I'm fascinated by how we can develop and better apply technologies to understand biological systems. So far this has taken me on a journey through classical biochemistry, scanning whole mouse brains to better understand processes of aging, the neurobiology of sleep & sexual behaviour in animals, deep into the the circuitry of the fruit fly brain to unpick the wiring of innate behaviours and learning & memory, and, finally, to the circus that is deep learning to improve drug discovery. Download my CV.
A more up-to-date list may be found on my Google scholar page
Message Passing Neural Processes 2020. Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò. Arxiv.
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures. 2020. Arian R. Jamasb, Pietro Liò, Tom L. Blundell. Workshop on Graph Representation Learning and Beyond at ICML 2020
The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry. 2020. Aditya Thawani*, Ryan-Rhys Griffiths*, Arian Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander Aldrick, Alpha Lee. ChemRxiv
Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain. 2020. Alexander S. Bates*, Philipp Schlegel*, Ruairí J. V. Roberts, Nikolas Drummond, Imaan F. M. Tamimi, Robert G. Turnbull, Xincheng Zhao, Elizabeth C. Marin, Patricia D. Popovici, Serene Dhawan, Arian R. Jamasb, Alexandre Javier, Feng Li, Gerald M. Rubin, Scott Waddell, Davi D. Bock, Marta Costa, Gregory S. X. E. Jefferis. Current Biology
Functional and Anatomical Specificity in a Higher Olfactory Centre. 2019. Shahar Frechter, Alexander S. Bates, Sina Tootoonian, Michael-John Dolan, James D. Manton, Arian R. Jamasb, Johannes Kohl, Davi Bock, Gregory S.X.E. Jefferis. eLife
Ethoscopes: An open platform for high-throughput ethomics. 2017. Quentin Geissmann, Luis Garcia Rodriguez, Esteban J. Beckwith, Alice S. French, Arian R. Jamasb, Giorgio F. Gilestro. PLOS Biology