Amir Arsalan Soltani

I am a research assistant in Professor Josh Tenenbaum's Computational Cognitive Science Lab at Massachusetts Institute of Technology, where I work on building computational models for perceptions inspired by cognitive science to endow future AI agents with visual intelligence.

I graduated in 2016 with a Master's Degree in Computer Science from State University of New York at Buffalo. Prior to that, I did my undergraduate at Isalamic Azad University in Iran, where I received my B.S. in Computer Software Engineering.

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Research

My long-term research goal is to enable AI agents to have the ability to imagine (produce mental or abstract imagery without sensory inputs) by building models of the visual world and come up with concise and generalizable theories/solutions that can explain phenomena beyond what low-level statistics of observable data spells.

Talks
Publications

Perceiving Fully Occluded Objects via Physical Simulation
Yildirim, I.*, Siegel, M.*, Soltani, A.**, Chaudhuri, S.** & Tenenbaum, J.
(* and ** indicate equal contribution)
Manuscript in Preparation, 2018

A model-based, compositional perception system for recovering 3D shapes covered by cloth, with low sample complexity.

3D generation, 3D reconstruction
Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks
Soltani, A., Huang, H., Wu, J., Kulkarni, T., & Tenenbaum, J.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
Paper (PDF)  /  Code  /  Poster  /  Slides (includes more results)

A generative model for generic 3D shapes to obtain abstract description of objects as a crucial component for building models of the environment through inverse graphics.


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