Ines De Miranda De Matos Lourenço

In November 2023 I defend my PhD "Learning from Interactions: Forward and Inverse Decision-Making for Autonomous Dynamical Systems"! You can find the thesis online here.

I started my PhD at KTH Royal Institute of Technology in July 2018, under the supervision of Prof. Bo Wahlberg and Alexandre Proutiere. I have been working with a variety of problems concerning decision-making in dynamical systems, including forward and inverse problems in adversarial, cooperative, and biological settings. You can find my PhD thesis (to be defended on the 23rd of November 2023) here, and the licentiate dissertation (from 2021) here.

I received the KTH Electrical Engineering Scholarship of Excellence and at ICDL-Epirob 2020 I was the winner of the Best Paper & Best Student Paper Award.

I am an affiliated student of the Wallenberg AI, Autonomous Systems and Software Program (WASP), where I belong to batch 1 of the WASP AI MLX (eXplainable Machine Learning) track. Within my batch, I belong to the Machine Learning, Deep Learning, and other AI cluster, and have taken the courses Deep Learning and GANs, and Graphical Models, Bayesian Learning, and Statistical Relational Learning. As a part of WASP, I have also attended two Winter Conferences, a Summer School, an International trip, and a WARA-PS Research Arena demo.

I was funded by the research environment New Directions in Learning Dynamical Systems (NewLEADS), and as a part of the System Identification group I have attended the 18th IFAC Symposium on System Identification, SYSID 2018, and three European Research Network System Identification (ERNSI) workshops (2018, 2019, and 2021).

I have been a TA for the Automatic Control course from 20219 to 2021 and have supervised two Bachelor projects.

In parallel with my research, I have been the Business Manager of the PhD Chapter board of KTH since February 2020. I organized the first edition of the Supervisor of the Year award 2020, reception lunches for PhD students, PhD pubs, and was responsible for doctoral student involvement in the THS Armada career fair. I have also been a board member of the female doctoral student network WOP@KTH since January 2019, where I was part of the organization of the Rising Stars event and networking mingles.

You can find more details about me on my personal webpage.

List of publications:

• I. Lourenço, A. Bobu, C. R. Rojas, and B. Wahlberg, “Diagnosing and
  Augmenting Feature Representations in Correctional Inverse Reinforcement Learning,” 62nd IEEE Conference on Decision and Control (CDC), 2023.
• R. Winqvist, I. Lourenço, F. Quinzan, C. R. Rojas, and B. Wahlberg,
  “Optimal Transport for Correctional Learning,” 62nd IEEE Conference on Decision and Control (CDC), 2023.
• I. Lourenço, R. Winqvist, C. R. Rojas, and B. Wahlberg, “A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning,” 61st IEEE Conference on Decision and Control (CDC), 2022
• I. Lourenço, R. Mattila, C. R. Rojas, and B. Wahlberg, “Hidden Markov Models: Inverse Filtering, Belief Estimation and Privacy Protection,” accepted for publication in the Journal of Systems Science and Complexity (JSSC), 2021
• I. Lourenço, R. Mattila, R. Ventura and B. Wahlberg, “A Biologically-Inspired Computational Model of Time Perception,” accepted for publication in the IEEE Transactions on Cognitive and Developmental Systems (TCDS), 2021.
• I. Lourenço, R. Mattila, C. R. Rojas, and B. Wahlberg, “Cooperative System Identification via Correctional Learning,” 19th IFAC Symposium on System Identification (SYSID), 2021.
• I. Lourenço, R. Mattila, C. R. Rojas, V. Krishnamurthy, and B. Wahlberg, “How to protect your privacy? A framework for counter-adversarial decision making,” 59th IEEE Conference on Decision and Control (CDC), 2020.
• R. Mattila, I. Lourenço, C. R. Rojas, and B. Wahlberg, “What did your adversary believe? Optimal filtering and smoothing in counter-adversarial autonomous systems,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.
• I. Lourenço, R. Ventura, and B. Wahlberg, “Teaching Robots to Perceive Time: A Twofold Learning Approach,” Joint IEEE 10th International Conference on Development and Learning (ICDL), 2020.
• R. Mattila, I. Lourenço, C. R. Rojas, V. Krishnamurthy, and B. Wahlberg, “Estimating private beliefs of Bayesian agents based on observed decisions,” IEEE Control Systems Letters (LCSS), vol. 3, no. 3, pp. 523–528, 2019.