IEEE Sección España

We are pleased to announce MISC'24, the Spanish Workshop on Machine Learning, Information Theory, Signal Processing, and Communications. In Madrid on July 2, 2024, MISC'24 expands upon the Signal Processing, Information Theory, and Communications (SIC) Workshop to include the Machine learning field. The technical program features speakers of renowned international prestige in these fields. Remarkably, the event will host a distinguished lecturer from the Information Theory Society. In the afternoon, we will have a poster session for Ph.D. students, postdocs, and researchers in the fields above to foster interaction and potential new collaborations. We encourage all participants to contribute. Registration will be open until June 21, 2024. Please fill out the form: https://forms.office.com/e/4YpXNajrQy Moreover, we will hold a welcome and networking dinner on July 1st to promote social interaction among participants. Each participant would cover the costs, with an estimated cost between 40 and 50 EUR. Please, let us know your interest in attending the dinner by answering the corresponding question within the registration form. Visit our website for more details: https://sites.google.com/view/misc24workshop/home Feel free to share the event details with potential interested participants. We look forward to your participation in this event. Kind regards, Grace Villacrés (URJC), Alejando Lancho (UC3M), Mihaela I. Chidean (URJC) MISC'24 Co-Chairs Co-sponsored by: Universidad Rey Juan Carlos, Universidad Carlos III de Madrid Speaker(s): Anand D. Sarwate, Tobias Koch, Daniel Hernández-Lobato, Antonio G. Marqués Agenda: https://sites.google.com/view/misc24workshop/program Room: Salón de Actos, Bldg: Universidad Rey Juan Carlos, Calle Quintana 21, Madrid, Madrid, Spain, 28008

We are pleased to announce MISC'24, the Spanish Workshop on Machine Learning, Information Theory, Signal Processing, and Communications. In Madrid on July 2, 2024, MISC'24 expands upon the Signal Processing, Information Theory, and Communications (SIC) Workshop to include the Machine learning field. The technical program features speakers of renowned international prestige in these fields. Remarkably, the event will host a distinguished lecturer from the Information Theory Society. In the afternoon, we will have a poster session for Ph.D. students, postdocs, and researchers in the fields above to foster interaction and potential new collaborations. We encourage all participants to contribute. Registration will be open until June 21, 2024. Please fill out the form: https://forms.office.com/e/4YpXNajrQy Moreover, we will hold a welcome and networking dinner on July 1st to promote social interaction among participants. Each participant would cover the costs, with an estimated cost between 40 and 50 EUR. Please, let us know your interest in attending the dinner by answering the corresponding question within the registration form. Visit our website for more details: https://sites.google.com/view/misc24workshop/home Feel free to share the event details with potential interested participants. We look forward to your participation in this event. Kind regards, Grace Villacrés (URJC), Alejando Lancho (UC3M), Mihaela I. Chidean (URJC) MISC'24 Co-Chairs Co-sponsored by: Universidad Rey Juan Carlos, Universidad Carlos III de Madrid Speaker(s): Anand D. Sarwate, Tobias Koch, Daniel Hernández-Lobato, Antonio G. Marqués Agenda: https://sites.google.com/view/misc24workshop/program Room: 501, Bldg: Universidad Rey Juan Carlos, Calle Quintana 21, Madrid, Madrid, Spain, 28008

On Tuesday, July 2nd, Prof. Anand Sarwate from Rutgers University give his Distinguished Lecture: Title: "Learning with Structured Tensor Decompositions” Abstract: Many measurements or signals are multidimensional, or tensor-valued. Vectorizing tensor data for statistical and machine learning tasks often results in having to fit a very large number of parameters. Using tensor decompositions to model such data can give a flexible and useful modeling framework whose complexity can adapt to the amount of data available. This talk will introduce classical decompositions (CP, Tucker) as well as more recent ones (tensor train, block tensor decomposition, and low separation rank) and show how they can be used to learn scalable representations for tensor-valued data and make predictions from tensor-valued data. Time permitting, we will describe applications in federated learning as well as open problems for future research. The event will be transmitted via Teams: (https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTgzMjdkYjUtZDYxNi00NmUxLTk0YjAtNDQwZTIxYWQzMDdh%40thread.v2/0?context=%7b%22Tid%22%3a%225f84c4ea-370d-4b9e-830c-756f8bf1b51f%22%2c%22Oid%22%3a%22b4bdccba-f532-48cd-8208-457d6bc3086c%22%7d) Co-sponsored by: Universidad Rey Juan Carlos, Universidad Carlos III de Madrid Speaker(s): Dr. Anand Sarwate, Virtual: https://events.vtools.ieee.org/m/425691

Por orden del presidente, se convoca la reunión ordinaria del capitulo correspondiente a Julio 2024 Virtual: https://events.vtools.ieee.org/m/426063