Mononito Goswami

I am a Robotics Ph.D. Student at the Auton Lab in School of Computer Science at Carnegie Mellon University, advised by Prof. Artur Dubrawski. I am interested in building foundation models for time series and tabular data. I am also interested in questions around data to (pre-)train machine learning model and also questions around model evaluation. I am excited about applications of ML to healthcare.

I’m currently a Student Researcher at Google Research. Previously, I was an Applied Scientist Intern at Amazon Web Services (AWS) AI Labs in the summer of 2022 and 2023.

Prior to pursuing my Ph.D., I earned my bachelor’s in computer engineering from Delhi Technological University (erstwhile Delhi College of Engineering) in India.

Feel free to drop me an email at mgoswami [at] andrew [dot] cmu [dot] edu if you are interested in my research and would like to collaborate!

My CV can be found here.

Awards

Center for Machine Learning and Health (CMLH) Fellowship 2021

Selected Publications

A complete list of my papers can be found on my Google Scholar and Semantic Scholar

MOMENT: A Family Of Open Time-series Foundation Models

Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski. International Conference of Machine Learning (ICML) 2024 [ArXiv, code, HuggingFace weights, Time Series Pile]

JoLT: Jointly Learned Representations of Language and Time-Series.

Yifu Cai, Mononito Goswami, Arjun Choudhry, Arvind Srinivasan, Artur Dubrawski. Neural Information Processing Systems Workshop on Deep Generative Models for Health (DGM4H NeurIPS) (2023) (Poster). [Paper]

PICSR: Prototype-Informed Cross-Silo Router for Federated Learning.

Eric Enouen, Sebastian Caldas, Mononito Goswami, Artur Dubrawski. *AAAI Conference on Artificial Intelligence (Student Abstract) (2024). 3-min presentation contest finalist [Paper]

JoLT: Jointly Learned Representations of Language and Time-Series for Clinical Time-series Interpretation.

Yifu Cai, Arvind Srinivasan, Mononito Goswami, Arjun Choudhry, Artur Dubrawski. AAAI Conference on Artificial Intelligence (Student Abstract) (2024). Best student abstract presentation award winner. [Paper]

AQuA: A Benchmarking Tool for Label Quality Assessment

Mononito Goswami, Vedant Sanil, Arjun Choudhry, Arvind Srinivasan, Chalisa Udompanyawit, Artur Dubrawski
Neural Information Processing Systems (NeurIPS) 2023 Datasets & Benchmark Track (Poster). [ArXiv, code]

Unsupervised Model Selection for Time-series Anomaly Detection

Mononito Goswami, Cristian Challu, Laurent Callot, Lenon Minorics, Andrey Kan
International Conference of Learning Representations (ICLR) 2023 (Spotlight).
[ArXiv, code]

Encoding Expert Knowledge into Federated Learning Using Weak Supervision

Sebastian Caldas, Mononito Goswami, Artur Dubrawski
International Conference of Learning Representations 2023 Workshop on Machine Learning for Internet of Things (ML4IoT).

Classifying Unstructured Clinical Notes via Automatic Weak Supervision

Chufan Gao*, Mononito Goswami*, Jieshi Chen, Artur Dubrawski
Machine Learning for Healthcare Conference 2022.
[ArXiv, code]

Weak Supervision for Affordable Modeling of Electrocardiogram Data

Mononito Goswami, Benedikt Boecking, Artur Dubrawski
AMIA 2021 Annual Symposium. American Medical Informatics Association.
[ArXiv]

Toward Learning at Scale in Developing Countries: Lessons from the Global Learning XPRIZE Field Study

Andrew A, McReynolds, Sheba P Naderzad, Mononito Goswami, Jack Mostow
ACM Conference on Learning@Scale 2020.
[ACM]

A binary PSO approach for improving the performance of wireless sensor networks

Ajay Kaushik*, Mononito Goswami*, Minkush Manuja*, S Indu, Daya Gupta
Wireless Personal Communications 113 (1), 263-297
[Springer]

A multi-task approach to open domain suggestion mining using language model for text over-sampling

Maitree Leekha*, Mononito Goswami*, Minni Jain
European Conference on Information Retrieval (ECIR) 2020
[Springer]

Discriminating Cognitive Disequilibrium and Flow in Problem Solving: A Semi-Supervised Approach Using Involuntary Dynamic Behavioral Signals

Mononito Goswami*, Lujie Chen*, Artur Dubrawski
AAAI Conference on Artificial Intelligence (Social Impact Track) 2020
[AAAI]

Modeling Involuntary Dynamic Behaviors to Support Intelligent Tutoring (Student Abstract)

Mononito Goswami, Lujie Chen, Chufan Gao, Artur Dubrawski
AAAI Conference on Artificial Intelligence (Student Abstract) 2020
[AAAI]

Rajni Jindal, Maitree Leekha*, Minkush Manuja*, Mononito Goswami*
European Conference on Artificial Intelligence (ECAI) 2020
[IOS Press]

Are you paying attention? detecting distracted driving in real-time

Maitree Leekha*, Mononito Goswami*, Rajiv Ratn Shah, Yifang Yin, Roger Zimmermann
IEEE Fifth International Conference on Multimedia Big Data (BigMM) 2019
[IEEE]

What’s Most Broken? Design and Evaluation of a Tool to Guide Improvement of an Intelligent Tutor

Shiven Mian*, Mononito Goswami*, Jack Mostow
International Conference on Artificial Intelligence in Education (AIED) 2019
[Springer]

* indicates equal contribution

Preprints

Towards Knowledge-driven Clinical Data Annotation Quality Assessment

Mononito Goswami, Benedikt Boecking, Patrick J. Coppler, Jonathan Elmer, Artur Dubrawski

Mononito Goswami*, Chufan Gao*, Benedikt Boecking, Saswati Ray, Artur Dubrawski

Mononito Goswami*, Minkush Manuja*, Maitree Leekha*
[ArXiv]