guadalupe gonzalez
guadalupe gonzalez
I joined Genentech/Roche in February 2023, after completing a PhD on graph deep learning for lead discovery advised by Michael Bronstein and Kirill Veselkov at Imperial College London. I am part of the Frontier Research team at Prescient/MLDD in Genentech Research and Early Development (gRED).
My expertise lies at the intersection of graph deep learning and causal inference and my focus is on (causal) graph deep learning for drug discovery/design, from the small-scale (e.g., proteins) to the large-scale (e.g., patient data) systems. I am particularly passionate about applying my knowledge to women’s health to catalyze breakthroughs in women-specific conditions such as endometriosis and preeclampsia.
Senior ML Scientist II
Prescient Design, Genentech
selected publications
On knowing a gene: A distributional hypothesis of gene function
by Kwon, J. J., Pan, J., Gonzalez G., Hahn W. C., Zitnik M. In Cell Systems (2024)
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
by Gonzalez G., Herath I., Veselkov K., Bronstein M., Zitnik M. In biorxiv (2024)
Towards Training GNNs using Explanation Directed Message Passing
by Giunchiglia V., Varun Shukla C., Gonzalez G., Agarwal, C. In Learning on Graphs Conference (2022)
Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19
by Laponogov, I.*, Gonzalez, G.*, Shepherd, M., Qureshi, A., Veselkov, D., Charkoftaki, G., Vasiliou, V., Youssef, J., Bronstein, M., Veselkov, K., * equal contribution. In Human Genomics (2021).
Phytochemically rich dietary components and the risk of colorectal cancer: A systematic review and meta-analysis of observational studies.
by Borgas, P., Gonzalez, G., Veselkov, K., Mirnezami, R. In World Journal of Clinical Oncology (2021).
Predicting anticancer hyperfoods with graph convolutional networks
by Gonzalez, G., Gong, S., Laponogov, I., Bronstein, M., Veselkov, K. In Human Genomics (2021).
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
by Gonzalez, G., Gong, S., Laponogov, I., Veselkov, K. and Bronstein, M. In NeurIPS Graph Representation Learning Workshop (2019).
education
2021 - 2023
Visiting PhD, Harvard Medical School, US
2018 - 2023
PhD Computing. Imperial College London, UK
2017 - 2018
MRes Data Science. Imperial College London, UK
2013 - 2017
BSc Biomedical Engineering. Polytechnic University of Madrid, Spain
teaching
Imperial College London
Fall term 2021
GTA for Machine Learning, Faculty of Medicine.
Summer term 2021
Instructor for workshop: Big Data and the ’Dark Matter’ of COVID, Faculty of Medicine.
Spring term 2020
GAT for Deep Learning, Faculty of Engineering.
Summer term 2019
GTA for Big Data and Machine Learning, Faculty of Engineering.
Non-profit organizations
Summer 2020
In2Science instructor, Machine Learning sessions to encourage Year 12 students to progress into STEM research-intensive degrees.
Fall 2018
CodeFirstGirls instructor, Web Programming in HTML, CSS, and JavaScript for girls with no experience in programming, to encourage them to pursue careers in STEM.
co-supervision
master's projects
2021
Emma Zhang. The overlap score of genetic targets of food and drug molecules predicts their synergistic anticancer effect.
Yoyo Zhong. Exploring dietary bioactive compounds on colorectal cancer radiation response using random walk with restarts.
Yuxi Wang. Drug combinations against colorectal cancer.
Jason Qiu. Combinatorial therapeutics against COVID-19.
Marco Gallotta. Drug combinations against lung cancer.
2020
Madelen Shepherd. The COVID-19 pandemic and cancer – an opportunity for drug repurposing.
Ahad Qureshi. COVID-19 and Lung Cancer: Can we Kill Two Birds with One Stone.
outreach
LOGML - co-organizer
2020 - 2021, 2023 - 2024
The London Geometry and Machine Learning workshop is a week-long event in which early career researchers worked on practical projects under the guidance of experienced mentors. The workshop also features prominent researchers in the fields of geometry and machine learning, as well as a company and networking night.
Imperial College London Women in Computing - co-president
2019 - 2021
Co-organized talks, workshops, and weekly activities for female and gender-minority PhD and staff members in the department of Computing. Led WiC Entrepreneur initiative to promote entrepreneurship among female students and staff members in the department of computing.