Research Statement

My research focuses on developing artificial intelligence techniques and their applications in real-world domains. In particular, my research interests include the following topics: multiagent systems, machine learning, reinforcement learning, generative artificial intelligence, and real options.

I also believe that successful research results involve fully implemented solutions for real-world problems.

[Long Research Statement]

Trustworthy Ad Hoc Teamwork (2022-2024)

Grant from the Air Force Office of Scientific Research (AFOSR), USA

Grant Agreement: FA9550-22-1-0475

Role in project: Principal Investigator

Description: Trustworthy Ad Hoc Teamwork project aims to create an autonomous agent that can collaborate efficiently and robustly with previously unknown teammates. In addition, we will create novel ad hoc teamwork algorithms and explore the issue of trust in human-robot interactions.

RELEvaNT - REinforcement LEarning in Non-stationary environmenTs (2022-2024)

Grant from Fundação para a Ciência e a Tecnologia, Portugal

Grant Agreement: PTDC/CCI-COM/5060/2021

Role in project: Co-Principal Investigator

Description: Reinforcement learning (RL), both classical algorithms and their deep variants, critically rely on the fact that the underlying system is assumed stationary. RELEvaNT will investigate new models and methods for efficient deep RL in non-stationary environments and the potential applications on several "human-centered" domains.

HOTSPOT - Human-robOt TeamS without PrecoOrdinaTion (2021-2024)

Grant from Fundação para a Ciência e a Tecnologia, Portugal

Grant Agreement: PTDC/CCI-COM/7203/2020

Role in project: Principal Investigator

Description: Ad hoc teamwork aims to build learning agents, such as softbots or robots, that engage in cooperative tasks with other unknown agents. This research project explores the challenges posed by the collaborative interaction between robots and humans in order to build novel decision-making algorithms.[More information]

TAILOR - Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization (2020-2023)

Grant from the European Commission

Grant Agreement: 952215

Role in project: Senior Researcher

Description: The EU-funded TAILOR project aims to bring European AI groups together in a single scientific network on the Foundations of Trustworthy AI. Hence, reducing the fragmentation and increasing the joint AI research capacity, helping it take the lead and advance the state-of-the-art in trustworthy AI. [More information]

Ad Hoc Teams With Humans And Robots (2018-2021)

Grant from the Air Force Office of Scientific Research (AFOSR), USA

Award Number: FA9550-19-1-0020

Role in project: Principal Investigator

Description: This project explores cutting-edge research on ad hoc teamwork and natural language processing for human-robot interaction. The project aims to create novel algorithms for ad hoc teamwork that are tailored for human-robot collaboration. [More information]

ILU - Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (2019-2021)

Grant from Fundação para a Ciência e a Tecnologia, Portugal

Grant Agreement ID: DSAIPA/DS/0111/2018

Role in project: Senior Researcher

Description: Our research work aims to create novel deep reinforcement learning approaches for traffic light control. In particular, we are interested in distributed RL approaches that take advantage of rich multiagent models, thus learning distributed policies that consider several traffic patterns (e.g., disruptive events, pedestrians, and vehicles). [More information]

INSIDE - Intelligent Networked robot systems for Symbiotic Interaction with children with impaired DEvelopment (2014 - 2018)

Grant from FCT within the CMU-Portugal Program

Grant Agreement ID: CMUP-ERI/HCI/0051/2013

Role in project: Senior Researcher

Description: The INSIDE project explored how to use symbiotic interactions between humans and robots in joint cooperative activities. The project strived to develop new hardware and software solutions to support real-world interactions between children with ASD and a robot in cooperative tasks with therapeutical purposes. [More information]

LIREC - LIving with Robots and IntEractive Companions (2011 - 2013)

Grant from the European Commission

Grant Agreement ID: EU FP7 ICT-215554 project

Role in project: Senior Researcher

Description: LIREC is a research project that explored how we live with digital and interactive companions. Throughout the project, we explored how to design digital and interactive companions who could develop and read emotions and act cross-platform. [More information]