CV

Download my CV (Last updated: June 22, 2026)

Education

PhD Student in Computer Science and Engineering, AI Laboratory for Molecular Engineering (AIME), Chalmers University of Technology

2024 – December 2026 (expected) · PI: Rocío Mercado Oropeza

Master’s Degree in Data Science and AI, Chalmers University of Technology

2023 · Avg. grade: 4.3/5 · Thesis: “Machine Learning for Predicting Targeted Protein Degradation”

Swedish Licentiate of Technology, Chalmers University of Technology

2021 · Thesis: “Multi-LSTM Acceleration and CNN Fault Tolerance”

Erasmus Exchange in Embedded Electronic System Design, Chalmers University of Technology

2016 · Avg. grade: 4.25/5

Master’s Degree in Computer Engineering Embedded Systems, Polytechnic University of Turin

2016 · Final grade: 101/110

Bachelor’s Degree in Computer Engineering, University of Modena and Reggio Emilia

2014 · Final grade: 96/110 · Thesis: “Study and Research on Responsive Technologies for Web Applications”

Work experience

PhD Student, AI Laboratory for Molecular Engineering (AIME), Chalmers University of Technology

March 2024 – Present · Researching AI-driven molecular engineering with applications in drug discovery

Research Student, AI Laboratory for Molecular Engineering (AIME), Chalmers University of Technology

Fall 2023 · Continuing master thesis work on predicting protein-ligand activity using deep learning models

Master Thesis Intern, AstraZeneca, Gothenburg

Spring 2023 · Worked on thesis titled “Machine Learning for Predicting Targeted Protein Degradation”

Hardware Engineer, Cobham Gaisler, Gothenburg

2020 – 2022 · Digital designer writing and testing RTL modules for RISC-V based processors

Teaching Assistant, Chalmers University of Technology

2016 – 2020 · Taught VHDL and assisted in courses related to signal processing and computer architecture

Intern, QUIX Software Solutions & Consulting, Italy

2014 · Software development focusing on public sector service management

Skills

• Deep learning frameworks and high performance computing: PyTorch, PyTorch Lightning, Optuna, TensorFlow, CuPy, Numba
• Cheminformatics and molecular ML: RDKit, XGBoost, Transformers, protein language models
• Programming: Python, C, C++, CUDA, OpenCL
• Proficient in Git and scripting in both Linux and Windows environments

Selected publications

See the publications page for the full list, including code, data and demos.

• “TACK: A statistical evaluation of degradation activity on a novel TArgeting Chimeras Knowledge dataset”, KDD 2026. With N. Dunlop and R. Mercado. arXiv:2605.19579
• “PROTAC-Splitter: a machine learning framework for automated identification of PROTAC substructures”, Journal of Cheminformatics 18, 30, 2026. With R. Zhang, T. Cropsal, A. Källberg, C. Tyrchan, E. Nittinger and R. Mercado. doi:10.1186/s13321-025-01135-9
• “Modeling PROTAC degradation activity with machine learning”, Artificial Intelligence in the Life Sciences 6, 2024. With E. Nittinger, C. Tyrchan and R. Mercado. doi:10.1016/j.ailsci.2024.100104
• “Mapping Multiple LSTM Models on FPGAs”, ICFPT 2020. With P. Trancoso, I. Sourdis and C.-S. Bouganis. doi:10.1109/ICFPT51103.2020.00010

Supervision

MSc thesis supervision at Chalmers University of Technology — see the supervision page for details.

• Tingting Mo and Jia Xin Zhu, Improving AI-based Synthesizability Scores for Next-Generation Protein Degradation Drugs, 2026. Examiner R. Mercado.
• Alexander Persson and Felix Erngård, reinforcement learning with language models for multi-objective PROTAC optimization, 2026. Examiner R. Mercado.
• Ranxuan Zhang, Machine Learning for PROTAC Decomposition and Enhanced Degradation Prediction, 2025. Co-supervised with E. Nittinger and C. Tyrchan (AstraZeneca); examiner R. Mercado. Led to the PROTAC-Splitter paper.

Talks and posters

Selected — see the talks page for the full list with venues and dates.

• Poster, TACK, ML2MD Symposium, Uppsala, 2026 (also KDD, Jeju, August 2026)
• Tutorial, Practical significance methods for molecular property prediction, AIME Lab Workshop, 2026
• Poster, PROTAC-Splitter, International Conference on Chemical Structures, Noordwijkerhout, 2025
• Seminar talk, DSAI Seminar, Chalmers CSE, 2025
• Poster, Modeling PROTAC degradation activity with ML, AI in Drug Discovery and Biomedicine, Barcelona, 2025
• Posters at the Generative Modeling Summer School (Eindhoven) and the Neuro-Symbolic AI Summer School (Salem), 2024
• Paper presentation, ICFPT, Maui, 2020

Service

• Peer reviewer since 2024 — 10 papers for venues including EurIPS ML4Molecules, the NeurIPS AI4Materials workshop, ICLR, and Nature Communications.

Funding

• Chalmers Gender Initiative for Excellence (Genie)
• Wallenberg AI, Autonomous Systems and Software Program (WASP), funded by the Knut and Alice Wallenberg Foundation
• Compute and storage provided by Chalmers e-Commons and the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council (grant agreement no. 2022-06725)

Languages

• English: Fluent (IELTS 6.5, 2015)
• Swedish: Colloquial level
• Italian: Mother Tongue

Interests and Activities

• Sketching, cooking, and playing football as a goalkeeper (but don’t ask me for my favorite team, I have no clue!)