Param Sangani

PhD Candidate, Computer Science
Saint Louis University

param.sangani@slu.edu · CV · GitHub · LinkedIn

Param Sangani

I am a PhD candidate at Saint Louis University advised by Dr. Hadi Ali-Akbarpour in the AIRLab. My research sits at the intersection of physics-based imaging and robot perception: I work on polarimetric imaging for material understanding, and on multimodal sensing pipelines that let robots decide not just what to do, but when it is safe to act.

Recently this has meant two threads of work: quantum–classical hybrid methods for classifying materials from their polarimetric response, and shared-autonomy control for assistive manipulators driven by synchronized EEG, EMG, and eye-tracking signals. I also TA graduate courses in algorithms and software engineering.

Publications

  1. Overview figure: EEG, EMG, and eye-tracking data collection, the NeuroCommitSSM pipeline, and evaluation on a Kinova Gen3 arm

    NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG–EMG–ET Commit Readiness

    T. Sultan, P. Sangani, K. Cool, P. Sikorski, G. Liu, H. AliAkbarpour, M. Babaiasl

    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026. Accepted.

    A framework that predicts a continuous commit-readiness score from synchronized EEG, EMG, and eye-tracking, and gates robot execution through a HOLD–ASSIST–COMMIT supervisor with real-time feasibility checks. Validated on a Kinova Gen3 arm across five activities of daily living with 32 subjects.

    paper

  2. Pipeline figure: a feature extractor trained on polarization cubes, with inference via amplitude encoding and a SWAP test

    Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification

    S. Shojaei, S. M. A. Tousi, E. Bennett, P. Sangani, A. Shiri Sichani, I. Ersoy, H. Ali-Akbarpour, F. Bunyak, G. N. DeSouza

    IEEE International Conference on Quantum Computing and Engineering (Quantum Week), 2026. Under review; preprint on arXiv.

    A quantum–classical hybrid pipeline that classifies materials from polarized light reflections by encoding learned voxel embeddings as quantum amplitude states and comparing them with a SWAP-test fidelity measure, benchmarked against optimal transport and Hungarian matching on 23 material classes.

    arXiv

Experience

2023–
Graduate Research Assistant, AIRLab — Saint Louis University. Polarimetric and event-based perception, 3D reconstruction, robotic manipulation.
2024–25
Graduate TA, Algorithms (CSCI 5100) — recitations, autograders, and visualization demos for 100+ students.
2024
Graduate TA, Software Development (CS 5030) — weekly demos, code reviews, and debugging help for 70+ students.
2020–22
Team Lead, MedLaunch — tools for oncology infusion centers (first place) and a cervical dilation measurement prototype.
2019–21
Space Systems Research Lab — embedded systems and telemetry for a satellite deployed from the ISS.