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Reflection Research Aurora

Build technical skills. Learn to think like a researcher. Complete a project you can explain.

Research mentorship in AI, data science, and healthcare applications for ambitious high school and undergraduate students.

Request a 15-minute consultation

Research mentorship by university faculty members, designed to support competitive university applications.

Reflection Research Aurora is a research mentorship agency that connects high school students with research programs delivered by tenure-track professors. Students gain hands-on research experience, develop technical expertise, and build competitive profiles for leading universities and scholarship opportunities worldwide.

Services

How We Help Students Move Forward

Research Mentorship

One-on-one guidance from university faculty through a complete, original research project.

AI & Data Science

Python, data analysis, and machine learning skills applied to real datasets and problems.

Scientific Communication

Writing research reports, presenting results, and explaining your work with confidence.

Competition Preparation

Targeted support for science fairs, data challenges, and research competitions.

Faculty-Led Research Mentorship

Students work directly with experienced university educators and researchers with backgrounds in computer science, AI, data analytics, and interdisciplinary research. Mentor details are provided during the consultation process so families can understand the expertise and fit before enrollment.

Request a 15-minute consultation

Programs

Explore Our Signature Programs

For High School Students

Foundations

A beginner-friendly 10-week program focused on coding fundamentals, data thinking, and a guided class schedule.

Level 1 foundations program
Applied CS × Research

A research-centered program where students solve two structured machine learning problems and build an independent portfolio piece.

Level 2 applied bioinformatics program
Custom Pathway

A fully tailored program built around the student's background, interests, timeline, and application goals.

Level X custom program

For Undergraduate Students

Undergraduate Research Track

A 1-on-1 research mentorship program for undergraduates building a research portfolio and faculty reference letter for master's and PhD applications.

Undergraduate research track

Example Project · AI + Healthcare

Cancer Prediction Using Cell-Free DNA and
Machine Learning

Research Question

Can machine-learning models distinguish cancer samples from healthy controls using patterns found in cell-free DNA (cfDNA)?

The Project
Students work with research-grade data derived from a public Nature Communications study. They build and compare machine-learning models, from logistic regression to neural networks in PyTorch, and learn how to evaluate them through carefully designed experiments.
What Students Learn
Python & PyTorch · Machine Learning · Healthcare Data Analysis · Model Evaluation · Experimental Design · Reproducible Research
Where Students Take the Lead
Students make and justify key decisions about data preprocessing, model design, class imbalance, evaluation, and interpretation, with guidance from their mentor.
Final Work
Reproducible Code · Analysis Notebook · Research Report · Presentation/Demo
Research Mindset
The goal isn't simply to achieve the highest model score. Students learn to ask whether results are reliable, understand limitations and uncertainty, and distinguish a research finding from a clinically validated healthcare application.

Track Record

Supervision Track Record*

Selected outcomes from students previously supervised by our mentors in university research settings. Research experience was one of many factors considered in university admissions.

Publications

  • Briefings in Bioinformatics Oxford University Press · in press
  • Pacific Symposium on Biocomputing (PSB 2027) Peer-reviewed conference proceedings · accepted

Admissions

  • University of Toronto, Engineering Offer, 2026
  • University of Waterloo, Engineering Offer, 2026
  • Carnegie Mellon University, Statistics & Machine Learning Offer, 2026

*These examples reflect the mentor's broader student-supervision experience and are not guaranteed outcomes of Reflection Research programs. University admission decisions depend on many factors, and research mentorship does not guarantee admission or publication.

Reflection Research Aurora is an independent educational service and is not affiliated with, sponsored by, or endorsed by universities where individual mentors may hold appointments.

Programs & Fees

Fees below cover our standard programs. For individualized research mentorship, we first discuss the student's background, research interests, goals, and expected scope before recommending a program.

Foundations

Build essential skills with confidence

Learn Python, data analysis, and research fundamentals through hands-on projects.

$1,200
per student · CAD
~30 hours total(about 10 weeks)
Max 6 studentsper cohort
Beginner friendlyNo prior experience needed
  • 1.5h live session / week (online)
  • Python & data analytics fundamentals
  • Guided mini-project
  • Asynchronous support (Q&A, feedback)
  • All learning materials included
Learn More
Applied CS × Research

Turn skills into a real research project

Apply your skills to more advanced problems and complete an original research project with mentorship.

$2,400
per student · CAD
~80 hours total(12–16 weeks)
Max 4 studentsper cohort
Some background helpful(we'll assess together)
  • 2h live session / week (online)
  • Structured machine learning problems
  • Guided project + original research project
  • Research report + presentation
  • Faculty reference letter (upon successful completion)
  • Ongoing support and feedback
Learn More
Custom Pathway

For unique goals and advanced students

A tailored program designed around your interests, timeline, and research goals.

Custom
quote at consultation
Duration TBDFlexible start dates
1-on-1 or small groupFully personalized
Any domain or goale.g., competitions, publication, specific topics
  • Customized curriculum
  • Individual research mentorship
  • Flexible pacing and schedule
  • Support for competitions, extended projects, or domain-specific interests
  • Reference letter available (upon successful completion)
Contact for Pricing
Taught by experienced university faculty 100% online (flexible for international students) Small cohort sizes for personalized attention A supportive and academically rigorous learning environment

Undergraduate research mentorship is available by consultation. See the Undergraduate Research Track for details.

All prices in CAD. Payment plans available. Contact us to discuss options.

FAQ

Common Questions

It depends on the level. Level 1 is designed for students with zero or minimal Python experience; no prior coding is required. Level 2 requires functional Python skills, either from Level 1 or equivalent self-study. Not sure where your child fits? The free 15-minute assessment call is the right starting point.

Yes, if the student already has solid Python experience (variables, loops, functions, and basic data handling). We confirm this at the assessment call. Students who skip Level 1 without sufficient background tend to struggle in the first few weeks of Level 2, so we take the check seriously rather than just accepting everyone.

Level 1 (Foundations) is fully online. Level 2 is primarily online as well, but in-person tutoring is available for students who prefer it. All online sessions run on Google Colab or equivalent cloud environments — no local installation needed, just a browser and a stable internet connection.

Level 1: approximately 3–4 hours per week (1.5h live session + 2h self-study). Level 2: approximately 6–7 hours per week (2h live session + 4–5h project work). These are realistic estimates. Students who treat it like a course assignment rather than a serious commitment tend to fall behind quickly in Level 2.

Indirectly, yes, and this distinction matters. The program builds the genuine technical depth and project ownership that makes application writing compelling. Students who complete Level 2 have real work to draw on: datasets they processed, models they debugged, results they interpreted, and failures they recovered from. That is exactly what Waterloo and U of T evaluators are looking for. We do not draft or edit application essays; students write their own materials, but they leave the program with far more to say.

No, and we are upfront about this. Journal submission is a goal we work toward, not a paid outcome. Peer review is competitive and results vary by project quality and scope. A well-executed, clearly scoped project submitted to a credible journal is a far stronger application signal than a rushed submission to a low-credibility open-access venue. We target journals such as BMC Bioinformatics, PLOS ONE, Computers (MDPI), and Frontiers journals where quality is the criterion for acceptance.

The program is CS × any domain, not limited to biology or health. Students have worked on projects in health data, environmental science, sports analytics, social systems, and more. The computational methods (data handling, machine learning modeling, research writing) transfer across fields. Students are encouraged to choose a domain that connects to their university goals and genuine interests, since that connection comes through clearly in application essays and interviews.

Life happens. Contact us as early as possible and we will work out a reasonable arrangement. For Level 2 cohort programs, mid-program withdrawals affect the small group dynamic, so we ask for at least two weeks' notice where possible. For individualized mentorship, the deposit is non-refundable as it reserves a block of mentor time, but remaining installment payments are handled on a case-by-case basis depending on how far the project has progressed.

Our mentors are tenure-track faculty at Greater Toronto Area universities. They are active researchers with institutional affiliations, not tutoring agency contractors. Students who complete Level 2 receive a formal reference letter describing their project, performance, and demonstrated capabilities. Students in extended 1-on-1 research mentorship receive a more detailed letter that speaks to research aptitude, intellectual curiosity, resilience, and specific original contributions observed over the course of the work. A letter from a faculty research collaborator carries considerably more weight than a program completion note, and is the type of letter graduate programs and scholarship committees find most compelling.

Yes. The Undergraduate Research Track is a separate 1-on-1 mentorship program designed for 2nd–4th year undergraduates (or recent graduates) who are building a research portfolio for master's or PhD applications. It runs 16–20 weeks and focuses on independent research design, end-to-end implementation, and graduate school positioning, including a strong reference letter from a GTA tenure-track faculty member. See the Undergraduate Research Track page for full details.

This is the most common scenario we plan around. Most Canadian and US graduate program deadlines fall in November or December. A 16-week engagement starting in late spring or early summer gives you a complete research project and manuscript draft by September, before SoP writing begins in earnest. At the initial consultation we map your application timeline against a realistic project scope so you are not finishing research and drafting statements simultaneously in October.

Email us at reflectionresearch.aurora@gmail.com to receive the free CS × Research Fit checklist and book a 15-minute background assessment call. The call covers the student's current skills, interests, and goals, giving us enough context to recommend the right level and cohort timing.

Contact

Start the Conversation

Tell us a bit about yourself and what you're looking for. We'll follow up within 1–2 business days.

Prefer to reach out directly? reflectionresearch.aurora@gmail.com · Aurora, ON