Robert Frank

Background

About Robert Frank

I am an ML scientist and Ph.D. candidate specializing in immunoinformatics and drug discovery. I design generative models for de novo antibody development with target-specific properties, build prediction models for antibody-antigen binding affinity estimation, and implement lab-in-the-loop frameworks using active learning. Throughout my Ph.D, I have managed collaborative research projects, supervised students through their theses, and mentored aspiring data scientists at hackathons. With a background spanning a B.Sc. in Cognitive Science and an M.Sc. in Computer Science (specializing in Computer Vision and ML), I have a strong theoretical background in ML. My industry experience at Fraunhofer IIS involved developing ML algorithms for real-world localization problems. Today, I channel this expertise into my Ph.D. research at the University of Oslo, focusing on generative models, immunoinformatics, and active learning. I am always open to discussing new research, collaborations, or opportunities where my skills in machine learning and computational biology can make an impact. Feel free to connect or send me a message. KEY AREAS OF EXPERTISE: Machine Learning (ML), Immunoinformatics, Data Science, Computational Immunology, Deep Learning, Generative Models (GANs, VAEs), Active Learning, Bioinformatics, AIRR Data, Drug Discovery, Computer Vision, Adaptive Immune Receptors, Python, PyTorch, Slurm (HPC).