About me
Hi there! I’m Krishnateja Killamsetty, a passionate researcher in the field of machine learning and AI. I recently completed my Ph.D. from the University of Texas at Dallas, where I had the pleasure of working in the CARAML Lab under the guidance of Dr. Rishabh Iyer. It was an incredible journey filled with exciting challenges and opportunities to grow as a researcher.
Now, I’m thrilled to be a Research Scientist at IBM Almaden Research Center, where I’m working on developing in-house conversational AI models for enterprise applications. My focus is on data subset selection and targeted synthetic data generation, aiming to make machine learning models more efficient and robust. I’m particularly excited about the potential of these techniques to achieve significant speedups, energy savings, and even reduced CO2 emissions.
My research interests lie in developing techniques that analyze the importance of data samples and leverage the underlying data structure to achieve data-efficiency, compute-efficiency, and robustness. I’m also keen on building machine learning systems that can learn efficiently from massive amounts of data, even when the labels are noisy or the classes are imbalanced. It’s a challenging but incredibly rewarding field of study.
I’m always eager to collaborate and discuss ideas with fellow researchers and enthusiasts. If you’re interested in learning more about my work, feel free to explore my research and publications pages. I believe that by working together, we can push the boundaries of AI and make a positive impact on the world, one efficient algorithm at a time.
Thank you for taking the time to get to know me a little better. I look forward to connecting with you and sharing more about my passion for machine learning and AI!
News
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Our paper “Orient: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift” got accepted at NeurIPS 2022!”
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Our paper “AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning” got accepted at NeurIPS 2022!”
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Our paper “How Out of Distribution Data Hurts Semi-Supervised Learning” got accepted at ICDM 2022!
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Our paper “GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning” got accepted at CVPR 2022!
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Our paper “Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming” got accepted at Findings of ACL 2022!
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Our paper “A Nested Bi-level Optimization Framework for Robust Few Shot Learning” got accepted at AAAI 2022!
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Served as Programme Committee member for AAAI 2022
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Our paper “RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning” got accepted at NeurIPS 2021!
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Our paper “SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios” got accepted at NeurIPS 2021!
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Served as Programme Committee member for NeurIPS 2021
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Excited to release CORDS (Github), a PyTorch-based open-source efficient deep model training and autoML library!
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Our paper “GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training” got accepted at ICML 2021!
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Our paper “Semi-Supervised Data Programming with Subset Selection” got accepted at Findings of ACL 2021!
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Served as Programme Committe member for AISTATS 2021
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Our paper “GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning” got accepted to AAAI 2021!
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Served as Programme Committe member for AAAI 2021