I am currently a Postdoctoral Researcher at the Max Planck Institute for Security and Privacy (MPI-SP), Germany with Director Meeyoung Cha. Previously, I was a Postdoctoral Researcher at the Institute for Basic Science, South Korea. I received my PhD from KAIST and my Bachelor’s and Master’s degrees from the Federal University of Rio de Janeiro.

Research Interests

My general research interests include Reinforcement Learning, Multi-Agent Systems, Robotics, and AI for Science. From these, I am particularly interested in:

  • Reinforcement Learning: improving sampling efficiency and performance by developing novel credit assignment and exploration methods, goal-conditioned RL, and continual learning.
  • Multi-Agent Systems: development of multi-agent RL algorithms in collaborative and collaborative-competitive environments, modeling of continual multi-agent frameworks.
  • Robotics: end-to-end control, foundation models for robotics, safety and risks of robotic systems.
  • AI for Science: generative AI for protein and antibody design, flexible AI architectures (graph neural networks, diffusion models).

My research vision is to develop algorithms that advance our knowledge on how humans and machines learn, interact, and collaborate.

News

  • [2026-04] Our work “Exploring LLM Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions” has been accepted at ACL Findings 2026.
  • [2026-01] Our work “Interpretable Machine Learning for Protein Science: Structure, Function, and Interactions” has been accepted for publication in ACM Computing Surveys.
  • [2025-12] Our work “Longitudinal Identification of Critical Factors in Nurse Turnover for Supporting Workforce Well-Being” has been accepted for oral presentation at HCI Korea 2026.
  • [2025-11] Our work “Dropouts in Confidence: Moral Uncertainty in Human-LLM Alignment” has been accepted as a poster at the AAAI 2025 AI Alignment track.
  • [2025-11] Our work “Computational Design and Glycoengineering of Interferon-Lambda for Nasal Prophylaxis against Respiratory Viruses” has been accepted for publication in Advanced Science.
  • [2025-10] I presented the lecture “From self-organized networks to deep reinforcement learning: perspectives on AI research” at the Center for Neuroscience-inspired AI, KAIST, South Korea.
  • [2025-07] Our work “Artificial intelligence-driven computational methods for antibody design and optimization” has been accepted for publication in mAbs.
  • [2025-07] Attended and helped organizing the MPI-SP Symposium on Challenges in a Digitalised Society in Bochum, Germany.
  • [2025-07] I presented the lecture “LLMs outside NLP Applications” at Ruhr University Bochum, Germany.
  • [2025-06] Our preprint “Predicting Individual Life Trajectories: Addressing Uncertainty in Social Employment Transitions” is now in SocArXiv.

Selected Publications

  1. Interpretable Machine Learning for Protein Science: Structure, Function, and Interactions
    Vecchietti, L. F. et al.
    ACM Computing Surveys, 2026
    paper
  2. Dropouts in Confidence: Moral Uncertainty in Human-LLM Alignment
    Kwon, J., Vecchietti, L. F. et al.
    Proceedings of the AAAI Conference on Artificial Intelligence, AI Alignment Track, 2026
    paper
  3. Artificial intelligence-driven computational methods for antibody design and optimization
    Vecchietti, L. F., Wijaya, B. N. et al.
    mAbs, 2025
    paper
  4. Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
    Vecchietti*, L. F., Lee*, M. et al.
    Proceedings of the International Conference on Machine Learning (ICML), 2024 — Spotlight Poster, co-first authors
    paper code
  5. AI World Cup: Robot Soccer-Based Competitions
    Hong, C., Jeong, I., Vecchietti, L. F. et al.
    IEEE Transactions on Games, 2021 — Main simulation environment for the AI World Cup competitions
    paper
  6. Sampling Rate Decay in Hindsight Experience Replay for Robot Control
    Vecchietti, L. F., Seo, M. et al.
    IEEE Transactions on Cybernetics, 2020
    paper
  7. Batch Prioritization in Multigoal Reinforcement Learning
    Vecchietti, L. F. et al.
    IEEE Access, 2020
    paper
  8. Rewards Prediction Based Credit Assignment for Reinforcement Learning with Sparse Binary Rewards
    Seo, M., Vecchietti, L. F. et al.
    IEEE Access, 2019 — Selected as featured research in KAIST Breakthroughs Magazine 2020
    paper

Invited Talks

  • [1] From self-organized networks to deep reinforcement learning: perspectives on AI research
    Center for Neuroscience-inspired AI, KAIST, South Korea, October 2025.
  • [2] LLMs outside Natural Language Processing applications
    Ruhr University Bochum (RUB), Germany, July 2025.
  • [3] Integrating data science and AI methods in multidisciplinary research to make discoveries with social impact
    WebImmunization Seminar, University of Oslo, Norway, December 2024.
  • [4] Robust optimization in protein fitness landscapes using reinforcement learning in latent space
    Cradle Bio, Zurich, Switzerland, November 2024.
  • [5] Developing and applying deep learning methods for protein design
    Graduate School of AI, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea, July 2023.
  • [6] Developing and applying deep learning methods to facilitate new scientific discoveries
    Max Planck Institute for Security and Privacy (MPI-SP), Bochum, Germany, May 2023.
  • [7] Target-conditioned protein and antibody design for drug discovery
    IBS Winter School on AI-Boosted Basic Science, Institute for Basic Science, Daejeon, South Korea, December 2022. Co-delivered with Prof. Ho Min Kim (KAIST)
  • [8] Identifying the key actions that lead an agent to accomplish a task in model-based reinforcement learning
    School of AI Convergence, Chonnam National University, Gwangju, South Korea, November 2021.
  • [9] Performance enhancement in multigoal model-based deep reinforcement learning
    Cho Chun Shik Graduate School of Mobility, KAIST, Daejeon, South Korea, October 2021.
  • [10] Identifying the key actions that lead an agent to accomplish a task in model-based reinforcement learning
    Data Science Group, Institute for Basic Science, Daejeon, South Korea, April 2021.

Diversity Statement

I am committed to increasing diversity in AI research. If you are from an underrepresented group and need research mentoring and guidance, feel free to contact me for a mentoring session.

Personal Interests

My personal interests include traveling, sports (football, kickboxing, skate, snowboard, surf, surfskate), music (classical guitar, cavaquinho) and reading (fantasy, science fiction, mountaineering).