Matteo Ceriotti

Rocket scientist ๐Ÿš€ ( systems ) at University of Glasgow ๐ŸŽ“ - Content and opinions my own.

Born in ๐Ÿ‡ฎ๐Ÿ‡น, living in , ๐Ÿด๓ ง๓ ข๓ ณ๓ ฃ๓ ด๓ ฟ

๐Ÿšด๐Ÿปโ€โ™‚๏ธ, ๐ŸŠ๐Ÿผโ€โ™‚๏ธ, ๐Ÿƒโ€โ™‚๏ธ, ๐Ÿ‚ (when possible)

He/Him ๐Ÿงโ€โ™‚๏ธ

Matteo Ceriotti boosted:
Sam Bowne :donor:sambowne@infosec.exchange
2025-06-13
Matteo Ceriottimtcerio
2025-06-13

@sambowne Genius!

Matteo Ceriottimtcerio
2025-06-11

European Space Agency's @esa Solar Orbiter gives first view of the 's South pole:

bbc.co.uk/news/articles/clyqry

Matteo Ceriottimtcerio
2025-06-11

With my colleague Dr Kevin Worrall , happy to support Craft Prospect Ltd on a new project from the European Space Agency @esa:

โ€˜Robust real-time constrained using machine learningโ€™ (ROC-ML)

It will demonstrate machine learning algorithms for motion planning in orbital missions, covering missions include active removal, in-orbit servicing and space station assembly.

gla.ac.uk/news/headline_118665

ROC-ML logo
Matteo Ceriottimtcerio
2025-06-09

As part of the conference, there will be a public lecture by Les Johnson of :

โžก Sailing on Sunlight: The Promise of Solar Sails in Space Exploration

๐Ÿ“Œ Tuesday 1 July 2025, 19:30-21:30, Delft University, The Netherlands

๐ŸŽซ Free, but limited places: aanmelder.nl/isss2025/public-e

Sailing on Sunlight: The Promise of Solar Sails in Space Exploration
Imagine a spacecraft that glides through space without fuel, propelled only by the pressure of sunlight. Solar
sail propulsion, once the stuff of science fiction, is now a reality, offering a revolutionary way to explore the
solar system and eventually, the cosmos. Scientist and author, Les Johnson, will uncover the fascinating
physics behind solar sails and how they harness photons, the particles that make up sunlight, to push
spacecraft across the void. Les will discuss recent space missions using solar sails, highlighting their
groundbreaking achievements. Looking ahead, he will discuss upcoming projects and the possibility
of interstellar travel powered by solar sails, envisioning humanity's journey beyond the solar system. Join us
in discovering how these elegant, light-driven sails may one day help us reach the stars, unlocking a future
of deep-space exploration once thought impossible.
Matteo Ceriottimtcerio
2025-06-04

Slam 2025

8 fantastic students will present their in unique and creative ways! This will be held at Cottiers, Glasgow on Thursday 3rd July from 19:00

events.bookitbee.com/college-o

Matteo Ceriottimtcerio
2025-06-04

Japanese lunar exploration company ispace will attempt to land its RESILIENCE spacecraft on the Moon no earlier than 5 June (CEST) 2025.

esa.int/Enabling_Support/Opera

Matteo Ceriottimtcerio
2025-06-03

: The $1.8 trillion for

A by McKinsey & Company at the World Economic Forum 2024

Key findings:
- Space will be a larger part of the global economy by 2035
- Space will become more about connecting people and goods
- Spaceโ€™s return on investment will be more than financial

Download the full report: mckinsey.com/industries/aerosp

Matteo Ceriottimtcerio
2025-05-15
Matteo Ceriotti boosted:
Science ScholarScienceScholar
2025-05-07

India plans manned space flight by 2027 phys.org/news/2025-05-india-sp

Matteo Ceriottimtcerio
2025-05-07

Edward Tomanek-Volynets' has just been published:

"The pointer network for reward maximisation in multi-target
space mission sequence selection"

This work uses machine learning to optimise sequences of targets to visit in a multi-target mission, for example rendezvous, removal, or servicing.

:
doi.org/10.1016/j.asr.2025.04.

Congrats Edward! ๐ŸŽ“

Multi-target space mission scenarios such as asteroid rendezvous, debris removal or satellite servicing, require targeting several orbits
in a single mission, often to be selected among a large set, and therefore choosing optimal sequences of these orbits to be visited. This
paper demonstrates a reinforcement-learning-based framework for selecting the sequence of targets to be visited in large-scale multitarget mission optimisation problems. The sequence selection is a NP-hard combinatorial optimisation problem. The proposed method
builds upon a neural network architecture for combinatorial optimisation originally developed for Euclidean problems, to produce estimates of the optimal sequence of targets in very short amounts of time. The neural network is trained using a policy-gradient
reinforcement-learning approach. Once training is complete, the network can be evaluated in two ways: one of these (greedy decoding)
produces solutions on average 15% less optimal than Ant Colony Optimisation (ACO); the other (stochastic search) is on average 5%
less optimal than ACO, using an iterative process that is slower than greedy decoding but still orders of magnitude faster than ACO. The
quality of the networkโ€™s solutions is shown both averaged over large amounts of problems, and demonstrated more closely on a few
specific instances.
Matteo Ceriottimtcerio
2025-05-01

on :

Made by Engineers: Making Glasgow a smart and sustainable city

๐Ÿ“… 10 June 2025
๐Ÿ•ก 6:00pm - 8:30pm
๐Ÿ“Œ The Exchange, 29 Royal Exchange Square, G1 3AJ

eventsportal.imeche.org/event/

Matteo Ceriottimtcerio
2025-04-02

@grb090423 Also, there is some interest. Look into Astroscale.

Matteo Ceriottimtcerio
2025-04-02

@grb090423 The problem is economic viability of ADR. It needs to start from Governments and Space Agencies.

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