Anisha Jain

I am currently pursuing Masters in Computer Vision (MSCV) at Robotics Institute at Carnegie Mellon University, advised by Prof. Lazlo A. Jeni and Michael Zollhoefer, Meta Reality Labs, where I am working on dynamic 3D reconstruction focusing non-rigidly deforming scenes captured by RGBD sensors. My primary interests lie in the intersection of Computer Vision and Machine Learning.

Prior to joining CMU, I worked as a Software Engineer at Microsoft (R&D) Pvt. Ltd., where my work focused on building agile deployment and experimentation of ML models for spam/phish detection during mail flow for Microsoft Defender for Office (MDO). I had the previledge of working with a team of talented engineers and researchers, and was mentored by Jay Goyal, Saurabh Shrivastav and Ganesh Pande.

In 2018, I worked under the supervision of Dr. Amarjot Singh and Dr S.N Omkar in the Computational Intelligence lab at Indian Institute of Science, Bangalore suspicious activity detection in surveillance using gait-based gender recognition system for subjects in loosely fitted clothing.

I completed my undergraduate education with a Major in Computer Science and Engineering from National Institute of Technology, Warangal in 2021. I had the pleasure of working under Dr. R Padmavathy for my undergraduate thesis.

In the late summer of 2020, I was a part of the Software Product Sprint at Google APAC, where I worked under the mentorship of Yash Jain. I have had the opportunity to intern at Microsoft Teams Mobile team, and The Garage at Microsoft (R&D) Pvt. Ltd., in the summer of 2020 and 2019 respectively.

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Carnegie Mellon University
MS in Computer Vision
Aug 2023 - Dec 2024

Microsoft (R&D) Pvt. Ltd.
Software Engineer
June 2021 - Aug 2023
SWE Intern
May 2020 - July 2020
SWE Intern
May 2019 - July 2019

National Institute of Technology, Warangal
B.Tech. Computer Science
Aug 2017 - May 2021

Google APAC
Software Product Sprint
July 2021 - Sept 2021

Indian Institute of Science, Bengaluru
Research Intern
May 2018 - June 2019


pose Bayesian Gait-Based Gender Identification (BGGI) Network on Individuals Wearing Loosely Fitted Clothing

Addressing the challenge of identifying individuals wearing loosely fitted clothes of the opposite gender to evade detection, our proposed Bayesian Gait-based Gender Identification (BGGI) excels in real-world scenarios. Utilizing the LFCI dataset for training, our technique demonstrates superior performance in both pose estimation and gender recognition, employing state-of-the-art methods on dense real-world videos.

pose Curiosity & Entropy Driven Unsupervised RL in Multiple Environments
[arVix] [slides]

Alpha-MEPOL method for unsupervised RL in multiple environments achieves enhanced performance with dynamic alpha and a higher KL-Divergence threshold. Curiosity-driven exploration proves effective in high-dimensional environments, fostering diverse experiences and improving learning outcomes. Our experiments confirm positive results, highlighting the effectiveness of these modifications in the current context.

News Updates
[Aug 2023] Starting my M.S. Computer Vision (MSCV) degree at the Robotics Institute, Carnegie Mellon University (CMU).
[Jun 2021] Started a new position as a Software Engineer, Security, Compliance and Management Org, Microsoft, Hyderabad, India.
[May 2021] Received my Bachelor's degree from National Institute of Technology Warangal, with a major in Computer Science and Engineering.
[May 2020] Started summer internship in the Microsoft Teams Mobile team at Microsoft, Hyderabad, India
[May 2019] Started summer internship in the The Garage at Microsoft, Hyderabad, India
[Feb 2019] Started a new position as Programmer at G-Bit Studios
[May 2018] Started summer internship at Indian Institute of Science, Bangalore

Source code from Jon Barron