Thomas Markovich

Computer Science · Physical Sciences

7h-index1.4kcitations29works0.02yr avg
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Research Topics

Advanced Graph Neural Networks(7), Quantum Mechanics and Non-Hermitian Physics(6), Quantum chaos and dynamical systems(5), Recommender Systems and Techniques(3), Cold Atom Physics and Bose-Einstein Condensates(3)

Publications28 total

Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
arXiv (Cornell University)·2025Open Access
Scaling Laws for Discriminative Classification in Large Language Models
arXiv (Cornell University)·2024Open Access
QDC: Quantum Diffusion Convolution Kernels on Graphs
arXiv (Cornell University)·2023Open Access
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
arXiv (Cornell University)·2023· 1 citedOpen Access
$$\textbf{k}$$NN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval
Lecture notes in computer science·2023· 1 cited
Causally-guided Regularization of Graph Attention Improves Generalizability
arXiv (Cornell University)·2022· 1 citedOpen Access
Graph Neural Networks for Link Prediction with Subgraph Sketching
arXiv (Cornell University)·2022· 17 citedOpen Access
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining·2022· 39 cited
Understanding convolution on graphs via energies
arXiv (Cornell University)·2022· 12 citedOpen Access
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
arXiv (Cornell University)·2022· 1 citedOpen Access
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
arXiv (Cornell University)·2022· 2 citedOpen Access
Towards Realistic Correlated Electronic Dynamics: New Developments in the Modeling of Harmonic Bath Models and Many Body Dispersion Interactions
Digital Access to Scholarship at Harvard (DASH) (Harvard University)·2017Open Access
Response to: “Comment on benchmarking compressed sensing, super‐resolution, and filter diagonalization”
International Journal of Quantum Chemistry·2016
Accelerating the computation of bath spectral densities with super-resolution
Theoretical Chemistry Accounts·2016· 4 cited
Calibration of the Many-Body Dispersion Range-Separation Parameter
arXiv (Cornell University)·2016· 1 citedOpen Access
Benchmarking compressed sensing, super-resolution, and filter diagonalization
International Journal of Quantum Chemistry·2016Open Access
Analytical nuclear gradients for the range-separated many-body dispersion model of noncovalent interactions
Chemical Science·2015· 42 citedOpen Access
Benchmarking Compressed Sensing, Super-Resolution, and Filter\n Diagonalization
arXiv (Cornell University)·2015Open Access
More accurate and efficient bath spectral densities from super-resolution
arXiv (Cornell University)·2013· 6 citedOpen Access
Adiabatic switching approach to multidimensional supersymmetric quantum mechanics for several excited states
Molecular Physics·2012· 5 cited
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Frequent Co-authors

Alán Aspuru‐Guzik(8), Donald J. Kouri(7), Jacob N. Sanders(5), Ahmed El-Kishky(4), Frank Portman(4), Aria Haghighi(4), Eric R. Bittner(4), Samuel M. Blau(4), Kaushik Maji(3), John Parkhill(3), Martin A. Blood-Forsythe(2), Chetan Verma(2), Baekjin Kim(2), Ramy Eskander(2), Yury Malkov(2), Sofía Samaniego(2), Nicholas Maxwell(2), Benjamin Paul Chamberlain(2), Nils Hammerla(2), Michael M. Bronstein(2)