Suchismita Sarker

Biography

Suchismita Sarker is a faculty member at the College of Engineering and Technology. She did her bachelor’s in Metallurgy and Materials Engineering. Right after her undergrad, she worked in the aluminum industry for 3 years as Process Engineer. After that, she did her Ph.D. from the University of Nevada, Reno and most of her dissertation work was performed at ESRF, NIST, and Los Alamos National Laboratory to understand the behavior of metallic glasses. After completion of her Ph.D., she joined <i>Max-Planck Institute</i><b><i> </i></b>in Germany and then joined <i>Stanford University</i> (SLAC) in California as a post-doctoral researcher. During her stay at Stanford for the last 4 years, she worked on accelerating material discovery by high throughput synthesis and characterization that is guided by Machine Learning (ML), especially, in the field of energy science (semiconductor, thermoelectric materials, water splitting) and structural materials (high, entropy alloys and metallic glasses).

Education

• Ph D, University of Nevada-Reno, Materials Science and Engineering
• BE, Foreign Institution, Metallurgy and Materials Engineering

Teaching Interests

The most exciting aspect of an academic career in a university environment is having the opportunity to work as a teacher and mentor for the students. I believe communicating effective ideas, interactive discussions, along discovering and demonstrating that mastery of the material is achievable, and fun are intellectually very satisfying. To organize a course, I built a solid foundation based on the coursework that is easy to follow, summarize all the necessary information considering prerequisites. Apart from well-organized content, I will provide additional information by covering the subject matter with practical result-oriented content to motivate students.

Research Interests

My research focuses on <b>materials design, discovery, and development through combining theory, machine learning (ML), and high-throughput experimentations for advanced manufacturing and renewable energy storage and conversion devices. </b>It will open affordable alternatives in the field of technological applications. As a young researcher in the field of material science, I have come to recognize the importance of understanding fundamental structure-property relationships using both classical and emerging advanced characterization techniques combined with machine learning (ML). I am convinced over time, the "materials genome" initiatives will reduce both times and cost to design new materials discovery and improve the socio-economic well-begin.