About

Mayank Singh Chauhan is an upcoming Analyst at Goldman Sachs with a diverse background in research and deep learning startups. He has experience in AI and Computer Networks research at both UCLA and IITD, and has worked as a python tutor and TA for AI courses at his institute. Mayank's research projects have resulted in publications at recognized conferences. Currently, Mayank works as an NLP Researcher at Sybill, where he spearheads the NLP research in the very early stages of the startup, which aims to add emotional intelligence to video calls. He has trained deep learning models to extract action items, pain points, and other important trackers from call transcripts, and has finetuned pre-trained language models for the task of multi natural language inference to handle oov text in production. Mayank has also deployed models to segment call transcripts based on the topic of discussion using Tensorflow's universal language encoder. He has formulated algorithms that build upon the above signals and extract questions and answers exchanged during the call. Previously, Mayank worked as a Summer Researcher at IITD, contributing to a research paper titled "Embedded CNN-Based Vehicle Classification and Counting in NonLaned Road Traffic," which was accepted in ICTD 2019 as the lead author. He ran object detection models on Raspberry Pi 3B, Intel Movidius stick, Jetson TX2, and Android Devices, and determined the mAP accuracy, benchmark scores, and energy values on the KITTI dataset. Mayank modified the caffe source to build an implementation for python that could profile all layers of object detection models. Mayank holds a Dual Degree (B.Tech + M.Tech) in Computer Science from the Indian Institute of Technology, Delhi. He is proficient in NLP, research science, AI, deep learning, language models, Python, and Tensorflow. Besides work, Mayank enjoys playing the piano, basketball, and his PS5. He is also a huge thriller buff.