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:
- 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. Drop by if you’re in town for AAAI!
- July 18, 2018: The Layer6 AI team headed by Maksims Volkovs and including D3M student Ga Wu (Wuga) won the RecSys Spotify Challenge!
- 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.
- Sept 5, 2017: Check out our IJCAI-17 paper on Planning in Deep Learned Models and NIPS-17 paper on Hybrid Planning with Tensorflow by Buser Say, Ga Wu, and Yu Qing (Ivan) Zhou.
- 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).