Prasanga Dhungel
Building production ML systems. Interested in robust, efficient, and interpretable AI.
Hi, I am Prasanga, a Machine Learning Engineer with a passion for building robust, scalable ML systems and conducting research in Reliable ML, Statistical Learning, and Machine Learning in non-Euclidean spaces.
Currently, I work as a Machine Learning Engineer at E.ON in Munich, where I architect end-to-end ML infrastructure, design production-grade CI/CD pipelines, and lead initiatives in MLOps and model lifecycle management. I recently completed my Master’s degree in Informatics at TUM, where my thesis explored efficient methods for pruning large-scale datasets through score extrapolation under the supervision of Prof. Stephan Günnemann. My coursework spanned Deep Learning, Machine Learning, Computer Vision, and Quantum Computing—providing me with a strong theoretical foundation alongside practical skills in building production ML systems.
Prior to my current role, I worked as a Data Scientist at Naamche Inc, where I developed end-to-end ML systems for real estate investment optimization, processing over $20M in property evaluations and building conversational AI assistants. I received my undergraduate degree in Computer Engineering from the Institute of Engineering, Pulchowk Campus at Tribhuvan University, where I built a strong foundation in algorithms, systems, and artificial intelligence.
Research Interests
The following areas represent my current research focus and published work:
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Data-Centric AI & Efficient Learning: My master’s thesis explored novel approaches to large-scale dataset pruning through score extrapolation, addressing the computational challenges of training on massive datasets. I’m interested in methods that improve data quality, reduce training costs, and make ML more accessible and sustainable.
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Robust and Interpretable ML: Building ML systems that are not only accurate but also reliable and explainable is crucial for real-world deployment. I’m particularly interested in developing methods that handle distribution shifts, outliers, and provide meaningful explanations for model decisions—essential for domains like energy systems and critical infrastructure.
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Machine Learning in Non-Euclidean Spaces: Graph neural networks and geometric deep learning open exciting possibilities for modeling complex relational data. My work has explored using geometric data structures for efficient learning, and I’m interested in applying these techniques to real-world problems involving networks, molecules, and spatial data.
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MLOps & Production ML: Bridging the gap between research and production is critical. I’m passionate about building scalable ML infrastructure, implementing robust monitoring systems, and establishing best practices that enable teams to deploy and maintain ML systems reliably at scale.
If you are interested in collaborating on research projects or discussing ideas, please feel free to reach out. I am always open to exploring new challenges and opportunities to create impactful solutions.
Beyond Work
I am a lover of literature, intellectual podcasts, and the beauty of nature. In my free time, you can find me lost in a good book, exploring the Bavarian Alps, or indulging in a thought-provoking film. I’m passionate about clear communication and enjoy breaking down complex technical concepts into accessible narratives. Thank you for visiting!