Nihal Murali

My research lies at the intersection of machine learning, medicine, and human–AI collaboration—developing algorithms that make AI systems safer, more interpretable, and clinically reliable.

Broadly, I study how AI models behave under uncertainty and how they interact with human expertise in high-stakes decision making. My work spans from understanding model failures such as shortcut and spurious learning to designing hybrid human–AI systems that integrate algorithmic predictions with human judgment for expert-level performance in healthcare applications.

“When should AI decide, and when should it defer?
How can human expertise be modeled and trusted in the loop?”

My broader goal is to advance AI systems that understand their limitations and collaborate effectively with humans—especially in domains where reliability and safety are non-negotiable.

I was a visiting researcher in the Machine Learning Research Group (MLRG) at the University of Guelph, advised by Prof. Graham W. Taylor, where I worked on computer-vision models for biological data in collaboration with Prof. Joel D. Levine. Our work showed that deep convolutional networks can recognize and re-identify individual Drosophila melanogaster across days, bridging biological vision and AI.

Previously, I interned at the Robotics Research Center (RRC) at the International Institute of Information Technology, Hyderabad, where I worked with Prof. Madhav Krishna and Dr. Krishna Murthy on robotics and perception.

I received my M.E. in Software Systems and B.E. (Hons.) in Electrical and Electronics Engineering from BITS Pilani, where I was advised by Prof. Surekha Bhanot.

Email / CV / Google Scholar / GitHub

“I build AI systems that know when to ask for help.”

	Hybrid Human–AI Systems for Reliable Intraoperative EEG Monitoring (in progress) Developing hybrid human–AI systems that enable reliable intraoperative EEG monitoring even in settings with limited expert availability.
	Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics Nihal Murali, Aahlad Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich TMLR (2023) Paper / Github / Video / Poster / Slides / Talk Investigates how neural networks learn spurious features during training and shows that monitoring early-layer dynamics can reveal harmful shortcuts before deployment.
	Augmentation by Counterfactual Explanation – Fixing an Overconfident Classifier Sumedha Singla^, Nihal Murali^, Forough Arabshahi, Sofia Triantafyllou, Kayhan Batmanghelich WACV (2023) ^*Equal contribution Paper / GitHub Proposed a counterfactual-based fine-tuning method (ACE) that uses GAN-generated augmentations to reduce overconfidence and improve uncertainty estimation in deep neural networks.
	Classification and re-identification of fruit fly individuals across days with convolutional neural networks Nihal Murali, Jon Schneider, Joel Levine, Graham Taylor WACV (2019) Paper / GitHub / Dataset Developed a CNN-based method to maintain individual fruit fly identities across days, enabling long-term behavioral tracking without physical tagging.
	Can Drosophila melanogaster tell who’s who? Jon Schneider, Nihal Murali, Graham Taylor, Joel Levine PLOS ONE (2018) Paper / News Demonstrated that individual fruit flies possess unique visual features detectable by deep convolutional networks—suggesting visual discrimination among conspecifics despite low-resolution vision.
	Classification and Re-Identification of Fruit Flies using Deep Convolutional Networks Bachelor’s Thesis, BITS Pilani Developed a deep learning system using ResNet architectures to automatically recognize and re-identify individual Drosophila melanogaster across days—eliminating manual tagging and enabling long-term behavioral experiments.

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