Quantitative Software Engineer
Goldman Sachs
Dallas, TX, United States
May 2022 - Present
- Asset & Wealth Management Division
I am a Quantitative Software Engineer currently working with Goldman Sachs in the Asset Management Division based in Dallas, Texas. I recently completed a Master's in Computer Science from The University of Texas at Dallas, where I specialized in Machine & Deep Learning. During my studies, I worked part-time with ArtSciLab as a Data Analyst and Web Developer, which involved managing web infrastructure and conducting research in the domain of Natural Language Processing.
Prior to my Bachelor's in Computer Science from Indian Institute of Information Technology in 2020, I had worked in the domains of Data Science, Machine Learning, and Full Stack Web Development in multiple companies and on various side-projects & hackathons. I have also been actively researching in the fields of Computer Vision and Natural Language Processing, and have published a few papers in the same. I was also a Google Explore ML Facilitator, training students on campus about Machine Learning.
In my free time, you can find me involved in various side projects, learning new skills, attending hackathons, and reading!
Goldman Sachs
Dallas, TX, United States
May 2022 - Present
ArtSciLab
Richardson, TX, United States
Jun 2021 - May 2022
ArtSciLab
Richardson, TX, United States
Jun 2021 - May 2022
The University of Texas at Dallas
Richardson, TX, United States
Feb 2021 - Aug 2021
Affine
Bengaluru, India
Jan 2020 - Jul 2020
Indian Institute of Technology - BHU
Varanasi, India
May 2019 - Jul 2019
The Bomway
Mumbai, India
May 2018 - Jul 2018
Machine & Deep Learning
Convolutional & Recurrent Networks
Natural Language Processing
Data Analysis
Data Visualization
Data Pipelines and Engineering
Computer Vision
Full Stack Application Development
Python, JavaScript, TypeScript, Kotlin, HTML, CSS, C
Djnago, Flask, Node, Express, React, Next.js, Spring, WordPress
Scikit-learn, TensorFlow, Keras, PyTorch, OpenCV, NLTK, IBM Watson Studio
Tableau, Excel, Pandas, Matplotlib, IBM Cognos
SQL, Oracle, MongoDB, Snowflake
ArtSciLab
Apr 2022
Social media has become an important part of digital web life, the effect it has on financial decisions is also increasing. The social media conversations on websites like Twitter, Reddit and Facebook are having an ever-increasing effect on stock prices as well as the way in which companies make decisions. This has made it important to analyze this data to make accurate predictions of stock prices in the future. In this paper, we try to analyze financial discussions among users of Reddit by extracting hidden patterns, themes and user characters to predict future actions and consequences on the market.
ICACNI 2019 - Springer, Singapore
Nov 2020
Sarcasm detection in media text is a binary classification task where text can be either written straightly or sarcastically (with irony) where the intended meaning is the opposite of what is seemingly expressed. Performing sarcasm detection can be very useful in improving the performance of sentimental analysis, where existing models fail to identify sarcasm at all. We examine the use of deep neural networks in this paper to detect the sarcasm in social media text(specifically Twitter data) as well as news headlines and compare the results. Results show that deep neural networks with the inclusion of word embeddings, bidirectional LSTM’s and convolutional networks achieve better accuracy of around 88 percent for sarcasm detection.
ICACNI 2019 - Springer, Singapore
Nov 2020
Newsreaders still look forward to well-established news sources for a more accurate and detailed analysis of the news or articles. In this paper, we present a model that can retrieve other news articles that provide essential context and background information related to the news article that is being looked into. The abstract topics have been recognized using Latent Dirichlet Allocation. A similarity matrix has been created after the extraction of topics using two methods, cosine similarity and Jensen–Shannon divergence. This paper describes the implementation and gives a brief description of both the similarity methods. It also gives a comparative study of both methods.
The University of Texas at Dallas
3.758/4.0
Aug 2020 - May 2022
Indian Institute of Information Technology Kalyani
9.17/10.0
Aug 2016 - May 2020
Jun 2021 - Apr 2022
Social media has become an important part of digital web life, the effect it has on financial decisions is also increasing. The social media conversations on websites like Twitter, Reddit and Facebook are having an ever-increasing effect on stock prices as well as the way in which companies make decisions. This has made it important to analyze this data to make accurate predictions of stock prices in the future. In this paper, we try to analyze financial discussions among users of Reddit by extracting hidden patterns, themes and user characters to predict future actions and consequences on the market.
We explore techniques based on natural language processing to pass conversations through data pipeline along with extracting stock tickers, manual as well as automatic theme extraction and word clouds. We also discuss common text processing techniques which can also be applied to other problems involving text analysis along with correlation model focusing on sentiment analysis as a predictor of stock movement.
Jun 2021 - Apr 2022
The pandemic moved us to virtual world and video meetings changed the way to interact with each other. The O.A.S.E.S (Our Art Science Experimental Seminars), previously called Watering Hole's conducted by ArtSciLab also moved to hybrid format, and it was a great opportunity to analyze the meetings using recordings from a data perspective. This white paper summarizes the step-by-step procedure to visualize these seminars, right from collecting data to creating visualizations.
We explore techniques based on natural language processing to pass conversations through a data pipeline, along with creating word cloud visualizations and analyzing sentiment of the text. We also discuss the ways to extend this idea to create time-varying word clouds and video recordings of the meetings.
Nov 2021 - Nov 2021
As a part of HackUTD sponsored challenge by EOG Resources, problem involved fetching total water, dividing into multiple operations and increment revenue as much as possible.
EOG wells are constantly producing water and we worked on finding ways to reduce expenditures, as well as reuse and recycle water whenever possible. The challenge was to develop an application which can process a steady stream of real-time sensor data to both optimize and visualize the distribution of water for company's upstream operations.
Feb 2021 - Feb 2021
It's not easy to find the perfect room and roommates when you go 1000 miles away from your home for international study. Roomies is a platform to help you automatically find roommates according to habits and preferences in an easy to use tinder-like style!
Developed as a part of HackUTD 2021 submission, the user is asked questions about gender, Age group, Country, University, Starting semester, Course, Food preferences, Smoking and Drinking habits, Cooking experience and expectations is similar areas from their roommates. Then a matching algorithm is used to calculate scores and top results are recommended to the user.
Jul 2019 - May 2020
Person Re-identification (Re-ID) is a fundamental subject in the field of computer vision technologies due to its application and research significance. The purpose of this task is to spot an individual in other cameras. This work explores conventional Re-ID approaches and then focuses on the latest developments, including methods and techniques using deep learning. In this work, we propose an efficient approach in which we first eliminate dissimilar images using k-mediods clustering. A silhouette part-based feature extraction scheme is adopted in each level of hierarchy to preserve the relative locations of the different body structures and make the appearance descriptors more discriminating in nature.
Jan 2019 - Dec 2019
Developed Cosine similarity and Jensen–Shannon divergence based news recommendation approach on 'TREC News Dataset' using background information linking, mentored by Prof. Tirthankar Dasgupta (TCS).
Newsreaders still look forward to well-established news sources for a more accurate and detailed analysis of the news or articles. In this paper, we present a model that can retrieve other news articles that provide essential context and background information related to the news article that is being looked into. The abstract topics have been recognized using Latent Dirichlet Allocation. A similarity matrix has been created after the extraction of topics using two methods, cosine similarity and Jensen–Shannon divergence. This paper describes the implementation and gives a brief description of both the similarity methods. It also gives a comparative study of both methods.
Jul 2018 - May 2019
Sarcasm detection in media text is a binary classification task where text can be either written straightly or sarcastically (with irony) where the intended meaning is the opposite of what is seemingly expressed. Performing sarcasm detection can be very useful in improving the performance of sentimental analysis where existing models fail to identify sarcasm at all. We examine the use of deep neural networks in this paper to detect the sarcasm in social media text(specifically Twitter data) as well as news headlines and compare the results. Results show that deep neural networks with the inclusion of word embeddings, bidirectional LSTM’s and convolutional networks achieve better accuracy of around 88 percent for sarcasm detection.
Jan 2019 - Mar 2019
Finalist at Smart India Hackathon - 2019
Medical Datasets: BraTS 2018 & MrBrains18
Models: Modified VGG16 & Modified UNetCNN
Interface: GUI using Python PyQt with the ability to visualize the results along with accuracy in real-time & the website made using Django that works the same as GUI but with easy and better visualization
Results: Brain Image Segmentation in the 4D dataset with average dice score of 0.84 and Tumor classification with average dice score of 0.96
Jan 2019 - Jan 2019
Designed as part of iHack 2019, IIT Bombay, it's a Fast-RCNN and YOLOv3 based object detection model for RGBD images. The model is able to detect occlusions in the RGBD images and also detect objects in the images and videos.
This solution helps in solving problems like better representation of crowded images in real-life situations like crowd controlling and traffic control, use of depth information for simultaneous image detection in noisy images and better side view prediction probability.
Jan 2019 - Jan 2019
Workflow: 1. When anyone realizes that their children or loved ones are lost in the crowd, he/ she will upload a single photo of who lost along with contact information of self. 2. The model will store the image into the database with proper encoding. 3. Whenever any other person will find that child or a person, he will just click a photo in the app and most probably 3 matches of the child will be shown along with contact numbers.
USP: 1. Easy to use interface with simple 2-step user actions 2. User just needs to upload one image of the child for face recognition instead of few hundreds required by other deep learning based approaches 3. Great accuracy of the model to identify the child or a person correctly.
Mar 2018 - Apr 2018
Developed as a part of Hack In The North 2019 submission, RealU is a deep learning based solution that helps you detect fake media and stay away from them. Along with news text, it also analyses the images related to news using Google's cloud APIs to give user the best possible prediction about news being fake or tampered.
Jan 2017 - Nov 2017
This is an image-processing based attendance system, coded entirely in Python.
The project is divided into 3 parts: 1. Registration: We collect data and images of the students. 2. Attendance Marking: We record a video, while the class is in session. Then, we extract some frames from the video and extract faces from it. Finally, we match the extracted faces to those registered and mark the attendance. 3. Displaying Attendance: Basic DBMS queries and displaying the result in a properly formatted way.
We used PyQt for GUI, OpenCV & face_recognize modules for detecting and matching faces.
+1 469 649 3928
omkarajnadkar@gmail.com
Dallas, TX, USA