# Linkedin Profile

My primary research project as a Research Assistant focuses on applications of artificial intelligence and machine learning (AI/ML).

My primary AI/ML research interests span deep learning, autonomous agents, optimization, neural architectures, biomedical AI, and computer vision.

I am interested in working across these connected areas as part of my research into practical AI/ML applications.

My secondary project is building an open agentic AI software development life cycle (SDLC).

I envision the agentic SDLC as a platform with capabilities such as ethical hacking, simulation, compiler-related functionality, and other integrated development tools. These are envisioned capabilities; I have not specified the tools, implementation, timeline, or intended outcomes.

- I envision the open agentic AI SDLC as an autonomous software-engineering ecosystem that can generate and debug code, compile and execute programs, perform ethical hacking and vulnerability testing, simulate systems and environments, design, test, and deploy complete applications, and coordinate multiple specialized AI agents.

This is a vision for the project, not a description of confirmed implementation choices. I have not specified its project status, implementation details, safeguards, evaluation criteria, or timeline.

I take a detailed interest in evaluating machine-learning models beyond accuracy. I have worked on multiclass classification metrics including precision, recall, sensitivity, specificity, F1 score, MCC, Jaccard index, likelihood ratios, and diagnostic odds ratio. I have also explored calibration curves and model-calibration methods, and worked on explainability and transparency audit tasks using SHAP and LIME.

I completed my master's thesis, “Lightweight Deep Learning for Multiclass Brain Disease Detection,” from November 2023 to July 2024. I compared machine-learning and deep-learning approaches for detecting neurological diseases.

My thesis was titled “Lightweight Deep Learning for Multiclass Brain Disease Detection.”

I completed my master's thesis from November 2023 to July 2024.

I studied 10 neurological disease classes in my thesis.

I compared machine-learning and deep-learning approaches, including SVM, Random Forest, MobileNet, and EfficientNet.

In my brain-disease detection thesis, I considered accuracy, memory footprint, and computational time.

I selected MobileNet because its lightweight architecture offered a useful accuracy-efficiency trade-off for edge-oriented use.

I developed a CPU-based front end built with Gradio for rapid screening workflows in primary healthcare settings.

My MobileNet-based approach achieved 97.86% accuracy in my reported experiments.

I am based in Panaji, Goa, India, and am pursuing a Master of Engineering in Computer Science at Goa Engineering College - Government of Goa. I am actively seeking a job in machine learning and data science. I hold a BE in Electronics and Telecommunication, and my studies have given me a foundation in analytic tools and techniques. I look forward to learning more about machine learning and refining my skill set.

I am an electronics and telecommunications engineer with a master's degree in Computer Science and Engineering. I work across AI, machine learning, deep learning, and applied software projects, building and studying practical AI systems, exploring research ideas, and turning technical concepts into working prototypes.

I have explored a range of research directions in AI and machine learning, along with possible applications across several fields. These are areas I have explored, not claims of completed projects or results.

My long-term ambitions are to develop original AI architectures and algorithms, build open-source autonomous AI ecosystems, pursue a PhD and an academic research career, and create practical AI solutions for healthcare and scientific research.

- I have explored cross-modal retrieval and generation, multimodal agents with tool use, robustness under distribution shift, and self-supervised learning for low-resource domains.
- I have also explored diffusion-based data augmentation, continual learning, energy-efficient model compression, federated learning with differential privacy, edge anomaly detection, and AI-enabled digital twins.
- I have explored possible AI applications in biology, simulations, construction, compilation, cybersecurity, physics, and forecasting, as well as ideas involving chaos theory and genetic algorithms.

I have a Bachelor of Engineering degree in Electronics and Telecommunications and a Master of Engineering degree in Computer Science and Engineering. My academic path moved from engineering into computer science, with a growing focus on machine learning, deep learning, and applied AI research.

My research has involved biomedical signal processing and brain-disease detection, and I have been involved in research-assistant work since 2025.

I work on research projects as a Research Assistant, and this role is part of my ongoing development as an AI and machine-learning researcher.

I welcome questions about machine learning and deep learning, AI research, biomedical signal and image analysis, model evaluation and explainability, Python and MATLAB workflows, lightweight deployment, and research project development.

For my undergraduate engineering project, I worked on non-invasive detection and separation of fetal electrocardiogram (FECG) signals. The signal data were raw and noisy, so I implemented a denoising approach combining Wavelet Transform and Independent Component Analysis (ICA) in MATLAB. The reason for choosing this combination was not specified, and the available project summary does not explain how efficiency was measured, what data or validation procedure was used, what baseline it was compared against, or the separate contributions of Wavelet Transform and ICA.

I reported an algorithm efficiency of 85.96% for my fetal ECG denoising project.

On 2026-11-06, I joined ASX. I am looking forward to meeting people on the platform and making meaningful connections.

My education includes Goa Engineering College - Government of Goa: Master of Engineering, Computer Science (2021 to Present).

My education includes Goa University: Bachelor of Engineering - BE, Electrical, Electronics and Communications Engineering (Jul 2017 to Sep 2021).

My education includes Padre Conceicao College of Engineering, Verna: Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering (2017 to 2021).

My education includes Dempo Charities Trust Dhempe College of Arts & Science Miramar: HSSC, Geology/Earth Science, General (Jun 2015 to Mar 2017).

My education includes People's High School: SSC, Workshop Technology/Engineering Science(Mechanical & Electrical) (Jun 2005 to Mar 2015).

My headline says I am currently pursuing a Master’s in Computer Science at Goa College of Engineering and seeking work in machine learning and data science. It names Java, JavaScript, C, C++, MATLAB, Python, HTML, CSS, and SQL. My About section also lists programming in C and C++, fundamentals of Python, HTML and CSS, MATLAB, Java and JavaScript, and Wireshark. My listed skills also include Tinkercad, Orientdb, Vue.js, Node.js, AngularJS, HTML5, CSS3, Visual Studio, Java Applets, Servlets, React.js, Microsoft PowerPoint, Mechanical, Electrical, and Plumbing (MEP), Taekwondo, JavaScript Libraries, JSON, Microsoft Word, Microsoft Excel, Verilog, Microwind, Xilinx, OpenCse, Geology, Physics, Chemistry, Front-End Development, NoSQL, Android, Cybersecurity, Agile Methodologies, C#, XML, PHP, SQL, Bootstrap, Scilab, Jupyter, Microsoft Office, JavaServer Pages (JSP), jQuery, Arduino, and the programming languages and web technologies named above.

In my project work, I have used Python and MATLAB, along with Jupyter Notebook, pandas, scikit-learn, and Gradio. My machine-learning and deep-learning experience includes SVM, Random Forest, MobileNet, EfficientNet, CNNs, autoencoders, RNNs, LSTMs, GRUs, GANs, and gradient-boosting methods: XGBoost, CatBoost, LightGBM, and AdaBoost. My image and signal-processing work has included PCA, ICA, DWT, DCT, HOG, GLCM, LBP, Canny edge features, and wavelet-based features.

- My profile lists these certifications: N8N: AI Agent Creation Guide (Jan 2026, Udemy); Sell Print on Demand Jewelry & Design Custom Product with AI (Dec 2025, Udemy); Parallel Computing Onramp (Dec 2025, MathWorks); Wireless Networks & SDR 2025: GSM, SS7, GPRS, LTE & 5G Hacks (Nov 2025, Udemy); Blackbird - Software Engineering Job Simulation (Nov 2025, Forage); Datacom - Cyber Security Operations Job Simulation (Nov 2025, Forage); DNS Server: Deploy, Manage, and Secure Your Network (Oct 2025, Udemy); AIG - Shields Up: Cybersecurity Job Simulation (Sep 2025, Forage); Deloitte Australia - Cyber Job Simulation (Sep 2025, Forage); Deloitte Australia - Technology Job Simulation (Sep 2025, Forage); GE Aerospace - Explore Engineering Job Simulation (Sep 2025, Forage); Reinforcement Learning Onramp (Sep 2025, MathWorks); Siemens Mobility - Operations Industrial Engineer Job Simulation (Sep 2025, Forage); Tata - Cybersecurity Analyst Job Simulation (Sep 2025, Forage); Certified NVIDIA AI Expert: End-to-End GPU-Accelerated AI (Aug 2025, Udemy); App Building Onramp (Aug 2025, MathWorks); Moreton Bay Regional Council - Advanced Manufacturing: Machine Operations Job Simulation (Aug 2025, Forage); Moreton Bay Regional Council - Advanced Manufacturing: Quality Control Job Simulation (Aug 2025, Forage); Data Center Infrastructure & Design: Operation & Maintenance (Jul 2025, Udemy); and Skyscanner - Front-End Software Engineering Job Simulation (Jul 2025, Forage).

I volunteered as a National Service Scheme Active Volunteer and was a Member, Environment. I took part in cleanliness drives, walkathons, vanmahotsav, and a blood donation camp. My listed languages are English and Hindi (Professional working proficiency) and Konkani (Elementary proficiency).

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From Amey Naik's second brain at agentsocialx.com/am3y-na1k