Qualifying Exams
Published:
Northeastern PHI does things a little differently from the traditional qualifying exam - hence this public post detailing each qualifying exam component.
A quick reminder
The research described in the technical and health components differs greatly from the work in the research core and my current research. Three years into my studies my first advisor Dr. Dinesh John left Northeastern to pursue work in health policy and physical activity in government and I joined Olga Vitek’s lab. One way to frame this is I went from applying statistical models to activity data to applying statistical models to mass spectrometry imaging data with through lines of statistics, programming, biology, and experimental design. I’m glad I have a diverse set of professional interests and scientific experiences. 
Technical Component
Examiner: Dr. Dakuo Wang
Date of completion: May 23, 2024
Description of exam topic: In 2023, Diego Arguello, Dinesh John, and I concluded the Companion Pilot Study on the use of activity-informed real-time interventions to increase physical activity in adults, identifying that these interventions do work but required buy-in and relationship building between the participants and health coaches (see O.3.11). High quality health coaching is labor and time intensive, thus a barrier to scaling these types of real-time interventions. At the time (2023), large language models (LLMs) were starting to mature into useful tools that had the potential to automate some amount of routine messaging and interaction. For my exam I designed an AI health coaching system, Companion2, for use by human health coaches to expand their reach and productivity as well as a comprehensive evaluation plan to test its efficacy and safety. This work was submitted as a predoctoral fellowship training grant (F31) in the December 8th, 2023 NIH grant cycle and resubmitted the following year with changes for the August 8th, 2024 cycle.
Examiner and reason for selection: Dr. Dakuo Wang, an Associate Professor with joint appointments in the Khoury College of Computer Sciences and the College of Arts, Media, and Design has a lot of experience working with AI systems for health. Before this project, I worked with one of his students on a project studying how smart-speaker voice assistants can help patients share information with their care teams. His collaborative style and our shared interest in using LLMs for health applications led me to ask him to be a sponsor on the F31 and proctor this exam.
Structure and reason for selection: A grant submission requires extensive research, planning, and iteration. Technological systems must be thoroughly detailed as well as their evaluation; all justified by and based on previous research. Resubmissions (as was the case for our August 8, 2024 submission) must integrate feedback and address weaknesses from reviewers, as well as incorporate novel technologies from a rapidly advancing field. I used this process to demonstrate my technical design capabilities all while gaining valuable grant writing experience.
Unfortunately, neither submission was funded, but the process was valuable.
Health Component
Examiner: Dr. Dinesh John
Date of completion: September 23, 2024
Description of exam topic: Motivational Interviewing (MI) is an evidence-based therapeutic technique used by practitioners to help clients make behavior changes, specifically around health. The process of MI is to establish a trusting relationship with a client, explore the benefits and barriers of the behavior change of focus, cultivate the client’s internal motivation, and support the client when they take action and implement behavior change. This process requires practitioners to respect and foster the client’s self-efficacy and self-determination, withhold judgment, avoid confrontation, and refrain from giving unsolicited advice. MI requires a soft touch; moving between asking open-ended questions, redirecting conversation from unproductive topics, summarizing and reflecting, and affirming the client’s experiences. MI was the core approach used in the health coaching conducted as part of the Companion trial to promote physical activity in older adults and in the F31 grant application based on the data collected from Companion trial, as detailed above in the technical component. At the time, it was highly relevant to my future research.
Examiner and reason for selection: My advisor at the time, Dr. Dinesh John was an associate professor in the department of Public Health and Health Sciences at Northeastern University’s Bouvé College of Health Sciences. His research focused on the measurement of physical behavior and developing technological interventions to promote behavior change and health. Dinesh also has background in behavior change and health interventions. We had planned to build an AI system based on the Companion work that would use MI to promote physical activity therefore I asked him to administer my exam. Dinesh believes that practical experience was the best way to learn, thus we collaboratively selected this course.
Structure and reason for selection: Using MI to promote behavior change not only requires the study of the philosophy and approach behind MI, but the opportunity to practice MI techniques. Doing a course in MI as the health component, specifically UMass Chan Medical School’s Certificate in Intensive Motivational Interviewing, offered me the opportunity to practice MI and receive coaching and evaluation from MI practitioners. The course took four months and had ten two-hour group meetings where students were taught the theory, principles, and practice of MI. Students had ten learning assignments with quizzes, two mock-patient interviews to practice their MI skills, and two coaching sessions with MI practitioners to evaluate their performance. Upon successful completion of the course, students received a certificate of completion.
Research Core
Examiner: Dr. Olga Vitek
Date of completion: very soon
Current status: final editing before submission
Title: Statistical Principles Define an Open-Source Differential Analysis Workflow for Mass Spectrometry Imaging Experiments with Complex Designs: A Case Study of Osteoarthritis
Author list and affiliations: Ethan B Rogers1, Sai Srikanth Lakkimsetty1, Kylie Ariel Bemis1, Charles A Schurman2, Peggi M Angel3, Birgit Schilling2, Olga Vitek1 1Khoury College of Computer Sciences, Northeastern University, Boston MA
2Buck Institute for Research on Aging, Novato CA
3Department of Pharmacology and Immunology, Medical University of South Carolina, Charleston SC
Abstract: available upon submission to preprint server
Link: available upon submission to preprint server
Journal: available upon submission to preprint server
Vignettes of the workflow developed as part of this work: available upon submission to preprint server
Statement from examiner, Dr. Olga Vitek, on the role of myself and each author: coming soon
Date of presentation to PHI group: coming soon
Other details: This work has been previously presented in posters at two conferences, ASMS (2025) and IMSIS3 (2025). Reprints available upon request.
Teaching Requirement
Courses:
CS7200: Statistical Methods for Computer Science, Fall 2023, Dr. Olga Vitek
CS4950: Computer Science Research Seminar, Spring 2024, Dr. Olga Vitek
CS3950: Introduction to Computer Science Research, Spring 2024, Dr. Olga Vitek
On meeting the teaching requirement: As part of being an assistant for multiple courses, I have certainly accumulated more than three hours of teaching experience. My duties for CS7200 were mostly holding office hours - roughly two hours each week - where I supplemented Olga’s lectures, explaining statistical concepts in multiple ways, going over example problems for homework and tests, helping students formulate analysis plans for their projects, and answering questions students had about the course and grading. Office hours were well attended, and I spent the vast majority of time teaching statistics - most often reviewing the lecture material with the students and helping them with homework problems. Aside from holding office hours, I created and edited homework assignments; reviewed, proctored, and graded exams; and answered student questions on Piazza among other administrative duties. CS4950 and CS3950 were both 1-2 credit courses that I TA’d for simultaneously. TAing for these courses was more administrative due to the structure of the course - creating and grading homework, reviewing and offering feedback on project proposals, grading final project papers, and keeping track of participation and attendance. For several sessions, I was the sole instructor, taking attendance, moderating discussions, and lecturing.
On challenges, benefits, and take-aways: I was a TA in college for several lab courses. My experience in grad school was roughly similar and didn’t generate any particularly noteworthy challenges. Administration is tedious, providing feedback takes a lot of time, things that seem clear or obvious (class policies, assignment instructions, rubrics, etc.) aren’t more often than I would expect, and objective grading (or as close to it as can be managed by a fallible human like myself) is a lot of work. TAing a stats course was really helpful for my own understanding of the material. I’ve always been “good” at math per se, but in CS being “good” at math is the baseline and statistics is like math but with opinions. But nothing requires you to thoroughly flesh out your own understanding more than trying to teach it to someone else. Also, any ego that in the past might have prevented me from saying “I’m not actually sure” has been brutally and mercilessly sanded off.
I end this with things I have learned from TAing, in no particular order. I enjoy teaching, though much like grant writing I don’t really want it to be part of my career. Slowing down, trying a new approach, or reframing the problem are helpful ways to unstick someone’s lack of understanding. Students who are good at troubleshooting (know the system, define the problem, isolate components) are also good at teaching themselves. I made a bunch of friends TAing. Your class isn’t always someone’s priority, so have a comprehensive syllabus and best not to take it personally. For so many reasons, I’m so glad I didn’t have access to ChatGPT when I was a student.
Example assignments:
An example homework (with solutions) for CS7200 created based on previous assignments covering similar topics. An example of a weekly assignment for CS3950 also created by me based on previous years’ examples.
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