CV

General Information

Full Name Anay Majee
Languages English, Hindi, German

Education

  • 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.
  • 2022 - 2025
    MS in Computer Science
    The University of Texas at Dallas, Texas, USA
    • MS in Computer Science for the Intelligent Systems track.
  • 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

  • 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.
  • 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.
  • 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.
  • 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.
  • 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

  • 2025
    • Appointed as a Reviewer for ICCV'25, NeurIPS'25, WACV'26, ICLR'26.
  • 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.
  • 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.
  • 2022
    • Appointed a Teaching Assistant at Department of Computer Science, UT Dallas.