Mohammadamin Mahmoudabadbozchelou

Applied Scientist at Amazon (Starting May 2025)

Generative AI & LLMsRetrieval-Augmented Generation (RAG)Physics-Informed Neural NetworksDigital TwinsDeep LearningRheology

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About

I'm Mohammadamin Mahmoudabadbozchelou, an incoming Applied Scientist at Amazon with a background rooted in the intersection of mechanical engineering and advanced AI. My journey has taken me from developing physics-informed neural networks during my Ph.D. at Northeastern to leading Generative AI and RAG initiatives as a Senior Engineer at Aspen Technology. I am deeply passionate about curiosity-driven problem solving and making AI more interpretable and scalable for real-world industrial applications. With 11 peer-reviewed publications and a patent in applied neural networks, I thrive on bridging the gap between academic research and production-level software. I’m always eager to connect with fellow innovators who are building transformative products or exploring the latest advancements in machine learning to exchange knowledge and identify potential synergies.

Networking

What I can offer

›Expertise in deploying production-level AI models
›Bridging complex physics with data science
›Insights into RAG and LLM implementation

Looking for

›expanding my professional network
›exploring mutual opportunities in AI innovation and transformative products

Best fit for

AI innovatorsProduct buildersMachine learning researchersEngineering leaders

Current Interests

Physics-AI IntegrationLarge Language Models (LLMs)Complex Systems ModelingProduct InnovationScalable AI Research

Background

Career

Transitioned from mechanical engineering research into data science and AI engineering. Progressed from research fellowships at Rutgers and Northeastern to a Senior Engineer role at Aspen Technology, specializing in Generative AI and RAG, before joining Amazon as an Applied Scientist.

Education

Ph.D. in Mechanical Engineering, Northeastern University (2019–2022); Licentiate Degree in Data Analytics, Northeastern University (2019–2021); MEng in Mechanical Engineering, Rutgers University (2018–2019); MS in Mechanical Engineering, Sharif University of Technology (2016–2018); BS in Mechanical Engineering, K. N. Toosi University of Technology (2012–2016).

Achievements

›Published 11 peer-reviewed papers, including work in Nature Scientific Reports
›Secured a patent in applied neural networks
›Led development of conversational AI systems at Aspen Technology
›Maintained a 4.0 GPA throughout Ph.D., Licentiate, and MEng programs

Opinions

Curiosity-driven problem solving is the bridge between traditional engineering and modern AI
AI should be made more interpretable, scalable, and impactful rather than just powerful
Academic research and industrial application must intersect to create real value