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Aditya Narendra
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I am an independent researcher working towards building safe, reliable, and fair ML systems for healthcare applications.
Previously, I was fortunate to work with Dr. Leland K Werden at ETH Zürich, Dr. Min Xu at Carnegie Mellon University (CMU), and Dr. J. Sivaswamy at IIIT Hyderabad. I also worked as a Software Engineer at Tech Mahindra's AI Lab.
Feel free to reach out to discuss research ideas or potential collaborations.
Email |
CV |
LinkedIn |
GitHub
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Experience
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Associate Software Engineer | Center of Excellence-Artificial Intelligence
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Aug 2022 - Nov 2024
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Worked on a GNN-based accident detection system for a smart traffic solution for the Govt. of Odisha, cutting emergency response times by over 60%. Also designed an EHR application handling 100,000+ daily records and improved record retrieval by 42%.
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Research Affiliate | Assisted Forest Regeneration lab
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Advisor: Dr. Leland K Werden
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Dec 2022 - Jan 2024
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Built a sapling detection algorithm that detects over 300 tree species, for savannah and
mangrove restoration projects. Finetuned a Llama2-13b model for a summarization platform with custom review tags for grey literature of regeneration practices on ASReview Lab.
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Research Intern | Xu lab
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Advisor: Prof. Min Xu
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Aug 2022 - Sept 2023
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Worked on a Contrastive Self-Supervised Learning (CSSL) approach for macromolecular structure classification from cryo-ET data with limited labels. Also contributed to an unsupervised multi-task learning framework for 3D subtomogram image alignment, clustering, and segmentation in cryo-ET environment.
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Research Volunteer | Summer School on Computational Neuroscience
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Advisor: Dr. José Biurrun Manresa
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July 2017 - Aug 2023
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Participated in the 2023 Neuromatch Academy Summer School on Computational Neuroscience. Designed regression models for future motion state prediction using time series analysis on ECoG data.
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Research Intern | Summer School on Deep Learning for Medical Imaging
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Advisors: Prof. Pierre-Marc Jodoin & Prof. Thomas Grenier
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Jul 2022 - Aug 2022
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Partcipated in the 3rd Edition Summer School on Deep Learning for Medical Imaging (DLMI-22). Evaluated various weakly supervised segmentation techniques for cardiac diseases diagnosis.
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Research Assistant | IHub-Data
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Advisors: Prof. Jayanthi Sivaswamy & Prof. C.V. Jawahar
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Jul 2021 - Jan 2022
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Worked on building the 'Indian Brain Segmentation Dataset'- a sub-cortical structure segmentation database for young population. Also contributed to a multi-scale attention architecture for COVID-19 detection from Chest-X Rays.
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Publications
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Optimizing Conformal Prediction Sets for Pathological Image Classification
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Shubham Ojha*, Aditya Narendra*, Abhay Kshirsagar, Shyam Sundar Debsarkar & Surya Prasath
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Pattern Recognition (Under Review), 2025
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Code
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CP Training method for controlling set compostionality.
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Ensuring Class-Conditional Coverage for Pathological Workflows
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Siddharth Narendra, Shubham Ojha, Aditya Narendra, Abhay Kshirsagar & Abhisek Mallick
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AAAI Conference on Artificial Intelligence (AAAI), 2025
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Webpage |
Code |
Poster |
Slides
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CP method for consistent coverage guarantee across all classes.
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Mitigating Feature Bias in DL Models for Cervical Cytology
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Subhashree Sahu, Shubham Ojha & Aditya Narendra
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WiML, Neural Information Processing Systems (NeurIPS), 2024
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Webpage |
Code |
Poster |
Slides
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Sampling based technique for feature bias mitigation.
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Uncertainty Quantification in DL Models for Cervical Cytology
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Shubham Ojha & Aditya Narendra
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Medical Imaging with Deep Learning (MIDL), 2024
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Webpage |
Code |
Poster |
Slides
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Effect of uncertainty incorporation on model's predictive capabilities.
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Deep Learning Based Classification of the Big Four Snake Species Using Visual Features
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Nishikanta Parida, Aditya Narendra, Pooja Reddy Kolimi, Priyansu Panda & Ipsit Misra
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IEEE Confluence, 2023
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Slides
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Transformer based model for Big-4 snake species classification.
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From Robots to Books: An Introduction to Smart Applications of AI in Education (AIEd)
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Shubham Ojha, Siddharth Mohapatra, Aditya Narendra & Ipsit Misra
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Springer ICRIC, 2023
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Slides
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Survey on AI-driven applications in Education
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Other Projects
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Prediction of Future Continuous Motion States from ECoG Recording
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Aditya Narendra, Andy Bonnetto, Chayanon Kitkana, Paola Juárez, Ruman Ahmed Shaikh & Taima Crean
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2023 NMA Summer School on Computational Neuroscience |
Slides |
Code
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Regression models for future motion state prediction on ECoG data.
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MoSwasthya: ML Based Application for Cardiac Disease Risk Prediction
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Aditya Narendra, Nishikanta Parida, S.R. Mohanty & Shaktee Biswal
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2022 Smart Odisha Hackathon (1st Prize Winner) |
Slides |
Code |
Video
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Ensemble method-based estimation cardiac risk estimation.
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Vision-Based Models for Sorting and Segregation of Waste
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Omdena Community Project |
Slides |
Code
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VGG 16-based model for waste materials sorting & segregation.
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Miscellaneous
- Taught (401-Deep Learning), an introductory DL course at Tech Mahindra to 50+ undergraduates from diverse academic backgrounds.
- Received the OUTR Merit Scholarship (2020-21) for ranking 1st in my department for the last 2 UG years.
- Beyond my work, I enjoy reading, sketching, cooking and playing team sports .
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Template adopted from: 1 and 2
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