Prospective Students

1) Should you do a PhD?

A PhD is not “more coursework” — it is training to become an independent researcher. You will spend years working on problems where the answer is not known, the path is unclear, and progress depends on your ability to think, read, build, and iterate.

✅ Strong reasons to do a PhD

You enjoy open-ended problem solving and deep thinking.
You want to publish and contribute new ideas to the field.
You want a research career (academia or industrial research labs).
You are excited by building systems that push the frontier in medical AI.
You are motivated by impact: improving diagnosis, clinical workflows, and patient outcomes.

❌ Common wrong reasons (please be honest with yourself)

“I want Dr. before my name.”
“I’m not sure what to do, so I’ll do a PhD.”
“A PhD guarantees higher salary.” (Not necessarily.)
“I want to delay joining industry.”
“I will only work if I’m given tasks.” (A PhD requires self-drive.)

The best indicator that a PhD is right for you: you can spend weeks on a difficult problem, fail repeatedly, learn from the failures, and still feel curious and motivated to try again.

2) Is my group a good fit for you?

I work on Personalized Medicine and Medical Imaging with an emphasis on multimodal AI that can: (i) understand images, (ii) reason clinically, (iii) ground decisions spatially, and (iv) remain trustworthy under distribution shifts.

Projects we love

Medical Foundation Models that unify tasks across radiology, pathology, ophthalmology, ultrasound, etc.
Vision–Language Models for clinical reasoning (multi-step, evidence-based, uncertainty-aware).
Spatial grounding: localization, alignment, region-level reasoning and explainability.
Trustworthiness: hallucination reduction, robustness, calibration, and evaluation protocols.
Personalized / longitudinal healthcare AI: modeling trajectories over time, multi-visit learning.

If your main interest is purely software engineering (without research), or purely theoretical work with no healthcare motivation, we may not be a great match.

3) How to choose a PhD supervisor (practical advice)

Many applicants email dozens of faculty members with the same template. This usually fails. A PhD is a long collaboration — compatibility matters.

What you should check before emailing

Do you genuinely like the supervisor’s recent papers and research direction?
Are students publishing regularly in strong venues?
Does the group culture match you (hands-on vs hands-off; applied vs theory; collaborative vs independent)?
Do you understand what *you* will likely work on in the first year?

The strongest emails are the ones that show you read at least 1–2 papers, can explain why they matter, and propose a direction you want to explore (even if it is preliminary).

4) How a PhD works (and what strong students do differently)

A PhD is driven by the student. I will guide, challenge, and support you — but you must own the research. The difference between struggling and thriving is often habits and process, not intelligence.

Strong students typically…

Read consistently (papers, code, evaluations) and keep notes.
Implement quickly, test ideas, and iterate with evidence.
Write early (a living draft beats a “perfect” draft later).
Communicate clearly: what you tried, what failed, what you learned, what’s next.
Build strong baselines and trust their experiments.

Students who struggle often…

Wait for tasks instead of defining next steps.
Spend too long “preparing” without building or testing.
Avoid writing until the end (which makes publishing hard).
Run experiments without careful evaluation or ablations.
Disappear for weeks, then try to compress progress into one meeting.

5) What you can expect from me

My goal is to help you become a strong independent researcher who can publish in top venues and build impactful medical AI systems. In practical terms, here is what you can expect:

Clear starting direction: reading list, baselines, milestones, and a realistic first project plan.
Regular supervision: weekly meetings (more when needed), and active iteration on ideas.
Strong writing mentorship: structure, positioning, clarity, and top-tier paper craft.
Top-venue focus: NeurIPS, ICLR, CVPR, AAAI, WWW, MICCAI, etc. (quality over quantity).
Research culture: high standards, fast iteration, and supportive collaboration.

6) What I expect from you

A successful PhD requires more than intelligence — it requires consistency, discipline, and ownership. If you join my group, I will expect:

Strong fundamentals: ML basics, deep learning, probability/optimization at a workable level.
Practical ability: clean code, reproducible experiments, and careful evaluation.
Independence: you can learn what you don’t know and come back with solutions.
Ambition: aiming for top venues and meaningful scientific contributions.
Integrity: honest reporting of results (including negative results) and research ethics.

If you feel stuck or overwhelmed, communicate early. Silence is the biggest productivity killer in a PhD.

7) How to apply at MBZUAI

For MSc and PhD study, submit a formal application through MBZUAI’s graduate admissions process. Emailing me is a way to discuss research fit and does not replace the university application.

See the official graduate admissions page for current requirements, deadlines, and application instructions, and explore the PhD and MSc in Computational Biology.

Contacting me about research fit

Email: imran.razzak [at] mbzuai.ac.ae

Subject suggestion: PhD Application — [Your Name] — Medical VLM / Foundation Models

Please include the following (PDF preferred, links are fine too):

CV (include GPA, ranks if relevant, awards, publications, and research experience)
Transcripts (unofficial is fine for initial contact)
Short research proposal (1–2 pages) covering: problem, why it matters, your idea, and what you would do in 3–6 months
Evidence of skills: GitHub, project portfolio, papers, Kaggle, or a strong reproducible project

How I evaluate applications (transparent)

Fit: does your interest match medical VLM / foundation models / clinical AI?
Evidence of ability: strong projects, code, publications, or rigorous coursework
Clarity: can you explain a technical idea clearly?
Research mindset: curiosity + persistence + willingness to iterate

After screening, I may send a small technical task (paper reading or implementation-style) and/or schedule a short call.