I’m currently a machine learning (research) engineer at Reverie Labs, an early-stage hybrid of tech and biotech. I work on building our machine learning platform for computational drug discovery. On the modeling side, this spans everything from message-passing (graph) neural networks, language models, generative models and voxel-based methods, to more traditional computational chemistry tools such as docking and molecular dynamics simulations. Other methods I enjoy exploring include meta-learning and reinforcement learning, and I’ve previously worked on computer vision applications as well. I am also, broadly speaking, interested in tech / startups / finance and their intersection.
IEEE Transactions on Network Science and Engineering (2021)
Machine Learning for Molecules Workshop @ NeurIPS 2020
Reverie Labs blog post about using ensemble filters to detect noisy data.
Ongoing project in collaboration with other Reverie and DeepChem team members, investigating large-scale self-supervised pretraining methods for attention-based language models. Models shared via HuggingFace 🤗. Builds on work previously shared at the ML4Molecules workshop at NeurIPS 2020.
Reverie Labs poster presented at the Computer Aided Drug Design (CADD) Gordon Research Conference 2019.
I contributed to the DoME project at the University of Vienna (Universität Wien) to help digitize modern European exhibition catalogues. I used a combination of OCR and named entity recognition via RNNs to automatically extract and characterize information from digital copies of catalogues, such as artist names and exhibit titles. The group’s work was featured in The Routledge Companion to Digital Humanities and Art History.
As an undergraduate, I was a research assistant for Professor Charles Hailey in the Columbia Astrophysics Laboratory. I helped fabricate and test lithium-drifted silicon x-ray detectors for the GAPS experiment.
walid [at] reverielabs.com