John Mark Ansermino
Relevant Degree Programs
Complete these steps before you reach out to a faculty member!
- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Requirements" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
- Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
- Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
- Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
- Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
- Be enthusiastic, but don’t overdo it.
G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - Nov 2019)
We developed novel algorithms for monitoring sleep, sleep breathing disorder (SBD)and instantaneous respiratory rate (IRR) in children using the characterization ofpulse oximetry photoplethysmogram (PPG). To evaluate the algorithms, we recordedthe oxygen saturation (SpO₂) and PPG signals from 160 children using a phone-basedoximeter consisting of a microcontroller-based pulse oximeter module interfacinga smartphone. This mobile oximeter was further developed to perform allprocessing on the smartphone through the audio interface.We evaluated the relative impact of SBD on sympathetic and parasympatheticactivity in children through the characterization of PPG and concluded that sympatheticactivity was higher in 30-second epochs with apnea/hypopnea event(s). Welater characterized the SpO₂ pattern in SDB and then combined SpO₂ pattern characterizationand PPG analysis to design a model with two binary logistic classifiersto identify the epochs with apnea/hypopnea events.We developed a novel model for identifying the cycles of random eye movement(REM) and non-REM of the overnight sleep based on the activity of cardiorespiratorysystem using the overnight PPG. We extracted the features associated withpulse rate variability (PRV), respiratory rate (RR), vascular tone and movementfrom PPG to build a model with two binary classifiers to identify wakefulness fromsleep (wake/sleep classifier) and REM from non-REM sleep (non-REM/REM classifier).We also developed a novel algorithm for extracting the instantaneous respiratoryrate (IRR) from PPG. The algorithm was performed in three steps: extractionof three respiratory-induced variation signals from PPG, estimation of IRR fromeach extracted respiratory-induced variation signal and fusion of IRR estimates. A time-frequency transform called synchrosqueezing transform (SST) was usedto extract the respiratory-induced variation signals from PPG. Later, a second SSTwas applied to estimate IRR from respiratory-induced variation signals. To fuseIRR estimates, a novel algorithm was proposed.This study would expand the functionality of conventional pulse oximetry beyondthe measurement of heart rate and SpO₂ to monitor sleep, to screen SBDs andmeasure the respiratory rate continuously and instantly.
The hypertensive disorders of pregnancy (HDPs), including pre-eclampsia, complicate up to 10% of pregnancies and are leading causes of maternal and perinatal morbidity and mortality. The fullPIERS model was developed to identify and quantify the risks of developing complications for women with pre-eclampsia in high-resource settings and to aid clinicians in managing such pregnancies. Prior to introducing the model into clinical practice, it is important to assess its external validity. Recalibration, if required, and addition of new biomarkers may be helpful to improve the predictive performance of the model. The objectives of this thesis were (i) to assess the external validity of the fullPIERS risk prediction model for women with pre-eclampsia (ii) recalibrate the model if necessary, and (iii) to assess the incremental value of adding the biomarker, placental growth factor (PlGF), to the model.Using abstracted medical records of women admitted into tertiary units in four high-income countries (HICs), the fullPIERS model was assessed for geographical and temporal validity. The model’s predictive ability in women with a broader spectrum of disease including early-onset pre-eclampsia, other HDPs and low and middle-income countries was also assessed using existing cohorts. Good performance was interpreted based on discrimination (AUROCs ≥0.7) and calibration (slope ≥ 0.7). Stratification and classification accuracy of the model were also assessed. The fullPIERS model showed good discriminatory performance on temporal and geographical validity (AUROCs >0.8) and also in broader HDPs (AUROCs >0.7). Medium to high likelihood ratios were estimated (>5 to >10) at a predicted probability cut-off of ≥30% for ruling in adverse maternal outcomes. Calibration was reduced in all cohorts (