I'm Pritom, a first year Computer Science Ph.D. student at the University of Texas at Dallas. I am experienced in research, development and deployment of deep learning and machine learning models and accustomed to data analysis and visualization.
CGPA: 3.74 on a scale of 4.00
GPA: 5.00 on a scale of 5.00
GPA: 5.00 on a scale of 5.00
Abstract: Bangla Optical Character Recognition (OCR) poses a unique challenge due to the presence of hundreds of diverse conjunct characters formed by the combination of two or more letters. In this paper, we propose two novel grapheme representation methods that improve the recognition of these conjunct characters and the overall performance of OCR in Bangla. We have utilized the popular Convolutional Recurrent Neural Network architecture and implemented our grapheme representation strategies to design the final labels of the model. Due to the absence of a large-scale Bangla word-level printed dataset, we created a synthetically generated Bangla corpus containing 2 million samples that are representative and sufficiently varied in terms of fonts, domain, and vocabulary size to train our Bangla OCR model. To test the various aspects of our model, we have also created 6 test protocols. Finally, to establish the generalizability of our grapheme representation methods, we have performed training and testing on external handwriting datasets. Experimental results proved the effectiveness of our novel approach. Furthermore, our synthetically generated training dataset and the test protocols are made available to serve as benchmarks for future Bangla OCR research.
Abstract: It is common to have continuous streams of new data that need to be introduced in the system in real-world applications. The model needs to learn newly added capabilities (future tasks) while retaining the old knowledge (past tasks). Incremental learning has recently become increasingly appealing for this problem. Task-incremental learning is a kind of incremental learning where task identity of newly included task (a set of classes) remains known during inference. A common goal of task-incremental methods is to design a network that can operate on minimal size, maintaining decent performance. To manage the stability-plasticity dilemma, different methods utilize replay memory of past tasks, specialized hardware, regularization monitoring etc. However, these methods are still less memory efficient in terms of architecture growth or input data costs. In this study, we present a simple yet effective adjustment network (SAN) for task incremental learning that achieves near state-of-the-art performance while using minimal architectural size without using memory instances compared to previous state-of-the-art approaches. We investigate this approach on both 3D point cloud object (ModelNet40) and 2D image (CIFAR10, CIFAR100, MiniImageNet, MNIST, PermutedMNIST, notMNIST, SVHN, and FashionMNIST) recognition tasks and establish a strong baseline result for a fair comparison with existing methods. On both 2D and 3D domains, we also observe that SAN is primarily unaffected by different task orders in a task-incremental setting.
Abstract: Keyboards are the primary devices for interaction with computer platforms and have been a central topic in HCI research for decades. The study of their layout design is important in deciding their efficiency, practicality and adoption. Multiple keyboard layouts have been developed for Bangla without any rigorous study for their comparison. In this paper, we take a quantitative data-driven approach to compare their efficiency. Our evaluation strategy is based on the key-pair stroke timing with data collected from standard QWERTY English keyboards. Our experiments conclude that the Bijoy keyboard layout is the most efficient design for Bangla among the four layouts studied. This quantitative approach can lay the groundwork for further study of these layouts based on other criteria.
Courses covered Linear Algebra for Machine Learning and Data Science, Calculus for Machine Learning and Data Science and Probability & Statistics for Machine Learning & Data Science.
Courses covered Linear Regression, Logistic Regression, Regularization, Gradient Descent, Artificial Neural Network, Decision Trees, Tree Ensembles, Xgboost, Recommender Systems, K-means Algorithm and Reinforcement Learning.
Courses covered Deep Learning, Optimization Techniques, Convolutional Neural Networks and Sequence Models.
Learned basic task automation with python, word, pdf and excel document parsing, desktop automation, web scrapping and email automation, regular expression etc.
Speaker verification system using deep learning model that can verify the identity of a speaker from raw waveform.
Coloring black and white sketches using generative adversarial network.
An e-commerce android application that uses augmented reality to preview a product before buying.
An autonomous line-follower robot that follows a line using an infrared sensor. Also capable of navigating through a cave in the absence of a line.
Planned chapter activities and conducted weekly meetings. Took basic robotics workshops as an instructor.
Planned events, wrote event proposals and event reports. Participated in all the workshops and seminars organized by the student branch.
Besides my professional and academic activities, I love to read books, watch TV shows and movies, travel to explore nature, and learn about diverse cultures of different nations throughout the world.