David Alvarez-Melis (Preferred)
David Alvarez-Melis
School of Engineering and Applied Sciences, Harvard University
Names
How do you usually write your name as author of a paper? Also add any other names you have authored papers under.
Emails
Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments.
****@mit.edu
, ****@microsoft.com
, ****@csail.mit.edu
, ****@gmail.com
, ****@seas.harvard.edu
Personal Links
Add links to your profiles on other sites. (Optional)
Education & Career History
Enter your education and career history. The institution domain is used for conflict of interest detection and institution ranking. For ongoing positions, leave the end field blank.
Assistant Professor
School of Engineering and Applied Sciences, Harvard University (seas.harvard.edu)
2023 – Present
Senior Researcher
Microsoft (microsoft.com)
2021 – Present
Postdoc
Microsoft (microsoft.com)
2019 – 2021
PhD student
Massachusetts Institute of Technology (mit.edu)
2014 – 2019
MS student
New York University (nyu.edu)
2011 – 2013
Undergrad student
ITAM (itam.edu)
2006 – 2011
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
Coworker
Nicolo Fusi
****@microsoft.com
2019 – Present
Coworker
Lester Mackey
****@microsoft.com
2019 – Present
Coauthor
Youssef Mroueh
****@us.ibm.com
2017 – Present
PhD Advisor
Tommi Jaakkola
****@csail.mit.edu
2014 – 2019
PhD Advisor
Mehryar Mohri
****@cs.nyu.edu
2011 – 2013
Expertise
For each line, enter comma-separated keyphrases representing an intersection of your interests. Think of each line as a query for papers in which you would have expertise and interest. For example: deep learning, RNNs, dependency parsing
Large Language Models
2021 – Present
Optimal Transport
2016 – Present
Interpretability
2016 – Present
Deep Learning
2015 – Present
Neural Networks
2014 – Present
Natural Language Processing
2013 – Present
Machine Learning
2011 – Present