Mala Radhakrishnan
Professor of Chemistry
Interested in computational biophysical chemistry, analysis/design of drugs and other biological molecules, and creatively teaching chemistry.
I develop and apply computational methods to analyze natural biological interactions and to design novel drugs and other biological molecules of therapeutic importance. My research interests are interdisciplinary, combining chemistry, physics, biology, applied mathematics, and computer science. I am especially interested in problems that require modeling at the molecular level as well as the systems or population levels, and I enjoy interdisciplinary collaboration with other researchers.
I teach introductory chemistry, computational chemistry, and physical chemistry for both chemists and biological chemists. I am particularly interested in encouraging strong numeracy skills and computational literacy among young scientists. As an alum of the Teach for America program, I am passionate about catalyzing educational opportunities for all youth.
I am also especially excited about combining creative writing with chemistry and have published Atomic Romances, Molecular Dances, a book of poetry that humorously teaches chemical concepts.
Education
- B.A., Harvard University
- Ph.D., Massachusetts Institute of Technology
Current and upcoming courses
Molecular basis of chemistry; intensive overview of theories, models, and techniques of physical chemistry; extensive coverage of quantum mechanics; applications of quantum mechanics to atomic and molecular structure, and spectroscopy; introductory statistical mechanics, with an emphasis on connections to thermodynamics; intermediate topics in chemical kinetics and introduction to reaction dynamics. The laboratory work involves learning elementary programming to quantitatively model data collected with various spectroscopies (UV-VIS, IR, NMR, fluorescence) using quantum theory.
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Provides a survey of fundamental principles in physical chemistry and how they relate specifically to the study of biological molecules and processes. Emphasis is placed on empowering students to understand, evaluate, and use models as approximations for the biomolecular world. Models are mathematically represented and provide both qualitative and quantitative insight into biologically relevant systems. Commonly used experimental techniques such as spectroscopy and calorimetry are explained from first principles with quantum mechanical and statistical mechanical models, and computational applications such as protein structure prediction and molecular design are explained through physical models such as molecular mechanics and dynamics. (BIOC 331 and CHEM 331 are cross-listed courses.)