Amir Alimohammadi

Ph.D. Candidate, Computing Science
GrUVi Lab, Simon Fraser University

I'm a Ph.D. candidate in computer vision and graphics at Simon Fraser University, advised by Prof. Ali Mahdavi-Amiri. I turn pretrained generative models into controllable tools for image editing, composition, and video segmentation, with work at SIGGRAPH, SIGGRAPH Asia, and ICLR. In summer 2026 I interned at Waabi, working on world models for driving.

Journey

  1. 2023 – present
    Ph.D. Computing Science
    GrUVi Lab, Simon Fraser University
  2. May – Aug 2026
    Research Intern
    Waabi · Toronto
  3. 2022 – 2023
    Research Intern
    Sharif University of Technology
  4. 2021 – 2022
    Research Intern
    VITA Lab, EPFL · Switzerland
  5. 2018 – 2023
    B.Sc. Computer Engineering
    Sharif University of Technology
  6. 2017 – 2022
    Math Olympiad Instructor
    500+ hours of number theory, algebra & combinatorics

Teaching

  • SFUVisual Computing Lab I & II (CMPT 742, 743) · Intro to AI · Cyber Security
  • SUTLinear Algebra · Data Structures & Algorithms · Design of Algorithms · Computer Structure · Computer Networks

Honors

  • 2017Gold · 35th Iranian National Mathematical Olympiad
  • 2016Silver · International Mathematics Competition, Thailand
  • 2015Bronze · International Mathematics Competition, China

© 2026 Amir Alimohammadi · Updated Oct 2026

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Publications * equal contribution

SIA animation: a dragon demonstration pair and a query image; switching the lava, open-wings and gold edits on and off changes the output

SIGGRAPH Asia 2026

SIA: Selective Image Analogy

Amir Alimohammadi*, Kian Izadpanah*, Bardiya Kariminia, Yiorgos Chrysanthou, Ali Mahdavi-Amiri

One before/after pair can carry several edits. SIA lets you choose which of them transfer to a new image.

SMITE animation: a face video segmented into parts, quadrants or bands, consistent across the clip

ICLR 2025

SMITE: Segment Me In TimE

Amir Alimohammadi, Sauradip Nag, Saeid Asgari Taghanaki, Andrea Tagliasacchi, Ghassan Hamarneh, Ali Mahdavi-Amiri

Flicker-free video segmentation at any granularity from one or a few reference images.

Projects