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nabu.app/discover/pet-imaging-innovation-lab
DiscoverThe PET Imaging Innovation Lab

University of Pennsylvania · Philadelphia, PA · Biomedical Engineering

The PET Imaging Innovation Lab

Suleman Surti

Save Targeted

Fit Score

72

Good Match

weakaveragestrong
Academic
70
Experience
73
Alignment
73

Lab Selectivity

72

Competitive Lab

OverviewWhy You MatchGaps · 5

Research Focus

Dr. Suleman Surti's lab at the University of Pennsylvania focuses on improving PET (Positron Emission Tomography) imaging techniques for enhanced cancer diagnosis and treatment. They develop innovative algorithms to refine the accuracy of PET images, particularly in challenging conditions like low radiation counts or non-standard isotopes.

PET imagingcancer diagnosisbreast cancerquantitative imagingmolecular imagingimage reconstruction

Research Aims

AIM #1

Develop advanced algorithms for scatter estimation in PET imaging.

AIM #2

Evaluate a novel breast PET scanner for improved cancer diagnostics.

AIM #3

Integrate molecular and anatomical imaging to guide cancer treatment decisions.

Skills the Lab Values

PET imaging techniquesdata analysisalgorithm developmentimage reconstruction methodsclinical imaging evaluationsignal processingsoftware implementationbiostatistics

Professor Priorities

What faculty actually evaluate.

We interviewed faculty at Harvard, Tufts, Boston University, and more and asked them to rate 13 factors by importance. Nabu's scoring weights are derived directly from those ratings.

1Research interest alignment
9.5
2Research experience
9.2
3Project work
8.3
4School reputation
8
5GPA
7.8
6Coursework relevance
7.7
7Publications
7.3
8Technical skills
6.8
9Achievements
6.3
10Standardized tests
6.2
11Competitions
6
12Internships
5.8
13Teaching experience
3.7

Ratings out of 10 · Faculty rated each factor's influence on admission decisions

Fit Score

Professor-calibrated

Scored the way professors actually think.

Nabu's scoring weights come from interviews with faculty at BU, Tufts, and Harvard — ranked by how much each factor actually influences admission decisions.

  • Professor-weighted scoring — calibrated from real faculty interviews, not guesswork
  • Research interest ranked #1 — faculty rated it above GPA, publications, and test scores
  • Three dimensions — academic profile, experience depth, and research alignment
Generate a fit report

Fit Score

87/100

Position Summary

Strong candidate. Your control systems background and ROS experience align with the lab's core research. Priority gap: peer-reviewed publications.

Score breakdown

Research alignment
88
Academic profile
74
Experience depth
62

Skill Gaps

across 3 target labs

CriticalPeer-reviewed publications3×
HighReal-time embedded deployment2×
MediumSLAM implementation experience2×
MediumDiffusion policy familiarity1×

Top recommended action

Reproduce a diffusion-policy paper on a small manipulation task and post the code publicly

~4 weeks

Gap Analysis

See exactly what's missing — and how to close it.

A high score is nice. A plan is better. Nabu maps your methodology coverage against what each lab actually does, then turns each gap into a concrete, weighted action.

  • Have / partial / missing on every method the lab uses
  • Concrete actions — every gap turned into a specific next step, so you spend effort where it counts
  • Shared gaps across your whole target set, ranked by impact
Map your gaps

Readiness Signal

Watch your profile get stronger.

Your profile signal is a single live number that moves as you ship projects, take courses, and close gaps. Nabu re-evaluates on every change — so you always know where you stand.

  • Live signal — one number tracked over weeks, benchmarked against your targets
  • Specific, actionable steps for each skill gap, aggregated across all target labs
  • Re-runs automatically when you add new work to your profile
Check your readiness

Profile Signal

62/100↑ +4 this month

Composite of academic + experience scores

Recommended actions

3 of 7
  1. 1

    Submit an abstract to the IROS or ICRA student competition track

    Motion Planning & Control Lab

  2. 2

    Deploy a PID controller on physical hardware and document results publicly

    Motion Planning & Control Lab

  3. 3

    Read 3 recent lab papers and write a 200-word technical summary for each

    Edge Intelligence Lab

New Message

ToProf. Ilya Sokolov <sokolov@mit.edu>
SubjectProspective MS student — trajectory optimization background

Dear Prof. Sokolov,

I'm a senior in Mechanical Engineering at Boston University, and your recent work on real-time trajectory re-planning under contact uncertainty (ICRA '24) caught my attention — particularly the model-predictive approach for online replanning.

My senior thesis implements a similar MPC formulation for a 6-DOF manipulator in simulation. I also have hands-on ROS2 experience from a summer internship at [Company], where I worked on motion primitive libraries for warehouse automation.

I'd love to learn more about current openings in your lab for Fall 2025.

— Generated from your profile · grounded in real papers

Outreach

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Generic cold emails get ignored. Nabu drafts outreach grounded in the professor's actual recent papers and your actual background — so every email leads with something real.

  • Cites real papers — references specific publications from the lab's indexed record
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  • One per lab — each draft is unique to that professor, not a mail-merge
Draft your first email
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25K+publications indexed

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Questions

The things students ask first.

Nabu compares your parsed profile — coursework, projects, skills, and any publications — against a lab's research aims, methodology, and recent papers. It scores five dimensions weighted by how confident the underlying evidence is. Every number traces back to a specific source, so you can audit it.
No. Nabu only reasons over evidence you've added — a course on your transcript, a project, a paper you've listed. If you don't have experience with something, it shows up as a gap, not an invented strength.
Yes. Whether you're a sophomore exploring research or a senior applying to PhD programs, Nabu meets you where you are. Set your year and track, and the readiness signal recalibrates to what's realistic for your stage.
Only you. Your documents are processed to extract academic signals — courses, skills, experiences — and only that structured data is stored, not your raw files. Nothing is shared with labs or third parties.
Building a profile and discovering labs is free forever. Pro ($5/mo or $25/yr) unlocks AI-powered fit reports, outreach email drafts, and cross-lab strategic planning — most students only need it during their application semester.
Lab profiles are built from NIH Reporter grant records and OpenAlex publication data — two of the largest open research databases available. Lab data and publications is pulled directly from these sources.
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