Harshavardhan Kamarthi

I’m Harsha. I’m a final-year Machine Learning PhD student in Department of Computational Science and Engineering at Georgia Institute of Technology . I am affiliated with AdityaLab and am advised by Dr. B Aditya Prakash. I graduated from Indian Institute of Technology Mardas and am fortunate to have worked with Dr. Balaraman Ravindran and Dr. Sutanu Chakraborti.

My research interests broadly revolves around robust time-series forecasting analysis with focus on uncertainty, scalability, and cross domain generalization. My current research interests include:

  1. Foundational time-series models that are prettrained on multiple domain datasets and generalizable across wide range of domains and tasks.(LPTM ‘24, PEMS ‘23, LstPrompt ‘24, TimeMMD ‘24).
  2. Scalable and efficient time-series forecasting models that can handle large-scale time-series data and provide robust and calibrated forecasts(HAILS ‘24, ProfHiT ‘23).
  3. Probabilistic time-series forecasting models that can provide uncertainty estimates and are robust to outliers, missing data and novel scenarios.(STOIC ‘23, CAMUL ‘22, EPIFNP ‘21, B2F ‘22).

news

Oct 1, 2024 Large Pre-Traind Time-series Models is accepted at NeurIPS 2024! See you at Vancouver.
Sep 16, 2024 Machine learning for data-centric epidemic forecasting will appear at Nature Machine Intelligence 6, 2024. Check out our survey on latest data-driven methods for epidemic forecasting.
May 1, 2024 Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting on Graph leanring for probabilistic multivariate time-series forecasting to appear at UDM workshop at KDD 2024!
Mar 1, 2024 Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity on large-scale industrial demand hierarchical forecasting is accepted at KDD 2024!
Feb 11, 2024 LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting on zero-shot time-series forecasting using large language models is accepted at ACL Findings 2024!

publications

  1. survey24
    Machine learning for data-centric epidemic forecasting
    Rodrı́guez, Alexander, Kamarthi, Harshavardhan, Agarwal, Pulak, Ho, Javen, Patel, Mira, Sapre, Suchet, and Prakash, B Aditya
    Nature Machine Intelligence 2024
  2. lptm23
    Large Pre-trained time series models for cross-domain Time series analysis tasks
    Kamarthi, Harshavardhan, and Prakash, B Aditya
    Conference on Neural Information Processing Systems 2024
  3. hails24
    Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
    Kamarthi, Harshavardhan, Sasanur, Aditya B, Tong, Xinjie, Zhou, Xingyu, Peters, James, Czyzyk, Joe, and Prakash, B Aditya
    KDD Applied Data Science Track 2024
  4. foil24
    Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
    In ICML 2024
  5. lstprompt24
    Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting
    Liu, Haoxin, Zhao, Zhiyuan, Wang, Jindong, Kamarthi, Harshavardhan, and Prakash, B Aditya
    ACL Findings 2024
  6. timemmd24
    Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis
    Liu, Haoxin, Xu, Shangqing, Zhao, Zhiyuan, Kong, Lingkai, Kamarthi, Harshavardhan, Sasanur, Aditya B, Sharma, Megha, Cui, Jiaming, Wen, Qingsong, Zhang, Chao, and others,
    arXiv preprint arXiv:2406.08627 2024
  7. nature24
    Title evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations
    Mathis, Sarabeth M, Webber, Alexander E, León, Tomás M, Murray, Erin L, Sun, Monica, White, Lauren A, Brooks, Logan C, Green, Alden, Hu, Addison J, Rosenfeld, Roni, and others,
    Nature Communications 2024
  8. stoic24
    Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
    KDD 2024 Workshop on Uncertainty Reasoning and Quantification in Decision Making 2024
  9. profhit23
    PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series
    Kamarthi, Harshavardhan, Kong, Lingkai, Rodrı́guez, Alexander, Zhang, Chao, and Prakash, B Aditya
    KDD 2023
  10. pems23
    PEMS: Pre-trained Epidmic Time-series Models
    Kamarthi, Harshavardhan, and Prakash, B Aditya
    arXiv preprint arXiv:2311.07841 2023
  11. camul21
    CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
    Kamarthi, Harshavardhan, Kong, Lingkai, Rodrı́guez, Alexander, Zhang, Chao, and Prakash, B Aditya
    ACM Web Conference (WWW) 2022
  12. back2future21
    Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
    Kamarthi, Harshavardhan, Rodrı́guez, Alexander, and Prakash, B Aditya
    Preprint 2021
  13. epifnp21
    When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
    Kamarthi, Harshavardhan, Kong, Lingkai, Rodrı́guez, Alexander, Zhang, Chao, and Prakash, B Aditya
    Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021
  14. selective20
    Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
    Nishtala, Siddharth, Madaan, Lovish, Mate, Aditya, Kamarthi, Harshavardhan, Grama, Anirudh, Thakkar, Divy, Narayanan, Dhyanesh, Chaudhary, Suresh, Madhiwalla, Neha, Padmanabhan, Ramesh, and others,
    arXiv preprint arXiv:2103.09052 2021
  15. patrol20
    Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty
    Venugopal, Aravind, Bondi, Elizabeth, Kamarthi, Harshavardhan, Dholakia, Keval, Ravindran, Balaraman, and Tambe, Milind
    20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2020
  16. missedcall20
    Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement
    Nishtala, Siddharth, Kamarthi, Harshavardhan, Thakkar, Divy, Narayanan, Dhyanesh, Grama, Anirudh, Hegde, Aparna, Padmanabhan, Ramesh, Madhiwalla, Neha, Chaudhary, Suresh, Ravindran, Balaraman, and others,
    Harvard CRCS Workshop on AI for Social Good 2020
  17. influence19
    Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
    Kamarthi, Harshavardhan, Vijayan, Priyesh, Wilder, Bryan, Ravindran, Balaraman, and Tambe, Milind
    19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Nominated for Best Paper Award<\b> 2019
  18. integrating19
    Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting
    Srinivasan, Aakash, Kamarthi, Harshavardhan, Ganesan, Devi, and Chakraborti, Sutanu
    International Conference on Natural Language Processing (ICNLP) 2019