As a research intern at Georgia Tech, I conducted a deep dive into various task-scheduling algorithms, leading to the discovery of Particle Swarm Optimization (PSO)'s superior efficiency. I also applied my theoretical knowledge to real-world contexts by evaluating PSO's application across diverse operational scenarios, such as streaming and auctioning platforms. This experience enhanced my skills in Python, Java, JavaScript, SQL, AWS, Docker, and Kubernetes, as well as my passion for software development.Currently, I am pursuing a Master of Science in Computer Engineering at New York University, where I continue to explore the realms of machine learning and cloud computing. My previous projects, such as "Feast Finder," an AI dining chatbot, and "Evento," an integrated event planning platform, demonstrate my ability to develop innovative, efficient, and beneficial technology-driven solutions. I am driven by a commitment to leverage technology for societal progress, and I am keen to connect with individuals and organizations that envision a future powered by collaboration and innovation in technology.
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Software EngineerAmazonSan Francisco, Ca, Us
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Graduate Teaching AssistantNew York University Sep 2024 - PresentNew York, United States• Assisted in teaching Computing Systems Architecture course at NYU, providing support to students and grading assignments.• Conducted office hours to help students with course material and provided guidance on assignments.• Collaborated with professors to develop course materials and assessments to enhance student learning experience. -
Research InternGeorgia Institute Of Technology Jan 2023 - May 2023United States• Conducted in-depth analysis of task-scheduling algorithms, comparing traditional methods with Particle Swarm Optimization (PSO). Successfully demonstrated that PSO achieves a remarkable 30% performance improvement, making it a superior alternative for optimizing computational tasks.• Led comprehensive evaluations of real-world scenarios through simulations on CloudSim, showcasing PSO's robust task handling capabilities. This research highlighted PSO’s effectiveness for managing streaming and auctioning workloads, underscoring its applicability to high-demand platforms such as Netflix and eBay.• Monitored and tracked the scalability of PSO by analyzing performance metrics. Observed a significant 40% efficiency boost as task volumes increased, confirming PSO’s exceptional suitability for handling expansive systems and large-scale applications. -
Research InternBennett University May 2022 - Dec 2022Indiana, United States• Designed and implemented a high-efficiency hyper ensemble model by combining StackingCV, bagging, and feature boosting techniques for heart disease detection, significantly improving classification accuracy.• Conducted comprehensive evaluations using UCI and Cleveland datasets, achieving remarkable results with 93.44% accuracy for binary classification and 60.5% for multiclass classification, outperforming existing state-of-the-art models by 8.5%.• Co-authored and presented the paper "H2EMDCO: Design of a Hyper Ensemble Model for Evaluation of Heart Diseases from Clinical Observations" at the International Conference on Machine Learning and Data Engineering, earning peer recognition and enhancing the visibility of our research contributions. -
Software Development EngineerDefence Research And Development Organisation (Drdo) Dec 2021 - Mar 2022India• Developed and deployed an advanced Face Recognition Attendance System utilizing Histogram of Oriented Gradients (HOG) technology. Achieved a 98.7% accuracy rate, significantly reducing manual attendance tracking time by 15 hours per week.• Implemented face landmark algorithms and leveraged Python libraries to encode faces with up to 128 unique features, greatly enhancing the system’s ability to differentiate between distinct individuals.• Optimized the face encoding process by implementing efficient sorting techniques and improving nearest neighbor search algorithms. These enhancements reduced attendance processing time by 20%, thereby boosting overall system efficiency.
Aditya Ojha Education Details
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4.0/4.0
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9.14/10
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Top 5% Of The Cohort
Frequently Asked Questions about Aditya Ojha
What company does Aditya Ojha work for?
Aditya Ojha works for Amazon
What is Aditya Ojha's role at the current company?
Aditya Ojha's current role is Software Engineer.
What schools did Aditya Ojha attend?
Aditya Ojha attended New York University, Bennett University, The University Of British Columbia.
Who are Aditya Ojha's colleagues?
Aditya Ojha's colleagues are Kerisha Bailey, Francesca Cioffi, Matt Stone, Saja Sayed, Jarah Alhawamdeh, Chun-Yen Fu, Gaurav Mohanty.
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Aditya Ojha
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Aditya Ojha
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