Here are some possibilities to define a project as part of the ARIA internship 2023-2024. For each of them, there is the possibility to continue further studying the subject in collaboration with a lab in France or abroad.
Topics:
- Graph theoretical Machine Learning
- Data clustering
- Statistical testing in high dimensions
- Interpretable/Explainable Machine Learning
- Social network analysis and diffusion of information
- Problems related to sequential decision-making
- Problems related to epidemic spreading and control
- Problems about urbanism and mobility (e.g. transportation, public safety, etc)
This event is an excellent opportunity to refresh my memory about the wonderful time -just over a decade- I spent in Ioannina, while studying at the University. I am looking forward to seeing again friends and familiar faces, and above all chat with the other colleagues and the young students of the department.
Many thanks to Evaggelia Pitoura for the invitation, and of course for organizing such events at the Computer Science and Engineering Department.
The Foundation FMJH has announced the call for post-doc funding 2023. Check our subject proposal entitled Graph operator pursuit for efficient graph machine learning. Applications are due to the 1st of December.
Contact me for more details on that.
Check also the other subject coming from the MLMDA group, which is related to Physics informed Neural Networks.
In the paper The Grey Hoodie Project: Big Tobacco, Big Tech, and the Threat on Academic Integrity, the authors investigate the very important issue of how the AI scientific agenda and policymaking around AI can be (and evidence imply they actually are) manipulated by Big Tech.
The abstract states:
“…Big Tech can actively distort the academic landscape to suit its needs. By comparing the well-studied actions of another industry (Big Tobacco) to the current actions of Big Tech we see similar strategies employed by both industries. These strategies enable either industry to sway and influence academic and public discourse. We examine the funding of academic research as a tool used by Big Tech to put forward a socially responsible public image, influence events hosted by and decisions made by funded universities, influence the research questions and plans of individual scientists, and discover receptive academics who can be leveraged…”
At the top of the post there is a figure showing how the different entities interact, and below the two main figures of the author’s study showing:
- Left: the percentage of Computer Science faculty of the sampled Ivy League Universities in US (UofT, MIT, Stanford, Berkeley) that have been funded by Big Tech (Google, Microsoft, Amazon, IBM, Facebook, Nvidia),
- Right: similarly for faculty affiliation.
The big questions are sort of obvious:
– How can Big Tech companies, which are among the most natural subjects of societal and legal monitoring/regulation, be the “pioneers” and “primary investigators” of the underlying ethics or democratic principles that those regulations should serve?
– How can Academia play its role to demand fair and equitable development, when part of it becomes a vehicle of legitimation for the big private interests?
The Franco-Bavarian AI Cup is now open for participation, for students and junior researchers from Europe interested in AI and Data Science.
Trophy: up to €95.000 for financing the launch of a start-up.
Procedure: Successful participation in one of the announced challenging use-cases, linked with sustainable development, which are out in collaboration with our partners from France and Germany:
- challenge from E.ON related to the energy distribution;
- challenge from Trading Hub Europe related to the pricing of energy;
- challenge from Deutsche Bahn Regio on the optimization of the planning of regional bus lines
- challenge from Centre Borelli related to the frailty of the elderly
- challenge from CEA on the detection of seismic activity.
You are invited to pass this information to your network.
Final registration date… this Friday!
More info: https://www.uni-passau.de/ai-
It is with great excitement that I share this piece of news. Machine Learning on Graphs is soon to have what deserves: the first major event of its own. And shall the name of it be:
Learning on Graphs Conference (LoG)
Below the announcement posted by Michael Bronstein (member of the Advisory Board):
Graph Machine Learning has become large enough of a field to deserve its own standalone event: the Learning on Graphs Conference (LoG). The inaugural event will take place in December 2022 and will be fully virtual and free to attend. We believe this event will stimulate research in the field by bringing together experts and practitioners from various subfields and adjacent disciplines.
Read the rest of his very interesting description of the scientific landscape around ML on graphs and how the advancements are scattered around the different big ML and Data Mining conferences.
The recent paper “The rise and fall of rationality in language” (PNAS, Dec 2021), by Marten Scheffera, Ingrid van de Leemputa, Els Weinansa, and Johan Bollenc, brings some quantification over the way we use language to communicate, express ideas, argue, etc, in the course of time.
Here are some of the subjects that the ML group (MLMDA) of Center Borelli offers for (but not limited to) MVA Masters program internships:
It’s been about two years since the appearance of the COVID-19 virus, and the pandemic is still a horrific roller coaster for most countries, and their citizens individually. From the beginning, and especially at the beginning, it was natural for doctors and researchers of various fields to put as top priority helping out with the situation. The research efforts continue, concentrating unprecedented energy and resources worldwide. However, moving forward on such a rapidly evolving subject brings challenges not only on the side of investigating new ideas, but also from the side of handling the sheer amounts of produced scientific literature. In essence, this means coping with reading, processing, exploiting, applying many, many,… many papers!
