Akshay Sharma

Akshay Sharma

Graduate Student @ UMass. An AI and Open Source Enthusiast.

I am a graduate student in College of Information and Computer Sciences at University of Massachusetts Amherst. I also worked as a software developer at Intuit IDC (TurboTax Desktop Team) from Aug 2017 to July 2021. I did my bachelors (B.Tech) from National Institute of Technology, Karnataka with a major in Information Technology. I like to solve real world problems with computer programming. My interests lie in the field of Artificial Intelligence viz. Machine Learning, Deep Learning, Natural Language Processing and Reinforcement Learning. I like to contribute to Open Source projects in my free time.

Resume

Here is the link to my resume.

Publications

  • I worked on improving a swarm optimisation algorithm during a research internship I got through Indian Academy of Sciences. Avinash Sharma, Akshay Sharma et al. , Ageist Spider Monkey Optimization Algorithm, In: Elsevier Journal, Swarm and Evolutionary Computation June 2016, Pages 58–77. Click here for paper link. I have also implemented the paper in Julia which you can find here.

  • I also enhanced the deep learning model based on LSTM and CNN by applying an inner attention mechanism for the LSTM and also used an information retrieval model along with it to generate answers for non factoid questions. Achieved Mean Reciprocal Rank (MRR) of ~0.74 on WikiQA and InsuranceQA datasets. Click here for paper link.

  • I did a poster presentation for my project during my GSoC 2017 intern at JuliaCon 2017 in Berkely, CA. Here is the link for its write up.

  • At IESL, UMass I worked on annotating a large dataset of scientific peer review text to highlight discourse structure and developed models to automatically detect this structure. Here is the link for the paper

Courses

  • Algorithms for Data Science
  • Machine Learning
  • Advanced Natural Language Processing
  • Conversational AI
  • Reinforcement Learning
  • Neural Networks
  • Systems for Data Sciences
  • Applied Statistics
  • Soft Computing
  • Data Structures and Algorithms
  • Linear Algebra
  • Graph Theory and Probability
  • Operating Systems