I'm a senior Reinforcement Learning researcher at Microsoft Research NYC.
Every Friday (4 PM ET) I cohost The Learning Salon (with Joshua Vogelstein and John Krakauer).
I study how we build models of the world and use them in memory, exploration, & planning.
I build and test neurally plausible algorithms for learning the structure of the environment. My approach combines reinforcement learning, neural networks, & machine learning with behavioral experiments, fMRI, & electrophysiology.
My training is in cognitive computational neuroscience. I've previously worked at Columbia University, Electrophysiology, Memory, and Navigation Lab, did my postdoc at Princeton (where I collaborated with Ken Norman, Matt Botvinick, Jon Cohen, Nathaniel Daw), my PhD was in psychology (Berlin, Germany, Bernstein Center for Computational Neuroscience), BSc in software engineering (Tehran, Iran), & MSc in Philosophy of Science (Utrecht, Netherlands).
Publications & preprints
Devlin S*, Georgescu R*, Momennejad I*, Rzepecki J*, Zuniga E*, Costello G, Leroy G, Shaw A, Hofmann K (2021) Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. ICML 2021. [ arXiv PDF ]
Momennejad I (2020) Learning Structures: Predictive Representations, Replay, and Generalization. Current Opinions in Behavioral sciences.
Brunec I, Momennejad I (2019) Predictive Representations in Hippocampal and Prefrontal Hierarchies.
[ bioRxiv preprint ]
Momennejad I, Norman KA, Cohen JD, Singh S, Lewis RL (2019). Rational use of Episodic and Working Memory: A Normative Account of Prospective Memory. BioRxiv, 580324 https://doi.org/10.1101/580324.
[ bioRxiv preprint, SfN poster ]
Momennejad I, Howard M (2018) Predicting the future with multi-scale successor representations.
[ bioRxiv preprint, status: in revision ]
Momennejad I, Otto RA, Daw N, Norman KA (2018) Offline replay supports planning in human reinforcement learning. eLife 2018;7:e32548.
Russek E*, Momennejad I*, Botvinick MM, Gershman SJ, Daw N (2017) Predictive representations can link model-based reinforcement learning to model-free mechanisms. Plos Comp Biol.
[ Journal link, PDF, bioRxiv preprint, CODE ]
Momennejad I*, Russek E*, Cheong JH, Botvinick MM, Daw N, Gershman SJ (2017) The successor representation in human reinforcement learning: evidence from retrospective revaluation. Nature Human Behaviour, 1. [ Nat Hum Beh paper , PDF, free preprint , my python tutorials comparing SR & SR-Dyna, blogpost from Deepmind ]
Coman A, Momennejad I, Drach R, Geana A (2016) Mnemonic convergence in social networks: The emergent properties of cognition at a collective level. PNAS. doi: 10.1073/pnas.1525569113.
[ Journal link , Podcast ]
Wisniewsky D, Reverberi C, Momennejad I, Kahnt T, Haynes J-D (2016) The role of parietal cortex in the representation of task-reward-association. The Journal of Neuroscience. [ Journal link ]
Haynes J-D, Wisniewsky D, Gorgen K, Momennejad I, Reverberi C (2015) fMRI decoding of intentions: compositionality, hierarchy, and prospective memory. Brain-Computer Interface (BCI), 3rd International Winter Conference. doi: 10.1109/IWW-BCI.2015.7073031. [ IEEE link ]
Momennejad I, Haynes J-D (2012) Human anterior prefrontal cortex encodes the 'what' and 'when' of future intentions. Neuroimage 61(1):139-48.
Russek E, Momennejad I, Botvinick MM, Gershman SJ, Daw N (in prep) Predictive representations in human prefrontal cortex and the hippocampus.
Momennejad I*, Tomov M*, Norman KA, Cohen JD (in prep) The strategic allocation of working memory and episodic memory in cognitive control: A neural network model of prospective memory. [ poster PDF file ]
Momennejad I, Cohen JD, Norman KA (in prep) MVPA evidence of episodic future simulation predicts goal-directed behavior in prospective memory. [ Ask me about it! ]
Momennejad I, Reverberi C, Haynes J-D (in prep) Sequence effects: Task similarity facilitation and dissimilarity costs in the planning and execution of task sequences. [ poster PDF file ]
MIND 2019 "Cognitive Maps" computational summer school (Methods In Neuroscience at Dartmouth)
- Lecture, tutorial, and hackathon team.
MIND 2018 computational summer school (Methods In Neuroscience at Dartmouth)
- Lecture, tutorial, and hackathon team.
MIND 2017 computational summer school (Methods In Neuroscience at Dartmouth)
- Two Lectures, tutorial, and hackathon team.
Introduction to cognitive neuroscience - with Mattew Botvinick at Princeton University
Recent Talks and Workshops (since April 2019)
April 21, 1-5 PM: Computational Justice Workshop (hands-on agent-based simulation in Python), Heart of the Machine series, Pioneerworks, Brooklyn, NY
May 13-14: Context and Episodic Memory (CEMS), Philadelphia, PA
May 16-18: Control Processes 2019, Brown University, Providence, RI
May 28-29: Talk and visit, Computational Neuroscience Center, University of Washington, Seatle, WA
July 10, 1-5PM: Structure for Efficient Reinforcement Learning (SERL) Workshop
4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Montreal, Canada
August 8-17: "Cognitive Maps" at MIND19 summer school for computational methods in neuroscience at Dartmouth
September 10-13: What is biological Computation? Santa Fe Institute
Momennejad I, Allahyari M (2017) Refiguring: in Conversation. Futureproof, Haverford College, Haverford, PA, pp 16-31.
Momennejad I (2011) Thinking in movements, rehearsing freedom. In: Wagner L and Gomes-Carrillo de Castro Eds., Sounds, Space, Body, a process. Ernst Schering Foundation and Association of Neuroesthetics: Berlin, pp:115-125.
Momennejad I (2011) Seeing with eyes closed: the neuro-epistemology of perceptual reality. In: Agudio E and Franke I Eds., Seeing with eyes closed. Association of Neuroesthetics: Berlin, pp:15-21.
Momennejad I and Franke I (2011) Two practices of seeing with eyes closed: contemporary art and science in dialogue. In: Agudio E and Franke I Eds., Seeing with eyes closed. Association of Neuroesthetics: Berlin, pp:9-13.
Media: Talks, outreach, press
A video of talk at the Stanford Psychology Colloquium "Predictive representations in memory and planning", Stanford University, 2017.
A video of talk on our elife paper"Offline Replay Supports Planning" at CCN 2017 (Cognitive Computational Neuroscience conference), Columbia Univesity, New York.
Video of MIND2017 workshop, "Thinking in graphs (Day 2): Social temporal networks and collective memory", Dartmouth college, 2017.
Video of MIND2017 workshop, "Thinking in graphs (Day 1): Cognitive maps & memory replay", Dartmouth college, 2017.
Video of MIND2018 workshop, Dartmouth college, 2018.
A video of keynote talk "Predictive maps: What does it mean to have human-like memory and agency?" (min 16 onward), at Facets Conference 2017, The Future of AI, Goethe Institut, New York.
Deep mind blog post featuring a new article on predictive representations as well as my paper on the successor representation.
Spinney L (7 March 2017) "How facebook, fake news and friends are warping your memory. Research on collective recall takes on new importance in a post-fact world". Nature feature on our work on collective memory