Experience

Aixelo Inc.

• United States
• Software Development
• 1 - 100 Employee

• Co-Founder and ML Chemist

  • Apr 2023 - Present

  Cambridge, Massachusetts, United States As the ML chemist at Aixelo in addition to being co-founder, I am responsible for developing AI tools, constructing the database for MOF (metal–organic frameworks) material synthesis recipe and integrating machine learning methods and large language models to data mining and synthesis condition prediction. I also design the interface of a ChatBot, which can allow humans to talk with Aixelo's machine learning models and data. This innovative technology facilitates seamless communication with… Show more As the ML chemist at Aixelo in addition to being co-founder, I am responsible for developing AI tools, constructing the database for MOF (metal–organic frameworks) material synthesis recipe and integrating machine learning methods and large language models to data mining and synthesis condition prediction. I also design the interface of a ChatBot, which can allow humans to talk with Aixelo's machine learning models and data. This innovative technology facilitates seamless communication with fosters conversation as natural as interacting with a human. Show less



Karlsruhe Institute of Technology (KIT)

• Germany
• Higher Education
• 700 & Above Employee

• Research Assistant

  • Jul 2022 - Present

  Eggenstein-Leopoldshafen, Baden-Württemberg, Germany Automated Synthesis: Developed, built and optimized machines and robotic systems for conducting autonomous SURMOF synthesis experiments, streamlining the production process. Experiment Optimization: Utilized these automated systems to conduct a series of experiments, optimizing the synthesis conditions for producing SURMOF materials. Machine Learning & Data Mining: Utilized Large Language Models (LLMs) and other machine learning methodologies for effective data mining, deepening… Show more Automated Synthesis: Developed, built and optimized machines and robotic systems for conducting autonomous SURMOF synthesis experiments, streamlining the production process. Experiment Optimization: Utilized these automated systems to conduct a series of experiments, optimizing the synthesis conditions for producing SURMOF materials. Machine Learning & Data Mining: Utilized Large Language Models (LLMs) and other machine learning methodologies for effective data mining, deepening our understanding of synthesis data and uncovering valuable insights for future experiments. Predictive Modeling: Built predictive models using machine learning techniques, aiding in the forecast of optimal synthesis conditions for different types of MOFs. Interdisciplinary Collaboration: Worked closely with interdisciplinary teams, bridging the gap between machine learning, chemistry and engineering, enabling the team to reach new heights in MOF research and development.

• Postdoctoral Fellow

  • Jul 2022 - Present



National Synchrotron Radiation Research Center (NSRRC)

• Research Assistant

  • Oct 2016 - Oct 2016

  Hsinchu Operation and data analysis with X-ray absorption spectroscopy (XAFS) to detect the oxidation state and coordination of the metal complex (Fe, Ni) in homogeneous organic reactions.

• Research Assistant

  • Apr 2016 - Apr 2016

  Hsinchu Operation and data analysis with X-ray absorption spectroscopy (XAFS) to detect the oxidation state and coordination of the metal complex (Cu) in homogeneous organic reactions.