Publications
Journals
Lee, S., Lee, E.H., Kwon, Y.-K., Koester, S.J., Avouris, P., Cherkassky, V., Tersoff, J., Low, T. Generalized energy band alignment model for van der Waals heterostructures with a charge spillage dipole. ACS Nano 19, 37749–37757 (2025).
Khaw, M.J., Zorko, N.A., Kennedy, P.R., Bendzick, L.E., Shackelford, M., Selleck, C., Hinderlie, P., Walker, J.T., Soignier, Y., Lyons, R.C., Femino, E.L., Stenger, T., Dasgupta, T., Kotz, L.E., Shetty, M., Phung, S.K., Lee, E.H., Lu, Q., Lim, J., Provenzano, P.P., Fujioka, N., Davis, Z.B., Geller, M.A., Wagner, J.E., MacMillan, M.L., Felices, M., Miller, J.S. Novel trispecific killer engager targeting B7-H3 enhances natural killer cell antitumor activity against head and neck cancer. Journal for Immunotherapy Cancer 13, e011370 (2025).
Cherkassky, V. & Lee, E. H. A perspective on large language models, intelligent machines, and knowledge acquisition. arXiv preprint arXiv:2408.06598 (2024).
Lee, E. H. & Cherkassky, V. Understanding double descent using VC-theoretical framework. IEEE Transactions on Neural Networks and Learning Systems 35, 18838–18847 (2024).
Cherkassky, V. & Lee, E. H. To understand double descent, we need to understand VC theory. Neural Networks 169, 242–256 (2024).
Lee, S., Seo, D., Park, S.H., Izquierdo, N., Lee, E.H., Younas, R., Zhou, G., Palei, M., Hoffman, A.J., Jang, M.S., Hinkle, C.L., Koester, S.J., Low, T. Achieving near-perfect light absorption in atomically thin transition metal dichalcogenides through band nesting. Nature Communications 14, 3889 (2023).
Lee, E. H., Jiang, W., Alsalman, H., Low, T. & Cherkassky, V. Methodological framework for materials discovery using machine learning. Physical Review Materials 6, 043802 (2022).
Book Chapters
Cherkassky, V., Lee, E.H.. VC-Theoretical Explanation of Double Descent.. in The Importance of Being Learnable: Essays Dedicated to Alexander Gammerman (eds An Nguyen, K. & Luo, Z.) 157-176, Springer Nature Switzerland, Cham (2026).
Patents
Lee, E.H. and Cheng, W.H. Lithography system and methods. US patent no.: US20220382167A1 (2022).
Posters
Khammanivong, A., Lee, E.H., Khonkhammy, D., Gunnarson, C., Sockalingum, G.D., Bedros, S., Petcavich, R., Pham, D.Q. Abstract LB409: OncodeAi™-Breast CED: A nanoparticle-enhanced IR molecular sensing platform for early breast cancer detection. Cancer Research 85, LB409–LB409 (2025).
Lee, E.H., Lee, S., Feummeler, E., Tadmor, E., Low, T., and Cherkassky, V. Enhancing materials discovery with the LUPI framework: a novel approach to predicting material properties. Knowledge Guided Machine Learning Workshop (2024).
Lee, E.H., Lee, S., Feummeler, E., Tadmor, E., Low, T., and Cherkassky, V. Enhancing materials discovery with the LUPI framework: a novel approach to predicting material properties. Midwest Machine Learning Symposium (2024).
Lee, E.H., Lee, S., Feummeler, E., Tadmor, E., Low, T., and Cherkassky, V. Enhancing materials discovery with the LUPI framework: a novel approach to predicting material properties. 3M Poster Symposium (2024).
Heller, N., Mallery, K., Bristow, N., Travadi, Y., Lee, E.H., Hong, J. Abstract 2304: Detection of early-disseminated cancer cells with deep learning-enabled holographic imaging. Cancer Research 84, 2304–2304 (2024).
Lee, E.H. and Cherkassky, V. VC theoretical explanation of double descent. Institute for Engineering in Medicine Annual Conference (2022).
Lee, E.H. and Cherkassky, V. VC theoretical explanation of double descent. CSE Graduate Students Welcome Session (2022).
Lee, E.H., Grassi, R. and Low, T. Theoretical investigation of phonon-polariton modes in cylindrical hexagonal boron nitride. Undergraduate Research Opportunity Program (2016).