CV
General Information
Full Name | Anay Majee |
Languages | English, Hindi, German |
Education
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2022 - Present
PhD in Computer Science
The University of Texas at Dallas, Texas, USA
- PhD candidate in Computer Science for the Intelligent Systems track.
- Research Assistant with Prof.Rishabh Iyer at the CARAML lab.
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2022 - 2025
MS in Computer Science
The University of Texas at Dallas, Texas, USA
- MS in Computer Science for the Intelligent Systems track.
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2014-2018
B.Tech in Electrical and Electronics Engineering
Vellore Institute of Technology, Chennai, India
- Graduated as a gold-medalist in the department by scoring a GPA of 9.68/10.0.
- Worked as a Undergrad Researcher at the Smart Grid Lab, advised by Prof. Gnana Swathika O.V.
Experience
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2025
Research Intern
Dolby Laboratories, San Francisco, CA
- Developed a Multi-Modal Generative Model (GenAI) for improving Video-Language understanding in long-form video sequences. To be submitted to CVPR'26.
- Inspired from Live Sports and Cinema content, not all frames in a video is relevant to understanding tasks, we developed a combinatorial approach to identify a set of video segments relevant to answering the question achieving \(\sim\)4% boost in performance on several Video LLMs.
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2024
Research Intern
Fujitsu Research of America (FRA), Santa Clara, CA
- Developed a Multi-modal Foundation Model, TabGLM towards cross-table generalization in tabular data. This work has been accepted to AAAI'25.
- TabGLM minimizes the consistency between graph (learnt from a Graph Transformer network) and text (learnt from a BART based Large Language Model (LLM) encoder) modalities generated from each record in large tables.
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2022
Data and Applied Scientist II
Microsoft, India
- Developed an Entity Centric Language model to improve identification of products, brands etc. resulting in 12% revenue gain in Search Advertising.
- Mentored an intern to develop an evaluation framework to benchmark entity centric language models which is used across 3+ teams in Microsoft Advertising.
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2018 - 2022
Applied Research Scientist
Intel, India
- Led the development of Few-Shot Object Detection (FSOD) and Few-Shot Incremental Learning (FSIL) algorithms in Pytorch for detecting rare or unseen road objects in unconstrained driving environments.
- Mentored two interns whose work on Few-Shot Object Detection has been submitted to conferences like ICCV and WACV.
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2017 - 2018
Undergraduate Technical Intern
Intel, India
- Developed Edge Inferencing framework to deploy Computer Vision models on multiple edge hardwares including Intel Neural Compute Sticks.
Services and Recognitions
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2025
- Appointed as a Reviewer for ICCV'25, NeurIPS'25, WACV'26, ICLR'26.
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2024
- Appointed as a Reviewer for ICLR'25, CVPR'25, WACV'25, TPAMI (journal).
- Appointed as a Research Assistant for Fall 25 and Spring 26.
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2023
- Appointed as a Reviewer for ICLR'24.
- Delivered a Keynote at VIT Chennai on Virtual Electrical Networks, Seamless Integration and Fault Clearance in Reconfigurable Microgrids.
- Appointed a Research Assistant at CARAML lab.
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2022
- Appointed a Teaching Assistant at Department of Computer Science, UT Dallas.