Interest flooding reduction in Content Centric Networks. - Vijay Ekambaram; Krishna M. Sivalingam (HPSR, 2013)

Appstrument - A Unified App Instrumentation and Automated Playback Framework for Testing Mobile Applications. - Vikrant Nandakumar; Vijay Ekambaram; Vivek Sharma (MobiQuitous, 2013)

Tell Me More: Digital Eyes to the Physical World for Early Childhood Learning. - Vijay Ekambaram; Ruhi Sharma Mittal; Prasenjit Dey; Ravindranath Kokku; Aditya K. Sinha; Satya V. Nitta (EDM, 2017)

Attention based Multi-Modal New Product Sales Time-series Forecasting. - Vijay Ekambaram; Kushagra Manglik; Sumanta Mukherjee; Surya Shravan Kumar Sajja; Satyam Dwivedi; Vikas Raykar (KDD, 2020)

Distributed Incremental Machine Learning for Big Time Series Data. - Dhaval Salwala; Seshu Tirupathi; Brian Quanz; Wesley M. Gifford; Stuart Siegel; Vijay Ekambaram; Arindam Jati (IEEE Big Data, 2022)

Hierarchy-guided Model Selection for Time Series Forecasting. - Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami (CoRR, 2022)

TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. - Vijay Ekambaram; Arindam Jati; Nam Nguyen; Phanwadee Sinthong; Jayant Kalagnanam (KDD, 2023)

Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. - Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami (KDD, 2023)

AutoMixer for Improved Multivariate Time-Series Forecasting on BizITOps Data. - Santosh Palaskar; Vijay Ekambaram; Arindam Jati; Neelamadhav Gantayat; Avirup Saha; Seema Nagar; Nam H. Nguyen; Pankaj Dayama 001; Renuka Sindhgatta; Prateeti Mohapatra; Harshit Kumar; Jayant Kalagnanam; et al. (CoRR, 2023)

AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data. - Santosh Palaskar; Vijay Ekambaram; Arindam Jati; Neelamadhav Gantayat; Avirup Saha; Seema Nagar; Nam H. Nguyen; Pankaj Dayama 001; Renuka Sindhgatta; Prateeti Mohapatra; Harshit Kumar; Jayant Kalagnanam; et al. (AAAI, 2024)

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. - Vijay Ekambaram; Arindam Jati; Pankaj Dayama 001; Sumanta Mukherjee; Nam Nguyen; Wesley M. Gifford; Chandra Reddy; Jayant Kalagnanam (NeurIPS, 2024)

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. - Vijay Ekambaram; Arindam Jati; Nam H. Nguyen; Pankaj Dayama 001; Chandra Reddy; Wesley M. Gifford; Jayant Kalagnanam (CoRR, 2024)

Activations Through Extensions: A Framework To Boost Performance Of Neural Networks. - Chandramouli Kamanchi; Sumanta Mukherjee; Kameshwaran Sampath; Pankaj Dayama 001; Arindam Jati; Vijay Ekambaram; Dzung Phan (CoRR, 2024)

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. - Claudius Krause; Michele Faucci Giannelli; Gregor Kasieczka; Benjamin Nachman; Dalila Salamani; David Shih; Anna Zaborowska; Oz Amram; Kerstin Borras; Matthew R. Buckley; Erik Buhmann; Thorsten Buss; et al. (CoRR, 2024)

Towards Unbiased Evaluation of Time-series Anomaly Detector. - Debarpan Bhattacharya; Sumanta Mukherjee; Chandramouli Kamanchi; Vijay Ekambaram; Arindam Jati; Pankaj Dayama 001 (ICASSP, 2025)

TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis. - Vijay Ekambaram; Subodh Kumar; Arindam Jati; Sumanta Mukherjee; Tomoya Sakai; Pankaj Dayama 001; Wesley M. Gifford; Jayant Kalagnanam (CoRR, 2025)