Kinkar Ch. Das is affiliated with Sungkyunkwan University in South Korea and has an extensive publication record in the fields of computer science and mathematics. Their work spans multiple subfields, including computational theory and mathematics, geometry and topology, organic chemistry, discrete mathematics and combinatorics, and materials chemistry.
The main areas of research focus around graph theory and its applications. Specific topics include graph labeling and dimension problems, computational drug discovery methods, advanced graph theory research, the synthesis and properties of aromatic compounds, free radicals and antioxidants, and topological and geometric data analysis.
Das has authored several notable papers from 2021 to 2022, including:
Das collaborates frequently with a number of coauthors, including Sourav Mondal, Yilun Shang, B. R. Rakshith, Shaowei Sun, and Xueyi Huang. These frequent collaborations highlight a network of researchers contributing to similar or overlapping fields of study.
Their research has been published in a range of academic venues, with a strong presence in:
Kinkar Ch. Das' research contributions focus on integrating computational methodologies, theoretical graph analyses, and chemical applications, particularly in the study of molecular graphs and topological indices. Their multidisciplinary approach crosses traditional subject boundaries, addressing problems in both pure and applied mathematical sciences.
Boris Furtula;Muhuo Liu;Kexiang Xu;Kinkar Ch. Das
Kinkar Ch. Das;Ivan Gutman;Boris Furtula
Kinkar Ch. Das
Kinkar Ch. Das;Ivan Gutman;Bo Zhou
Kinkar Ch . Das;Ivan Gutman;Boris Furtula
Kinkar Ch. Das;N. Trinajstić
Kinkar Ch. Das
Unknown
Unknown
Kinkar Ch. Das;Kexiang Xu;Junki Nam
Kinkar ch. Das
Kexiang Xu;Kinkar Ch. Das
Kinkar Ch. Das;Bo Zhou;N. Trinajstić
Kinkar Ch. Das;Kexiang Xu;Ivan Gutman
Kinkar Ch. Das;Ivan Gutman;Igor Milovanović;Emina Milovanović
Kinkar Ch. Das
Kexiang Xu;Kinkar Ch. Das;Kechao Tang
Kinkar Ch. Das;Sang-Gu Lee
Unknown
Batmend Horoldagva;Kinkar Ch. Das
Kinkar Ch. Das;Kexiang Xu;Ivan Gutman
Kinkar Ch. Das
Kexiang Xu;Kinkar Ch. Das;Haiqiong Liu
If you think any of the details on this page are incorrect, let us know.
Students interested in Mathematics often explore related fields that complement their analytical skills. One popular pathway is pursuing a master's degree in data-driven disciplines. For example, a data analytics master’s degree equips graduates with advanced techniques in statistical modeling and big data interpretation, making them highly valuable in industries like finance, healthcare, and technology.
For those aiming to blend mathematical expertise with business acumen, MBA programs offer a practical option. When selecting a program, students might consider schools featured in lists like the mba transfer credits programs, which allow for greater flexibility in coursework and reduce time to degree completion.
Some students may prefer less intensive business programs. Exploring the easiest mba specialization can be a strategic way to enhance leadership skills without the stress of highly competitive admissions.
Additionally, fully remote students can benefit from resources highlighting the easiest online mba program options, perfect for balancing work, study, and personal commitments while gaining qualifications that support career advancement beyond pure mathematics.
Helmholtz-Zentrum Berlin für Materialien und Energie
University of Manchester
City, University of London
University of Aberdeen
United States Department of Agriculture
Radboud University
Washington State University Spokane
Kagoshima University
Lamont-Doherty Earth Observatory
University of Pittsburgh
Queensland University of Technology
University of Paris-Saclay
University of Chicago
Boston College
Luleå University of Technology
New York University
