Abrar Majeedi

I'm a PhD candidate at the University of Wisconsin-Madison, working on multimodal deep learning. I am fortunate to be advised by Prof. Yin Li.

I have also gained valuable research experience through internships at Amazon, Microsoft and Dell EMC.

Email / CV / Scholar / Github

Research

My research focus is multimodal deep learning for sequential data. I am also interested in Vision Language Models, Large Language Models, and their applications in sequential data such as videos, sensor data (time series) and text.

News and Updates

July 2025: Our paper "Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering" accepted as an Oral presentation at BioNLP @ ACL 2025.
June 2025: Our team was awarded 2nd position in the Shared Task on grounded question answering (QA) from electronic health records (ArchEHR-QA 2025) at BioNLP@ACL 2025.
June 2025: Our paper "LETS Forecast: Learning Embedology for Time Series Forecasting" has been accepted at the International Conference on Machine Learning (ICML) 2025.
April 2025: Our team was awarded 1st place in the Machine Learning Challenge at the Pediatric Academic Societies (PAS) 2025 Conference, Honolulu, Hawaii.
Jan 2025: Our Poster "Deep learning to quantify care manipulation activities in neonatal intensive care units" won an Award for Best Innovation in Neonatology at the Cleveland Clinic Children's SHINE (Syposium on Health Innovation and Neonatal Excellence) Conference, Orlando, FL.
Nov 2024: Our paper "RICA2: Rubric-Informed, Calibrated Assessment of Actions" won the Best Poster Award at NSF Poster Competition at Purdue University, West Lafayette, IN.

Publications

	LETS Forecast: Learning Embedology for Time Series Forecasting Abrar Majeedi, Viswanatha Reddy Gajjala, Satya Sai Srinath Namburi GNVV, Nada Elkordi, Yin Li International Conference on Machine Learning (ICML) 2025 Project Page / Paper Link A novel time series forecasting method that combines principles from nonlinear dynamical systems with deep learning to model latent temporal structure for accurate forecasts.
	RICA²: Rubric-Informed, Calibrated Assessment of Actions Abrar Majeedi, Viswanatha Reddy Gajjala, Satya Sai Srinath Namburi GNVV, Yin Li, European Conference on Computer Vision (ECCV) 2024 Project page / Paper Link Action quality assessment in videos by incorporating human designed scoring rubrics while providing calibrated uncertainty estimates.
	Glottic Opening Detection using Deep Learning for Neonatal Intubation with Video Laryngoscopy Abrar Majeedi, Patrick Peebles, Yin Li, Ryan McAdams Nature - Journal of Perinatology, Nov 2024 Paper link Auotmatically detect the glottic opening during neonatal intubation using video laryngoscopy to improve intubation outcomes.
	Deep learning to quantify care manipulation activities in neonatal intensive care units Abrar Majeedi, Ryan McAdams, Ravneet Kaur, Shubham Gupta, Harpreet Singh, Yin Li npj Digital Medicine, June 2024 Paper Link / Code Automatically quantify care manipulation activities in neonatal intensive care units (NICUs), while integrating physiological signal data to monitor neonatal stress in NICUs.
	Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs Abrar Majeedi, Babak Naderi, Yasaman Hosseinkashi, Juhee Cho, Ruben Alvarez Martinez, Ross Cutler Preprint, 2022 Paper / Code Assess perceptual quality of videos encoded by Machine Learning-based Video Codecs.
	Detecting Egocentric Actions with ActionFormer Chenlin Zhang, Lin Sui, Abrar Majeedi, Viswantha Reddy Gajjala, Yin Li EPIC @ CVPR Workshop 2022 Report / Code Won 2nd place in the Egocentric action detection challenge

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