About
Analytical and dynamic professional with comprehensive experience in ML, Optimization and…
Activity
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It was a magical reunion of brilliant minds and big hearts at #BITSiansDay! Reliving college days, celebrating entrepreneurial journeys, and forging…
It was a magical reunion of brilliant minds and big hearts at #BITSiansDay! Reliving college days, celebrating entrepreneurial journeys, and forging…
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Did you know Arkieva has been recognized as a Challenger in the 2024 Gartner® Magic Quadrant™ for Supply Chain Planning Solutions? We're honored to…
Did you know Arkieva has been recognized as a Challenger in the 2024 Gartner® Magic Quadrant™ for Supply Chain Planning Solutions? We're honored to…
Liked by Shahrukh Quraishi
Experience
Education
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Indiana University Bloomington
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* B555 Bayesian Machine Learning
* B659 Machine Learning through Approximate Inference in Graphical Models
* I529 Machine learning for Bio informatics
* S520 Statistics
* D523 Advance Database concepts
* I519 Introdution to Bio informatics
* B505 Applied Algorithms -
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* Statistics and EDA
* Predictive Analysis - I
* Predictive Analysis - II
* Big Data and Analytics
* E-commerce Analytics (Elective) -
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Major: Data structures and Algorithm
Licenses & Certifications
Projects
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Bayesian Linear Regression vs MLE and Bayesian Model Selection
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The objective of this project is to assess the performance of linear regression, its regularized variant, and the Bayesian model, incorporating model selection.
This algorithm is implemented from Bishop: [https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf] -
Experiments on Conditional Variational Autoencoders based on Google's paper - https://arxiv.org/pdf/1406.5298.pdf
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This project draws inspiration from Conditional Variational Autoencoders. Primarily, I have incorporated two main concepts: the first involves Professor Roni, an IU professor, and the second entails a modification of a semi-supervised learning approach using a deep generative model as outlined in the Google deepmind research paper found at https://arxiv.org/pdf/1406.5298.pdf.
This report showcases the implementation of the aforementioned methods and provides a comparative analysis of…This project draws inspiration from Conditional Variational Autoencoders. Primarily, I have incorporated two main concepts: the first involves Professor Roni, an IU professor, and the second entails a modification of a semi-supervised learning approach using a deep generative model as outlined in the Google deepmind research paper found at https://arxiv.org/pdf/1406.5298.pdf.
This report showcases the implementation of the aforementioned methods and provides a comparative analysis of their results against those presented in the referenced papers. -
Generative Normalizing Flow: Forward, Backward Flows and Density Estimation
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In this comprehensive exercise, I apply both forward and backward normalizing flows to assess their bijection properties. Additionally, I conduct density estimation on a dataset, exploring how the estimation is influenced by varying parameters.
This exercise is guided by the principles outlined in Kevin P. Murphy's book chapter on normalizing flows.
Link: https://probml.github.io/pml-book/ -
Review of Variational Probabilistic Matrix Factorization Algorithm from Netflix Competition
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This project adopts a variational probabilistic algorithm, grounded in the formulation outlined in the paper found at https://www.stats.ox.ac.uk/~teh/research/bayesml/kddcup2007.pdf. The algorithm builds upon the traditional SVD matrix factorization approach by introducing priors on both the U and V matrices. The estimation of the posterior distribution is achieved through the utilization of Evidence Lower Bound (ELBO) and the variational Expectation-Maximization (EM) algorithm.
I…This project adopts a variational probabilistic algorithm, grounded in the formulation outlined in the paper found at https://www.stats.ox.ac.uk/~teh/research/bayesml/kddcup2007.pdf. The algorithm builds upon the traditional SVD matrix factorization approach by introducing priors on both the U and V matrices. The estimation of the posterior distribution is achieved through the utilization of Evidence Lower Bound (ELBO) and the variational Expectation-Maximization (EM) algorithm.
I present the implementation of this algorithm on the MovieLens dataset, conducting experiments with various hyperparameters to assess the model's performance.
Languages
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English
Full professional proficiency
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Introducing the Higgs Family of LLM. Since founding Boson AI in 2023, we have dedicated ourselves to empower enterprises with AI technologies, with…
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The most popular univariate drift detection method is... Kolmogorov-Smirnov for continuous variables and L-infinity for categorical Should you…
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In distributed systems, generating unique identifiers (IDs) is crucial for tasks like creating session IDs/tokens and transaction IDs,etc. Here are…
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🚨 Calling all students! Tesla released its AI Engineer Internship for Fall 2024 Here's what you need to know: 🚗 Located in Palo Alto, CA 🚗…
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Stanford offers FREE Data Science Education! - Machine Learning https://lnkd.in/dY9ZXJyM - Statistical learning https://lnkd.in/dUE5jkXr -…
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https://lnkd.in/eW5HUY-H
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