👋 Hi! I’m currently a Grad Student at Boston University pursuing my Masters in AI. I did my undergrad from Manipal Institute of Technology, India, in Electronics and Instrumentation with a minor in Computational Intelligence, and had developed a keen interest in all things ML during my first year and was fortunate to gain research experience along the way. Prior to joining BU, I worked with the Content and User Understanding team at ShareChat, and was fortunate to work on projects with the Serre Lab (Brown University), Human Dynamics Group (MIT Media Lab, Massachusetts Institute of Technology), ETS, Montreal and FOR.ai (now Cohere for AI).
At BU, I’m currently working with Prof. Joshua Peterson at the Cognitive Data Science Lab, working on the intersection of ML and Human Behaviour.
Curriculum Vitae / xavierohan1@gmail.com
Education
M.S. in Artificial Intelligence
Boston University
2023 - Present
B.Tech. in Electronics and Instrumentation (minor: Computational Intelligence)
Manipal Institite of Technology
2018 - 2022
Publications, Preprints and Working Papers (also see Google Scholar)
MAViC: Multimodal Active Learning for Video Captioning
Gyanendra Das, Xavier Thomas, Anant Raj, Vikram Gupta
Preprint, 2022
Experience
Center of Computing & Data Sciences (Boston University), Teaching Assistant
- TA for CDS DS 598 B1 - Deep Learning for Data Science, with Prof. Thomas Gardos
Cognitive Data Science Lab (Boston University), Research Assistant
- Working on building interpretable, high-precision Machine Learning models of Human Behavior.
- Advisor: Prof. Joshua Peterson
ShareChat, Machine Learning Engineer Intern
- Worked on topics related to Few-shot Learning, Multimodal Learning and Generative AI with the Content and User Understanding Team at ShareChat.
- Advisors: Vikram Gupta, Dr. Hisham Cholakkal, Dr. Anant Raj
- Paper
Serre Lab (Brown University), Research Intern
- Worked on a project to create a new framework to evaluate one-shot generative models along two axes: sample recognizability vs. diversity (intra-class variability)
- Using this framework, conducted a systematic evaluation of representative one-shot generative models (VAE, Neural Statistician, Data Augmentation GAN) on the Omniglot handwritten dataset.
- Paper
- Code
- Advisors: Dr. Thomas Serre, Dr. Victor Boutin
École de technologie supérieure (ÉTS), Montréal, Research Intern
- Summer research intern, funded through the Mitacs Globalink Research Internship Program.
- Worked on Weakly Supervised Semantic Segmentation.
- Code
- Advisor: Dr. Jose Dolz
Massachusetts Institute of Technology, Research Assistant
- Worked on an algorithm that recovers domain information in an unsupervised manner, by carefully removing class-specific noise from features.
- Used this carefully selected feature space to partition inputs and learn a domain-adaptive classifier.
- Paper
- Code
- Advisors: Dr. Abhimanyu Dubey, Dr. Alex Pentland
For.ai (now Cohere For AI), Research Member
- FOR.ai is a multi-disciplinary team of scientists and engineers who like doing research for fun. Our only objective is to publish good machine learning research that is useful and interesting. Our collaborators include researchers from research institutions such as Google Brain, University of Oxford, and Vector Institute for Artificial Intelligence. Published experiments and tools can be found at github.com/for-ai
- Worked on an Out-of-Distribution Detection Benchmarking Project. Integrated the Glow architecture in the codebase for a large-scale study of Generative Models.
- Advisor: Sheldon Huang
Miscellaneous
- 🚀 This website is built with the help of https://github.com/Renovamen/renovamen.github.io which uses Astro, Solid and UnoCSS
- This website is still a Work in Progress!