Aakansha Mathur
Teaching Assistant at Brown University
M.S. Computer Science at Brown University
I am currently a graduate student at Brown. I am pursuing M.Sc in Computer Science and graduating in 2022. My interests are in full-stack development and machine learning. I have worked on multiple projects that have strengthened my knowledge and experience in my areas of interest. I am passionate about working and leading projects and like to keep myself updated with the current developments in tech.  I have also published two papers in the area of machine learning. I am actively seeking full-time positions for 2022.
Brown University
https://www.linkedin.com/company/brown-university
Teaching Assistant for CSCI 1270: Database Systems
Providence, Rhode Island, United States
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Teaching Assistant
Brown University
https://www.linkedin.com/company/brown-university
Teaching Assistant for CSCI 1951A: Data Science
Providence, Rhode Island, United States
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Teaching Assistant
BITS Pilani, Dubai Campus
https://www.linkedin.com/company/the-birla-institute-of-technology-science-pilani-dubai-campus
Designed the frontend using HTML5, CSS3 and Javascript.
Regularly updated the UI of the website to enhance user experience. 
Designed dynamic and browser compatible pages.
Dubai, United Arab Emirates
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Professional Assistant: Front End Web Developer
Master of Science - MS
Computer Science
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Brown University
https://www.linkedin.com/school/brown-university/
Bachelor of Engineering - BE
Computer Science
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Birla Institute Of Technology and Science, Pilani Dubai
https://www.linkedin.com/school/bitspilanidubai/
Pioneered the first women in tech student chapter in BITS Pilani Dubai Campus. BPDC ACM-W advocates women in computing and provides various workshops, programs and services to help women enhance their skills and broaden their horizons.
Association for Computing Machinery-W
Vice Chair
Speech recognition is a broad topic that primarily involves sub-topics like language identification, speaker identification, speech emotion recognition, speech to text systems, text to speech systems, dialogue systems and much more. While, human beings are quickly able to recognize or identify a language because of the corpora of knowledge built over the years. However, it is a challenging task to have a machine identify a spoken language. So, to build a system that can correctly identify multiple languages irrespective of the dialect and speaker characteristics is an interesting area of research. One benefit of such a LID system is that the barrier between people caused due to language differences will be broken. Such a system will further the progress of globalization. The latest developments of machine learning in speech and language are described as a detailed state of the art in this paper.
A Study of Machine Learning Algorithms in Speech Recognition and Language Identification System
Springer
https://doi.org/10.1007/978-981-33-4543-0_54
Nowadays, users express their opinions about services via online reviews. Reviews on online platforms like TripAdvisor and Yelp assist tourists in decision making while simultaneously providing feedback to tourism authorities. This research aims to analyze Yelp reviews of restaurants in the USA by developing an automated system beneficial to both tourists and restaurant business owners. The automated system performs three sub-tasks: predicting star ratings from reviews, feedback model and knowledge based recommendation system. Results show that Random Forest and Decision Tree classifiers give the highest accuracy for predicting star ratings. Machine learning techniques like clustering and topic modeling are used to identify the topics from the reviews. Later, the salience and valence of the topics are obtained and are used to generate the feedback model. Lastly, a recommendation system is developed using user preferences, restaurant knowledge and valence of each topic of each restaurant.
Undergraduate Thesis: Analyzing Online Reviews of Tourism Businesses
Birla Institute of Technology and Science Pilani - Dubai
Abstract: With the increase in availability of paintings on the web, the importance of organizing art collections cannot be overstated. By classifying paintings based on art movements, information about paintings on the web can be well structured. This will also help us garner insights into more obscure paintings and the styles they embody. This paper discusses a method of classifying paintings into two art movements, namely Cubism and Romanticism, using two texture descriptors: Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The paper also extends the applicability of the method to different classifiers. Some classifiers used for classification are KNN, NuSVC, LinearSVC, GaussNB, Decision Tree, Random Forest, AdaBoost and Gradient Boost. A subset of the Pandora database is used as a dataset. From the results, it can be inferred that using the Gradient Boost classifier gives the highest overall accuracy for Cubism and Romanticism when LBP and HOG are used as texture descriptors. Moreover, it can be seen that LBP emerges as the best feature for classifying paintings into the Cubism and Romanticism Art Movements.
Classifying Paintings into Movements using HOG and LBP Features
ICBDR 2019: Proceedings of the 2019 3rd International Conference on Big Data Research
https://dl.acm.org/doi/abs/10.1145/3372454.3372483
Creating Modern Web and Mobile Applications
CSCI 1320
Data Science
CSCI 1951A
Database Systems
CSCI 1270
Deep Learning
CSCI 1470
Created an intelligent model that would predict acceptance rate of a student into a university using quantitative information.
Acceptance rate prediction
Programmed a web chatroom application that would connect users with similar interests.
Developed features to allow users to create a chatroom, send messages in a chatroom and communicate with other users.
ChatUp
Created a mobile application that assisted user with inventory management of perishable goods.  
Developed features such as user authentication, scan product name and expiration date (text recognition), storing product information and customizing notification settings.
Fridgey
Built a Deep Learning model that predicts a caption given an input image. Applied LSTM, Attention Mechanism and CNNs to build the model. Evaluated performance of the model using a BLEU score.
Image Captioning
https://www.linkedin.com/in/tarunrajnish
Providence, RI
Tarun Rajnish
Actively Seeking Internship Opportunities for Summer '22 | Graduate Student at Brown University | MS in Computer Science
https://www.linkedin.com/in/sayanchak
Providence, RI
Sayan Chakraborty
Computer Science - Brown University | Software Engineer
https://www.linkedin.com/in/carrie-ruoyu-deng-16b588122
Providence, RI
Carrie (Ruoyu) Deng
Incoming Analyst @ QES, LLC. History of Art and Architecture & Economics @ Brown University
https://www.linkedin.com/in/virak-pond-tor
Providence, RI
Virak Pond-Tor
Systems Programmer @ Brown University
https://www.linkedin.com/in/nadabenabla
Providence, RI
Nada Benabla
Math-CS @ Brown University
https://www.linkedin.com/in/adityaparameswaran
Providence, RI
Aditya P
MS Computer Science @ Brown University
https://www.linkedin.com/in/neha-chaudhari
Providence, RI
Neha Chaudhari
Brown University 2020, Computer Science
https://www.linkedin.com/in/shipra-priyadarshini
New York City Metropolitan Area
Shipra Priyadarshini
Computer Science Student at Brown University
https://www.linkedin.com/in/monica-roy-1471661ab
Cambridge, MA
Monica Roy
Computer Science Student at Brown University
https://www.linkedin.com/in/yuming-fu-7b054114b
Irvine, CA
Yuming Fu
Incoming Software Engineer @Google Ads NYC | Master of Computer Science @Brown University | Ex Software Engineer Intern @Google | UCI Alumni
https://www.linkedin.com/in/semanti-basu-969220111
Providence, RI
Semanti Basu
CS PhD Graduate Student at Brown University
https://www.linkedin.com/in/amritsinghrana
Providence, RI
Amrit Singh Rana
CS Grad @ Brown University | Seeking Summer '22 Internships
https://www.linkedin.com/in/ashley-heewon-chung-9b95a3187
Providence, RI
Ashley Heewon Chung
Head Teaching Assistant at Brown University
https://www.linkedin.com/in/linghai-liu-679a12191
Providence, RI
Linghai Liu
Undergraduate Research Assistant
https://www.linkedin.com/in/hiloni-mehta
Providence, RI
Hiloni Mehta
Computer Science @ Brown University | Software Developer Intern at BNY Mellon
https://www.linkedin.com/in/rutvik-s
Providence, RI
Rutvik S.
Sc.M Computer Science student at Brown University
https://www.linkedin.com/in/pragadeesh-c-79364082
Providence, RI
Pragadeesh C
ScM in Computer Science, Brown University
https://www.linkedin.com/in/sohum-gupta
Aurora, IL
Sohum Gupta
Computer Science at Brown University
https://www.linkedin.com/in/floriatsui
Ann Arbor, MI
Floria T.
CS @ Brown
https://www.linkedin.com/in/mithun-ravisankar-41365a124
Providence, RI
Mithun Ravisankar
Ph.D. Student at Brown University
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Just completed the Feature Engineering course on Kaggle! :)#featureengineering #kaggle
Liked by Aakansha Mathur
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Meet Gracia! Back in September, Gracia joined our team and today she continues to work with Questrade as a Junior Data Scientist Intern. Here is…
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Nobody ever told me how hard it would be to get a job that fits me. And I’m acknowledging that I am a student at one of the most privileged…
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Jaipur
aakansha mathur
Student at Apex Institute of Management & Science
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India
Aakansha Mathur
Student at DMS, jnvu
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India
Aakansha Mathur
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West Delhi
Aakansha Mathur
Senior Analyst at NatWest Group (formerly known as Royal Bank of Scotland)
Microsoft University Recruiting Team
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