My research interests are lossless data compression, information theory, and AI. Data sources usually have some type of structure, such as graphs and sets. This means we can compress them better by taking this structure into account during compression. Unfortunately, existing methods that do this are either sub-optimal (don’t achieve the Shannon bound) or computationally intractable. I’m interested in building computationally efficient compression algorithms that can be used with deep generative models on structured data, as well as their connections to bayesian methods.
Originally, I am from Florianópolis (Brazil) but I’ve lived in New Jersey, Orlando, Toronto (now), São Paulo, as well as other smaller cities in the south of Brazil.
I spend more time than I should on Twitter.
Check out my CV (last updated: Nov/2021).
I’ve joined Google Brain as a Student Researcher with Lucas Theis and Johannes Ballé.
I’m accepting invitations to speak at conferences, workshops, and academic events in general.
Selected Publications and Preprints
For a complete list, please see my Google Scholar profile.
Data-driven Optimization for Zero-delay Lossy Source Coding with Side-Information
Elad Domanovitz, Daniel Severo, Ashish Khisti, Wei Yu
International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2022
Compressing Multisets with Large Alphabets
Daniel Severo*, James Townsend*, Ashish Khisti, Alireza Makhzani, Karen Ullrich
Data Compression Conference (DCC), 2022
[arXiv] [code] [tweet 1] [tweet 2] [slides]
This paper was also presented as
Your Dataset is a Multiset and You Should Compress it Like One
Best Paper Award at NeurIPS Workshop on DGMs, 2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan*, Karen Ullrich*, Daniel Severo*, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison
Long talk at International Conference on Machine Learning (ICML), 2021
[arXiv] [code] [tweet] [video]
Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes
Arthur D Reys, Danilo Silva, Daniel Severo, Saulo Pedro, Marcia M Sá, Guilherme AC Salgado
Brazilian Conference on Intelligent Systems (BRACIS), 2020
* Equal contribution
Vector Scholarship in Artificial Intelligence Recipient, 2020-21
The Vector Scholarship in Artificial Intelligence supports the recruitment of top students to AI-related master’s programs in Ontario. Valued at $17,500 for one year of full-time study at an Ontario university, these merit-based entrance awards recognize exceptional candidates pursuing a master’s program recognized by the Vector Institute or who are following an individualized study path that is demonstrably AI-focused.
NSERC Applied Research Rapid Response to COVID-19 Grant, 2020
Our project titled “Canadian Hospital Simulator For Management of COVID19 Cases and Contact Tracing” was awarded $75,000.00.
Virtual Design Challenge Winner, 2019
Won 1st place at the VDC hosted by The University of British Columbia with my paper Proof of Novelty. Received a cash prize of $ 3,000.00.
Student Merit Award & Medal, 2015
Science Without Borders Scholarship, 2013
Talks and Media
- IT@UofT Source Coding with Latent Variable Models, 2021
- Two ECE grad students receive Vector Institute Scholarships in AI., 2020
- Commit 77338d2 Pursuing a Career in Data Science (pt-BR), 2020
- Hipsters #106 Cool Data Science Cases (pt-BR), 2018
You may reach me at