Saswati Krishnan

AI Researcher

Delhi, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Developed a predictive model for brain stroke diagnosis.
Researched IoT techniques for pollution monitoring.
Strong foundation in machine learning and data analytics.

Stackforce AI infers this person is a data-driven professional with a focus on healthcare and environmental analytics.

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Skills

Core Skills

Machine LearningProgramming LanguagesEnvironmental Data AnalysisData Analytics

Other Skills

ArtBadmintonC (Programming Language)C++CSSCalligraphyChessCommunicationElectronic EngineeringEmbedded SystemsEnglishEvent ManagementHTMLIoTJavaScript

About

I am a 2025 B.Tech graduate in Electronics and Communication Engineering from Maharaja Agrasen Institute of Technology, Delhi. I am passionate about Artificial Intelligence, Machine Learning, and Data Analytics, with a keen interest in applying technology to solve real-world problems. Alongside my technical journey, I actively engage in arts and sports, which help me stay creative, disciplined, and team-oriented. I am eager to contribute my skills, learn from industry experts, and grow in the field of data-driven technologies.

Experience

0 mo

Total Experience

Average Tenure

Current Experience

Techciti technologies pvt ltd

Machine Learning Intern

Jan 2025 – Apr 2025 · 3 mos

Developing a machine learning model to predict brain stroke likelihood using key health indicators such as hypertension, blood glucose levels, and smoking habits. Implementing a Random Forest Classifier to optimize prediction accuracy beyond 85%. Using front-end technologies like HTML, CSS, and JavaScript to build a web-based ML model for brain stroke diagnosis.

Machine LearningRandom ForestHTMLCSSJavaScriptProgramming Languages

Indian institute of tropical meteorology (iitm), delhi

Environmental Analytics Intern

Aug 2024 – Sep 2024 · 1 mo

Researched IoT-based techniques for atmospheric pollution monitoring under Dr. Atul Kumar Srivastava (Scientist E). Collected and analyzed air quality data from Aethalometer and Grimm Dust Monitor, utilizing an IoT cloud platform. Improved pollution trend analysis accuracy by 25% using Origin software for visualization and statistical evaluation.

Environmental Data AnalysisIoTOrigin SoftwareData Analytics