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Research & Publications

Selected publications at ICML, ICLR, NeurIPS, AAAI, KDD, and other venues. Full list on Google Scholar and Semantic Scholar.

Agents

SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
Shravan Chaudhari, Rahul Thomas Jacob, Mononito Goswami, Jiajun Cao, Shihab Rashid, Christian Bock
ArXiv Preprint, 2025
Dense embedding retrieval for localizing code changes from natural language issue descriptions in large software repositories.
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TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents
Yifu Cai, Xinyu Li, Mononito Goswami, Michał Wiliński, Gus Welter, Artur Dubrawski
ArXiv Preprint, 2025
A benchmarking environment for evaluating ML engineering agents on the full time-series modeling stack, from data exploration and modeling to testing and deployment.
PDF Code

Foundation Models

Chronos-2: From Univariate to Universal Forecasting
Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider
ArXiv Preprint, 2025
Extends the Chronos forecasting model to handle multivariate, covariate-conditioned, and probabilistic forecasting in a single unified architecture.
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Exploring Representations and Interventions in Time Series Foundation Models
Michał Wiliński, Mononito Goswami, Willa Potosnak, Nina Żukowska, Artur Dubrawski
International Conference on Machine Learning (ICML), 2025
First to show that time series foundation models learn interpretable concepts (trends, seasonality) despite self-supervised training, and that these representations can be targeted for intervention.
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MOMENT: A Family of Open Time-series Foundation Models
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski
International Conference on Machine Learning (ICML), 2024
One of the first open-source time series foundation models. 2.5M+ downloads on HuggingFace, 700+ GitHub stars.
PDF Code HuggingFace Data

Evaluation Science

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
Małgorzata Gwiazda, Yifu Cai, Mononito Goswami, Arjun Choudhry, Artur Dubrawski
International Conference on Learning Representations (ICLR), 2026
Benchmark for temporal reasoning in LLMs, with scalable task generation via LLM agents and item response theory.
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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 and Benchmarks Track
A comprehensive benchmarking tool for evaluating label error detection methods across diverse datasets and annotation types.
PDF Code
Unsupervised Model Selection for Time-series Anomaly Detection Spotlight
Mononito Goswami, Cristian Challu, Laurent Callot, Lenon Minorics, Andrey Kan
International Conference on Learning Representations (ICLR), 2023
Shows that ensembles of unsupervised heuristics and weak supervision can accurately select anomaly detection models without any ground-truth labels.
PDF Code

Healthcare & Education

JoLT: Jointly Learned Representations of Language and Time-Series for Clinical Time-Series Interpretation Best Student Abstract
Yifu Cai, Arvind Srinivasan, Mononito Goswami, Arjun Choudhry, Artur Dubrawski
AAAI Conference on Artificial Intelligence (AAAI), 2024 Student Abstract. Also at NeurIPS 2023 DGM4H Workshop.
Learns joint language-time series embeddings so clinicians can query physiological signals with natural language descriptions.
Paper OpenReview
Using Weakly Supervised Machine Learning to Label Atrial Fibrillation in Real-World Intensive Care Unit Telemetry Data
Sydney R. Rooney, Roman Kaufman, Mononito Goswami, Michael R. Pinsky, J. Kyle Miller, Salah Al-Zaiti, Artur Dubrawski, Gilles Clermont
Circulation, 2022
Applies weak supervision to detect atrial fibrillation episodes in noisy ICU telemetry without expert-labeled training data.
Paper
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
Chufan Gao, Mononito Goswami, Jieshi Chen, Artur Dubrawski
Machine Learning for Healthcare Conference (MLHC), PMLR, 2022
Automatically generates labeling functions from clinical notes to train classifiers without manual annotation.
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Counterfactual Phenotyping with Censored Time-to-Events
Chirag Nagpal, Mononito Goswami, Keith Dufendach, Artur Dubrawski
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
Combines causal inference with survival analysis to discover patient phenotypes under counterfactual treatment scenarios.
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Weak Supervision for Affordable Modeling of Electrocardiogram Data
Mononito Goswami, Benedikt Boecking, Artur Dubrawski
AMIA Annual Symposium Proceedings, 2021
Applies weak supervision and data programming to build ECG classification models without manual expert labels.
Paper
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 (AAAI), 2020
Uses semi-supervised learning on involuntary behavioral signals (eye gaze, facial expressions) to detect cognitive states during problem solving.
Paper