Research

I am currently a researcher at the Microsoft Semantic Machines.

I completed my PhD from School of Computer Science at Carnegie Mellon University, advised by Taylor Berg-Kirkpatrick. 

Prior to that, I completed my Bachelors degree in Computer Science and Engineering from Indian Institute of Technology (IIT) Roorkee.

Before joining CMU, I worked at Adobe Research Labs in Bangalore, India. During my PhD, I have interned at Allen Institute for AI Seattle (Summer 2019; Mentor: Peter Clark), and Facebook AI Research (Summer 2020)

Link to Research Statement (Last updated: August 2021)

Github // Twitter // LinkedIn // G-Scholar // S-Scholar 

Reviewing: Reviewer for NEURIPS 2018/19, NAACL 2019/21, ACL 2019/20, EMNLP 2019/20(outstanding reviewer)/21(outstanding reviewer), COLING 2020, AACL 2020, INLG 2021, ACL-ARR (2022 - Present)(oustanding reviewer in Oct 2023, Feb 2024), AC for ACL 2023, AC for EMNLP 2023, AC for ARR 2024-Present

Teaching: TA for 11-777 Advanced Multimodal Machine Learning (Fall 2018), CSE 151 Intro to AI: A Statistical Approach (Fall 2019)

Mentoring: Vishal Keswani (IITK undergrad): 2021 ; Bodhisattwa Majumder (UCSD PhD) : 2020-2021. Nathaniel Weir (Summer intern 2022). Nikita Moghe (Summer intern 2023)

Fellowship: I am one of the winners of Adobe 2020 Fellowship

RESEARCH PUBLICATIONS

See Publications 

For the most up-to-date list of publications, please visit my Google Scholar profile page

Grounded Natural Language Generation via Interpretable Hierarchical Operations. Thesis. November 2021

Harsh Jhamtani [Thesis] [Bibtex]  

Grounded Natural Language Generation via Interpretable Hierarchical Operations. Thesis. November 2021

Harsh Jhamtani [Thesis] [Bibtex]  

My thesis research was in interpretable machine reasoning for grounded text generation, especially in those problems which require uncovering of latent discrete structures.

Interpretable Latent Abstractions on Data

Discrete Plans for Long-Form Text Generation