First Name Last Name Address +12-3456789 | username@gmail.com | username | username | DOB dd/mm/yy Skills Languages Python, C++, MATLAB, SQL, HTML Tools PyTorch, TensorFlow, Scikit‑learn, Pandas, NumPy, Keras, PySpark, OpenCV, Tensorboard, MLflow, Matplotlib, Jupyter Notebook, Git, Selenium, TensorRT, Jira Areas Computer Vision, Network Pruning, Deep Learning, Natural Language Processing, Web Scraping OS Linux (Ubuntu, Pop!_OS), Windows Experience Company location COMPUTER ViSiON EXPERT (PART TiME) Jun. 2022 ‑ Present • Implemented the paper “The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks” on various Computer Vision models and reduced the memory footprint and inference time to obtain low‑cost and low‑power models without compromising the accuracy of the models. Company location COMPUTER ViSiON INTERN Feb. 2022 ‑ May 2022 • Improved the accuracy of existing billboard change detection models at company by 12%. Techniques include Deep Metric Learning, Siamese networks, Convolutional Neural Networks, Triplet loss functions and PyTorch Metric Learning library University Name Location TEACHiNG ASSiSTANT March 2022 ‑ Feb. 2023 • Data Science course. Topics: Data Mining, Information Extraction & Natural Language Processing, Computer Vision & Image Classification, Semi‑ structured Data, Time Series • Machine Learning • Foundations of Information Retrieval Company Location TECHNiCAL INTERN July 2016 ‑ Sep. 2016 • Learned about the usage of <technology> and the <technology> used for power reduction. Also implemented <command> command in <tech‑ nology>, one of <compnay>’s flagship models. Company Location SUMMER INTERN May 2015 ‑ July 2015 • Developed a system which compares the performance of a company with its competitors in the same sector by comparing their stock values and shows the comparison graphically. Projects Project Name (2022) University Name • Investigated the effect of <technology> in human activity classification of <data> using various Transformer models. Proposed an architecture which is 20 times less complex, with more interpretability and explainability. • Tools : PyTorch, Scikit‑learn, Tensorboard, MLflow, Transformers, Convolutional Neural Networks Project Name (2022) University Name • Improving the performance of a Variational Autoencoder model which generates music from sample inputs. • Tools : PyTorch, Pandas, PyPianoRoll, NumPy Project Name (2021) University Name • Developed a model which predicts the genre of a song based on its lyrics using features like TF‑IDF, word2vec, etc.. • Tools : Python, Scikit‑learn, Scipy, Word2Vec, TF‑IDF Project Name (2021) University Name • Developed an image retrieval system which takes a query image and finds other similar images from a given set of images. • Tools : Python, Scikit‑learn, OpenCV, Scipy, K‑means clustering Project Name (2022) University Name • Analyzed nearly 40000 notebooks from a public dataset to check if there is any relation between the number of errors in a Python notebook and the degree of PEP8 violations. • Tools : Python, PySpark, HDFS Project Name (2020) University Name • Developed a model which predicts hand gestures based on features extracted from sEMG signals. • Tools : Python, Scikit‑learn, XGBoost, SVM, KNN Project Name (2020) Miscellaneous • Developed a script for notifying users by SMS when a slot is available for taking COVID‑19 vaccine shots in their preferred area. • Tools : Python, Co‑Win API, Twilio API Project Name (2016) University Name • Developed a model that will analyze the sentiments of user generated tweets and categorizes them into positive, negative or neutral. • Tools : Python, LibSVM, Twitter Search API Education MS in Computer Science (Data Science & Technology); CGPA: x/10 Location UNiVERSiTY NAME 2020‑2023 • Relevant courses: Machine Learning, Foundations of Information Retrieval, Natural Language Processing, Image Processing & Computer Vision, Advanced Machine Learning, Managing Big Data, Data Science M. Tech in Computational Mathematics; CGPA: xx/10 Location UNiVERSiTY NAME 2017‑2019 • Relevant courses: Computational Linear Algebra, Number Theory & Cryptography B. Tech in Computer Science & Engineering; CGPA: xx/10 Location UNiVERSiTY NAME 2012‑2016 • Relevant courses: Data Structures & Algorithms, Database Management Systems, Operating System, Computer Networks Conference Papers paper name xxxxx conference name , location paper name xxxxx conference name , location paper name xxxxx conference name , location Achievements • Completed an online course on Machine Learning by Andrew Ng from Stanford University • Won the “Best Paper Award” at an international conference <name> at <location> for the paper <paper_name>. Positions of Responsibility • Core committee member of <Technical_Event>. • Company Ambassador of College (2014‑15). • Headed <Technical_Event> sub teams.