Constance Ferragu
I am a Computer Science M.S.E. Student at Princeton University, advised by Professor Adji Bousso Dieng.
I graduated from École Polytechnique with a Bachelor of Science in Mathematics and Computer Science.
While in France, I was a Research Intern at InstaDeep, where I had the chance to work on a
Contrastive Learning Model in the context of FewShot Learning and MetaLearning for Image Classification.
Email /
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Linkedin /
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Research
I am interested in the generalization of probabilistic machine learning models. I have recently joined
Vertaix where I will be working on developing scalable energybased models.


Contrastive Fewshot Learning with OpenAI's CLIP Algorithm
OpenAI tested CLIP on a fewshot learning baseline which trains, with only a few examples per class, a linear
regression from CLIP's image embeddings. Their results indicate that this linear regression would need to be trained
on a number of examples before matching the zeroshot performances of CLIP. We suppose that this behavior is due to the
image encoder being used independently, and thus losing some information from the text encoder. To address this, we built
a fewshot image classification model (Alg. 1) that uses both encoders. We find that this approach yields an equivalent or
higher performance than in the zeroshot setting, even for oneshot learning. This indicates that CLIP holds a
sustainable amount of prior knowledge.


Contrastive Fewshot MetaLearning for Image Classification
CLIP's unique embedding space holds information from two modalities: visual and semantic. We test CLIP in the
metalearning for fewshot learning environment with a contrastive training loop. In the same environment,
we implemented two algorithms that test the CLIP visual and semantic prior information. Based on
prototypical networks, which learns a text encoder and image encoder independently and a mixing
network to combine the encoder outputs, we implemented our own mixing network to combine the CLIP encoder outputs.
Our results surpass the current benchmarks of models trained for this particular environment.


MathsSolver
The MathsSolver program will allow a user to upload a picture of a handwritten maths problem or type the problem which will then be solved by the program.
I was the Team Leader for the Differential Calculator. My teams task consisted of creating a practical way to handle functions given as string inputs which
would be used for the entirety of the project (not only the differential solver) and also to come up with an algorithm that finds the derivative of a
function along with its stepbystep solution.


Concurrent NBody Simulation
In this project, we aim to do a working simulation of N bodies, first by implementing it sequentially and then
we parallelise it. Lastly we implemented a more advanced simulation algorithm,
the BarnesHut algorithm and discussed ways of parallelizing it.

Excellence Scholarship,
École Polytechnique.
Teaching Assistantship,
Princeton University.
Summa Cum Laude,
École Polytechnique.
Outstanding Student Distinction,
École Polytechnique.
COS 240: Reasoning About Computation Princeton University, Fall Semester 2022.
