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.
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)
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
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.
In2Science instructor, Machine Learning sessions to encourage Year 12 students to progress into STEM research-intensive degrees.
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.
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.
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.
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.