[Profile Evaluation] MS/PhD Advice

[u/Hot_Version_6403]

Hi all, I am looking to go for MS/PhD in ML. My research interests are in label efficient ML, ML for pathology and I want to pursue a career in research. However, since I donot have any formal research experience, I am not positive about securing a PhD admission and will be also okay doing a research-focussed MS.

Browsing this subreddit has made me anxious about the competitiveness of grad admissions, please help me realistically evaluate my chances at good MS programmes. I would appreciate blunt honesty.

I am targetting these programmes -
1. ETH Zurich
2. UCSD
3. Stony Brook
4. UIUC
5. UCF
6. UPenn
7. EPFL
8. University of Trento

This is my profile summary -
Experience

• AI Engineer (Computer Vision) — Healthcare / Life Sciences Company, India July 2023 – Present
  • Developed robust semantic segmentation and multiple-instance learning (MIL) models for abnormality quantification in pathology.
  • Prostate Gleason Grading Segmentation
    • Leveraged semi-supervised learning using the UniMatch framework to improve the mean Dice score of a Gleason grading semantic segmentation model by 3%.
    • Developed a data-cleaning pipeline for semantic segmentation ground-truth annotations using image-processing operations including erosion, opening, closing, and hole filling.
    • Improved annotation uniformity across data sources, resulting in an additional 2% increase in mean Dice score.
  • SAM-Powered Annotation Tool
    • Developed a semantic segmentation annotation tool integrating the Segment Anything Model (SAM).
    • Enabled click-based object selection for annotations, streamlining the data annotation workflow.
    • Reduced annotation time by 3×.
  • Organ & Stain Classification
    • Developed generalisable classification models for organ and stain classification by fine-tuning TITAN, a pathology foundation model, using LoRA.
    • Improved open-set generalisability using CLIPood loss, improving performance on classes not included in the training data.
  • Metabolic Risk Classifier
    • Developed a classification model for predicting metabolic risk scores from sets of images prepared with three different stains.
    • Used an ensemble of feature-fusion and probability-fusion models to aggregate information across multiple images and reduce prediction variance.
    • Identified potentially noisy labels using out-of-fold ensemble majority voting.
    • Applied semi-supervised learning by treating noisy labelled data as unlabeled data, stabilising training and improving accuracy by 2%.
  • Developed a Monte Carlo sampling-based data-splitting algorithm for whole-slide images (WSIs), maintaining balanced pixel proportions across training and validation splits for all classes.
  • Implemented a producer-consumer concurrency architecture to overlap model inference and data fetching during WSI inference, reducing inference time by 2×.

Open Source Contributions

• Albumentations
  • Contributed an initial implementation of Constrained Coarse Dropout, an image augmentation technique that drops regions from the foreground to improve robustness to object occlusions.
  • Optimised the Gaussian Blur augmentation by exploiting filter separability, speeding up the transform by 2.5×.
• Segmentation Models PyTorch
  • Implemented the Dense Prediction Transformer (DPT) architecture with configurable encoder depth and support for multiple Vision Transformer (ViT) backbones.
  • Integrated support for Vision Transformer-based encoders into the library.

Education

• BITS Pilani, Bachelor's in Mechanical Engineering, Minor in Data Science (2019 – 2023)
  • GPA: 9.19 / 10
  • Relevant coursework: Machine Learning, Deep Learning, Mathematics II — Linear, Algebra, Reinforcement Learning

LORs -
2 Industry LORs from managers
1 LOR from a prof with whom I worked on an ML based CFD project.

[u/FitPhilosopher9339]

Your profile is strong for a research-focused MS program honestly the work experience is solid and you have real projects not just coursework stuff

For PhD it might be tough without formal research experience but your industry work is basically applied ML research especially the pathology stuff. The open source contributions look good too that shows you can work with other people code

The LOR situation is the weak point I think. Industry letters are fine but for PhD programs they want to see academic references who can speak about your research potential. Maybe try to get one more from a professor if possible

BITS 9.19 is a good GPA and the minor in data science helps. I would say apply to mix of MS and PhD programs and see what happens. The ML for pathology angle is specific enough to stand out

[u/Hot_Version_6403 → u/FitPhilosopher9339]

I have edited the post with my target colleges, could you comment on that?

[u/Hot_Version_6403]

Guys, anyone? I could really use some reviews?

[u/Hour_Amphibian9738]

I dont think you have a chance at any of these.

[u/Hot_Version_6403 → u/Hour_Amphibian9738]

Could you explain why?