Ahmad Mustafa Anis

I'm a Deep Learning Computer Vision Engineer at Roll.ai and AI Research Fellow at Fatima Fellowship advised by Dr. Wei Peng from Stanford University. I completed my Bachelors in Computer Science from International Islamic University Pakistan, advised by Dr. Muhammad Nadeem.

I was an AI Fellow at the 14th Batch of PI School of AI with a scholarship worth 12,500€.

I serve as a community lead (For Geo-Regional Asia and ML-Maths Subgroups) at Cohere.for.ai, led by Sara Hooker. In this role, I've hosted over 50 researchers from Asia to present their work at Cohere for AI, completed a study group on NYU Deep Learning by Yann LeCun.

See my Resume here (Updated Sep 2024).

Email  /  Twitter  /  Google Scholar  /  Hugging Face  /  Github  /  Medium  /  Linkedin

Research

I am interested in Deep Learning in general, and read literature from different fields. In-particular, I try to follow the research in the following areas:

• Improving Reasoning in Vision-Language Models
  • Visual Question Answering (VQA)
  How well can a model reason on an image/video and answer questions. How can we extend it to multi-cultural and multi-lingual settings while maintaining alignment.
  • Image Generation
  How well can a model reasonate with the textual caption and generate image. How can we extend it to a multi-cultural and multi-lingual settings while maintaining alignment.
• Self-Supervised Learning
How to learn good representations from a huge amount of data that can be used for down-stream tasks without huge efforts.
• World Models
How can we make models that can plan and reason in an environment.

News

Publications

Consent in Crisis: The Rapid Decline of the AI Data Commons

Shayne Longpre, Robert Mahari, Ariel Lee, Campbell Lund, [11 authors], Ahmad Mustafa Anis, [29 more authors], Daphne Ippolito, Sara Hooker, Jad Kabbara, and Sandy Pentland

NeurIPS, 2024 (Under review)

Project page/ arXiv

This paper examines the rapid decline of consent and data availability in AI training corpora, highlighting the challenges and implications for the AI data commons.

Favourite Papers

List of papers I really admire (and hope to do similar impactful work).