Welcome to my PhD Journey!
Milestones
IAAP Scholarship Award
Selected by the Iranian-American Academics & Professionals (IAAP) Scholarship Selection Committee to receive an IAAP Scholarship Award, recognizing academic achievement and contributions to the community.
Reflections from CVPR 2026
I attended CVPR 2026 in Denver this June. Three themes stood out.
Efficiency and on-device AI took center stage. The field has clearly pivoted from building capable models to running them on real hardware. Quantization, model compression, and hardware-aware optimization were everywhere, with Apple and NVIDIA both pushing on-device deployment and acceleration. It was encouraging to see efficiency treated as a first-class research problem.
Diffusion models have become a general-purpose tool. Beyond image generation, diffusion now powers classification, 3D reconstruction, and world models, with a strong focus on making inference fast enough for real-time and edge use.
World models and physics-aware vision are the next frontier. The growing consensus is that models must understand physical and causal structure, not just predict pixels, pointing toward vision systems that reason about the world rather than only recognize patterns within it.
I left energized about the field and the role efficient, deployable vision models will play in it.
PhD Research Day — Poster Presentation
Presented research on Digital Twin systems for Mild Cognitive Impairment (MCI) at the University of Denver PhD Research Day.
Summit Hack 26 — 2nd Place
Built an e-waste sustainability assistant using computer vision and RAG to identify electronics and return carbon footprint, recoverable value, and safe disposal guidance.
Joined PhD Program at University of Denver(Dr. Mohammad Mahoor ↗)
Began doctoral studies in Computer Science under Dr. Mohammad Mahoor, focusing on deep learning, computer vision, and multimodal foundation models.
Papers I'm Reading
Paper reading log coming soon — check back shortly.
CVPR 2026 — Key Takeaways
Three themes defined the conference for me, from the deployment story of today to the research frontier of tomorrow.
Edge AI and efficient inference are the dominant deployment story
The biggest shift was from "can we build it" to "can we run it on real hardware." Quantization, model compression, and hardware-aware optimization (TensorRT, SNPE on Snapdragon and Jetson) were everywhere. Apple led the on-device push — deploying LLMs on iPhone via llama.cpp and Metal, and tiny VLMs on Snapdragon, with Oncel Tuzel presenting on the frontiers of on-device AI and their AToken unified vision tokenizer. The pattern is quantization plus memorization-style efficiency: cache and reuse computation, keep the active footprint small.
Apple · Oncel Tuzel · TensorRT · SNPE · llama.cpp + Metal · AToken
Selected papers
Diffusion models have become a general-purpose modeling tool
Diffusion is no longer just for image generation. Three dedicated tutorials, plus EDGE workshop talks from Stefano Ermon (Stanford) on diffusion inference acceleration and Han Cai (NVIDIA) on post-training acceleration. The conceptual expansion is the real story — diffusion now powers discriminative classification, 3D pose via flow matching, dynamic scene reconstruction, and world models. Kaiming He's "Back to Basics" even challenged the core noise-prediction objective, arguing models should predict the clean image directly.
Stefano Ermon · Han Cai (NVIDIA) · Kaiming He (JiT / Back to Basics)
Selected papers
World models and physics-aware models are the future of CV
If edge efficiency and diffusion were the present, world models were the consensus future — dozens of workshops on world models, video world models, and world-model-driven VLA systems. Jack Parker-Holder (DeepMind) presented Genie 3, toward open-ended world creation. The key sub-trend is physics-awareness: pixel prediction alone is insufficient, models need physical and causal structure, reflected in CVPR papers like PHANTOM (physics-infused video generation). Thomas Serre's keynote reinforced it from neuroscience — scaling alone won't close the gap with biological vision; we need "neural laws," not just scaling laws.
Genie 3 (Parker-Holder / DeepMind) · PHANTOM · Serre keynote