Carlos de la Torre

Carlos de la Torre

PhD Researcher in Computer Science

University of Helsinki

Hey, I’m Carlos! 👋

I spend my time bridging Machine learning and Neuroscience, currently developing novel approaches in Brain-Computer Interfacing. My vision is to have computing systems augmenting our cognitive abilities and supporting our well-being, naturally integrating with our cognition. I found home at the Cognitive Computing group led by Academy Research Fellow Tuukka Ruotsalo at the University of Helsinki.

I do some Cognitive modeling and used to spend my days slicing brains and herding cells as I started my research in Neurobiology and Neuropharmacology.

  • Machine Learning
  • Physiological computing
  • Human-computer interaction
  • PhD Computer Science, 2021-

    University of Helsinki

  • BSc Biotechnology, 2019

    Universidad CEU San Pablo

  • BSc (MSc) Pharmacy, 2018

    Universidad CEU San Pablo

  • PCert Clinical Trials, 2017

    University of Chicago


(2022). Brain-Supervised Image Editing. CVPR.

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(2021). [Re] Neural Network Model of Memory Retrieval. ReScience C.

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(2021). Improving Artificial Teachers by Considering How People Learn and Forget. In IUI.

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(2020). Brain Relevance Feedback for Interactive Image Generation. UIST.

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(2018). Endogenous pleiotrophin and midkine regulate LPS-induced glial responses. Neuroscience Letters.

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I use electroencephalography (EEG) recordings to implicitly control generative models’ (generative adversarial neural networks or GANs) output visual features as a model task.

🧠 What are the similarities between human cognition and machine learning models?

I investigated the similarity between images measured as GAN-learned distances and compared them to human perceptual distances (Psychophysiology, submitted).

📈 How can we use physiological recordings or medical data in user-centered machine learning methods?

I use EEG as feedback to steer the GAN and match the user’s intentions (e.g., I think of a blond and smiling person, the GAN generates a blond and smiling person UIST'20). I extend the applications to brain-based semantic image editing (CVPR'22) and implicit multi-user image generation (in progress).

🤖 Can such data inform us to enhance or design novel machine learning algorithms?

In progress.

📚 Can we model human memory and help humans learn?

I have combined a cognitive model of memory (infer how the user learns and forgets) and model-based planning (when to review or show a new item) for intelligent tutoring systems IUI'21. I have also contributed to open science by replicating a connectionist memory model, leading to correcting the original manuscript (ReScience C).


  • Mentoring, networking, advocacy, and event planning.
  • Tech: GNU+Linux (Gentoo/Arch/Debian), FLOSS, Vim, UNIX philosophy, refurbishing computers.
  • Sports, personal growth, and music: certified coach and personal trainer. I play triad chords on a (music) keyboard.


See all technical and leadership.

Machine learning

scikit-learn, PyTorch

Neurophysiological data

MNE-Python, EEG

Data science

pandas, numpy, seaborn, matplotlib

Recent Timeline

See all technical and leadership skills, including courses.

University of Helsinki
Course: Project Management and Leadership (21 h)
Sep 2022 – Sep 2022 Online

Projects are temporary endeavors to create unique outcomes; they have a scope, a schedule, a budget, a certain level of quality, and other essential parts. To animate them, somebody needs to manage and lead the team. On completion of the course, the participants will:

  • Know what the discipline of Project Management is, its current state, and the existing standards,
  • Learn what does and does not work in a Research environment and why,
  • Learn how to organize work towards a goal efficiently,
  • Be able to develop a professional project’s schedule and budget,
  • Learn good practices for communication within a project, influence others, and act in a crisis.
Course: Introduction to Machine Learning Problem Framing (0.75 h)
Sep 2022 – Sep 2022 Online

I revised concepts about outlining ML solutions:

  • Identify if ML is a good solution for a problem.
  • Learn how to frame an ML problem.
  • Understand how to pick the suitable model and define success metrics.
Université Paris 1 Panthéon-Sorbonne
Course: Entrepreneurial PhD Academy (24 h) — Una Europa online training
Jun 2022 – Jun 2022 Online

The online training for selected PhD students consisted of 3 full days of online sessions with the following learning objectives:

  • To strengthen transversal skills, including entrepreneurship, market analysis, and business modeling, and apply them to the PhD student’s research project.
  • To develop international and multicultural networking skills by cooperating in an interactive program with peers, mentors and, academics from Una Europa partner universities.
  • To acquire practical knowledge about tools for the development of business ideas.



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