NRT Core Faculty
Arthi Jayaraman
(PI, NRT director) has expertise in the development and use of molecular models, theory, simulation (using HPC), and DS towards the design of novel polymers. She has extensive collaborations with experimentalists in academia, industry, and national labs. Given her research expertise in soft materials, polymers, high-performance computing, and data science, and her regular interdisciplinary interactions with chemists, computer scientists, mechanical engineers, and biologists, who use different technical vocabulary, she understands the graduate training needs and the current training gaps in graduate programs involving the cultural and communication barriers that challenge such interdisciplinary interactions. Through such interdisciplinary interactions, the graduate students and postdocs trained in her research laboratory have gone on to become successful researchers in industry, national laboratories, and academia. She also has extensive experience teaching core and elective classes to both undergraduate and graduate students over the last 12+ years. One of these course relevant to this NRT is a graduate elective on ‘Molecular modeling and simulation of soft materials’.
In her past role as director of graduate program in Chemical and Biomolecular Engineering, she learned how graduate programs could better integrate students’ technical training and professional skills development and how the success of graduate students is closely linked to their advisor(s)’s involvement in their mentoring. These observations motivate her to lead this NRT program focused on graduate education and mentoring. Lastly, Jayaraman’s role as Associate Editor for Macromolecules (2019 – present) and Deputy Editor (2021- present) for the new open access journal on Polymers from the American Chemical Society (ACS Polymers Au) gives her an understanding of current challenges of data sharing and access in the publishing world, which she will also share with the graduate trainees in this NRT.
Sunita Chandrasekaran (co-PI, NRT Technical Training co-Director) is an expert in HPC, parallel computing, programming models/abstractions, and connecting their tools to domain sciences (e.g., bioinformatics, atmospheric research). Chandrasekaran, with co-author Dr. Guido Juckeland (member of our NRT IAC), has published an edited textbook on OpenACC for Programmers: Concepts and Strategies and has organized GPU Hackathons. Prof. Chandrasekaran also plans to offer a graduate level ‘Parallel programming and data science’ elective based on the success and impact of an undergraduate core course led Chandrasekaran and Stephen F. Siegel from CIS. This course is expected to cover the fundamentals of parallel concepts, patterns, methodologies, and programming models used for parallelization, parallelization of both multicore systems and systems with accelerators and will be expanded to integrate NRT training by studying programming challenges from soft materials domain scientists.
Joshua Enszer (NRT Professional Development co-Director) is an expert in pedagogy, teaching and learning, gamification and game-based learning, development of engineering curriculum, and chemical process safety education. He serves as an academic consultant for the AIChE Center for Chemical Process Safety and is a member of the Advisory Board of the UD Center for Teaching and Assessment of Learning (CTAL). Prof. Enszer will serve as the advisor for trainees who opt to do the pedagogical training and teaching fellowship during their NRT traineeship. He has been involved in the Teaching Fellowship program in CBE since 2016 and has mentored graduate students who have served as teaching fellows. He guides the training fellows through this program in preparing for lectures for two weeks of the assigned course, effective modes of delivery of lectures and incorporation of in-class active learning techniques, going over the evaluation post-lectures, and outlining steps for improvement of the teaching fellow as an instructor. Outside this fellowship program, he supervises about 20 graduate students each year as they serve as teaching assistants for lab and lecture courses in the department of chemical and biomolecular engineering.
Laure Kayser (NRT Professional Development co-Director) is an expert in synthesis and characterization of soft organic electronics (conjugated polymers) for biological applications; she leads an interdisciplinary research group working at the intersection of organometallic chemistry, polymer synthesis, and materials and device engineering. Her joint appointments in two colleges (Arts & Sciences and Engineering) brings a unique perspective of differences and similarities in graduate training in these colleges. Her role as a member of the steering committee member for the UD (NIH Training program) Chemistry-Biology Interface (CBI) program will also be useful in translating their successful training practices into our program. Prof. Kayser mentors an interdisciplinary and diverse research group of five graduate students from MSE and CBC at UD since Fall 2019. Individual and group meetings are held weekly to track progress toward the graduate degree and discuss career opportunities and goals. She is also a faculty trainer and a member of the steering committee for the NIH-funded Chemistry-Biology Interface graduate training program. As part of this program, her focus is on outreach, diversity, and community support. She has also taken courses on effective mentoring including the CAM workshop.
Austin Brockmeier(NRT Technical Training co-Director) is an expert in data science, machine learning, and signal processing, with a focus on the design of statistical models, numerical optimization, and data mining algorithms. He brings in a unique expertise having developed and applied DS approaches to analyze data from a wide range of disciplines: biomedical engineering, neuroscience, and natural language processing. Relevant to this NRT program are his recent experiences in the Inclusive Teaching Professional Development Workshop Series at UD, UD College of Engineering Diversity Working Group, as well as launching the DSI’s Data Science Community Hour. Prof. Brockmeier has also developed a graduate course in machine learning (ML) that combines the three foundational fields of data science(DS): mathematics, statistics, and computer science, and introduces students to the review of current literature and semester-long independent research projects with peer review. He currently mentors a group of four PhD students and one master’s student.