Research

Author: Anju ChhetriIn a now-famous study, expert radiologists were asked to scan CT images of lungs for nodules. Hidden in one of those scans was something no one expected: a gorilla, 48 times the size of the average nodule. However, interestingly more than half the radiologists never noticed it. Their attention was so finely tuned to detecting tumors that they overlooked an unexpected and obvious anomaly. This phenomenon is known as inattentional blindness, where focused expertise can paradoxically limit perception [1].While this finding is surprising, its implications extend far beyond human cognition. It offers a powerful analogy for understanding a critical challenge in machine learning. Consider a model trained to detect malignant cancer cells. It performs well when the input data resembles what it has seen during training. But what happens when it encounters a completely new disease, something outside its learned distribution? This scenario is known as out-of-distribution data, where the statistical properties differ from the training set.In such cases, the model does not recognize its own uncertainty. Instead, it forcefully maps the unfamiliar input to one of its known categories. The result can be a confident but incorrect prediction, potentially leading to dangerous misdiagnoses. This limitation arises because most machine learning systems operate under what is called a closed-world assumption. They assume that every input belongs to one of the predefined classes.[2]This challenge is not limited to healthcare. In domains like self-driving cars, encountering unexpected objects or rare environmental conditions can lead to similarly flawed decisions. A plastic bag drifting across the road or an unusual vehicle shape may not fit neatly into the model’s learned categories, yet the system must still respond.To address this, researchers focus on out-of-distribution detection. The goal is to identify when an input does not belong to the training distribution and flag it instead of forcing a classification. But this raises a key question. If a model is trained only to assign inputs to known classes, how can it recognize something unfamiliar?One approach is to look beyond final predictions. Instead of relying solely on class labels, we can analyze intermediate signals within the model. These include internal representations, logits, and probability scores. Patterns in these signals can help distinguish familiar inputs from anomalous ones, sometimes using a combination of multiple indicators [3,4,5].But OOD detection alone isn't enough. As models grow more complex, a deeper question emerges: how do we understand and trust the decisions they make? This demands collaboration beyond machine learning robustness, drawing in interpretability research and explainable AI to make model behavior transparent, not just accurate.[1] Drew, Trafton, Melissa L-H. Võ, and Jeremy M. Wolfe. "The invisible gorilla strikes again: sustained inattentional blindness in expert observers." Psychological science 24.9 (2013): 1848-1853.[2] Hou, Mun. “Detecting Out-of-Distribution Samples with kNN | Mun Hou’s Blog.” Detecting Out-of-Distribution Samples with kNN , 2022, blog.munhou.com/2022/12/01/Detecting Out-of-Distribution Samples with Knn/.[3] Wang, Haoqi, et al. "Vim: Out-of-distribution with virtual-logit matching." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.[4] Hendrycks, Dan, and Kevin Gimpel. "A baseline for detecting misclassified and out-of-distribution examples in neural networks." arXiv preprint arXiv:1610.02136 (2016).[5] Lee, Kimin, et al. "A simple unified framework for detecting out-of-distribution samples and adversarial attacks." Advances in neural information processing systems 31 (2018). 

Mahesh Shakya and Alok Khatri represented NAAMII at the 5th Annual Conference & General Meeting of the National Young Academy of Nepal (NaYAN), held on December 5–6, 2025, at National College, Dhumbahari, Kathmandu. The conference, themed “AI for Science and Society,” brought together young academics and researchers to discuss the role of AI in education, research, and societal impact.Mahesh Shakya, research associate at the TOGAI lab, spoke on AI in Scientific Research, highlighting practical applications of AI in advancing research in Nepal and promoting innovation in low-resource settings.Similarly, Alok Khatri, founder of Tangible Careers (a startup currently being incubated at NAAMII), presented on AI in Education, sharing insights on how AI-driven approaches can enhance learning, capacity building, and talent development.The event also featured the 4th episode of NiCHOD, a nationwide student competition where participants presented their research in three minutes, competing for awards at undergraduate and graduate levels. 

Dr. Binod Bhattarai, research scientist at NAAMII's B Bhattarai Multimodal Learning Lab,  recently spoke at the Embassy of Nepal in London on AI's role in healthcare, focusing on World Models and Safe AI.World Models enable AI systems to understand contextual relationships in clinical settings, and Safe AI addresses the trustworthiness of these systems when clinical decisions affect patient outcomes. Dr. Bhattarai's presentation brought these technical discussions into the public forum, engaging the audience with what these technologies mean for healthcare delivery and accountability.Reflecting on the event, Dr. Bhattarai emphasized how bridging the global digital divide requires not just cutting-edge technology, but strong community will, a principle reinforced through exchanges like this between Nepal's research community and international audiences. 

Philippe Ballaire, CEO of AI Planning Logic, the company funding the Osteotomy project at the MAP-MED Lab visited the NAAMII office in early December. It was a pleasure for the team to meet him in person. During his visit, he shared thoughtful insights on working efficiently as a team and collaborating effectively on shared tasks, while also engaging in meaningful discussions about ongoing projects. The visit provided a valuable opportunity for team bonding, and the team enjoyed a pleasant lunch together, making his stay both productive and memorable.

Date: 19–21 November 2025Venue: Alice Reception, Lalitpur (Day 1) & Alliance Française, Lalitpur (Day 2 and 3)NAAMII, with support from FAO Nepal, concluded a three-day workshop designed to bring key stakeholders together around a unified approach for post-disaster agricultural damage assessment. The sessions were part of the ongoing A2 Innovation Lab project with FAO Nepal, which aims to strengthen Nepal’s capacity to use drones, geospatial systems, and AI for timely and transparent crop loss estimation.The workshop centered on aligning perspectives from farmers, local governments, technical agencies, and national institutions to validate and refine standard operating procedures. Alongside these discussions, it also educated students and young practitioners in UAV operations, GIS workflows, and AI-based image analysis.Day 1The first day focused on establishing a shared technical foundation. Bal Kumar Lamsal, Drone and GIS Data Officer at NAXA, provided an overview of UAV types, autonomous systems, and mapping software including PICTERRRA and PIX4D React. Er. Moti Ram Itani, Aeronautical Engineer and Founder of Pushpak Udaan Aviation Academy, shared his expertise on aircraft design, drone policies, and licensing regulations in Nepal. Practical demonstrations by Sishir Lamsal helped participants understand flight planning, geospatial data collection, and software applications, providing a foundational knowledge of drones, mapping, and AI integration, setting the stage for practical problem-solving.Day 2The second day brought stakeholders into problem-driven discussions. Suyog Chalise from Impact 477 guided students through design thinking and problem identification. A representative from Amit drone consultancy demonstrated LiDAR applications for analyzing Nepal’s land and resources. Tashi Bista and Sajal Pradhan from Salt, a wildlife media company, shared their experiences in wildlife photography and videography, including collaborations with the BBC, and highlighted challenges in acquiring filming rights. The day concluded with Sishir Lamsal and Lalit BC demonstrating AI/ML model training for image analysis, enabling students to understand the intersection of drones and AI.Day 3The final day centered on feedback, real-world constraints, and pathways to scale. Arun GC from FAO Nepal delivered opening remarks and feedback, followed by Ritesh Jha from the Department of Hydrology who discussed challenges in precision agriculture and flood damage assessment, emphasizing how drone-based AI/ML solutions can improve accuracy and decision-making. Participants explored scaling prototypes, integrating with existing systems, and using baseline data for predictive analysis, gaining practical insights into turning technology into tangible impact. The sessions concluded with discussions on how these projects could be further developed to benefit farmers, local governments, and disaster management efforts across Nepal.The workshop strengthened coordination among institutions, technical experts, and young practitioners, creating momentum for a national system that links drone data, geospatial analysis, and AI into a unified and reliable process for agricultural damage assessment across Nepal.

From mid-September to late October, Dr. Binod Bhattarai, Research Scientist at NAAMII’s B Bhattarai Multimodal Learning Lab (MMLL) and  Assistant Professor at the University of Aberdeen, shared his expertise in trustworthy AI at key international venues.The Multimodal Learning Lab (MMLL) is a cross-border initiative between NAAMII, Nepal, and the University of Aberdeen, developing robust, interpretable, and reliable AI algorithms for complex, heterogeneous data—from surgical videos to low-resource languages—to tackle challenges in healthcare, energy, and agriculture.A highlight of Dr. Bhattarai’s trip was presenting the lab’s latest research at MICCAI 2025 in Daejeon, South Korea, a premier conference in medical imaging. In China, at Xidian University and the NORA student chapter, he delivered two talks on “Towards Trustworthy AI in Healthcare,” engaging students, postdocs, and faculty, and sparking discussions on ethical, interpretable, and high-impact AI applications.The visit generated meaningful discussions, fresh ideas, and new directions for research, reinforcing MMLL’s commitment to advancing trustworthy AI and fostering international research partnerships.

Best wishes to the TOGAI Lab team Nishant Luitel, Mahesh Shakya, and Prasiddha Bhandari who are traveling to Uganda to provide mentorship and support for the upcoming Global AI Hackathon in Obstetric Ultrasound. The hackathon focuses on federated learning and AI applications, transforming maternal and fetal healthcare in low-resource settings.This event is a follow-up to a three-week virtual training program held earlier this year, where mentors trained participants on both technical AI methods and medical applications. Five teams from each of five participating countries Nepal, Uganda, Kenya, Nigeria, and Pakistan will be competing, with the winning team having their work showcased at the 20th AI in Health Africa Conference and potentially receiving project support.

Dr. Bipendra Basnyat, Adjunct Research Scientist leading NAAMII’s Agri AI (A²) Innovation Lab, recently shared his insights on AI-driven agriculture in a podcast hosted by Sushant Pradhan. With over two decades of experience in AI and machine learning, Dr. Basnyat discussed the challenges and opportunities of integrating advanced technologies into Nepali farming, sustainable agriculture, and climate-smart practices.NAAMII’s A² Innovation Lab combines cutting-edge technology with traditional farming knowledge to develop resilient, scalable, and sustainable agricultural systems. Its work spans climate-smart agriculture, regenerative practices, and permaculture, using AI, IoT, satellite imagery, and computer vision to optimize resource management, improve crop resilience, and preserve local knowledge.Dr. Basnyat’s conversation also addressed the misconceptions around AI, data security, and the role of technology in empowering farmers. He highlighted practical applications of AI in Nepali agriculture, from precision farming to intelligent systems for real-world use, demonstrating how innovation can bridge the technology gap and strengthen local farming communities.The A² Innovation Lab continues to develop tools and insights that support farmers, researchers, and communities, while advancing AI for conscious living and sustainable agriculture.

Mahesh Shakya (Research Associate), along with Research Assistants Prasiddha Bhandari and Nishant Luitel from the TOGAI Lab, travelled to Makerere University in early November to represent NAAMII at the AI in Health Africa Conference 2025 and the AI4GH Synergy Hackathon. Their participation highlighted NAAMII’s growing involvement in global efforts to advance equitable healthcare AI across low-resource settings. Mahesh delivered a talk on how NAAMII has built research capacity in Nepal without seed funding and shared examples of applying AI to maternal health and medical imaging challenges.Prasiddha and Nishant supported the AI4GH Synergy Hackathon by mentoring teams from Uganda, Kenya, Nigeria, and Pakistan as NAAMII continues to work on AI-powered gestational-age estimation from blind-sweep ultrasound videos. The visit helped expand collaborative opportunities in maternal and public health AI while strengthening ties between institutions working on similar healthcare challenges in Africa–Asia research networks.

Dr. Bipendra Basnyat from the Agri-AI (A²) Innovation Lab  represented NAAMII at the FAO World Food Forum (WFF), highlighting Nepal’s pioneering efforts in AI-enabled disaster assessment and agricultural resilience. The presentation underscored FAO/NAAMII leadership role in fostering data-driven innovation and positioned the initiative as a model for future regional scaling. He presented the ongoing work under the FAO–NAAMII partnership for AI-Driven Agricultural Disaster Response System in Nepal, showcasing innovations in cloud repository automation, data-driven modeling, and the integration of UAV, Sentinel, and field survey datasets for improved agricultural intelligence.The project strengthened coordination among FAO Nepal and NAAMII, and will next focus on a stakeholder workshop to review outcomes and demonstrate AI workflows. Upcoming efforts include finalizing the cloud offloading SOP, advancing preparing training materials, and enhancing project outreach through communications.

Dr. Bishesh Khanal, research scientist at the TOGAI Lab, traveled to Japan this October to present at the U.N. Open Science and Open Scholarship Conference 2025 on building an ecosystem for AI knowledge generation, dissemination, and translation to society for equitable advancement in the AI era. Alongside representing NAAMII, Dr. Khanal also represented the National Young Academy of Nepal, where he serves as a founding executive member. His talk highlighted the role of open science and scholarship in democratizing knowledge and enabling institutions like NAAMII to excel even in low-resource settings. Strong AI research relies on open collaboration and sharing of code, datasets, and ideas, and embodies this commitment, advancing AI research, education, and innovation in practice.

As part of the ongoing four-month project with the Food and Agriculture Organization (FAO) under the A² Innovation Lab, the NAAMII team visited Koshi Province from September 27–29, along with NAXA. The visit was aimed to capture farmers’ testimonials, document on-ground realities, and film the ongoing process of using drone and AI technologies to assess flood-induced crop damage.During the visit, NAXA led drone surveys and data processing across multiple municipalities using LiDAR and multispectral imaging, producing high-resolution aerial maps and 3D surface models that reveal crop conditions, soil patterns, and how floods have altered the landscape. These spatial datasets will feed into the AI-based analysis being developed by the team to evaluate flood damage, recommend post-flood crop options, and help farmers replant more effectively.The team plans to return after the October floods to capture new data for near real-time assessment, supporting evidence-based recovery planning. Through this collaboration, NAAMII continues to bridge AI research with on-the-ground impact, strengthening resilience in vulnerable regions.