Vishal’s prominent early observations were how different classes of people react to the same situation. Performing well in Computer Science tests and Physics Olympiads, he also explored his critical thought processes through art, music and philosophy. He started realizing that the biases in the human brain deep-set through societal classes may be the reason of observing differential reactions, thus triggering his explorations in Multi-Modal machine learning. In 2013, he reached out to ML-NLP faculties in IIT during his undergrad, back when Deep Learning was not as popular. He actively self-learned NLP concepts through Collobert et al’s NLP almost from scratch and Richard Socher’s initial work. He then explored high-finance trading at Goldman Sachs, fintech at Visa Inc., Open Source ML at Google Summer of Code and industry machine-learning at Uber Inc. Finally, doing research at Columbia University, he realized the best use of his expertise and passion is by going into NLP/Multimodal-HCI research.

Vishal holds a Master of Science diploma in Computer Science from Columbia University, where he worked closely with Prof. Smaranda Muresan, and Prof. Ching-Yung Lin. He also holds a Bachelors degree in Computer Science from IIT Guwahati (one of the 7 old IITs). Due to pressing financial constraints, he had to go into industry after Masters, with a resolve to get back into academia through a PhD program in a few years.

He has contributed to open-source through Google Summer of Code, Eclipse Foundation (Machine Learning Interpolation), and has worked at Goldman Sachs, Uber, Visa Inc., and Microsoft. He is proficient in C++, Python, Java, Scala, C and has earlier worked with Fortran, Shell, PHP, SQL, and JavaScript. Among frameworks, he has worked extensively on Tensorflow (Uber), Git, Cloudera (Visa), Docker, CUDA, IntelliJ, and Eclipse.

Research goals:

  • Natural Language Processing
  • Conversational AI
  • Low Resource Learning
  • Multi-Modal Machine Learning
  • Human Computer Interaction

Standardized Tests:

  • GRE: 331/340 [Quant:170/170, Verbal:161/170]
  • TOEFL: 115/120