Prasanth Sagar Kottakota

Experience | Projects | Skills | Achievements | Contact

Actively looking for full-time positions as a Data Scientist, Data Analyst, Software Engineer and Software quality assurance(QA) Engineer.

~ Email | Resume | Github | LinkedIn ~

I am Prasanth, currently pursuing an MS in Computer Science at Syracuse University with an expected graduation May 2024. I am actively seeking full-time and co-op opportunities in the fields of data science, software engineering and quality assurance. My academic journey began at NITK Surathkal, a prestigious technological institute in India, where I completed my Bachelor's degree in Computer Science and Engineering. After my undergraduate studies, I gained valuable experience by working at McAfee and Trellix Cybersecurity as a Software Engineer for two years. In these roles, I was involved in Data Analysis, software development, Quality Assurance and Automation.

Recognizing the importance of continuous learning and career advancement, I decided to pursue a Master's degree in Computer Science in the United States at Syracuse University, known for its excellent reputation. I have maintained an impressive academic record with a GPA of 3.93/4.00, consistently earning A grades. Additionally, I secured a Graduate Teaching assistantship at the university.

One of my significant achievements was the creation of an automation framework for McAfee's NSP device, which greatly streamlined software testing processes. This effort led to the discovery of a critical bug, earning me accolades from the head of engineering at McAfee. Furthermore, I worked on automating performance testing for the NSP device, effectively scaling network traffic testing. I achieved this by integrating Rest APIs from the IXIA Breaking Point system with NSP APIs and developing wrapper functions for test automation.

My passion for data science represents another dimension of my skill set, underscored by hands-on experience in various projects. Notably, I've delved into time-series data analysis for authentication purposes using machine learning algorithms. This involved projects during both my academic and internship tenures. At McAfee, I focused on developing algorithms to detect anomalies in network traffic, create data pipelines for data insignts from customer servers. Presently, I am engaged in advancing my expertise by delving into Language Learning Models (LLMs) and recommendation systems.

I am excited about the opportunity to contribute my skills and expertise to innovative projects in the fields of data science, software engineering and quality assurance. Thank you for considering my qualifications, and if you have relevant openings, I'd be eager to connect for a quick discussion.

Syracuse University, New York

Master of Science | Computer Science and Engineering
Specialization: Data Science
Aug '22 - May '24
GPA : 3.93 / 4.00
Relevant Coursework:
• [CIS735] - Machine Learning for Security
• [CIS675] - Design and Analysis of Algorithms
• [CIS662] - Machine Learning
• [CIS657] - Principles of Operating Systems
• [CIS655] - Computer Architecture
• [CIS628] - Cryptography
• [CIS623] - Structured Programming and Formal Methods
• [CIS600] - Social Media and Data Mining
• [CIS600] - Applied Natural Language Processing(NLP)
• [CIS563] - Data Science

National Institute of Technology Karnataka, Surathkal

Bachelor of Technology | Computer Science and Engineering
July '16 - July '20

Graduate Teaching & Research Assistant, Syracuse University
Syracuse, USA
August '23 - May '24

• Co-ordinating with Professor J.J Waclawski, Lectured a class of over 60 students, focusing on software implementations.
• Orchestrated the aggregation of multi-dimensional sensor data from cattle herds, employing 20+ accelerometers to capture granular insights into behavior dynamics.
• Engineered robust data pipelines leveraging AWS infrastructure and Apache Spark, employing Lambda architecture for real-time and batch processing of time-series data.
• Collaborated cross-functionally with domain experts and research teams to synthesize insights and drive innovations in precision agriculture and smart farming methodologies.
• Proficiently instructed unit testing in Java, Python, and C++ using JUnit, pytest, and Google Test frameworks. [code]
• Managed 14 group projects with project planning strategies and creating sprints.
• Designed efficient priority affinity scripts for optimizing process scheduling in Operating Systems.[code]

Software Engineer, McAfee
Bangalore, India
August '20 - July '22

Data Analysis and Automation
• Worked on automation framework using python, REST API to automate NSP release testings.
• Executed thorough Release testing, identifying 80+ high severity bugs across Regression, Performance, Upgrade,and Hetero Testing. Collaborated cross-functionally for prompt issue resolution.
• Created and executed over 50 complex SQL queries involving sub queries, stored procedures, triggers, views.
• Designed and implemented 20+ Airflow pipelines tailored to team requirements, encompassing both inbound and outbound data pipelines, trigger rules, sensors, and pipeline orchestration.
• Automated ETL processes, model training, & evaluation via scalable end-to-end data infrastructure pipelines in Azure Databricks & Azure Data Factory, leveraging Spark clusters, saving 120 labor hours monthly.
• Built pipelines for querying and processing over 10 attributes, including top attacks, anomalies, traffic types, and firewall health status, etc.., within the real-time dashboard.
• Improved real-time dashboard performance by optimizing querying pipelines and data processing for diverse attributes. Applied Python, SQL, Apache Kafka, Apache Spark, and visualization libraries, resulting in a 20% reduction in load time.
• Configured and managed complex test environments including 60+ client-server setups, 40+ McAfee IPS devices, and 5+ VMware ESXi, traffic simulator devices, ensuring seamless testing operations.
• Constructed a Random Forest model as part of advanced machine learning techniques to predict the severity of potential attacks, resulting in a 32% improvement in response action performance.

Machine Learning Research Intern, NITK Surathkal
Mangalore, India

ML Research Intern, January '19 - June '19
• Utilized the CMU DSL-StrongPasswordData.csv benchmark dataset, comprising keystroke dynamics data from 51 individuals, to develop a fraud detection system.
• Explored various ML algorithms (LSTM, Random Forest, Logistic Regression, KNN, SVM, etc.) on keystroke dynamics data. ARIMA outperformed others with 69.8% accuracy in fraud detection.

Software Programmer, January '18 - May '20
• Researched and analyzed 20+ Git repositories to pinpoint issues pertaining to battery drain during "doze mode," employing advanced search strategies and data analysis techniques to identify key trends.
• Created automated scripts using Python and Bash scripting languages to streamline data extraction from over 50+ Git repositories, reducing manual effort and increasing efficiency in issue identification.
• Applied web scraping methodologies to gather data from diverse online sources, enabling comprehensive analysis and informed decision-making regarding solutions to battery drain issues.
• Skills: Github, API, Python, AWS, Django, HTML, CSS, Javascript

	AI Based URL Detection Data mining, Python, URL's, Feature Engineering, NLP, ML [code] The project aims to detect URL's based on AI using a kaggle datatset. • The Algorithm was trained over 600000 URL's on various classification algorithms. • Over 22+ features were engineered and added into training model to achieve more meaningfull accuracy and detection. • The project was successfully integrated into django framework and hosted in cloud.
	Reddit-Based Sentiment Analysis of Israel-Palestine Conflict Data mining, Python, Sentiment Analysis, VADER, Time series illustrations, plots [code] The project aims to find the sentiments over israelpalestine conflict from reddit data. The key features and milestones in the project are the following, • Using Reddit API(PRAW) to extract data from reddit, the data consists of 50000+ comments, description, owner info, and all other meta data. • Performed data pre-processing and employed the VADER sentiment analyzer to categorize Reddit posts into 3 categories positive, negative, and neutral sentiments, enhancing understanding of sentiment trends • Analyzed Reddit sentiment trends, correlating with real-time events user spikes and disruptions. Identified over 10 observations including density, word counts etc to provide insights into public sentiment on the Israel-Palestine conflict.
	Priority and affinity in Operating System C Programming, Operating Systems, Process Scheduling, Priority and affinity [code] • Writing a cpu affinity code to demonstrate the priority and affinity setting of a process. . • The program takes an input file containing the process burst times, priorities and the core numbers which these processes are specified to execute. • This project enables setting specific cores to few processes which needed zero waiting time and real time executions not following the scheduling concept in Operating Systems.
	McAfee CyberInsight Pipeline Python, SQL, Apache airflow, Snowflake, K8’S • Utilizes Snowflake and Apache Airflow for efficient ETL processes, extracting feature information and monitoring firewall attacks from 40,000+ McAfee customers’ servers. • Deployed Kubernetes to host 20+ versions of McAfee software in containers. Integrated Apache Airflow with Apache File Sensor for extracting debug files from customer servers. Leveraged Kubernetes for seamless deployment and data extraction, driving product enhancement initiatives. • The project served as the customer software information retrival and helped NSP in decision making during software update releases.
	Wearable Device-Based Security: ML Authentication Python, ML Algorithms, Double KNN [code] • Conducted analysis on head and torso movement data collected from 34 individuals as time series data. • Evaluated multiple classification algorithms (KNN, SVM, decision trees, random forests) to identify the most accurate algorithm for classifying head and torso movements. • Implemented Double KNN algorithm and a hybrid combination of classification algorithms to leverage their strengths for improved accuracy and performance in classification tasks. • Evaluated various ML models, identifying a top-performing model with a 29% correlation, indicating authentication from two wearable devices is independent, enhancing security.

Machine Learning in Agricultural Crop management and Disease detection
R Programming, SQL, Rapid Miner, Machine Learning Algorithms

• The project aims to showcase the practical applications of machine learning within agricultural production systems.
• The project's focus encompasses various aspects of crop cultivation, such as yield prediction, disease detection, weed detection, crop quality assessment, and species recognition.
• Led analysis of 20-year crop data from Indian government, encompassing 500k+ records from various states. Demonstrated leadership by collaborating with a team of 3 to leverage RapidMiner and SQL for feature extraction and processing.
• The machine learning model(SVR Anova) deployed in the project demonstrated remarkable success by achieving an accuracy rate of approximately 84% in predicting crop yields, surpassing other models that could only achieve an accuracy of 75%.
• Multiple machine learning models were employed in the project, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Bayesian Models (BM), and clustering techniques, to address specific agricultural production challenges and improve overall performance.

	Automation of Performance testing for McAfee NSP using IXIA BPS Python, SQL, Git, Selenium, Ixia Breaking point, Rest API • Led 11-month project automating network security performance testing using Ixia BreakingPoint API’s and Python which Significantly reduced testing time from 6 to 2 hours per device and 2 weeks per release cycle • Led a team of 3, ensuring successful project integration with the existing automation framework. • Developed comprehensive results reporting through a user-friendly UI and private server. • Recognized as best TOP3 initiative in the network security team.
	Customer Database Backup Automation and Bug Detection Python, SQL, Git, Selenium, Rest API • Developed Python automation code to test customer DB backups during software releases at McAfee. • Integrated code into testing framework for comprehensive cross-software deployment and functional testing. • Improved code segment to verify software functionality post-version changes in backups, detecting critical bug,averting potential catastrophic collapse, and safeguarding $50 million in device value.
	Online Examination System PHP, MySQL, Apache HTTP, HTML/CSS, AWS [code] • Designed a web based platform using PHP ,making it dynamic and interactive for hosting online examinations with flexibility of various user roles. • The system is hosted on the XAMPP platform, utilizing an Apache server for web hosting and database management, ensuring a seamless online examination experience. • The system emphasizes data security and employs Role-Based Access Control (RBAC) to ensure authorized access for admin, teachers, and students. • It provides user-friendly panels for managing users and subjects, conducting exams, and includes a feedback system for user input, enhancing usability.
	Lossless Image Compression technique using Combination Methods Matlab, Algorithms, Encryption, Decryption • It provides a redundancy-based improvement to the present lossless data compression approach. LZW, a lossless data compression method, is combined with the BCH encoding process in the suggested approach. The new merging method reduced the size of an image file by 22 percent. • Improved Lempel–Ziv–Welch algorithm by combining it with well known Bose–Chaudhuri–Hocquenghem algorithm which corrects multiple bit errors. • Reference : https://www.scirp.org/journal/paperinformation.aspx?paperid=23911

Programming Python, C/C++, Haskell, SQL, Java, Bash, HTML/CSS, R	Developer Tools Git, Docker, Kubernetes, AWS, Jenkins, Maven, VS Code, VM Ware, PyCharm, IntelliJ, Eclipse
Data Analysis and ML Spark, Kafka, nltk, Pandas, NumPy, PySpark, Matplotlib, Scikit-learn, TensorFlow, Keras, PyTorch, RapidMiner, AWS S3, AWS Glue, AWS Athena, Databricks, Tableau, K8's	Testing Regression, Performance, Stability, Integration, Unit, Functional, Sanity, Penetration, Acceptance, Upgrade, End to End
Database Technologies and Web Development MySQL, RDS, PostgreSQL, MongoDB, Django.	Testing Framework Selenium, PyTest, JUnit
Protocols HTTP, TCP, IP, VLAN, FTP, SNMP, DNS, SSL, TLS, MPLS and SNMP	Performance Testing Ixia BreakingPoint, JMeter, Spirent TestCenter
Operating Systems Fedora, Ubuntu, Linux, Windows, MacOS, Nachos, Xinu.	Collaboration Tools Confluence, Jira, Github

Licenses and Certifications

• AWS Certified Solutions Architect - Associate

• Google Data Analytics Specialization

• Algorithmic Toolbox

• Data Structures and Algorithms

Issuing Organization

Amazon Web Services (AWS)

Coursera

Coursera

NPTEL -IIT Madras

Issue Date

March 2024

June 2022

June 2020

May 2018

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Medals, Awards and Rankings

• I have been awarded a graduate position at Syracuse University in recognition of my consistently high CGPA, showcasing my dedication to academic excellence.
• Received a prestigious gold medal🥇 at the International Mathematical Olympiad, a globally recognized competition that showcases exceptional mathematical talent and problem-solving skills.
• Secured the 4th position in the state-wide Little Champs Education Academy Exam.
• Achieved the 2nd🥈 position in the Ramanujan Talent Search Test in Mathematics at the county level.
• Recipient of a $2,000 Scholarship from the Government of India for achieving a Top 2000 rank in the IITJEE.
• Achieved the 49th State Rank in the Common Entrance Exam for Polytechnic.
• Received recognition and accolades for achieving an outstanding All India Rank of 1082 out of 2 million applicants in the highly competitive JEE 2016, conducted by the Government of India.

Recognition

• Earned 👏 high praise and commendation from Martin Stecher, distinguished McAfee Fellow and Head of Engineering, for delivering substantial and impactful contributions that propelled framework enhancements forward, resulting in a 25% increase in efficiency and a 30% reduction in system errors.
• Recognized as a distinguished individual, having achieved the rare distinction of being featured twice in McAfee's monthly spotlights for outstanding contributions to their products.