Hello! I am a PhD candidate in Computing at Imperial College London, supervised by Michael Bronstein and Kirill Veselkov. My research focuses on the application of graph machine learning to biomedical problems, with a special emphasis on drug repositioning, combinatorial therapeutics and hyperfoods (i.e. molecules in foods with disease-beating properties) prediction.

I am also passionate about increasing the presence of minorities in STEM, a goal I work towards by acting as the Co-President of the Women in Computing society at Imperial, and regularly volunteering as an instructor in courses aimed at empowering girls and children from disadvantaged backgrounds to pursue careers in STEM.

I am currently co-organizing LOGML, a week-long workshop scheduled for summer 2021, featuring prominent researchers in the fields of geometry and machine learning, and offering practical tutorial projects targeted at PhD students. 

Check it at logml.ai

 

Publications

2021

Laponogov, I.*, Gonzalez, G.*, Shepherd, M., Qureshi, A., Veselkov, D., Charkoftaki, G., Vasiliou, V., Youssef, J., Bronstein, M., Veselkov, K. Human Genomics 15(1): 1. * equal contribution.

We introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome.

2021

Borgas, P., Gonzalez, G., Veselkov, K., Mirnezami, R. World Journal of Clinical Oncology, 12(6), 482–499.

Meta-analysis studying the association of several food items (garlic, nuts, tomatoes, cruciferous vegetables and citrus fruits) and the risk cancer.

2021

Gonzalez, G., Gong, S., Laponogov, I., Bronstein, M., Veselkov, K. Human Genomics, 15(1), 33.

Graph neural model for anticancer hyperfoods prediction

2019

Gonzalez, G., Gong, S.,  Laponogov, I., Veselkov, K. and Bronstein, M. 2019 NeurIPS Graph Representation Learning Workshop.

We introduce a graph  neural network model for the anticancer hyperfood prediction task. 

2019

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

Here, we propose an interpretable model that learns disease self-representations for drug repositioning.

2019

Veselkov, K., Gonzalez, G., Aljifri, S., Galea, D., Mirnezami, R., Youssef, J., Bronstein, M. and Laponogov, I. Nature Scientific Reports, 9(1).

We introduce a machine learning approach to predict molecules in food with anticancer properties ('anticancer hyperfoods'). 

 

Education

2018-2023

Imperial College London, UK

PhD, Computing

Department of Computing, Faculty of Engineering

2017-2018

Imperial College London, UK

Master of Research, Data Science

Department of Surgery and Cancer, Faculty of Medicine

Distinction

2013-2017

Polytechnic University of Madrid, Spain

Bachelor of Science, Biomedical Engineering

Top of the class

 

Teaching and co-supervisions

Co-supervision of master's projects

Yuxi Wang (2021). Drug combinations against colorectal cancer

Jason Qiu (2021). Combinatorial therapeutics against COVID-19

Marco Gallotta (2021). Drug combinations against lung cancer

Madelen Shepherd (2020). The COVID-19 pandemic and cancer – an opportunity for drug repurposing

Ahad Qureshi (2020). COVID-19 and Lung Cancer: Can we Kill Two Birds with One Stone

Teaching experience

Spring term 2020. Deep Learning. Department of Computing, Imperial College London

Summer term 2019. Big Data and Machine Learning. Dyson School of Design Engineering, Imperial College London.

 

Outreach and voluntary positions

LOGML - Co-Organizer

May 2020 - Present

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

ICL Women in Computing - Co-President

Oct 2019 - Present

ICL Women in Computing is a collective of students and researchers in the Department of Computing dedicated to promoting initiatives that aim to tackle the gender imbalance in computer science. Our goals are to empower gender minorities and inspire the younger generation to pursue careers in STEM as well as creating a community open and welcoming of people of all genders.

As a Co-President I contribute by overseeing the work of the society, in planning events, liaising with contacts within and outside the department, and representing the society in general.

In2Science - Instructor

Jul 2020 - Aug 2020

Delivered webinar sessions on the topic of machine learning aimed at students from low-income backgrounds to empower them to achieve their potential and progress to STEM research intensive degrees.

CodeFirstGirls - Instructor

Oct 2018 - Dec 2018

Teaching web programming in HTML, CSS, and JavaScript to girls with no experience in programming, to encourage them to pursue careers in STEM