Vineet Bhat

Vineet Bhat

He/Him

PhD Candidate

Experience

Accenture Labs India - Research Intern (Oct 2021)

I developed a framework to enhance the diversity of a small in-lab gesture video dataset by researching state-of-the-art synthetic video generation methods and implementing augmentations using OpenCV and Pillow. The automated framework increased video samples by 100 times through systematic application of blurring, random cropping, and affine transformations. Using the synthetic dataset, I trained and evaluated deep learning models like MocoGAN and TecoGAN, identifying GAN-based methods as optimal for limited gold-standard datasets. This approach led to a 10% relative improvement in classification performance over competitive baselines. Additionally, I identified the limitations of mechanical augmentations and recommended diffusion models for more sophisticated data augmentation.

ABInBev India - Automation Intern (May 2021)

I analyzed €8.4M in correction amounts for the Belgium Business Division, identifying invoicing errors and developing seven KPIs to enhance billing accuracy while cataloging SAP tables for data aggregation. For the Mexico Business Division, I debugged 10+ data metrics and developed knowledge models for Overdues and Accounts Receivable, enabling effective rework tracking. I also designed three global sales order trackers and documented analysis dashboards for business-friendly use.

Huawei India RnD - Data Science Intern (July 2020)

I developed a machine learning model for speech language detection, leveraging mel-spectrogram images and multi-label classification techniques from computer vision. Using CNN architectures like InceptionV3, DenseNet201, and VGG16, I trained models on datasets such as LibriSpeech and CommonVoice, achieving 97% accuracy in detecting European languages. For Automatic Speech Recognition (ASR), I trained the Jasper Network with CTC loss for English and applied transfer learning to Spanish, achieving a competitive 19.78% WER for Spanish ASR. To improve transcription accuracy, I integrated spelling correction models like DeepPavlov and Enchant, reducing English ASR errors by 7%. Additionally, I explored ASR for music lyric transcription by isolating vocals using Spleeter but identified challenges due to background noise and artistic pronunciation variations.

MedPrime Technologies - Data Science Intern (December 2019)

MedPrime Technologies is a pioneering medical device company dedicated to creating customer-centric solutions for global healthcare needs. During my second year of undergrad, I collaborated closely with the CTO through an internship to design a Python-based backend software that assists doctors in predicting the suitability of sperm samples for Human IVF.