Michael Scherm
Senior Scientist at Bicycle Therapeutics
Cambridge, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMichael Scherm is a Senior Scientist with 11 years’ experience bridging biochemistry, protein science and vaccine design across academia and industry in the UK and US. He holds a PhD from the University of Cambridge and has led translational projects from target production and screening at Bicycle Therapeutics to cross-reactive influenza and SARS-CoV-2 vaccine research during a Mount Sinai postdoc. Comfortable in interdisciplinary teams, he combines hands-on lab skills, project management and consulting experience to advance biologics and cell-based vaccines. His background includes structural biology, immune response analysis and even anti-venom optimization, reflecting a knack for tackling diverse biological problems. Unusually for a wet-lab scientist, he has contributed code to reinforcement-learning tooling, signaling a practical interest in ML applications to science and biotech. Based in Cambridge, he pairs multicultural communication skills with a penchant for rowing and fermentation outside the lab.
12 years of coding experience
Doctor of Philosophy (PhD), Biochemistry, Christ's College, Doctor of Philosophy (PhD), Biochemistry, Christ's College at University of CambridgeBachelor of Science (B.Sc.), Biochemistry and Cell Biology, Bachelor of Science (B.Sc.), Biochemistry and Cell Biology at Jacobs University Bremen
German, English
Github Skills (6)
deep-reinforcement-learning10
tensorflow10
python10
reinforcement-learning10
machine-learning9
algorithms8
Programming languages (2)
Github contributions (5)
tensorforce/tensorforce
Oct 2016 - Nov 2016
Tensorforce: a TensorFlow library for applied reinforcement learning
Role in this project:
ML Engineer
ML EngineerContributions:44 commits in 1 month
Contributions summary:Michael's commits primarily involve the development of core components related to a DQN (Deep Q-Network) agent for reinforcement learning. Their work focuses on building the DQN agent architecture, including replay memory management, network construction, and training operations. The user implements the value function and sets up the necessary placeholders and optimizers for efficient learning within the TensorFlow framework. The commits reveal ongoing work on implementing the fundamental logic for a DQN model.
reinforcement-learningtensorflowdeep-reinforcement-learningtensorflow-librarytensorforce
mschaars/twitter-sentiment-tracker
Nov 2014 - Dec 2014
Contributions:17 commits in 11 days
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