Chris Fietkiewicz

Chris Fietkiewicz is an Associate Professor in the Department of Mathematics & Computer Science at Hobart and William Smith Colleges. He joined the faculty in 2019 and is based in Lansing Hall, with research interests in computational neuroscience and high-performance computing.

Research Interests: His work focuses on Computational Neuroscience, particularly the development of neural simulators for educational and clinical applications. He leads the Applied Neural Control Toolkit (ANC Toolkit), which enables simulation of neural and axonal responses for neuroprosthetic development. His research also spans High Performance Computing, including software optimization, parallel computing, and cluster management tools.

Collaborations: He collaborates with Dr. Thomas Mortimer (Case Western Reserve University) on neural stimulation and with Dr. Chen Liu (Tianjin University) on Parkinson’s disease modeling and deep brain stimulation. His publications reflect a strong interdisciplinary focus on biomedical signal processing, control systems, and neuromorphic computing.

Publication Trends: His recent work (2016–2022) centers on closed-loop control of pathological neural oscillations, computational models of Parkinson’s disease, and real-time neural simulation. These studies appear in top journals like IEEE Transactions and Biomedical Signal Processing and Control, emphasizing algorithmic innovation and clinical relevance.

Scientific Contributions:

• Co-developer of the ANC Toolkit for neuroscience education.
• Researcher in neural control systems and HPC optimization.
• Active contributor to computational models of movement disorders.

Advising and Grants: While no formal students are listed, his collaborative projects suggest mentorship of undergraduate and graduate researchers. Grant details are not provided, but his research cluster and web tools indicate funded infrastructure development.

Labs and Teams: He is building a research computing cluster and developing web-based batch programming tools, indicating a focus on scalable, accessible computational neuroscience platforms.