Aditya Narendra

	Research Affiliate \| Assisted Forest Regeneration Dec '22 - Jan '24 Working under the supervision of Dr. Leland K Werden at the Earthshot lab on vision based models for estimating ground biomass, carbon sequestration, and reforestation cover of assisted forest restoration projects using remote sensing and GIS data.
	Associate Researcher \| Tech Mahindra Aug '22 - Oct '23 Worked under Dr. Jibitesh Mishra and Ipsit Misra at Tech Mahindra's Center of Excellence of Artificial Intelligence. We developed various deep-learning models for smart traffic systems and intelligent healthcare systems. Additionally, I also taught an introductory undergraduate course “401-Deep Learning” to over 50 students across various backgrounds.
	Research Intern \| Xu Lab Aug '22 - Sept '23 Worked under the supervision of Dr. Min Xu at his lab. We developed an unsupervised clustering model for structural pattern mining of cryo-Electron Tomography (cryo-ET) data to sort and visualize marco-molecules without labels. Also contributed to a project focused on macromolecular structure classification using cryo-ET images with self-supervised learning. [ Code ]
	Research Volunteer \| Neuromatch Academy July '22 - Aug '23 Working under the supervision of Dr. José Biurrun Manresa and Dr. Xi-He Xie at the 2023 NMA Summer School on Computational Neuroscience. We worked on deep learning based models for predicting future continuous motion states of human movement from ECoG recordings. [ Code ] [ Slides ] [ Notes ]
	Summer School Research Intern \| ETS Montreal Jul '22 - Aug '22 Worked under Prof. Thomas Grenier and Prof. Pierre-Marc Jodoin at the 2022 Summer School on Deep Learning for Medical Imaging (3rd Edition). We worked on weakly supervised techniques for cardiac MRI segmentation and the modeling and simulation of brain images containing stroke lesions. [ Code ]
	Junior Machine Learning Engineer \| Omdena India Chapter July '22 - Aug '22 Worked as a task lead for the modelling and deployment teams to develop vision models for sorting and segregation of waste. We tested various state-of-art neural network architectures and hosted them on Hugging Face platform. Also presented a proof of concept to the UN SDG India team.
	ML Intern \| iNeuron.ai Jan '22 - Apr '22 Worked at the intersection of data science and business intelligence on deep learning models for cardiac disease analysis and presented a data analysis report for business modelling team. [ Code ] [ Slides ]
	Summer Research Intern \| Centre for Visual Information Technology July '21 - Jan '22 Worked under the supervision of Prof. Jayanthi Sivaswamy and Prof. CV Jawahar on building attention based models for COVID-19 diagnosis from X-ray images and segmentation models for sub-cortical structures. Additionally, also participated in the 5th Summer School on Artificial Intelligence with focus on Computer Vision.

	Uncertainty Quantification in DL Models for Cervical Cytology Shubham Ojha & Aditya Narendra Venue: Medical Imaging with Deep Learning-2024 (MIDL'24) [ Paper ] [ Code ]
	Deep Learning Based Classification of the Big Four Snake Species Using Visual Features Nishikanta Parida, Aditya Narendra, Pooja R Kolimi, Priyansu Panda & Ipsit Misra Venue: 14th IEEE International Conference on Cloud Computing, Data Science & Engineering(Confluence'24) [ Paper ] [ Slides ] [ Code ]
	From Robots to Books: An Introduction to Smart Applications of AI in Education (AIEd) Subham Ojha, Siddharth Mohapatra, Aditya Narendra & Ipsit Misra Venue: 7th Springer International Conference on Recent Innovations Computing (ICRIC'23) [ Paper ] [ Slides ]
	Applications of Artificial Intelligence in Fashion Industry Aditya Narendra (Undergraduate Thesis) Venue: Odisha University of Technology and Research (OUTR), Bhubaneswar [ Code ]

	MoSwasthya: ML Based Application for Cardiac Disease Risk Prediction [ Code ] [ Slides ] [ Video ] Developed a bilingual app featuring Ensemble Methods for First Action Prediction System (FAPS) to estimate cardiac disease risk using non-medical inputs like height, weight, age, health history, sleep, alcohol intake etc. with a real-day accuracy of 91.24%. Additionally, this application also provides details of nearby hospitals, clinics and doctors for public usage. This work was awarded the First Prize worth 2500 USD among 1000 teams nationwide at the 2022 Smart Odisha Hackathon.
	Vision-Based Models for Sorting and Segregation of Waste [ Code ] [ Slides ] This work was completed during my tenure at Omdena. We developed object classification and detection-based models to segregate trash into common categories. Furthermore, we introduced a novel dataset comprising more than 16,000 images of trash items upon which evaluated multiple CNN and SSD architectures to achieve an overall accuracy of 96.14% for 10-class classification.
	Satellite Data-based Pollution Forecasting using CNNs [ Code ] In this research, we studied the feasibility and dependability of employing CNNs for predicting the BreezoMeter Air Quality Index (BAQI) using satellite imagery. We also introduced a novel BAQI dataset with over 10,000 satellite images and corresponding BreezoMeter air quality data from 56 cities and achieved an overall accuracy of 87.14%. This study indicates that cost-effective and widely available satellite images are efficacious for accurate air pollution forecasting.
	CoughVid: Covid-19 Detection from Cough Voice Samples [ Code ] As part of the Pfizer Digital Medicine Challenge, we devised an RNN and MFCC feature-based model to identify Covid-19 from cough audio samples. In real-world tests with over 320 patients, our model achieved an accuracy of 82.42%. We find that audio signal spectrograms can serve as image datasets for constructing models to classify audio samples.
	Pancreatic 3D-Segmentation using CT Scans [ Code ] We developed a transformer architecture for 3D medical image segmentation, based on an encoder-decoder design with linear complexity. Evaluated on CT pancreatic segmentation datasets for cancer diagnosis, our approach achieved a 91.32% accuracy with fewer annotations, ensuring practical segmentation performance and precise uncertainty quantification.