache Spark

Michael X. Cohen (Radboud University Nijmegen) [introductory]  Dimension Explosion and Dimension Reduction in Brain Electrical Activity

Ian Fisk (Flatiron Institute) [introductory]  The Infrastructure to Support Data Science

Michael Freeman (University of Washington) [intermediate]  Interactive Data Visualization Using D3 + Observable

David Gerbing (Portland State University) [introductory]  Derive Meaning from Data with R Visualizations

Christopher W.V. Hogue (Ericsson Inc.) [introductory]  Applied Information Theory for Scalable Database Schema and Query Templates

Ravi Kumar (Google) [intermediate/advanced]  Clustering for Big Data

Victor O.K. Li (University of Hong Kong) [intermediate]  Deep Learning and Applications

Wladek Minor (University of Virginia) [introductory/Advanced]  Big Data in Biomedical Sciences

José M.F. Moura (Carnegie Mellon University) [introductory]  Graph Signal Processing

Panos Pardalos (University of Florida) [intermediate/advanced]  Optimization and Data Sciences Techniques for Large Networks

Valeriu Predoi (University of Reading) [introductory]  A Beginner's Guide to Big Data Analysis: How to Connect Scientific Software Development with Real World Problems

Karsten Reuter (Max Planck Society) [introductory/intermediate]  Machine Learning for Materials and Energy Applications

Ramesh Sharda (Oklahoma State University) [introductory/intermediate]  Network-based Health Analytics

Steven Skiena (Stony Brook University) [introductory/intermediate]  Word and Graph Embeddings for Machine Learning

Alexandre Vaniachine (VirtualHealth) [intermediate]  Open-source Columnar Databases

Sebastián Ventura (University of Córdoba) [intermediate/advanced]  Supervised Descriptive Pattern Mining

Xiaowei Xu (University of Arkansas, Little Rock) [introductory/advanced]  Language Models and Applications

Organizers:

Ben-Gurion University of the Negev

Institute for Research Development, Training and Advice (IRDTA) – Brussels/London

More info: https://irdta.eu/bigdat2021a