I'm a senior Reinforcement Learning researcher at Microsoft Research NYC.
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.
Predictive cognitive maps: My work shows that multi-scale predictive map representations updated via memory replay support abstraction & hierarchical planning.
Collective memory: In another line of research I've used graph theory & multi-person experiments to study how conversational networks shape collective memory (this podcast).
Computational justice: using multi-agent simulations I study systemic and structural injustice, e.g., how collective behavior incur unjust costs on minorities in institutions, and how to compare long-term policies to alleviate these costs (see Computational Justice below).
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
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, Duker A, Coman A (2019) Bridge ties bind collective memories. Nature Communications.
[ open access paper ]
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 , 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 + PDF file ]
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