(2cc) Model-Based Process Design

The vision of my future research group is the data-driven and mechanistic modeling of various processes from bio to small molecules production or energy. Both academia and industry produce tremendous amount of data which can pave the way of smart design of processes. Every researcher should benefit from the development of new technologies and algorithms for conducting more efficient and well-planned research. Data-based thinking especially helps young researchers focus on the theories and science (modeling) behind the system of interest and plan their research/experiments more efficiently. This also helps specialize in the usage of experimental tools and developing new methods. I believe in the marriage of experimental work and systems modeling to leverage our understanding of our surroundings and intensify various processes. I am interested in the fundamentals and model-based/data-driven design of crystallization systems, bioreactors, and drug delivery along with process systems engineering. Furthermore, my future group will use the latest developments in machine learning to solve problems of not only pharmaceutical crystallization processes but also other processes in industry to provide practical solutions.

Postdoctoral Research: MIT, Advisor: Allan S. Myerson and Richard D. Braatz

My postdoctoral research has two main lines. One is the development (building and improving) and intensification continuous pharmaceutical processes. I have been working on the synthesis (reactions) of a drug substance and downstream processes after synthesis involving analytical methods for data collection, systems engineering for process control, and process modeling for process optimization. The second line is the investigation of the volume effect on the nucleation of polymorphic compounds. The stochasticity of the nucleation changes with the volume effect on top of the polymorphic behavior of the system and I am researching the mechanisms behind this phenomenon.

Industrial Internship: Genentech

During my industrial internship at Genentech, I used convolutional neural networks to perform image analysis to extract real time crystallization kinetics information. This culminated in the development of a user-friendly graphical user interface (GUI) and dissemination at the International Forum on Process Analytical Chemistry (IFPAC).

PhD Research: Purdue University, Advisor: Zoltan K. Nagy

During my PhD at Purdue, I worked on the model-based design of crystallization processes to produce active pharmaceutical ingredients as well as understanding the kinetics and mechanisms causing different behaviors of the crystals in various crystallization or dissolution systems such as batch crystallizers, continuous oscillatory baffled crystallizers, and hot melt extrusion. I developed a methodological workflow to design crystallization processes including system identification, smart experimentation for data acquisition and preliminary detection of key process parameters, modeling and digital twin development followed by process optimization and experimental validation of the resulting model.

Teaching Interests:

During my PhD, I had the chance to be a teaching assistant for two undergrad courses: 1) senior process control and 2) sophomore reaction engineering. I was awarded two teaching awards for excellence in teaching for senior process control class by both the College of Engineering and Davidson School of Chemical Engineering Department. My experience has taught me that conveying materials as simple as possible while making analogies to more absorbed topics by students on top of showing my enthusiasm is valuable. Students always recognize when the teacher loves what they are doing, and they become more receptive. I know it because my English wasn’t great back then, in addition I felt insecure when I had to teach Laplace transform to 180 students; I love teaching and I did my best to teach, and I know they could tell how much I loved teaching. I showed them that I understood them because I knew how it felt in my senior year when the Laplace transform was first introduced to me. I know and have experienced that both as a student and a teacher that the teacher’s energy always radiates in the lecture hall, and it matters. It changes the students’ attitude towards the topic and gives them courage to put more effort to understand the topic. I also believe in listening to the students’ feedback and following their instructions such as including more numerical examples or repeating the same topic. My goal in teaching style is based on showing them that they can solve most of the problems and working with the students to excel the communication during the lectures.

Given my educational background (BS, MSc, PhD, and postdoc in chemical engineering) and experience, I feel confident in teaching several chemical engineering courses at both undergraduate and graduate levels such as Numerical Methods, Process Control, Introduction to Chemical Engineering, Statistical Thermodynamics, or Heat and Mass Transfer. I have taken and worked on computational biophysics classes and topics such as molecular dynamics simulations and drug discovery during my master’s, and I am confident in teaching those topics as well.