Building predictive models of real-world human movement with data-driven and normative approaches using data science, nonlinear optimization, control theory, and nonlinear dynamics.
Postdoctoral Researcher in Bioengineering and Neuroscience, University of Pennsylvania (2018 - Now)
PhD in Mechanical Engineering, The Ohio State University (2012 - 2018)
B. Tech in Mechanical Engineering, VJTI, Mumbai, India (2008 - 2012)
Lysenko, S. , Seethapathi, N. , Kording, K. P. & Johnson, M. J. (2020). Towards automated emotion classification of atypically and typically developing infants, To appear in 2020 8th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).
Brown, G. L.^ , Seethapathi, N.^ & Srinivasan, M. (2020). Energy optimality predicts curvilinear locomotion, arXiv:2001.02287. ^ equal contribution
Chambers, C. , Seethapathi, N. , Saluja, R. , Loeb, H. , Pierce, S. , Bogen, D. , Prosser, L. , Johnson, M. J. & Kording, K. P. (2019). Computer vision to automatically assess infant neuromotor risk. bioRxiv preprint, bioRxiv:756262.
Seethapathi, N. , Jain, A. & Srinivasan, M. (2019). Walking for short distances and turning in lower-limb amputees: a study in low-cost prosthesis users. arXiv preprint, arXiv:1909.03139.
Seethapathi, N. , Wang, S. , Saluja, R. , Blohm, G. , & Kording, K. P. (2019). Movement science needs different pose tracking algorithms. arXiv preprint, arXiv:1907.10226.
Seethapathi, N. & Srinivasan, M. (2019). Step-to-step variations in human running reveal how humans run without falling. eLife, e38371.
Seethapathi, N. & Srinivasan, M. (2015). The metabolic cost of changing walking speeds is significant, implies lower optimal speeds for shorter distances, and increases daily energy estimates. Biology Letters, 11.9: 20150486.