Download CV (PDF) Contact: felipe.vecchietti at mpi-sp.org

General Information

I am currently a Postdoctoral Researcher at the Max Planck Institute for Security and Privacy (MPI-SP), Bochum, Germany, with Director Meeyoung Cha.

Research Interests

  • Reinforcement Learning (Credit Assignment, Exploration, Goal-conditioned RL, Continual Learning)
  • Multi-Agent Systems (Collaborative, Collaborative-Competitive)
  • Robotics (Foundation Models, End-to-End Control, Safety and Risks)
  • AI for Science (Generative AI, Protein and Antibody Design, Drug Discovery, Graph Neural Networks, Diffusion Models)

Positions

2024.11–Present — Max Planck Institute for Security and Privacy (MPI-SP), Germany Postdoctoral Researcher. Adviser: Meeyoung Cha

2021.10–2024.9 — Data Science Group, Institute for Basic Science (IBS), South Korea Senior Researcher. Adviser: Meeyoung Cha

2021.3–2021.9 — Mechanical Engineering Research Institute, KAIST, South Korea Postdoctoral Researcher. Adviser: Dongsoo Har

Education

2017–2021 — Korea Advanced Institute of Science and Technology (KAIST) PhD in Green Transportation. Thesis: Performance Enhancement in Multigoal Reinforcement Learning using Hindsight Experience Replay. Adviser: Dongsoo Har

2015–2017 — Federal University of Rio de Janeiro (UFRJ) Master’s Degree in Electrical Engineering. Dissertation: Comparison between rule-based and data-driven NLP algorithms for Brazilian Portuguese speech synthesis. Adviser: Fernando Gil Vianna Resende Junior

2013 — Korea Advanced Institute of Science and Technology (KAIST) Exchange Student, Electrical Engineering Department

2009–2015 — Federal University of Rio de Janeiro (UFRJ) Bachelor’s Degree in Electronic and Computer Engineering

Internships

Summer 2013 — Hyundai Motor Company, Namyang R&D Center Eco Vehicle Control System Development Team

Publications

The † symbol denotes a mentored student.

Peer-Reviewed Journals

  1. Vecchietti, L. F., & others. (2026). Interpretable Machine Learning for Protein Science: Structure, Function, and Interactions. ACM Computing Surveys. paper
  2. Vecchietti, L. F., Wijaya, B. N., & others. (2025). Artificial intelligence-driven computational methods for antibody design and optimization. MAbs. https://doi.org/10.1080/19420862.2025.2528902 paper
  3. Yun, J., Yang, S., Kwon, J. H., Vecchietti, L. F., Lee, M., & others. (2025). Computational Design and Glycoengineering of Interferon-Lambda for Nasal Prophylaxis against Respiratory Viruses. Advanced Science.
  4. Ribeiro, T. B., Vecchietti, L. F., & others. (2025). Overabundance of abelisaurid teeth in the Açu Formation (Albian-Cenomanian), Potiguar Basin, Northeastern Brazil: morphometric, cladistic and machine learning approaches. Journal of Vertebrate Paleontology.
  5. Mishra, S., Rajendran, P. K., Vecchietti, L. F., & others. (2023). Sensing accident-prone features in urban scenes for proactive driving and accident prevention. IEEE Transactions on Intelligent Transportation Systems.
  6. Hong, C., Jeong, I., Vecchietti, L. F., & others. (2021). AI World Cup: Robot Soccer-Based Competitions. IEEE Transactions on Games. paper
  7. Kim, T., Vecchietti, L. F., & others. (2021). Two-stage training algorithm for AI robot soccer. PeerJ Computer Science.
  8. Lee, S., Jin, H., Vecchietti, L. F., & others. (2021). Cooperative decentralized peer-to-peer electricity trading of nanogrid clusters based on predictions of load demand and PV power generation using a gated recurrent unit model. IET Renewable Power Generation, 15, 3505–3523.
  9. Vecchietti, L. F., Seo, M., & Har, D. (2020). Sampling Rate Decay in Hindsight Experience Replay for Robot Control. IEEE Transactions on Cybernetics. paper
  10. Vecchietti, L. F., & others. (2020). Batch Prioritization in Multigoal Reinforcement Learning. IEEE Access, 8, 137449–137461. paper
  11. Lee, S., Vecchietti, L. F., & others. (2020). Power Management by LSTM Network for Nanogrids. IEEE Access.
  12. Lee, S., Jin, H., Vecchietti, L. F., & others. (2020). Short-term predictive power management of PV-powered nanogrids. IEEE Access, 8, 147839–147857.
  13. Kim, T., Vecchietti, L. F., & others. (2020). Machine Learning for Advanced Wireless Sensor Networks: A Review. IEEE Sensors Journal.
  14. Kim, S., Kim, I., Vecchietti, L. F., & others. (2020). Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization. Applied Sciences, 10(24), 8876.
  15. Seo, M., Vecchietti, L. F., Lee, S., & Har, D. (2019). Rewards Prediction Based Credit Assignment for Reinforcement Learning with Sparse Binary Rewards. IEEE Access. paper

Peer-Reviewed Conferences

  1. Kwon, J., Vecchietti, L. F., & others. (2026). Dropouts in Confidence: Moral Uncertainty in Human-LLM Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, AI Alignment Track. paper
  2. Kim, J., Kwon, J., Vecchietti, L. F., & others. (2025). Exploring persona-dependent LLM alignment for the moral machine experiment. ICLR BiAlign Workshop.
  3. Vecchietti, L. F., Lee, M., & others. (2024). Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space. Proceedings of the International Conference on Machine Learning (ICML). code
  4. Wijaya, B. N., Vecchietti, L. F., & others. (2023). Evaluation of Antibody Structure Reconstruction with an SE(3)-Equivariant Graph Neural Network. Korea Software Congress (KSC).
  5. Jung, H., Vecchietti, L. F., & others. (2023). Protein Structure Tokenizer for Efficient Learning. Peptalk.
  6. Hangeldiyev, B., Rzayev, A., Armanuly, A., Jung, H., Vecchietti, L. F., & others. (2022). Antibody Sequence Design With Graph-Based Deep Learning Methods. Korea Software Congress (KSC).
  7. Lee, M., Vecchietti, L. F., & others. (2022). Protein Sequence Design in a Latent Space via Model-based Reinforcement Learning. NeurIPS Machine Learning in Structural Biology (MLSB) Workshop.
  8. Lee, M., Rzayev, A., Jung, H., Vecchietti, L. F., & others. (2022). Structure-based representation for protein functionality prediction using machine learning. Korea Computer Congress (KCC).
  9. Rajendran, P. K., Mishra, S., Vecchietti, L. F., & others. (2022). RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training. ECCV IWDSC Workshop.

Preprints

  1. Vecgaile, L., Spata, A., Vecchietti, L. F., & Zagheni, E. (2025). Predicting Individual Life Trajectories: Addressing Uncertainty in Social Employment Transitions. SocArXiv.
  2. Vecchietti, L. F., & others. (2024). Recent advances in interpretable machine learning using structure-based protein representations. arXiv.

Academic Services

Reviewer — Journals: IEEE Transactions on Cybernetics, IEEE Transactions on Games, IEEE Sensors, Frontiers in Robotics and AI

Reviewer — Conferences: AAAI ICWSM 2022, NeurIPS 2024, ACM WSDM 2025, ICLR 2025, AISTATS 2025, ICML 2025, NeurIPS 2025, AAAI 2026, ICLR 2026

Reviewer — Workshops: ICML LatinX 2021, ICLR Reincarnating RL 2023, NeurIPS MLSB 2023, ICML ML4LMS 2024, NeurIPS MLSB 2024, NeurIPS MLSB 2025

Invited Talks

[I1] From self-organized networks to deep reinforcement learning: perspectives on AI research Center for Neuroscience-inspired AI, KAIST, South Korea, October 2025.

[I2] LLMs outside Natural Language Processing Applications Ruhr University Bochum (RUB), Germany, July 2025.

[I3] Integrating Data Science and AI methods in multidisciplinary research to make discoveries with social impact WebImmunization Seminar, University of Oslo, Norway, December 2024.

[I4] Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space Cradle Bio, Zurich, Switzerland, November 2024.

[I5] 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.

[I6] Developing and applying deep learning methods to facilitate new scientific discoveries Max Planck Institute for Security and Privacy (MPI-SP), Bochum, Germany, May 2023.

[I7] 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)

[I8] 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.

[I9] Performance enhancement in multigoal model-based deep reinforcement learning Cho Chun Shik Graduate School of Mobility, KAIST, Daejeon, South Korea, October 2021.

[I10] 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.

Mentored Students

It is a great pleasure to work with very talented and hardworking students.

I am very thankful for the support and trust given from my advisors throughout my career: Prof. Fernando Gil Vianna Resende Junior (MsC), Prof. Dongsoo Har (PhD), and currently Prof. Ho Min Kim and Prof. Meeyoung Cha.

Current

Bryan Nathanael Wijaya (2023–present) — MsC Candidate, KAIST Topic: Antigen-conditioned Antibody Design. Published a first-author paper in mAbs.

Begench Hangeldiyev (2022–present) — MsC Candidate, KAIST Topic: Antibody Sequence Design with Graph-Based Methods. Published first paper on antibody design at KSC 2023.

Alumni

Jiseon Kim (2024–2025) — PhD Candidate, KAIST. Topic: Human-AI Alignment. Presented a first-author paper at ACL Findings and the ICLR BiAlign Workshop 2025.

Hyunkyu Jung (2021–2025, now PhD Candidate at KAIST) — PhD Candidate, KAIST. Topic: Protein-Protein Interactions using Equivariant Point Cloud Transformers.

Anar Rzayev (2022–2025, now Intern at ISTA) — CS Undergraduate, KAIST. Topic: Antibody Structure Representation Learning.

Sangmin Lee (2023) — CS Undergraduate, KAIST. Topic: AI-based methods for thermostable protein design.

Maxim Krassimirov Mintchev (2022, now Research Associate at TU Berlin) — ME Master’s Candidate, KIT/KAIST (Double Degree). Topic: Recommendation Systems for Logistics. Successfully defended master’s thesis.

Minji Lee (2022, now PhD Candidate at Columbia University) — CS Undergraduate, KAIST. Topic: Protein Optimization using Deep RL. First-author paper at ICML 2024 (Spotlight). Papers at KCC and NeurIPS MLSB Workshop 2022.

Lucas Santiago Peixoto (2022) — Computer Engineering Undergraduate, UFRJ. Topic: Sentence-level Text Analysis using Transformers. Successfully defended undergraduate thesis.

Matheus Tymburiba Elian (2021–2022, now Faculty at UFMG) — Industrial Design PhD Candidate, University of Tsukuba. Topic: Gender-Ambiguous Voice Agents. Published first-author paper in Estudos em Design 2025.

Kien Hoang (2021–2022, now MsC Candidate at EPFL) — Mathematics Undergraduate, KAIST. Topic: Graph structure optimization with Deep RL.

Sumit Mishra (2020–2022, now PhD Candidate at KAIST) — Robotics Master’s Candidate, KAIST. Topic: Intelligent Transportation Systems. First-author paper in IEEE Transactions on ITS.

Praveen Kumar Rajendran (2020–2022, now CV Engineer at Neubility) — Future Vehicle Master’s Candidate, KAIST. Topic: Camera Pose Estimation. First-author paper at ECCV IWDSC Workshop 2022.

Taeyoung Kim (2019–2021, now PhD Candidate at KAIST) — Green Transportation Master’s Candidate, KAIST. Topic: Deep RL, Cooperative-Competitive Multi-agent Systems. First-author review paper in IEEE Sensors Journal; first-author paper in PeerJ Computer Science.

Kyujin Choi (2019–2020, now ML Engineer at KT) — Green Transportation Master’s Candidate, KAIST. Topic: Deep RL, Cooperative-Competitive Multi-agent Systems.

Minah Seo (2018–2019, now ML Engineer at KT) — Green Transportation Master’s Candidate, KAIST. Topic: Credit Assignment in Deep RL. First-author paper in IEEE Access.