Christopher Schulze serves as a Principal Data Scientist and Principal Investigator at Jacobs, where he leverages his extensive expertise in artificial intelligence and complex systems to drive innovative solutions for clients in the Internet of Things (IoT) sector. With a robust background in machine learning,...
Christopher Schulze serves as a Principal Data Scientist and Principal Investigator at Jacobs, where he leverages his extensive expertise in artificial intelligence and complex systems to drive innovative solutions for clients in the Internet of Things (IoT) sector. With a robust background in machine learning, deep learning, and stochastic control, Christopher is at the forefront of developing novel algorithms that enhance the identification and management of unknown IoT devices across expansive networks. His current project, a collaboration with DARPA, utilizes one of the largest public domain IoT datasets alongside proprietary data to tackle the challenges of device recognition and network security.
In his role, Christopher employs advanced techniques in convex optimization and deep reinforcement learning to create sophisticated models that not only improve the accuracy of device identification but also optimize the overall performance of IoT systems. His proficiency in programming languages such as C++ and MATLAB, combined with his skills in natural language understanding and language modeling, enables him to develop robust data-driven solutions that address complex real-world problems.
Christopher's work is characterized by a commitment to pushing the boundaries of AI and robotics development methodologies. By integrating convolutional neural networks (CNN) and leveraging tools like Scikit-Learn and SciPy, he ensures that his projects are not only innovative but also scalable and applicable across various industries. As he continues to lead groundbreaking research at Jacobs, Christopher remains dedicated to advancing the field of data science and contributing to the evolution of IoT technologies.
