Curriculum Vitae

Dr. Ing. Simon Stock was born in May 1991 in Aalen, Germany. He began his academic career at the Karlsruhe Institute of Technology (KIT), where he obtained both his Bachelor's and Master's degrees in Electrical and Information Engineering.

For his Bachelor's thesis, Dr. Stock worked on an interdisciplinary project at the Institute for Anthropomatics and Robotics (H2T) within the Faculty of Computer Science. During his Master's thesis, he developed innovative approaches for automated capture of 3D images in the human eye at Carl Zeiss Meditec Inc. in California.

Since November 2017, Dr. Stock has been a research associate at the Institute for Information Technology (ITIV). During his doctoral studies, he was a fellow of the Karlsruhe School of Optics and Photonics (KSOP). In 2020, he received the Faculty Teaching Award for initiating and conducting the Laboratory for Applied Machine Learning Approaches (LAMA).

In January 2022, he completed his doctoral degree with the thesis "An Objectivation of Visual Perception using Virtual Reality, Brain-Computer Interfaces and Deep Learning."

In addition to his work as a post-doctoral researcher, Dr. Stock is also involved in research-transfer. He is a co-founder of the start-up and transfer project Metis Neurotec (www.MetisNeurotec.com).

Teaching

Supervision of the Laboratory for Applied Machine Learning Algorithms (LAMA)

Research Interests

• Software development in medical technology
• Machine learning
• Brain-computer interfaces (BCI) and electroencephalography (EEG)
• Computer vision
• Ophthalmology
• Virtual and augmented reality (VR and AR)

Collaborations

Dr. Stock has collaborated with renowned institutions and companies, including: 

• Massachusetts Institute of Technology (MIT), Cambridge, MA, USA 
• David J Apple Laboratory - Heidelberg University Hospital, Heidelberg, Germany 
• Carl Zeiss Meditec, Inc., Dublin, CA, USA 
• Apic.ai, Karlsruhe, Germany

Further information

• https://www.linkedin.com/in/simonclausstock/
• https://www.researchgate.net/profile/Simon-Stock

Supervised completed student theses

• MA: "Simulation of Progressive Eye Diseases using Virtual Reality".
• MA: "Object Recognition Using Transfer Learning by Convolutional Neural Networks (CNN): A Case Study for Automated Meal Recognition" (Collaborative MA by two Master-students)
• BA: "Design and Prototyping of a Virtual Reality Head Mounted Display with Adaptable Vergence and Accommodation".
• BA: "Instance Segmentation of Objects Close to the User for Improved Perception in Augmented Reality Using Transfer Learning".
• BA: "Gaze Estimation using Convolutional Autoencoders"
• MA: "DeepBees: Using Multi-Task Learning for Joint Detection, Pose Estimation and Classification of Pollen Bearing Bees".
• BA: "Prototyping and Implementation of a Smart Foot Bracelet for Navigation and Guidance Using Haptic Feedback"
• BA: "Integration of pupil tracking in an ophthalmologic virtual reality application for improved immersion and pupil movement analysis"
• BA: "Signal processing in ophthalmology: How the retina defines what we are seeing"
• MA: "Highly Parallelized Rendering of the Retinal Image through a Computer Simulated Human Eye".
• BA: "Rapid Prototyping and Evaluation of a Connected Distributed Load Measurement System".
• MA: "Real Time Monitoring System for Laser Marking of Polymeric Materials
• BA: "Implementation of an Intuitive Multimedia Interface for an Ophthalmologic VR Device"
• MA: "Examining the Human Neuro-visual System using Electroencephalography: A Deep Learning Approach toward Objective Perimetry"
• MA: "An Electroencephalography based System for Objective Refraction and Visual Acuity Assessment using Convolutional Neural Networks"
• MA: "A Machine Learning Approach to Production: A Software Framework for Artificial Neural Network Execution in C++"
• MA: "Adaptive Gesture Control for Automotive Applications with Few-Shot Learning"
• MA: "Correction of choroidal and retinal thickness maps using widefield optical coherence tomography"
• Seminar: "Artifact detection in electroencephalography signals for human brain computer interfaces"
• MA: "A rapid prototyping approach to EEG electrodes for use in machine learning supported brain computer interfaces"
• BA: "Exploring visual stimulation for brain-computer interfaces: A comparison of stimulus designs for ssvep-based applications"
• BA: "Measurement of astigmatism using a SSVEP-based brain-computer interface"
• BA: "Comparison of convolutional neural network and canonical correlation analysis in ssvep-based brain-computer interfaces"
• BA: "Visual re-identification of bumblebees returning from foraging trips"
• BA: "Closed-loop user-experience in a brain-computer interface for neuro-visual experiments"
• MA: "A cross-platform approach to brain-computer interfaces for monitoring and device control using augmented reality"
• BA: "Development of a low-cost open-source electroencephalograph focused on scientific research"
• MA: "Development of an automatic laser diode lifetime testing setup"
• BA: "Key classification in music recordings using deep convolutional neural networks"
• BA: "Detection and classification of higher cognitive functions with SSVEP using a brain-computer interface"
• BA: "Artifact detection in an EEG-based BCI for the assessment of neuro-visual function"
• BA: "Using brain computer interface for the recognition and classification of various mental states using neural networks"
• MA: "Smart home room sensor for person behavior recognition".
• Seminar: "On steady-state visual evoked potential detection in short response time for human brain computer interfaces".
• Seminar: "Quality assessment and noise estimation of EEG data"
• BA: "Development of a firmware for an ophthalmologic VR-device towards the treatment of presbyopia by vergence and accommodation training".
• MA: "Improving user experience and user interface by designing an optimized model for convoaid app"
• MA: "EEG-based objective ADHD diagnosis using convolutional neural networks"
• BA: "Investigating cognitive enhancement through binual beats utilizing brain-computer interfaces"