******************************************************************
9th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2023 Spring
Bari, Italy
April 3-7, 2023
https://irdta.eu/deeplearn/2023sp/
***********
Co-organized by:
Department of Computer Science
University of Bari “Aldo Moro”
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
******************************************************************
Early registration: December 23, 2022
******************************************************************
SCOPE:
DeepLearn 2023 Spring will be a research training event with a global scope
aiming at updating participants on the most recent advances in the critical and
fast developing area of deep learning. Previous events were held in Bilbao,
Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães, Las Palmas de Gran
Canaria, Luleå and Bournemouth.
Deep learning is a branch of artificial intelligence covering a spectrum of
current exciting research and industrial innovation that provides more
efficient algorithms to deal with large-scale data in a huge variety of
environments: computer vision, neurosciences, speech recognition, language
processing, human-computer interaction, drug discovery, health informatics,
medical image analysis, recommender systems, advertising, fraud detection,
robotics, games, finance, biotechnology, physics experiments, biometrics,
communications, climate sciences, bioinformatics, geographic information
systems, etc. etc. Renowned academics and industry pioneers will lecture and
share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified
through 24 four-hour and a half courses and 3 keynote lectures, which will
tackle the most active and promising topics. The organizers are convinced that
outstanding speakers will attract the brightest and most motivated students.
Face to face interaction and networking will be main ingredients of the event.
It will be also possible to fully participate in vivo remotely.
An open session will give participants the opportunity to present their own
work in progress in 5 minutes. Moreover, there will be two special sessions
with industrial and recruitment profiles.
ADDRESSED TO:
Graduate students, postgraduate students and industry practitioners will be
typical profiles of participants. However, there are no formal pre-requisites
for attendance in terms of academic degrees, so people less or more advanced in
their career will be welcome as well. Since there will be a variety of levels,
specific knowledge background may be assumed for some of the courses. Overall,
DeepLearn 2023 Spring is addressed to students, researchers and practitioners
who want to keep themselves updated about recent developments and future
trends. All will surely find it fruitful to listen to and discuss with major
researchers, industry leaders and innovators.
VENUE:
DeepLearn 2023 Spring will take place in Bari, an important economic centre on
the Adriatic Sea. The venue will be:
University of Bari “Aldo Moro”
STRUCTURE:
3 courses will run in parallel during the whole event. Participants will be
able to freely choose the courses they wish to attend as well as to move from
one to another.
Full live online participation will be possible. However, the organizers
highlight the importance of face to face interaction and networking in this
kind of research training event.
KEYNOTE SPEAKERS:
Vipin Kumar (University of Minnesota), Knowledge Guided Deep Learning: A
Framework for Accelerating Scientific Discovery
William S. Noble (University of Washington), Deep Learning Applications in Mass
Spectrometry Proteomics and Single-Cell Genomics
Emma Tolley (Swiss Federal Institute of Technology Lausanne), Physics-Informed
Deep Learning
PROFESSORS AND COURSES: (to be completed)
Babak Ehteshami Bejnordi (Qualcomm AI Research), tba
Patrick Gallinari (Sorbonne University), [intermediate] Physics Aware Deep
Learning for Modeling Dynamical Systems
Sergei V. Gleyzer (University of Alabama), [introductory/intermediate] Machine
Learning Fundamentals and Their Applications to Very Large Scientific Data:
Rare Signal and Feature Extraction, End-to-End Deep Learning, Uncertainty
Estimation and Realtime Machine Learning Applications in Software and Hardware
Jacob Goldberger (Bar-Ilan University), [introductory/intermediate] Latent
Random Variables, Generative Models and Variational Autoencoders
Christoph Lampert (Institute of Science and Technology Austria), [intermediate]
Training with Fairness and Robustness Guarantees
Yingbin Liang (Ohio State University), [intermediate/advanced] Bilevel
Optimization and Applications in Deep Learning
Miaoyuan Liu (Purdue University), [introductory/intermediate] Edge of the
Future: AI in Real Time Systems of Scientific Instruments
Xiaoming Liu (Michigan State University), [intermediate] Deep Learning for
Trustworthy Biometrics
Michael Mahoney (University of California Berkeley), [intermediate] Practical
Neural Network Theory
Liza Mijovic (University of Edinburgh), [introductory/intermediate] Deep
Learning & the Higgs Boson: Classification with Fully Connected and Adversarial
Networks
Razvan Pascanu (DeepMind), [intermediate] Understanding Learning Dynamics in
Deep Learning and Deep Reinforcement Learning
Bhiksha Raj (Carnegie Mellon University), [introductory] An Introduction to
Quantum Neural Networks [with Rita Singh and Daniel Justice]
Bart ter Haar Romeny (Eindhoven University of Technology),
[intermediate/advanced] Explainable AI from First Principles
Tara Sainath (Google), [advanced] E2E Speech Recognition
Martin Schultz (Research Centre Jülich), [introductory/intermediate] Deep
Learning for Air Quality, Weather and Climate
Hao Su (University of California San Diego), [intermediate/advanced] Neural
Representation for 3D Capturing
Adi Laurentiu Tarca (Wayne State University), [intermediate] Machine Learning
for Cross-Sectional and Longitudinal Omics Studies
Zhi Tian (George Mason University), [intermediate] Communication-Efficient and
Robust Distributed Learning
Michalis Vazirgiannis (Polytechnic Institute of Paris), [intermediate/advanced]
Graph Machine Learning with GNNs and Applications
Atlas Wang (University of Texas Austin), [intermediate] Sparse Neural Networks:
From Practice to Theory
Guo-Wei Wei (Michigan State University), [introductory/advanced] Discovering
the Mechanisms of SARS-CoV-2 Evolution and Transmission
Lei Xing (Stanford University), [intermediate] Deep Learning for Medical
Imaging and Genomic Data Processing: from Data Acquisition, Analysis, to
Biomedical Applications
Xiaowei Xu (University of Arkansas Little Rock), [intermediate/advanced] Deep
Learning Language Models and Causal Inference
OPEN SESSION:
An open session will collect 5-minute voluntary presentations of work in
progress by participants. They should submit a half-page abstract containing
the title, authors, and summary of the research to da...@irdta.eu by March 26,
2023.
INDUSTRIAL SESSION:
A session will be devoted to 10-minute demonstrations of practical applications
of deep learning in industry. Companies interested in contributing are welcome
to submit a 1-page abstract containing the program of the demonstration and the
logistics needed. People in charge of the demonstration must register for the
event. Expressions of interest have to be submitted to da...@irdta.eu by March
26, 2023.
EMPLOYER SESSION:
Organizations searching for personnel well skilled in deep learning will have a
space reserved for one-to-one contacts. It is recommended to produce a 1-page
.pdf leaflet with a brief description of the company and the profiles looked
for to be circulated among the participants prior to the event. People in
charge of the search must register for the event. Expressions of interest have
to be submitted to da...@irdta.eu by March 26, 2023.
ORGANIZING COMMITTEE:
Donato Malerba (Bari, local chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
https://irdta.eu/deeplearn/2023sp/registration/
The selection of 8 courses requested in the registration template is only
tentative and non-binding. For the sake of organization, it will be helpful to
have an estimation of the respective demand for each course. During the event,
participants will be free to attend the courses they wish.
Since the capacity of the venue is limited, registration requests will be
processed on a first come first served basis. The registration period will be
closed and the on-line registration tool disabled when the capacity of the
venue will have got exhausted. It is highly recommended to register prior to
the event.
FEES:
Fees comprise access to all courses and lunches. There are several early
registration deadlines. Fees depend on the registration deadline. The fees for
on site and for online participation are the same.
ACCOMMODATION:
Accommodation suggestions will be available in due time at
https://irdta.eu/deeplearn/2023sp/accommodation/
CERTIFICATE:
A certificate of successful participation in the event will be delivered
indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
da...@irdta.eu
ACKNOWLEDGMENTS:
University of Bari “Aldo Moro”
Rovira i Virgili University
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
_______________________________________________
uai mailing list
uai@engr.orst.edu
https://it.engineering.oregonstate.edu/mailman/listinfo/uai
