Researcher and engineer, with a focus on applying techniques from
machine learning and optimization to solve difficult decision problems -
plan a logistics network, trade futures, run a power grid, or solve a video game.
Currently living in Manhattan, NY, 🇺🇸.
Contact: iaindunning 📧 gmail.
Team Lead & Researcher (August '18 —)
I run "HAIL" - HRT AI Labs. We make deep learning work for systematic trading by integrating and translating techniques from other domains and developing proprietary extensions for our unique needs. We're hiring!
Senior Research Engineer (July '16 — July '18)
Applying large-scale artificial intelligence techniques like deep reinforcement learning to complex environments, using cutting-edge deep learning hardware. Team lead for five engineers, tech lead for multiagent research engineering.
Teaching & Research Assistant (Sep. '11 — May '16)
See research below. Co-created XX,000-person EdX class The Analytics Edge. Taught MBA and executive MBA residential versions of the class at MIT Sloan School of Management.
Decision Support Engineer intern (June 13 — Aug. '13)
Worked on search engine crawler. Designed & implemented algorithms to improve the crawl prioritization, analyzed impacts on very large (O(1010) rows, O(103) TB) datasets with MapReduce/Flume.
Human-level performance in 3D multiplayer games with
population-based reinforcement learning
M. Jaderberg*, W. Czarnecki*, I. Dunning*, L. Marris, G. Lever, A. García Castañeda, C. Beattie, N. Rabinowitz, A. Morcos, A. Ruderman, N. Sonnerat, T. Green, L. Deason, J. Z. Leibo, D. Silver, D. Hassabis, K. Kavukcuoglu, T. Graepel.
[Science] [arXiv] [video] [DeepMind blog]
Malthusian Reinforcement Learning
J. Z. Leibo, J. Perolat, E. Hughes, S. Wheelwright, A. Marblestone, E. Duéñez-Guzmánn, P. Sunehag, I. Dunning, T. Graepel.
The Hanabi Challenge: A New Frontier in AI Research
N. Bard*, J. Foerster*, S. Chandar, N. Burch, M. Lanctot, F. Song, E. Parisotto, V. Dumoulin, S. Moitra, E. Hughes, I. Dunning, S. Mourad, H. Larochelle, M. Bellemare, M. Bowling.
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
J. Foerster, F. Song, E. Hughes, N. Burch, I. Dunning, S. Whiteson, M. Botvinick, M. Bowling.
IMPALA: Scalable distributed deep-RL with importance weighted
L. Espeholt*, H. Soyer*, R. Munos*, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, S. Legg, K. Kavukcuoglu.
[arXiv] [DeepMind blog]
What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO
I. Dunning, S. Gupta, J. Silberholz.
INFORMS Journal on Computing, 2018.
Inequity aversion improves cooperation intertemporal social dilemmas
E. Hughes*, J. Z. Leibo*, M. Philips, K. Tuyls, E. Duéñez-Guzmán, A. García Castañeda, I. Dunning, T. Zhu, K. McKee, R. Koster, H. Roff, T. Graepel.
Learning Fast Optimizers for Contextual Stochastic Integer Programs
V. Nair, K. Dvijotham, I. Dunning, O. Vinyals.
Population based training of neural networks
M. Jaderberg, V. Dalibard, S. Osindero, W. Czarnecki, J. Donahue, A. Razavi, O. Vinyals, T. Green, I. Dunning, K. Simonyan, C. Fernando, K. Kavukcuoglu.
DeepMind tech report, 2017.
[arXiv] [DeepMind blog]
Extended formulations in mixed integer conic quadratic programming
J. P. Vielma*, I. Dunning, J. Huchette, M. Lubin.
Mathematical Programming Computation, 2017.
Multistage robust mixed-integer optimization with adaptive partitions
D. Bertsimas, I. Dunning*.
Operations Research, 2016.
Reformulation versus cutting-planes for robust optimization
D. Bertsimas, I. Dunning*, M. Lubin*.
Computational Management Science, 2016.
Computing in operations research using Julia
M. Lubin, I. Dunning.
INFORMS Journal on Computing, 2015.
Google Scholar for other references.
Some papers use alphabetical ordering - asterix indicates "first" author.
B.E.(Hons), Engineering Science (Mar. '07 — Dec. '10)