Reeshad Khan, Ph.D.

Efficient AI (EdgeAI) & Autonomous Systems Perception — BEV • Radar–Vision Fusion • RAW-to-Task Co-Design

GitHub Google Scholar CV (PDF) LinkedIn
Real-Time BEV EdgeAI Deployment Radar–Vision Fusion RAW-to-Task Co-Design 3D Perception
Reeshad Khan headshot

About

I work on efficient perception systems for autonomy with an emphasis on real-time deployment: BEV perception, multimodal fusion, and sensing-aware learning under noisy and resource-constrained settings.

Currently
Ph.D. (CS), University of Arkansas
Dissertation defended Dec 8, 2025

News

acceptances • awards • milestones
2026-01-01
✨
Paper accepted: Adaptive Extensions of Unbiased Risk Estimators for Unsupervised MRI Denoising (CVC 2026).
2025-12-08
🎓
Successfully defended Ph.D. dissertation: “Efficient Deep Neural Networks for Autonomous Perception.”
2025-01-01
🚗
Paper published: TinyBEV (ICCV WDFM 2025).
2025-01-01
🧠
Paper published: From Noise Estimation to Restoration (VISAPP 2025).
2025-01-01
🧾
Paper published: Learning From Oversampling (IEEE Access 2024).
2024-01-01
🏅
Award: Reginald R. “Barney” & Jameson A. Baxter Graduate Fellowship (2024).
2023-01-01
🏅
Award: EECS Graduate Fellowship, University of Arkansas (2023).
2022-01-01
🏅
Award: College of Engineering Graduate Fellowship, University of Arkansas (2022).
2019-01-01
🌏
Scholarship: Chinese Government Belt and Road Scholarship (2019).

Research

Efficient 3D Perception & BEV Autonomy
Resource-aware BEV perception/planning, distillation, and modular pipelines for autonomy.
Robust Learning under Noisy Sensing
Sensing-aware learning, RAW-to-task pipelines, and restoration methods for challenging conditions.

Publications

click a title to open the PDF

Contact

Email: rk010@uark.edu
LinkedIn: linkedin.com/in/reeshad-khan-a50207106
GitHub: github.com/Reeshad-Khan

Visitors

World visitor map + country counts.

World Map
Country Counter
Flag Counter