Daniela Gonzalez

About

Daniela Gonzalez is a Research Scientist at the Institute for Experiential AI at Northeastern University, with over ten years of research experience spanning wet-lab biology, computational modeling, and data science. She holds a Ph.D. in Biological Sciences, an M.S. in Data Science, and a B.S. in Biotechnology. Before joining the Institute, she developed machine learning pipelines for biologics in the pharmaceutical industry, with hands-on experience in molecular docking, QSAR modeling, and cancer cell line assays. This combination of experimental and computational expertise shapes her approach to research, grounding algorithmic development in biological and clinical relevance.

Her research interests include deep learning for drug discovery, pharmacological modeling, multi-omics data integration, and molecular feature engineering. Her current work focuses on extending deep learning architectures to predict drug combination synergy and therapeutic windows for personalized cancer treatment, integrating tumor transcriptomics with rich molecular drug representations. She is particularly interested in how computational predictions can be validated experimentally and translated toward clinical decision-making, an area where most existing models fall short.

She has authored over ten peer-reviewed publications spanning early drug discovery, computational biology, and data science. Her long-term goal is to strengthen the bridge between computational prediction and experimental validation in precision oncology, contributing to a future where in silico models reliably inform therapeutic decisions at the patient level.

Research Interests

Deep learning for drug discovery, drug combination modeling, pharmacological modeling, multi-omics integration, and precision oncology.

Education

Ph.D. in Biological Sciences, Universidad Nacional de Tucumán, 2023
M.S. in Data Science, Northeastern University, 2026
B.S. in Biotechnology, Universidad Nacional de Tucumán, 2018