Quantum Transport

My research focuses on quantum transport in two-dimensional (2D) materials, where reduced dimensionality and strong electronic correlations give rise to exotic quantum transport phenomena. I employ a combination of first-principles electronic structure methods and the non-equilibrium Green’s function (NEGF) formalism to quantitatively model electronic transport at the atomic scale. A central goal of my work is to engineer and control quantum transport pathways in atomically thin systems subjected to external fields. In particular, I investigate how edge states, interface effects, and electrode functionalization can be exploited to realize stable and selective electronic transport channels for single-molecule identification.

Molecular Bioelectronics

My research in molecular bioelectronics focuses on the design and theoretical modeling of nanoscale electronic devices that integrate molecular electronics concepts for the identification and sequencing of biomolecules. By exploiting quantum transport through molecular junctions, I investigate how electronic signatures can be used to distinguish complex biological entities such as DNA, RNA, proteins, and carbohydrates at the single-molecule level. My research further emphasizes predictive modeling of nanomaterial properties for applications across diverse omics sciences, including genomics, transcriptomics, proteomics, and glycomics, as well as the electronic detection of drugs, vitamins, and natural products.  

Machine Learning

My research focuses on the development of advanced and interpretable machine learning (ML) methodologies that can be seamlessly integrated with nanoscale electronic devices to enable reliable and targeted predictions. Rather than treating ML as a black-box tool, my approach emphasizes physical interpretability and robustness, ensuring that model predictions remain consistent with the underlying device physics. I employ a broad spectrum of learning paradigms, including supervised, unsupervised, and semi-supervised learning, to extract meaningful patterns from complex, high-dimensional datasets generated by quantum transport and electronic structure calculations. Through this integrated ML-physics approach, my research aims to accelerate the design and optimization of nanodevices for applications ranging from materials discovery to molecular sensing and bioelectronics. 

2D Materials and Heterostructures

My research explores the design and theoretical investigation of two-dimensional (2D) materials and their in-plane/van der Waals heterostructures, where reduced dimensionality and weak interlayer coupling enable unprecedented control over electronic properties. Atomically thin materials provide a versatile platform in which structural, chemical, and electronic degrees of freedom can be tuned at the monolayer level. I study the intrinsic in-plane properties of individual 2D materials, including their electronic structure, charge distribution, and transport behavior, as well as the emergent properties arising from vertical stacking in van der Waals heterostructures. By systematically varying layer composition, stacking order, and interfacial alignment, I investigate how interlayer interactions modify band alignment, charge transfer, and transport pathways. 

Catalysis

In collaborative experimental–theoretical studies, I investigate reaction mechanisms and optical properties of metal nanoclusters relevant to catalytic applications. Through detailed electronic structure analysis, including Kohn–Sham molecular orbital and energy-level diagrams, I elucidate how cluster geometry, electronic states, and frontier orbitals govern reactivity, selectivity, and optical transitions. These insights establish structure–property–reactivity relationships that enable the rational design of functional nanoclusters for catalysis and optoelectronic applications.