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PhD candidate at Imperial College London

I am a fourth-year PhD candidate at Imperial College London in the Department of Computing. I received my B.Sc. in Biomedical Engineering and M.Res. in Data Science before adventuring into the exciting field of deep learning on graphs advised by Michael Bronstein and Kirill Veselkov.

During the first part of my PhD I enjoyed developing graph machine learning methods leveraging genomic data to uncover molecules with disease-beating properties in foods within the HyperFoods project. Motivated by the drawbacks of modeling genetic perturbations using machine learning approaches alone, I currently work on developing causal graph machine learning algorithms to model genetic and chemical perturbations together with Marinka Zitnik at Harvard Medical School.

email ggonzalezp16[at]gmail[dot]com

LinkedIn  GScholar  Twitter

 

publications

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).

Learning interpretable disease self-representations for drug repositioning

by Frasca, F*., Galeano, D.*,Gonzalez, G., Laponogov, I., Veselkov, K., Paccanaro, A. and Bronstein, M., * equal contribution.  In NeurIPS Graph Representation Learning Workshop (2019).

 

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods

by Veselkov, K., Gonzalez, G., Aljifri, S., Galea, D., Mirnezami, R., Youssef, J., Bronstein, M. and Laponogov, I. In Nature Scientific Reports (2019)

education

2021 - 2022 (in progress)

Visiting PhD, Harvard Medical School, US

2018 - 2023 (in progress)

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

The London Geometry and Machine Learning workshop was a week-long event scheduled for July 2021, in which early career researchers worked on practical projects under the guidance of experienced mentors. The workshop also featured prominent researchers in the fields of geometry and machine learning, as well as a company and networking night.

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.