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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
The search for applying machine learning to real world problems led me to work in the area of Astro-turfing. This is a filtering model with machine learning algorithm to filter-out the fake reviews and hence the fake reviewers from committing review frauds. I used amazon reviews data sets to built a stochastic model on top of it.
The search for applying machine learning to real world problems led me to work in the area of Astro-turfing. This is a filtering model with machine learning algorithm to filter-out the fake reviews and hence the fake reviewers from committing review frauds. I used amazon reviews data sets to built a stochastic model on top of it.
Working with the accuracies and flaw removal strategies with re-implementation of Open-Face/Googles Face-Net, for improving the range of applications in the domain of Security.
Resolved the false positive 2-D inputs by introducing more features in Stage 1 (face detection) as a.) Orientation Normalization b.) 3D surface representation.
Working with the accuracies and flaw removal strategies with re-implementation of Open-Face/Googles Face-Net, for improving the range of applications in the domain of Security.
Resolved the false positive 2-D inputs by introducing more features in Stage 1 (face detection) as a.) Orientation Normalization b.) 3D surface representation.
Seq-GAN is a unique approach which models the data generator as a stochastic policy in reinforcement learning to solve the problem.
The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search.
Seq-GAN is a unique approach which models the data generator as a stochastic policy in reinforcement learning to solve the problem.
The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search.
Successfully collected more than 900 Gb of public webcam data, by capturing image frames periodically from over 80 webcams across United States.
Applied Coco-annotation semi-automated labelling to develop ground-truth labels.
Re-implemented 4 state-of-the-art face detection algorithms for face detection & face mask detection to analyze their effectiveness in real-world dataset.
Reported face mask usage across United States from Jun 23’ 2020 to Feb 10’ 2020.
Successfully collected more than 900 Gb of public webcam data, by capturing image frames periodically from over 80 webcams across United States.
Applied Coco-annotation semi-automated labelling to develop ground-truth labels.
Re-implemented 4 state-of-the-art face detection algorithms for face detection & face mask detection to analyze their effectiveness in real-world dataset.
Reported face mask usage across United States from Jun 23’ 2020 to Feb 10’ 2020.
Undergraduate Course - Teaching Assistant, Lehigh University, Department of Computer Science & Engineering, 2019
Course Description
Instructors -
Prof. Arielle Carr - CSE017(010), CSE017(011)
Prof. Houria Oudghiri - CSE017(012)
This course will cover the design and implementation of algorithms using Java. It assumes that students have had prior experience using conditional statements, loops, arrays, etc., in Java, and will build on this knowledge to develop a full understanding of proper object-oriented programming, algorithmic techniques (e.g., divide-and-conquer, recursion), and the design of data structures (e.g., queues, stacks, trees).
Undergraduate Course - Teaching Assistant, Lehigh University, Department of Computer Science & Engineering, 2020
Course Description
Instructors -
Prof. Houria Oudghiri
This course will cover the design and implementation of algorithms using Java. It assumes that students have had prior experience using conditional statements, loops, arrays, etc., in Java, and will build on this knowledge to develop a full understanding of proper object-oriented programming, algorithmic techniques (e.g., divide-and-conquer, recursion), and the design of data structures (e.g., queues, stacks, trees).