David Earl Hostallero
Hello!
I'm David. I am currently a PhD student at McGill University and Mila. I am a member of the Computational Biology and Artificial Intelligence (COMBINE) Lab, under the supervision of Prof. Amin Emad. My current research interests are machine learning and graph representation learning in pharmacogenomics, with special applications to cancer and lung diseases. Prior to coming to McGill, I was part of LANADA (KAIST) and worked extensively in multi-agent reinforcement learning.
I play an unhealthy amount of Cities: Skylines, Civilization VI and Overwatch. I play other video games (mostly single-player) but not as often as these three. I am also a mechanical keyboard enthusiast (linear switch gang!).
Education
McGill University / Mila - Quebec AI Institute
PhD student (PhD in Electrical Engineering)
Computational Biology and Artificial Intelligence (COMBINE) Lab — PI: Prof. Amin Emad
Awards:
McGill Engineering Doctoral Awards (MEDA): 2019-2022
Fonds de recherche du Québec – Nature et technologies (FRQNT): 2023-2025
Korea Advanced Institute of Science and Technology (KAIST)
Master of Science in Electrical Engineering
LeArning in Networking: Algorithm, Design, and Analysis (LANADA) Lab — PI: Prof. Yung Yi
University of the Philippines Diliman
Bachelor of Science in Computer Science, Magna Cum Laude
Computer Vision and Machine Intelligence Group (CVMIG) — PI: Prof. Prospero Naval Jr.
Articles
Peer-reviewed Journals
Interpretable deep learning architectures for improving drug response prediction: myth or reality? [link]
Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification using TINDL [link]
Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction [link]
A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil Extracellular Traps as mediators of thrombosis in COVID-19 [link]
Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals [link]
On the Efficiency of Running Machine Learning Tasks for Drone-Based Target Tracking : Cloud-Based vs. Drone-Based (in Korean Language)
Conference Proceedings
Inducing Cooperation through Reward Reshaping based on Peer Evaluations in Deep Multi-Agent Reinforcement Learning [link]
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning [link]
Learning to Schedule Communication in Multi-agent Reinforcement Learning [link]
DemNet: A Convolutional Neural Network for the Detection of Alzheimer’s Disease and Mild Cognitive Impairment [link]
Conference and Workshop Presentations
Talk
Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction
Bellairs Workshop on Machine Learning and Statistical Signal Processing for Data on Graphs, December 2021
Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction
Machine Learning in Computational Biology (MLCB), November 2021
Inducing Cooperation through Reward Reshaping based on Peer Evaluations in Deep Multi-Agent Reinforcement Learning
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), May 2020
Poster
A deep learning model of preclinical-to-clinical anti-cancer drug response prediction and biomarker identification
Great Lakes Bioinformatics Conference, May 2023
DemNet: A Convolutional Neural Network for the Detection of Alzheimer’s Disease and Mild Cognitive Impairment
IEEE Region 10 Conference (TENCON), November 2016