I am more than happy to provide students with letters of recommendations or professional references for scholarships, internships, co-ops, fellowships*, graduate school/professional program applications*, jobs, or other similar uses. Applications to graduate schools, scholarships, fellowships, research-related internships/REUs, and some internships typically require letters of recommendations. Meanwhile, industry-related employment and some internships typically request professional references, which typically involve contact by the employeer to ask for feedback or insight on the candidate's work ethic and performance, ability to work in teams, general personality or characteristics, and non-technical skills (writing, presenting, communication, etc.).
In order to request a letter of recommendation or professional reference, please review the guidelines below for both requesting and ensuring that you qualify for a strong recommendation.
QUALIFICATIONS
To qualify for a strong letter of recommendation or professional reference as an undergraduate student, you should:
meet at least one of the following criteria:
have completed at least 1 quarter of class with me and earned a grade of 3.0 or higher in each course taken with me
worked with me on undergraduate research projects related to engineering education
served on a committee or student organization with me (ex: DEIA Committee or WChE)
be one of my advisees for at least 1 quarter
AND have interacted with me significantly and meaningfully outside of class (ex: stopping by my office or other interactions) or through in class participation (ex: office hours, class attendance, etc.)
To qualify for a strong letter of recommendation or professional reference as a graduate student, you should:
meet at least one of the following criteria:
have TA-ed or graded for me for at least 1 quarter and exhibited a strong performance (reliable, timely, supporting of students)
served with me on a committee or student organization (ex: DEIA Committee or WChE)
had me serve on your thesis committee
have interacted with me significantly and meaningfully outside of these contexts (ex: informal advising)
*Since letters of recommendation for external graduate/professional programs and national scholarships are longer and require more detail, students must meet additional requirements. Please schedule a meeting and discuss with me at least 1 month prior to the deadline to see if I feel I will be able to write you a strong enough letter based on your performance and our interactions.
TO REQUEST A LETTER
Please submit the request in writing via email at least 3 weeks in advance of the deadline, and you must ask me before listing me.
Include the following with the initial request:
Deadline and mechanism of submission
Current resume or CV
The following information:
Brief description of the position/program/fellowship you're applying
Reason for applying
How this opportunity supports and/or relates to your ultimate career goal
What aspects of your performance and/or characteristics you would like me to comment on and examples if you can provide them
Anything else you think would be useful for me to know to make sure I can write you a strong, personalized letter
TO REQUEST A PROFESSIONAL REFERENCE
Please submit the request via email, and ask me before putting down my name and/or contact information.
If I agree, I will provide you with the best contact information to use in the application.
REFERENCES
LearnChemE (https://learncheme.com/) is a wonderful, free resource created by those at the University of Colorado Boulder to help student and instructors alike with learning and using chemical engineering content. They have fantastic videos, interactive simulations, and student resources for a variety of chemical engineering courses/subjects. They also have FE Exam Review resources for any of you considering taking the FE.
The Rosen Review (https://rosenreview.cbe.princeton.edu/) is another fantastic and free resource for learning chemical engineering, chemistry, mathematics, and other STEM concepts. Compiled by Dr. Andrew Rosen, a friend and colleague of mine, the Rosen Review is a compilation of notes Dr. Rosen has taken since high school and throughout his college and graduate degrees. He is a fantastic teacher, and I really recommend checking out this site as a learning tool to supplement and support any concepts you’re interested in or struggling with. Content is organized by subject and chemical engineering content is sorted by undergraduate vs graduate level.
The University of Michigan created a companion website for both the Elements and Essentials Fogler textbooks (http://websites.umich.edu/~elements/5e/index.html) that has interactive notes, software, and example problems. The chapters are sorted and numbered based on the Elements book, but you can find the full chapter name, and thus content, by looking at the Objectives sorted by chapter to find sections corresponding to those in Essentials or that are of interest to you.
Northwestern University’s NICO 101 Introduction to Python Programming and Data Science course by Dr. Luis Amaral, as the materials are free and publicly available on GitHub (https://github.com/amarallab/Introduction-to-Python-Programming-and-Data-Science). This was designed to be a crash course in Python for graduate students at Northwestern who had no prior exposure to the coding language. The course takes place roughly over 2 weeks, and exclusively uses Jupyter Notebooks. The first few days of material are introduction to Python in general (starting from what a Jupyter Notebook is, what different data types are, what a function is, and expanding to basic plotting and function development), and more advanced material far beyond the scope of this class are included in the later half of the course (including text analysis, complex data science, web scraping, and other really cool and fun things you can do with Python). This course is fantastic, and extremely well made—it’s actually how I taught myself Python in graduate school. You do not need to complete all the days to get something out of it. Simply reading through some of the introductory Jupyter Notebooks (which you can do via GitHub without downloading them) is a good starting point for those new to coding.
Codecademy (https://www.codecademy.com/catalog/language/python) is a great resource for learning all types of coding languages. Learning on Codecademy happens purely via a web browser and does not require any program installations. You can filter the listings so that only free things are shown, and basic Python introductory courses are included in that. I found these useful when learning, but I think the NICO 101 course did a better job starting from the ground up.
Stack Overflow (https://stackoverflow.com/) is a forum where code developers post inquiries about debugging code or how to approach a coding problem. You can gain so much from Stack Overflow without explicitly posting. If you’re encountering a code problem, I promise you’re not the first one to encounter it and I bet you somebody on Stack Overflow has and common solutions are posted already. Please feel free to use existing postings as a resource to debug code. If you google specific error messages in Python, generally the first few links to come up are Stack Overflow postings. You may not copy code directly from Stack Overflow or post on it asking to solve your homework problems, but you may use it as guidance for debugging your code.
Python Programming and Numerical Methods: A Guide for Engineers and Scientists (https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html) is a free textbook from Berkeley that introduces Python basics, data structures, and then applications of Python in numerical methods that engineers might encounter. I haven't used this extensively, so I'm not super familiar with it, but a fellow teaching faculty knew of it and sent it to me when I was looking for Numerical Methods textbooks for engineers that weren't based in MATLAB.
Dr. Anna Marie LaChance, a Lecturer in the Department of Chemical Engineering at UMass Amherst, created a highly informative YouTube video (https://www.youtube.com/watch?v=zOGrxJ4AAXk) that outlines the capabilities, limitations, and ethics of AI.