Works

Publications

[9] A. R. Ellis-Mohr, "On the Information Dynamics of Biological, Bioengineered, and Artificial Intelligence," May 2026. [Dissertation]

[8] A. R. Ellis-Mohr, M. Hartman, L. R. Varshney, "Energy-Aware Routing to Large Reasoning Models," April 2026.

[7] A. Wadell, A. Bhutani, V. Azumah, A. R. Ellis-Mohr, A. J. Stier, K. Hegazy, A. Brace, H. Zhao, C. Kelly, A. K. Nayak, Y. Chen, D. Simatos, H. Lin, M. Emani, V. Vishwanath, K. Gering, M. Alkan, T. Gibbs, J. Wells, W. W. Qian, R. C. Gerkin, B. Amorelli, A. B. Wiltschko, L. R. Varshney, B. Ramsundar, K. Duraisamy, M. W. Mahoney, A. Ramanathan, V. Viswanathan, "Foundation models for discovery and exploration in chemical space," arXiv, 2026.

[6] A. R. Ellis-Mohr, A. K. Nayak, and L. R. Varshney, “A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search,” Philosophical Transactions of the Royal Society A, February 2026.

• Long-form manuscript: A. R. Ellis-Mohr, A. K. Nayak, and L. R. Varshney, “A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search,” arXiv [cs.LG], 2025.

[5] A. R. Ellis-Mohr, M. Gazzola, L. R. Varshney, “Synthetic Neurocomputers: Advancing Scientific Research through Computing with Living Neurons,” in U.S. Department of Energy Analog Computing for Science Workshop, Bethesda, Maryland, 11-13 September 2024. [White Paper]

[4] A. R. Ellis-Mohr and L. R. Varshney, “Directed Information Flow in Computing Systems with Living Neurons,” in Proceedings of the 2024 IEEE International Symposium on Information Theory Workshops, Athens, Greece, 7-12 July 2024.

[3] K.-Y. Huang, G. Upadhyay, Y. Ahn, M. Sakakura, G. J. Pagan-Diaz, Y. Cho, A. C. Weiss, C. Huang, J. W. Mitchell, J. Li, Y. Tan, Y.-H. Deng, A. R. Ellis-Mohr, Z. Dou, X. Zhang, S. Kang, Q. Chen, J. V. Sweedler, S. G. Im, R. Bashir, H. J. Chung, G. Popescu, M. U. Gillette, M. Gazzola, H. Kong, “Neuronal Innervation Regulates the Secretion of Neurotrophic Myokines and Exosomes from Skeletal Muscle,” Proceedings of the National Academy of Sciences, Vol. 121, No. 19, April 2024.

• News: Nerves prompt muscle to release factors that boost brain health

[2] X. Zhang, Z. Dou, S. H. Kim, G. Upadhyay, D. Havert, S. Kang, K. Kazemi, K.-Y. Huang, O. Aydin, R. Huang, S. Rahman, A. R. Ellis-Mohr, H. A. Noblet, K. H. Lim, H. J. Chung, H. J. Gritton, M. T. A. Saif, H. J. Kong, J. M. Beggs, M. Gazzola, “Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons,” Advanced Science, Vol. 11, No. 11, March 2024.

• News: Researchers design new open-source technology for interfacing with living neurons

[1] Y. Zhang, J. Cui, K.-Y. Chen, S. H. Kuo, J. Sharma, R. Bhatta, Z. Liu, A. R. Ellis-Mohr, F. An, J. Li, Q. Chen, K. D. Foss, H. Wang, Y. Li, A. M. McCoy, G. W. Lau, Q. Cao, "A smart coating with integrated physical antimicrobial and strain-mapping functionalities for orthopedic implants," Science Advances, Vol. 9, No. 18, May 2023.

• • News: Smart surgical implant coatings provide early failure warning while preventing infection

Presented Works

External

[11] A. R. Ellis-Mohr et al., “A Playbook for Scalable Scientific Foundation Models,” The Trillion Parameter Consortium, Baltimore, Maryland, 3 June 2026.

[10] A. R. Ellis-Mohr, D. Burnham, R. Engelken, L. R. Varshney, D. Chklovskii, “Data-Driven Identification of a State Projection for Bandwidth-Constrained Feedback Stabilization,” Control Theory & Neuroscience Workshop, Simons Foundation, New York, New York, 21 May 2026.

[9] M. Zenari et al., Self-Supervised Learning of Local Predictive Directions in Dynamical Systems, Computational and Systems Neuroscience (COSYNE), Lisbon, Portugal, 13 March 2026.

[8] A. R. Ellis-Mohr, D. Burnham, R. Engelken, L. R. Varshney, D. Chklovskii, “Data-Rate Constraints for Linear Stabilization in Neural Systems,” NeuroML Workshop, University of Chicago, Chicago, Illinois, 25 February 2026.

[7] A. R. Ellis-Mohr, D. Burnham, R. Engelken, L. R. Varshney, D. Chklovskii, “Data-rate theorems explain neuronal tuning,” Junior Scientist Workshop on Theoretical Neuroscience, HHMI-Janelia Research Campus, Ashburn, Virginia, 10 November 2025.

[6] A. R. Ellis-Mohr, "Scaling Laws for Autonomous Discovery," University of Toronto, Toronto, Canada, 2 July 2025.

[5] A. R. Ellis-Mohr, "Scaling Autonomous Science," Air Force Research Laboratory, Dayton, Ohio, 25 June 2025.

[4] A. R. Ellis-Mohr, A.K. Nayak, "A Theory for Training and Inference Compute Scaling Paradigms," IBM Research, Virtual, 1 May 2025.

[3] A. R. Ellis-Mohr, A. K. Nayak, and L. R. Varshney, “On theories for interference-compute scaling and information batteries,” Scientific Meeting on Bits, Neurons, and Qubits for Sustainable AI, The Royal Society, London, UK, 7 April 2025.

[2] A. R. Ellis-Mohr and L. R. Varshney, “Directed Information Flow in Computing Systems with Living Neurons,” presented at NeurIT: Information Theory in Neuroscience and Neuroengineering Workshop in IEEE International Symposium on Information Theory, Athens, Greece, 7 July 2024.

[1]  X. Zhang, Z. Dou, G. Upadhyay, S.-H. Kim, D. Havert, K. Y. Huang, A. R. Ellis-Mohr, R. Huang, O. Aydin, S. Kang, T. Saif, H. J. Kong, J. M. Beggs, M. Gazzola, "A versatile, accurate, low-cost and open-source electrophysiology solution for in-vitro neuronal cultures," presented at American Physical Society March Meeting, Las Vegas, Nevada, 6 March 2023.

Internal

[3] A. R. Ellis-Mohr, “A finite-time Lyapunov exponent analysis pipeline,” Mind in Vitro Symposium, September 2023.

[2] A. R. Ellis-Mohr, “Dual instability regions in saccadic eye movements: A finite-time Lyapunov exponent analysis,” Summer@Simons Symposium, August 2023.

[1] A. R. Ellis-Mohr, “Living Computers for Artificial Biological Intelligence,” Harnessing Data for Materials Symposium, August 2022.