Welcome to the Data-Driven Decision Making Lab (D3M)
Mission:
Our lab reenvisions urban and consumer informatics from a data-driven decision-making perspective to forge sustainable, smarter cities and more efficient, consumer-responsive businesses.
Research Areas:
Machine Learning and Large-scale Data Analysis, Artificial Intelligence (AI), Information Retrieval, Social Media, Recommender Systems, Sequential Decision Optimization, Operations Research, Smart Cities Applications.
Lab News:
- Jan 9, 2023: Check out an outstanding summary overview of our IPM 2023 article on Unintended Bias in Language Model-driven Conversational Recommendation written by D3M MASc graduate Tianshu (Tina) Shen.
- Oct 9, 2022: D3M is running the ICAPS 2023 Probabilistic Planning and Reinforcement Learning Competition. Please contact Dr. Ayal Taitler if you have any questions, suggestions, or are interested in participating.
- June 17, 2020: Congratulations to D3M MASc student Zheda (Marco) Mai who won 1st place in the CVPR 2020 CLVISION Challenge with his entry Batch-level Experience Replay with Review.
- Feb 28, 2020: Professor Sanner has received a Google Faculty Research Award in Machine Learning and Data Mining to pursue research on Deep Conversational Recommender Systems.
- Aug 17, 2019: We’re co-organizing the AAAI-20 Workshop on Interactive and Conversational Recommendation Systems (WICRS) with colleagues from Google and U. of Michigan.
- May 25, 2018: Zhijiang (Tony) Ye’s paper with Buser Say entitled Symbolic Bucket Elimination for Piecewise Continuous Constrained Optimization has received the Student Paper Award at CPAIOR-18.
- June 28, 2017: Professors Scott Sanner and William O’Brien (Carleton) and D3M student Brent Huchuk have been awarded an NSERC CRD to pursue research on machine learning for residential HVAC with Ecobee, Inc.
- Dec 29, 2016: Iain Guilliard has won the 2016 Kikuchi-Karlaftis Best Paper Award of the Transport Research Board for his paper A Non-homogeneous Time Mixed Integer LP Formulation for Traffic Signal Control (video).