• Hi!
    I'm Chakradhar

    Web Developer and AI Engineer focused on building innovative web and AI solutions

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About Us

Who Am I?

Hi I'm P. Chakradhar Reddy, I am an undergraduate student at SRM University, pursuing a degree in Computer Science and Engineering with a specialization in Cloud Computing, maintaining a CGPA of 9.65. Passionate about Artificial Intelligence, I have published research papers in the field and interned as an AI Engineer at Hydromind. Under the mentorship of Pramit Saha, MASc, a DPhil (PhD) candidate at The Oxford Biomedical Image Analysis (BioMedIA) cluster, University of Oxford, I worked on a project focusing on parameter-efficient finetuning of large language models for text generation, which honed my skills in NLP and model optimization. Additionally, I serve as Treasurer for the iCAN Chennai chapter and was awarded the 2024 Summit Scholarship by the International Children’s Advisory Network.

Web Design

Software

Application

My Specialty

My Skills

I possess a strong command of programming languages such as Python, JavaScript, and C++, alongside full stack development skills with React, Node.js, and Flutter. My expertise extends into AI and machine learning, particularly in neural networks, NLP, and computer vision. I’m also skilled in data analytics, with certifications in Google Data Analytics, and have experience in cloud computing (AWS, Azure) and database management with MongoDB and SQL.

Web development

85%

AI & Machine Learning

80%

Database Management Systems

75%

Cloud Computing

90%

Data Analytics

75%

Object Oriented Programming

70%
My Journey

Education

I am currently pursuing a Bachelor of Technology (BTech) at SRM University, specializing in AI and full stack development, with a CGPA of 9.64(Till 6th Sem). This program has equipped me with a strong foundation in both software engineering and data science.

  • Awarded a performance-based scholarship of 50,000 INR for exceptional academic achievement during my 4th year, reflecting my commitment to excellence in my studies.
  • Published several research papers in AI and cloud computing.

I completed my Intermediate education at Raju Junior College with a focus on Math, Physics, and Chemistry (MPC), achieving a total of 855 marks. This academic milestone helped sharpen my analytical thinking and laid a strong foundation for my future studies in engineering.

  • Excelled in subjects that formed the basis of my technical expertise in AI and software development.
  • Built a strong academic record that set the stage for advanced learning in technology and innovation.

I completed my 10th class education at Red Cherries School, achieving an impressive total of 9.7 CGPA. This foundational education played a vital role in developing my core skills and knowledge in various subjects.

Experience

Work Experience

AI Engineer 2024 - Present

I am currently involved in the development of Brolance.com (formerly Makeitjob.com), where my team and I have completely restructured the architecture and added numerous innovative features. We are on the verge of deploying this new system, designed to function as an all-in-house product, integrating components like ATS algorithms, ST algorithms, and much more. This project reflects our commitment to delivering a comprehensive and efficient solution tailored to user needs.

Project Program on Artificial Intelligence and Machine Learning under Pramit Saha @ Oxford University Mar 2024 - May 2024

Under the mentorship of Pramit Saha, I participated in a research project on parameter-efficient finetuning of large language models (LLMs) for text generation. We focused on adapting pre-trained LLMs, like GPT, to specific tasks with minimal computational costs using techniques such as low-rank adaptation (LoRA), adapter layers, and prompt tuning. Our experiments on benchmark datasets demonstrated significant improvements in text generation quality and efficiency, emphasizing a balance between performance and resource utilization while enhancing my expertise in advanced NLP techniques.

Treasurer – iCAN Chapter, Chennai 2024

As Treasurer for the iCAN Chapter in Chennai, I manage the financial operations, including budgeting, fundraising, and financial planning for events and initiatives. My role involves ensuring proper allocation of resources and maintaining transparent financial records, supporting the chapter's goal of empowering young voices in health and research. Additionally, I was honored with the 2024 Summit Scholarship by iCAN in recognition of my contributions, leadership, and commitment to the network's mission of advancing children's advocacy on a global scale.

Project Program on Competitive Intelligence and Startup Competitivenes under Atul Banerjee @ London Business School Mar 2024 - May 2024

I attended an intensive cohort project program on Competitive Intelligence for Startups, led by Atul Banerjee, where I gained expertise in market analysis, competitor strategy, and business positioning. Through hands-on workshops and scenario analysis, I developed skills in assessing market opportunities, analyzing competitor strengths and weaknesses, and formulating go-to-market strategies. This program provided practical experience in leveraging competitive intelligence to enhance startup competitiveness.

Research Works

Efficient CAPTCHA Image Recognition Using Convolutional Neural Networks and Long Short-Term Memory Networks

International Journal of Scientific Research in Engineering and Management (IJSREM)

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This research paper presents a novel approach to CAPTCHA image recognition using a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs). CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems are widely employed to differentiate between human users and bots in online applications, ensuring security and preventing automated attacks. The purpose of this study is to develop an efficient and accurate CAPTCHA recognition system capable of handling complex and distorted CAPTCHA images commonly used on websites. The methodology involves preprocessing CAPTCHA images, including cropping and resizing, followed by feature extraction using CNNs to capture spatial patterns and structures. Achieved the Top Maximum Accuracy of 99.54% and made it the best model available on the field

Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization

International Journal of Machine Learning and Cybernetics (IJMLC)

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This research paper dives into "Neural Sequence-to-Sequence Modeling with Attention," exploring how deep learning architectures can enhance abstract text summarization. Through advanced techniques, we aim to improve contextual understanding for more accurate summaries. Join us as we unravel the complexities of neural networks and their impact on summarization tasks!

Real-time Underwater Garbage Detection with YOLO based Object Detection and Image Segmentation Models

Journal of Emerging Technologies and Innovative Research (JETIR)

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In this research, we tackle the urgent issue of real-time underwater garbage detection using state-of-the-art technologies. By integrating YOLO-based Object Detection and Image Segmentation Models, we've developed a robust system that can accurately identify and classify various types of underwater debris, even in challenging underwater environments.

Empowering Information Retrieval: A Framework for Effective Data Summarization Using NLP and SBERT

International Research Journal of Modernization in Engineering Technology and Science (IRJMETS)

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This project tackles the complexities of data collection and synthesis, aiming to create a robust framework for summarizing the vast knowledge available on the Internet. In today's information-heavy environment, quickly finding relevant details is a major challenge.Our framework addresses this by combining morphological content and semantic information to filter and distill important data from various online sources. A key feature is "summary summarization," which condenses essential insights from large datasets into concise formats while retaining the original meaning.

Evaluation Methodologies and Performance Metrics in Supervised Named Entity Recognition (NER) for Journal Articles: A Critical Analysis

International Research Journal of Modernization in Engineering Technology and Science (IRJMETS)

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This paper delves into the critical field of Named Entity Recognition (NER) in natural language processing, specifically focusing on journal articles. We provide an in-depth analysis of supervised learning approaches for NER, highlighting key evaluation metrics like precision, recall, and F1-score at both token and entity levels.

Hypergraph Fusion and Pseudo-Relevance Feedback for Enhanced Tag-Based Image Retrieval

International Research Journal of Modernization in Engineering Technology and Science (IRJMETS)

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We present an innovative method for tag-based image retrieval (TBIR) on platforms like Flickr. Our approach combines global and local visual features using a hypergraph-based technique. Here's a quick rundown:
Hypergraph Creation: Integrates visual features and tag data.
Pseudo-Relevance Feedback: Identifies likely relevant images.
Hypergraph Learning Algorithm: Calculates relevance scores for images.
Our findings show this method greatly improves image search efficiency. Dive into our publication to learn more!

Contrast-Induced Nephropathy in the Context of Interventional Cardiology

Journal of Software Engineering (JSE)

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This study explores Contrast-Induced Nephropathy (CIN), a serious complication following invasive cardiac procedures linked to increased morbidity and mortality. We delve into the multifactorial pathophysiology of CIN and highlight key risk factors such as chronic renal insufficiency, diabetes, and advanced age. While hydration remains a cornerstone in CIN prevention, our research also examines emerging strategies like controlled hydration and diuresis. We discuss the mixed results on the use of prophylactic N-acetylcysteine and caution against ineffective or harmful agents.

Enhanced Insights into Temporal and Seasonal Fluctuations of Arsenic in Groundwater: Incorporating Hydrochemical Analysis for Sustainable Management of Pollution

International Journal of Healthcare Information Systems and Informatics (IJHISI)

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This study addresses the pressing issue of arsenic contamination in groundwater, a significant global threat to human health and ecosystems. We explore the hydrochemistry and contamination processes in the Ganga Brahmaputra river basin, focusing on seasonal and temporal variations in arsenic levels. Our findings reveal how rainfall and groundwater dynamics influence arsenic concentrations, with advanced hydrochemical techniques shedding new light on this critical environmental challenge.

Artificial Intelligence and Machine Learning: The Influence of Machine Learning on Predictive Analytics in Healthcare

International Journal of Machine Learning and Cybernetics (IJMLC)

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This research on the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare. Our paper delves into how predictive analytics, powered by ML algorithms, is revolutionizing patient outcomes, reducing costs, and optimizing resource management. Discover the applications, benefits, challenges, and future prospects of this cutting-edge technology in healthcare.

Get in Touch

Contact

pchakradharreddy2004@gmail.com

SRM University, Chennai, India

+91 8897613458