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<p>I currently work at Mastercard as a Senior Data Scientist. My research interests include temporal graphs and NLP for transactional data.</p>
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<p>Welcome to my personal website 👋!</p>
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<p>I currently work at Mastercard as a Senior Data Scientist. I manage transactional data, which encompasses tabular and temporal dimensions. It involves intricate temporal data modeling utilizing time-series analysis, temporal point processes, and temporal graph neural networks. Additionally, I am actively engaged in prototyping the integration of Large Language Model (LLM)–based embeddings, harnessing their capabilities to optimize performance across transactional data scenarios. My daily responsibilities encompass the end-to-end process of designing, developing, and deploying machine learning and deep learning models at scale, ensuring robust and efficient solutions.</p>
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<h2data-anchor-id="education">Education</h2>
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<p><imgsrc="assets/mortarboard-fill.svg"> Masters in Computational Data Sciences, 2020 <br> <spanstyle="color:gray"> Indian Institute of Science | Bangalore, India </span></p>
<li><p><strong><ahref="https://arxiv.org/abs/2210.15294">Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes</a><br> Govind Waghmare, Ankur Debnath, Siddhartha Asthana, Aakarsh Malhotra</strong><br><em>Conference on Information & Knowledge Management (CIKM), 2022</em></p></li>
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<li><p><strong><ahref="https://link.springer.com/chapter/10.1007/978-3-030-93736-2_24">Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data</a><br> Ankur Debnath, Nitish Gupta, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora</strong><br><em>Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021</em></p></li>
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<li><p><strong><ahref="https://kdd-milets.github.io/milets2021/papers/MiLeTS2021_paper_7.pdf">Exploring generative data augmentation in multivariate time series forecasting: opportunities and challenges</a><br> Ankur Debnath, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora</strong><br><em>KDD Workshop on Mining and Learning from Time Series (MileTS), 2021</em></p></li>
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<li><p><strong><ahref="https://arxiv.org/pdf/2008.01388.pdf">Unsupervised cross-modal alignment for multi-person 3d pose estimation</a><br> Jogendra Nath Kundu, Ambareesh Revanur, Govind Vitthal Waghmare, Rahul Mysore Venkatesh, R Venkatesh Babu</strong><br><em>European Conference on Computer Vision (ECCV), 2020</em></p>
<li><p><strong><ahref="https://ieeexplore.ieee.org/abstract/document/7413746/">Badminton shuttlecock detection and prediction of trajectory using multiple 2 dimensional scanners</a><br> Govind Waghmare, Sneha Borkar, Vishal Saley, Hemant Chinchore, Shivraj Wabale</strong><br><em>IEEE First International Conference on Control, Measurement and Instrumentation (CMI), 2016</em></p></li>
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* **[Learning Temporal Representations of Bipartite Financial Graphs](https://dl.acm.org/doi/abs/10.1145/3604237.3626911)
<br/> *Conference on Information & Knowledge Management (CIKM), 2022*
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* **[Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data](https://link.springer.com/chapter/10.1007/978-3-030-93736-2_24)
<br/> *Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021*
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* **[Exploring generative data augmentation in multivariate time series forecasting: opportunities and challenges](https://kdd-milets.github.io/milets2021/papers/MiLeTS2021_paper_7.pdf)
* **[Badminton shuttlecock detection and prediction of trajectory using multiple 2 dimensional scanners](https://ieeexplore.ieee.org/abstract/document/7413746/)
<td><strong><ahref="https://arxiv.org/abs/2210.15294">Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes</a></strong><br>
<td><strong><ahref="https://link.springer.com/chapter/10.1007/978-3-030-93736-2_24">Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data</a></strong><br>
<td><strong><ahref="https://kdd-milets.github.io/milets2021/papers/MiLeTS2021_paper_7.pdf">Exploring generative data augmentation in multivariate time series forecasting: opportunities and challenges</a></strong><br>
<td><strong><ahref="https://ieeexplore.ieee.org/abstract/document/7413746/">Badminton shuttlecock detection and prediction of trajectory using multiple 2 dimensional scanners</a></strong><br>
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"title": "Govind Waghmare",
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"text": "I currently work at Mastercard as a Senior Data Scientist. My research interests include temporal graphs and NLP for transactional data."
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"text": "Welcome to my personal website 👋!\nI currently work at Mastercard as a Senior Data Scientist. I manage transactional data, which encompasses tabular and temporal dimensions. It involves intricate temporal data modeling utilizing time-series analysis, temporal point processes, and temporal graph neural networks. Additionally, I am actively engaged in prototyping the integration of Large Language Model (LLM)–based embeddings, harnessing their capabilities to optimize performance across transactional data scenarios. My daily responsibilities encompass the end-to-end process of designing, developing, and deploying machine learning and deep learning models at scale, ensuring robust and efficient solutions."
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"title": "Publications",
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"text": "My Google Scholar 📝 profile.\n\n\nLearning Temporal Representations of Bipartite Financial Graphs Pritam Kumar Nath, Govind Waghmare, Nikhil Tumbde, Nitish Kumar, Siddhartha Asthana International Conference on AI in Finance (ICAIF), 2023\nTBoost: Gradient Boosting Temporal Graph Neural Networks Pritam Nath, Govind Waghmare, Nancy Agrawal, Nitish Kumar, Siddhartha Asthana Temporal Graph Learning Workshop @ NeurIPS, 2023\nModeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes Govind Waghmare, Ankur Debnath, Siddhartha Asthana, Aakarsh Malhotra Conference on Information & Knowledge Management (CIKM), 2022\nAdversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data Ankur Debnath, Nitish Gupta, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021\nExploring generative data augmentation in multivariate time series forecasting: opportunities and challenges Ankur Debnath, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora KDD Workshop on Mining and Learning from Time Series (MileTS), 2021\nUnsupervised cross-modal alignment for multi-person 3d pose estimation Jogendra Nath Kundu, Ambareesh Revanur, Govind Vitthal Waghmare, Rahul Mysore Venkatesh, R Venkatesh Babu European Conference on Computer Vision (ECCV), 2020\n\nProject page\n\nBadminton shuttlecock detection and prediction of trajectory using multiple 2 dimensional scanners Govind Waghmare, Sneha Borkar, Vishal Saley, Hemant Chinchore, Shivraj Wabale IEEE First International Conference on Control, Measurement and Instrumentation (CMI), 2016"
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"text": "My Google Scholar 📝 profile.\n\n\n\n\n\n\n\n\n\n\n\nLearning Temporal Representations of Bipartite Financial Graphs\nPritam Kumar Nath, Govind Waghmare, Nikhil Tumbde, Nitish Kumar, Siddhartha Asthana\nInternational Conference on AI in Finance (ICAIF), 2023\n\n\n\nTBoost: Gradient Boosting Temporal Graph Neural Networks\nPritam Nath, Govind Waghmare, Nancy Agrawal, Nitish Kumar, Siddhartha Asthana\nTemporal Graph Learning Workshop @ NeurIPS, 2023\n\n\n\nModeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes\nGovind Waghmare, Ankur Debnath, Siddhartha Asthana, Aakarsh Malhotra\nConference on Information & Knowledge Management (CIKM), 2022\n\n\n\nAdversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data\nAnkur Debnath, Nitish Gupta, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora\nJoint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021\n\n\n\nExploring generative data augmentation in multivariate time series forecasting: opportunities and challenges\nAnkur Debnath, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora\nKDD Workshop on Mining and Learning from Time Series (MileTS), 2021\n\n\n\nUnsupervised cross-modal alignment for multi-person 3d pose estimation\nJogendra Nath Kundu, Ambareesh Revanur, Govind Waghmare, Rahul Mysore Venkatesh, R Venkatesh Babu\nEuropean Conference on Computer Vision (ECCV), 2020\n\n\n\nBadminton shuttlecock detection and prediction of trajectory using multiple 2 dimensional scanners\nGovind Waghmare, Sneha Borkar, Vishal Saley, Hemant Chinchore, Shivraj Wabale\nIEEE First International Conference on Control, Measurement and Instrumentation (CMI), 2016"
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