Vageesh Saxena
Postdoctoral Researcher at Maastricht University Institute of Data Science
Netherlands
Summary
👤
Senior🎓
Top SchoolVageesh Saxena is a postdoctoral researcher and applied ML engineer with 8 years of experience specializing in NLP, computer vision and multimodal AI, currently based at Maastricht University. He combines deep research—PhD-level work on model optimization and interpretability—with hands-on engineering using PyTorch, Hugging Face, DeepSpeed and GPU-distributed training to productionize LLMs, VLMs and RAG systems. Past projects include privacy-preserving datasets and authorship identification for combating human trafficking, plus TX‑Ray, an interpretable framework that surfaced stable neurons across pretraining and fine-tuning to improve generalization. Comfortable across the ML stack from data scraping and visualization to model explainability and quantization, he emphasizes responsible AI, multilingual and zero/few-shot strategies. Colleagues describe him as interdisciplinary and pragmatic—able to bridge academic rigor with tooling and pipelines that solve real-world societal problems.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Maastricht UniversityBachelor of Technology (BTech), Electrical, Electronics and Communications Engineering, Bachelor of Technology (BTech), Electrical, Electronics and Communications Engineering at Dr. A.P.J. Abdul Kalam Technical UniversityMaster of Science - MS, Cognitive Systems : Language, Learning and Reasoning, Master of Science - MS, Cognitive Systems : Language, Learning and Reasoning at University of Potsdam
English, Hindi, Gujarati, German, Dutch
Github Skills (20)
medicine8
predict8
simplification7
predictive7
regularizer6
classification6
architecture5
convolutional-neural-networks5
pytorch4
machine-learning4
readability4
deep-learning4
natural-language-processing4
nlp4
neural-network2
Programming languages (2)
Github contributions (5)
RoshanRane/PredNet-and-Predictive-Coding-A-Critical-Review
Nov 2018 - Feb 2020
Performing video classificaiton by using the predictive processing architecture. The model is trained in a self-supervised manner to predict the next frames in videos along with the supervised video action classification task.
Contributions:22 commits, 1 push in 1 year 3 months
pytorchclassification-tasksuperviseddeep-learningvideos
vageeshSaxena/Android-Apps
May 2018 - Apr 2020
Contributions:2 pushes, 1 branch in 1 year 11 months
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