Ankan Dash

Ankan Dash

PhD in Computer Science. Building privacy-preserving ML, foundation models, and generative AI at Apple 🧠🤖💻

Mountain View, California

bio

Ankan Dash is a Senior Machine Learning Engineer at Apple, where he works on foundation models, generative AI, and Apple Intelligence for iPhone. He develops privacy preserving machine learning algorithms that ship to hundreds of millions of users. He previously served as Senior Research Scientist – Foundation Models at Ambient.ai, where he designed and fine-tuned vision-language models for real-time multimodal understanding. He completed his PhD in Computer Science at New Jersey Institute of Technology, focusing on Deep Learning, Computer Vision, and Generative AI. His research focuses on developing AI systems that benefit society while advancing the frontiers of computational intelligence.

experience

2026 -

I am Senior Machine Learning Engineer at Apple, working on foundation models, generative AI, and Apple Intelligence for iPhone. I develop privacy preserving machine learning algorithms that ship to hundreds of millions of users worldwide.

2025 - 2026

I was Senior Research Scientist – Foundation Models at Ambient.ai, where I designed and fine-tuned vision-language models for real-time multimodal understanding in physical security. I optimized models for efficient edge deployment.

2024 - 2025

I was AI-ML Computer Vision Intern at Samsung Research America, where I successfully developed real-time reverse pass-through feature for Samsung VR.

2023 - 2024

I was AI Computer Vision Intern at Samsung Research America, where I developed POC for reverse pass-through feature and Diffusion Models for image inpainting.

education

2020 - 2025

My PhD was focused on Deep Learning, Computer Vision, and Generative AI at New Jersey Institute of Technology. My advisor was Dr. Guiling Wang.

2017 - 2019

MSc in Aerospace Engineering at the University of Illinois at Urbana-Champaign, where I worked on Finite Element Methods and CFD models for flow field analysis.

publications

Eye-See-You: Reverse Pass-Through VR with Full Head Avatars
A. Dash, J. Gu, G. Wang, C. Chen
International Joint Conference on Artificial Intelligence (IJCAI) • 2025
Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding
J. Ye, A. Dash, G. Wang, W. Yin
Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL) • 2025
GAN based universal NeRF output refinement model framework
A. Dash, J. Gu, G. Wang
IEEE International Conference on Metaverse Computing, Networking, and Applications • 2025
A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing
A. Dash, J. Ye, G. Wang
IEEE • 2024
Self-Supervised Learning for User Localization
A. Dash, J. Gu, G. Wang
International Conference on Computing, Networking and Communications (ICNC) • 2024
Hier-GAN: Hierarchical Inpainting GAN with Auxiliary Inputs for Combined RGB and Depth Inpainting
A. Dash, J. Gu, G. Wang
IEEE/CVF Conference on Computer Vision and Pattern Recognition - CVPR Workshop on Computer Vision in the Wild (CVinW) • 2024
Attentive Partial Convolution for RGBD Image Inpainting
A. Dash, G. Wang
Companion Proceedings of the ACM Web Conference (WWW) • 2024
Safety in Traffic Management Systems: A Comprehensive Survey
W. Du, A. Dash, J. Li, H. Wei, G. Wang
Designs, 7(4), Article 100 • 2023
High-Resolution Solar Image Generation Using Generative Adversarial Networks
A. Dash, J. Ye, G. Wang
Annals of Data Science • 2022
Free Fall of Homogeneous and Heterogeneous Cones
J. Kim, Y. Jin, S. Shen, A. Dash, L. Chamorro
Physical Review Fluids, 5(9), 093801 • 2020

links

LinkedIn • Google Scholar • Email