Connectionists: Call for Chapters: Multi-Task Learning in Science and Engineering by Springer

[MTL]

We are pleased to invite researchers, academics, and practitioners to
contribute chapters to the upcoming edited book titled Multi-Task
Learning in Science and Engineering.
This book will explore the intersection of Deep Learning and
Multi-Task Learning (MTL), offering insights into methods, techniques,
and applications across a wide range of scientific and engineering
domains

The book will be structured into two volumes:

Volume I: Science – Dedicated to foundational scientific applications
of MTL in fields such as biology, chemistry, physics, and mathematics.
This volume will focus on MTL’s role in data-driven discovery,
cross-disciplinary learning, and hypothesis generation within
scientific research.

Volume II: Engineering – Focused on the use of MTL in engineering and
practical real-world applications, including biomedicine, robotics,
automation, and materials design. This volume will emphasize MTL’s
impact on optimization, industry-specific adaptations, and performance
efficiency.

Deadlines:

31 January 2025: Submission of intention to contribute, specifying the
volume (Science or Engineering), the topic(s), and a tentative title
of your chapter

1 May 2025: Full chapter submission

1 July 2025: Feedback on submitted chapters

1 September 2025: Final version submission


Each chapter should be between 20 and 40 pages, including figures and
references. Original research, review articles, and case studies are
all welcome.

Submissions should be emailed to multitasklearning.book at gmail.com and
should include the following editors' email addresses in CC:
pardalos at ufl.edu , panos.pardalos at gmail.com ,
giuseppe.nicosia at unict.it , giuseppe.nicosia.1 at gmail.com ,
giulio.giaquinta at studium.unict.it , giulio.giaquinta7 at gmail.com

Editors:

Panos Pardalos, University of Florida, USA
Giuseppe Nicosia, University of Catania, Italy
Giulio Giaquinta, University of Catania, Italy