A5092467276- Quantum Computing Algorithms and Architecture
- Molecular Communication and Nanonetworks
- Innovative Microfluidic and Catalytic Techniques Innovation
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Computational Physics and Python Applications
- Homotopy and Cohomology in Algebraic Topology
- Geometric and Algebraic Topology
- Quantum-Dot Cellular Automata
- Tensor decomposition and applications
- Genomics and Phylogenetic Studies
- Protein Structure and Dynamics
- Advanced Combinatorial Mathematics
- Scientific Computing and Data Management
IBM Research - Almaden
2024
University at Buffalo, State University of New York
2021
Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present comprehensive analysis of tensor methods such Tucker, CANDECOMP/PARAFAC, spiked decomposition, etc. and their diverse applications across domains imaging, multi-omics, spatial transcriptomics. To systematically investigate the literature, applied topic modeling-based approach that identifies groups distinct thematic sub-areas biomedicine where been used,...
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to expectation that near future computers will be capable performing simulations for natural science applications, operations research, machine learning at scales mostly inaccessible classical computers. Whereas impact already started recognized fields such as cryptanalysis, simulations, optimization among others, very little is known about full potential...
Clinical trials are pivotal in the drug discovery process to determine safety and efficacy of a candidate. The high failure rates these attributed deficiencies clinical model development protocol design. Improvements design could therefore yield significant benefits for all stakeholders involved. This paper examines current challenges faced trial optimization, reviews established classical computational approaches, introduces quantum algorithms aimed at enhancing processes. Specifically,...
Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With rapid evolution of quantum computing, it is natural to ask whether computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific instances amenable advantage, and estimating resources required are equally tasks. Here, we share our perspective on how create framework...
In this paper, we give a combinatorial description of the concordance invariant $\varepsilon$ defined by Hom in \cite{hom2011knot}, prove some properties using grid homology techniques. We also compute $(p,q)$ torus knots and that $\varepsilon(\mathbb{G}_+)=1$ if $\mathbb{G}_+$ is diagram for positive braid. Furthermore, show how behaves under $(p,q)$-cabling negative knots.