Research

Journal and Conference Papers

[2] N. Welsh*, L. J. Shikhman*, M. N. Attzs, S. K. Putane, V. M. Nguyen, R. T. White. Post-Launch Capability Expansion of Vision-Language Models via Prompting for On-Orbit Spacecraft Inspection. CVPR 2026 Workshop on AI4Space. [paper]

[1] L. J. Shikhman. One Operator to Rule Them All? On Boundary-Indexed Operator Families in Neural PDE Solvers. Workshop on AI & PDEs at the 14th International Conference on Learning Representations. [paper][arxiv][code]

Invited Talks

[1] L. J. Shikhman, L. Lawler. Deploying Physics Models at the Edge: Quantized AI for Weather and Fluid Simulation on Dell Pro Max with GB10. Optimized AI Conference, 2026, Atlanta, GA.

Manuscripts in Review

[4] L. J. Shikhman. Self-Evolving Mathematical Stress Tests for Neural Operators. AI4Math Workshop at the 43rd International Conference on Machine Learning.

[3] L. J. Shikhman. Semigroup Consistency as a Diagnostic for Learned Physics Simulators. AI4Physics Workshop at the 43rd International Conference on Machine Learning.

[2] L. J. Shikhman, M. Galarnyk, A. Dash, N. Welsh. Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes. The 40th Annual Conference on Neural Information Processing Systems.

[1] L. J. Shikhman. Diagnosing Failure Modes of Neural Operators Across Classes of PDEs, Transactions on Machine Learning Research. [arXiv][code]

Manuscripts in Preparation

[2] L. J. Shikhman, S. Gilbertie. Cellular Sheaf Neural Operators for Autoregressive Representation Learning in PDE Surrogates

[1] L. Papadopoulos, E. Reck, L. J. Shikhman. Accounting-Informed Machine Learning for Detecting Financial Reporting Fraud.