1st Conference on Machine Learning for Gravitational Waves, Geophysics, Robotics, Control System and CA17137 MC2 meeting

Contribution List

1. "Imbedding large reasearch infrastructures in the earth environment, the case for Virgo".

Prof. Katsanevas Stavros

14/01/2019, 09:30

Introduction to Gravitational Waves

2. The future of Gravitational Waves detectors: toward Einstein Telescope

Michele Punturo (INFN)

14/01/2019, 10:15

Introduction to Gravitational Waves

3. Coupling between seismic noise and diffuse light in GW detector

Dr Antonino Chiummo (European Gravitational Observatory)

14/01/2019, 11:15

Introduction to Gravitational Waves

4. Hunting environmental noise in Virgo

Irene Fiori (European Gravitational Observatory)

14/01/2019, 11:45

Introduction to Gravitational Waves

22. Applications of Machine Learning in seismology

Dr Alberto Michelini (alberto.michelini@ingv.it)

14/01/2019, 12:15

Introduction to Gravitational Waves

5. WG3 introduction

Jan Harms

14/01/2019, 14:00

ML for Advanced Control techniques

6. Angular controls in Virgo

Julia casanueva diaz

14/01/2019, 14:30

ML for Advanced Control techniques

7. Feed-forward noise cancellation in Virgo

Dr Diego Bersanetti (INFN Genova)

14/01/2019, 15:00

ML for Advanced Control techniques

8. ML for noise cancellation in systems with non-linear coupling

Alberto Iess (University of Rome Tor Vergata)

14/01/2019, 15:30

ML for Advanced Control techniques

9. ML and genetic algorithms for noise cancellation

Alessio Cirone

14/01/2019, 16:15

ML for Advanced Control techniques

10. Non-stationary noise cancellation with causal, stable parametric filters

Dr Gabriele Vajente

14/01/2019, 16:50

ML for Advanced Control techniques

20. WG2 introduction

Alessandro Bertolini

15/01/2019, 09:00

ML for low-frequency seismic measurement

11. Newtonian Noise subtraction test array at Virgo

Tomasz Bulik

15/01/2019, 09:15

ML for low-frequency seismic measurement

12. Robots for GW detectors and infrastructure monitoring and operation: Can Networks of Autonomous Robotics Vehicles help the characterization of Newtonian, Acoustic and Other Source Noise in GW detection?

Prof. Fabio Bonsignorio (Scuola Superiore Sant'Anna)

15/01/2019, 09:45

ML for low-frequency seismic measurement

13. Machine Learning for seismic events

Ms Jozinović Dario (INGV)

15/01/2019, 10:15

ML for low-frequency seismic measurement

14. Generalized maximum entropy inference for seismic models

Dr Velimir Ilic

15/01/2019, 10:45

ML for low-frequency seismic measurement

15. GW detector sites characterization with seismic arrays

Soumen Koley (Nikhef)

15/01/2019, 14:00

ML for low-frequency seismic measurement

23. Short Term Scientific Mission Call

Apolinario Liliana

15/01/2019, 14:40

ML for low-frequency seismic measurement

16. WG1 introduction

Michal Bejger

15/01/2019, 15:00

ML for GW astronomy

17. Total-variation and dictionary learning methods for gravitational-wave data analysis

Jose Antonio Font-Roda

15/01/2019, 15:20

ML for GW astronomy

18. Gravitational Wave Open Science Center: how to access LIGO/Virgo open data

Dr Agata Trovato

15/01/2019, 16:15

ML for GW astronomy

19. Image-based transient signal classification with deep learning

Massimiliano Razzano

15/01/2019, 16:55

ML for GW astronomy

21. Deep Learning Methods for Predictive Tasks with Large Scale Sensor Data

Dr Roberto Corizzo

15/01/2019, 17:35

ML for GW astronomy