Pantea Karimi

Pantea Karimi
  • Ph.D. Student at Massachusetts Institute of Technology

About

6
Publications
314
Reads
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2
Citations
Introduction
I am Pantea Karimi, a Ph.D. student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. I am advised by Mohammad Alizadeh in the Networking and Mobile Systems Group. Currently, I'm interested in improving video conferencing applications using advanced computer vision and video compression techniques. I received my B.S. in Electrical Engineering at the Sharif University of Techology, where I worked on Decentralized Systems and Blockchains
Current institution
Massachusetts Institute of Technology
Current position
  • Ph.D. Student

Publications

Publications (6)
Preprint
Assessing and enhancing human learning through question-answering is vital, yet automating this process remains challenging. While large language models (LLMs) excel at summarization and query responses, their ability to generate meaningful questions for learners is underexplored. We propose Savaal, a scalable question-generation system with three...
Conference Paper
Full-text available
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for op...
Preprint
Full-text available
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for op...
Conference Paper
Full-text available
Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates. Recently, several neural alternatives have been proposed that reconstruct talking head videos at very low bitrates using sparse representations of each frame such as facial lan...
Preprint
Full-text available
Real-time video streaming relies on rate control mechanisms to adapt video bitrate to network capacity while maintaining high utilization and low delay. However, the current video rate controllers, such as Google Congestion Control (GCC) in WebRTC, are very slow to respond to network changes, leading to link under-utilization and latency spikes. Wh...
Preprint
Full-text available
Video conferencing systems suffer from poor user experience when network conditions deteriorate because current video codecs simply cannot operate at extremely low bitrates. Recently, several neural alternatives have been proposed that reconstruct talking head videos at very low bitrates using sparse representations of each frame such as facial lan...

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