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2019 IEEE Data Science Workshop
June 2—5 2019
Minneapolis, Minnesota, USA
Technical Program
Sunday, June 2
08:50 - 12:00
Tutorial
T1: Large-scale Optimization for Machine Learning - Julien Mairal, Inria Grenoble (France)
12:00 - 13:20
Lunch
13:20 - 16:30
Tutorial
T2: Tensors in Data Science - Nikos Sidiropoulos, University of Virginia (USA)
16:40 - 18:00
Panel
Panel Discussion
18:15 - 21:00
Welcome Reception
Monday, June 3
08:30 - 09:30
Plenary
PLEN-1: Andrea L. Bertozzi - "The Mathematics of Crime"
09:30 - 10:00
Coffee Break
10:00 - 12:00
SS
Data Analytics for Power Systems
Lecture Room 2
Graph learning
Lecture Room 1
12:00 - 13:20
Lunch
13:20 - 14:20
Plenary
PLEN-2: Robert D. Nowak - "Active Machine Learning: From Theory to Practice"
14:30 - 15:50
Anomaly Detection
Lecture Room 1
Tensor factorizations
Lecture Room 2
15:50 - 16:20
Coffee Break
16:20 - 18:00
SS
Omics Data Processing and Analysis
Lecture Room 2
SS
Tensor Methods
Lecture Room 1
Tuesday, June 4
08:30 - 09:30
Plenary
PLEN-3: Bin Yu - "PCS Workflow, Interpretable Machine Learning, and DeepTune"
09:30 - 10:00
Coffee Break
10:00 - 12:00
Graph signal processing
Lecture Room 1
Learning, modeling and inference with data - I
Lecture Room 2
12:00 - 13:20
Lunch
13:20 - 14:20
Plenary
PLEN-4: Rong Jin - "Optimization in Alibaba: Beyond Convexity"
14:30 - 15:50
Applications in social media and recommendation systems
Lecture Room 1
Topics in Data Science
Lecture Room 2
15:50 - 16:20
Coffee Break
16:20 - 18:00
SS
Nonconvex Optimization for Data Science
Lecture Room 1
SS
Statistical Inference in Complex Network Data
Lecture Room 2
19:00 - 21:00
Gala Dinner
Wednesday, June 5
08:30 - 09:30
Plenary
PLEN-5: Xiao-Li Meng - "Is it a Computing Algorithm or a Statistical Procedure: Can you tell or should you care?"
09:30 - 10:00
Coffee Break
10:00 - 12:00
Applications in Biology and Medicine
Poster Area
Distributed algorithms
Poster Area
Learning, modeling and inference with data - II
Poster Area
12:00 - 13:20
Lunch
13:20 - 14:20
Plenary
PLEN-6: Jennifer Neville - "Towards Relational AI – The good, the bad, and the ugly of learning over networks"