Computer Science Enthusiast | Passionate About Probability & Machine Learning Research
I’m a Computer Science student at
Sharif University of Technology, with a deep passion for Probability and
Machine Learning . I’m always searching for the math
and reasoning behind everything, which drives my focus into the
mathematical concepts of Machine Learning.
I naturally do a lot of programming as well, you may see some of my
public codes on my
GitHub.
I’ve also had the opportunity to work on exciting research projects
during my B.Sc. which you can see
here.
As part of the quest to understand our reality, and understand how the world and our brain works, I am focused on the underlying mathematics and principles of intelligence and learning in Neural Networks.
B.Sc. in Computer Science at Sharif University of Technology
This was a graduate course since it contained some topics of ML Theory as well. It was my first official ML course where I learned some of the theoretical foundations and topics ranging from classics like random forests and mixture models to Neural Networks and RL. And I implemented some methods from scratch, like MAP, SVM and MLP.The final project was also Nueral Style Transfer.
This was my first advanced probability course. We learned about Markov Chains and its variations, Poisson and Gaussian processes, martingals and many other topics. The final project and my extra project was about MCMC: Metropolis Hastings algorithm, where I was introduced to sampling methods.
This was a graduate course where we study probabilistic methods and concentration inequalities in high-dimensional spaces, with applications in areas such as machine learning, random matrix theory, and statistics.
This was a graduate course where we learned many of the mathematical concepts and the classic background, that lead to today's Machine Learning and Deep Learning.
In this course I learned a formal statistical learning view that is incorporated in ML. This was an introductory course compared to the ones above.
These courses were also among my favorite courses.
These are also foundation courses that I enjoyed.
These courses also helped me through my CS journy and are foundations of my Computer Science knowledge.
“You keep on learning and learning, and pretty soon you learn something no one has learned before.”
We worked on Expressive measures of graph similarity for ML tasks. Starting with non-parametric and kernel methods, and then incorporating deep parametric methods. Here I had the opportunity to introduce many ideas and theoretical analysis, create theoretical proofs and conduct benchmark experiments. I studied concepts in Geometric Deep Learning and Graph NNs, Approximation on Graphs, Graph Theory, Optimization and Topology.
We are experimenting with many aspects of Trustworthy ML on KANs, such as Robustness, Membership Inference, Certified Robustness. Collaborated with students at the University of Massachusetts
We studied and experimented on a model based on Markov Random Fields and Bayesian Inference, inspired by their use in computer vision tasks.
Feel free to reach out to me at hoomanzolfaghari84@gmail.com or hooman.zolfaghari84@sharif.edu.