Resources
At each and every stage of our careers, there is so much to learn and share. Below are a few initiatives that I have gained many insights from—some of which I even had the honor of contributing to. Please do not hesitate to reach out with suggestions to further enrich this list.
Machine Learning Tutorials, Workshops, and Summer School
NeurIPS 2023 Workshop: New Frontiers of AI for Drug Discovery and Development
Drug discovery and development is cost-intensive, high risky, and time-consuming. Since its emergence, AI has been applied to nearly every phase of drug discovery and development to accelerate the discovery process of effective drugs while minimizing the risk of adverse outcomes for patients. In this workshop, we aim to foster discussion about the challenges, discoveries, and opportunities of AI for drug discovery and development. The workshop will be in-person at NeurIPS in New Orleans, Louisiana, USA.
Link: Website
Towards Precision Medicine with Graph Representation Learning
Graph representation learning has matured immensely as a field within the last few years. Graph machine learning approaches, also known as geometric deep learning or graph neural networks, have become widely used in biomedical applications. Our tutorial, presented at the 30th International Conference on Intelligent Systems for Molecular Biology (ISMB), surveys impact areas in precision medicine (e.g., modeling disease progression, candidate biomarker discovery for targeted therapies, rapid disease diagnostics, treatment regimen recommendations) and highlights new opportunities enabled by these approaches.
Link: Website
Deep learning for biomedical networks: Methods, challenges, and frontiers
The Zitnik Lab (Michelle M. Li and Marinka Zitnik) at Harvard University presented a primer and discussion on graph representation learning for biomedicine at the Broad Institute’s Models, Inference, and Algorithms (MIA) Meetings 2021.
Learning on Graphs (LoG) Conference
LoG is an annual research conference that covers areas broadly related to machine learning on graphs and geometry. LoG has a proceedings track with papers published in Proceedings for Machine Learning Research (PMLR), and a non-archival extended abstract track. LoG also holds a series of tutorials. All programming is virtual.
Graph ML Tutorials
The Zitnik Lab at Harvard University actively adds to and maintains tutorials for graph machine learning. Each tutorial provides example datasets and an easy-to-use Jupyter notebook to immediately begin experimentation.
Link: Github repository
London Geometry and Machine Learning (LOGML) Summer School
The London Geometry and Machine Learning Summer School (LOGML) aims to bring together geometers and machine learners to work together on a variety of problems. There is a selection of group projects, each overseen by an experienced mentor, talks by leading figures in the field, a variety of social events, and a company networking night.
Link: Website
Mentorship Opportunities
Harvard College Women in STEM (WiSTEM) Mentorship Program
The WiSTEM Mentorship Program was established to build community among women in STEM by matching undergraduates with graduate students in similar fields as well as create programming that fosters the growth and development of women students in the STEM community at Harvard.
Link: Website
Harvard Graduate Women in Science and Engineering (HGWISE): Mentoring Girls in India
HGWISE has been working closely with Learn with Leaders, an India-based educational organization, to get young girls in India excited about science and engineering. In the workshops we host throughout the year, we openly discuss some of the issues related to being a woman in STEM, share our own personal and scientific journeys, and offer some resources to continue pursuing their interests in STEM.
Link: Website
Journal of Emerging Investigators (JEI)
The Journal of Emerging Investigators is an open-access journal that publishes original research in the biological and physical sciences that is written by middle and high school students. JEI provides students, under the guidance of a teacher or advisor, the opportunity to submit and gain feedback on original research and to publish their findings in a peer-reviewed scientific journal.
Link: Website