Event type: Workshops

On July 14th at 2:30 pm, join the CMU Portugal Large Scale Collaborative Project “Building Hope” for its online closing session and showcase the project’s results in advancements in energy management.

Building HOPE aimed to revolutionize energy management practices in smart urban environments by developing and validating a cutting-edge tool called Holistic Optimization of Prosumed Energy (HOPE) for buildings. By harnessing the power of data collection, sensing advancements, and AI-based methodologies, the project strived to pave the way towards a cleaner energy future.

The HOPE platform has been extensively tested throughout the project in diverse contexts, including industrial, office, and retail buildings. These real-world trials have highlighted the platform’s innovative features and showcased its significant impact on energy management. As a result, the HOPE platform is poised to become the go-to energy management solution for buildings in the next decade, potentially reshaping the industry.

The project was led by dstsolar in collaboration wit  WATT-IS, InnovationPoint, S.A., Instituto Superior Técnico, Universidade de Coimbra, and the Electrical Engineering Department at Carnegie Mellon University.

Registration to attend online is free but mandatory here. 

AGENDA

14h30 Opening Session
Silvia Castro, CMU Portugal

14h45 Presentation of the Building Hope Project
Mauro Costa, dstsolar

15h00 Presentation of the developed models:

Watt.IS | Smarter and more efficient buildings through data analytics: contributions of load forecasting and non-intrusive disaggregation of consumption
IST | Flexibility, Performance and SmartContracts: New services in intelligent building management
UC | Optimization of energy efficiency in buildings through the integration of flexibility resource

16h00 Overview Building Hope platform (innovationpoint)
Ricardo Lopes, innovationpoint

16h20 Closing Session
Catarina Nunes Soares – SONAE MC

Building Hope – Building Holistic Optimization of Prosumed Energy  – project is co-financed by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalisation (COMPETE 2020) and by the Portuguese Foundation for Science and Technology (FCT) under the CMU Portugal Program.

On June 30th at 2 pm, the CMU Portugal Large Scale Collaborative Project IntelligentCare will host a hybrid final workshop at Hospital da Luz Lisboa Auditorium 2. The event aims to present the closing remarks and results of the  IntelligentCare project,  bringing together e members of the consortia and a diverse audience.

The IntelligentCare project, promoted by Hospital da Luz Learning Health, is dedicated to developing a patient-centric solution for managing Multimorbidity (MM).  By utilizing analytical methods to explore data from the electronic health records (EHR) and the remotely reported patients measures through smart sensors and mobile solutions, the project aims to enhance the lives of patients living with MM. 

The collaborations involve researchers from Hospital da Luz Learning Health, the Institute of Robotic Systems of Instituto Superior Técnico (ISR Lisboa), INESC-ID, Heinz College (CMU), and Priberam. Clinical teams from Hospital da Luz Lisboa, with the support of the Information Systems and Technologies (DSTI), Planning and Business Intelligence (DPIN), and Health Value Management (DGVS) divisions of the Luz Saúde group, were actively involved in the project.

Key achievements under the IntelligentCare project include:

• Early-stage identification and characterization of patients with multimorbidity
• Evidence-based suggestions of clinical pathways to guide treatment 
• Predictive insights on interactions with the hospital 

The workshop aims to provide the audience with an understanding of the challenges associated with health data research and offer insights into the main results achieved by the IntelligentCare project. Attendees will gain valuable knowledge on the transformative potential of patient-centric solutions and analytical insights in managing multimorbidity.

Workshop Agenda
REGISTRATION (hybrid)

More about the project

The growing population aging with multimorbidity (MM) hinders the sustainability of the healthcare sector. MM is typically associated with high healthcare usage and costs, which do not always translate to better patient outcomes. Consequently, there is a significant need to develop new tools to manage this condition. The application of artificial intelligence (AI) techniques in healthcare is a topic that promises to revolutionize healthcare as we know it today. Using real-life health data for research and AI projects presents several challenges and opportunities. The IntelligentCare project aims to develop a patient-centric and personalized tool to manage MM using AI methods to explore data from electronic health records as well as measures reported remotely by patients (sensors and questionnaires). In this final workshop, we intend to present the main results from the project IntelligentCare.

The IntelligentCare – Intelligent Multimorbidity Management System – the project is co-financed by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalisation (COMPETE 2020) and by the Portuguese Foundation for Science and Technology (FCT) under the CMU Portugal Program.

CMU Portugal IntelligentCare Webpage

Connected Mobility – FLOYD Workshop and Presentations

In addition to the FLOYD project, this workshop will feature presentations from other projects addressing related topics such as Route 25, 5G-Auto, InSecTT, and STEROID. The event will conclude with a talk by Peter Steenkiste from Carnegie Mellon University on the subject of “Computational Offloading with 5G & Edge Computing: Are We There Yet?”

Don’t miss this opportunity to REGISTER and join us online or at Altice Labs (Aveiro) to witness the future of connected mobility.

FLOYD  – 5G/SDN Intelligent Systems For LOw latencY V2X communications in cross-Domain mobility applications is led by Capgemini Engineering in collaboration with ALTICE LABS, S.A. , Instituto Superior de Engenharia do Porto,  Instituto de Telecomunicações, VORTEX Colab and the Computer Science Department at CMU. Project FLOYD (POCI-01-0247-FEDER-045912) is co-financed by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalisation (COMPETE 2020) and by the Portuguese Foundation for Science and Technology (FCT) under the CMU Portugal Program.

On May 4th, the CMU Portugal Large Scale Collaborative Project Safeforest will host a Workshop to present the closing remarks and results of the project, gathering the consortia members and a broader audience at Instituto de Sistemas e Robótica da Universidade de Coimbra  (Polo II, Universidade de Coimbra).

The Safeforest – Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention – funded through PT2020 under the CMU Portugal Program, is led by the company Ingeniarius in collaboration with SILVAPOR, ADAI (Associação para o Desenvolvimento da Aerodinâmica Industrial) and ISR from Universidade de Coimbra.

The project aims at developing innovative actions for the prevention of forest wild fires, developing an advanced robotic system for the prevention of wildland and wildland-urban interface (WWUI) fires, in particular the wide protection corridors used as a fuel barrier, in a way to control and reduce the spread of large forest fires.

This closing workshop, is aimed both at the academic sector and also at a wider audience, including key players in the forestry sector. The morning includes a more technical session with the presentation of developments in robotic systems, followed by a session focused on topics related to the importance of forest management as a measure to promote their resilience. In the afternoon there will be onsite demonstrations of the equipment developed under the project.

Between July 10 and 16, the CMU Portugal Large Scale project iFetch will be part of VISUM 2022 a Summer School focused on Computer Vision and Machine Learning  organized by INESC TEC.

This event will be an inspiring opportunity for young researchers to get feedback from senior scholars and industrial researchers in Machine Learning and Computer Vision.

The iFetch team led by Ricardo Sousa (LinkedIn/Twitter), the project’s principal Investigator at FARFETCH, will support a Mentorship Program on Conversational Agents and will mentor one of the students to work on a computer vision problem while getting acquainted with the latest developments in conversational agents.

More about iFetch participation in our News article and at VISUM Website.

The LxMLS 2021 will take place from July 7th to July 15th in an online format (via zoom and slack). This year the event is organized jointly by Instituto Superior Técnico (IST), a leading Engineering and Science school in Portugal, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), Unbabel, Cleverly and Priberam, with the support of the CMU Portugal Program.

Important Dates
—————
* Application Deadline: May 15, 2021
* Decision: June 1, 2021
* Early Registration: June 15 – July 1, 2021
* Summer School: July 7 – 15, 2021

Topics and Intended Audience
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The school will cover a range of Machine Learning (ML) topics, from theory to practice, that are important in solving Natural Language Processing (NLP) problems that arise in the analysis and use of Web data.

The target audience is:
* Researchers and graduate students in the fields of NLP and Computational Linguistics;
* Computer scientists who have interests in statistics and machine learning;
* Industry practitioners who desire a more in depth understanding of these subjects.

Features of LxMLS:
* No deep previous knowledge of ML or NLP is required, but the attendants are assumed to have some basic background on mathematics and programming;
* Days are divided into morning lectures and afternoon lab sessions and practical talks (see schedule);
* The Labs guide will be provided one month in advance. Last year’s guide is available on the website.
* A day zero is scheduled to review basic concepts and introduce the necessary tools for implementation exercises
* Both basic (e.g linear classifiers) and advanced topics (e.g. deep learning, reinforcement learning) will be covered
* Lecturers are leading researchers in machine learning and natural language processing

Online Format:
Due to the current COVID-19 pandemic, the 11th Lisbon Machine Learning School will be held online (via zoom and slack). Similar to last year, we are excited for the opportunity to create a virtual school, where you will be able to attend all the lectures, and participate in the Q&As and labs remotely. We will also provide the tools for students to engage with each other remotely. The lectures will also be streamed to YouTube, and will become freely available later in our YouTube channel. The Q&A, labs and social activities will remain restricted to the accepted students only.

List of Confirmed Speakers
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LUIS PEDRO COELHO Fudan University | China
MÁRIO FIGUEIREDO Instituto de Telecomunicações & Instituto Superior Técnico | Portugal
ANDRE MARTINS Instituto de Telecomunicações & Unbabel | Portugal
IRYNA GULEYVICH Technical University Darmstat | Germany
NOAH SMITH University of Washington & Allen Institute for Artificial Intelligence | USA
SLAV PETROV Google Inc. | USA
XAVIER CARRERAS Artificial Intelligence Research Institute, CSIC | Spain
GRAHAM NEUBIG Carnegie Mellon University | USA
BHIKSHA RAJ Carnegie Mellon University | USA
CHRIS DYER Google Deep Mind | UK
ELIAS BARENBOIM Columbia University | USA
ADELE RIBEIRO Columbia University | USA
STEFAN RIEZLER Institut für Computerlinguistik, Universität Heidelberg | Germany
BARBARA PLANK IT University of Copenhagen | Denmark
SASHA RUSH Cornell Tech | USA

Please visit the event oficial page for up to date information: http://lxmls.it.pt/2021.

To apply, please fill the form in https://lisbonmls.wufoo.com/forms/application-form-lxmls-2021/
Any other questions should be directed to: lxmls-2021@lx.it.pt.