Hi, I’m Chandrasekar Pasumarthi
Computer Engineering | AI Research | Responsible AI
Building intelligent systems for meaningful impact across healthcare, education, and scientific discovery.
Featured Project
Responsible AI for Students, Families & Schools.
Building safer and more accountable generative AI experiences for students.
Designed and developed a Responsible AI platform exploring how generative AI can be safely used by students while providing appropriate parental and school oversight. The project combines cloud-based AI governance with an ESP32-based edge computing extension, connecting software intelligence with embedded systems, sensor processing, and edge inference.
Identity & Consent
Google and school authentication with student, parent, teacher, and administrator roles.
AI Prompt Analysis
Classifies prompts by category and evaluates usage patterns through an AI safety layer.
Parent Oversight
Parent dashboards provide visibility into AI usage while maintaining appropriate student privacy.
School Integration
Teacher and school-level workflows support responsible classroom AI adoption and academic-integrity use cases.
System Architecture
From Cloud AI to Edge Intelligence
I explored multiple architectures for bringing responsible AI closer to the user—ranging from an edge-cloud architecture using NVIDIA Jetson to a lightweight TinyML design using ESP32.
Architecture 01
Edge-Cloud Responsible AI — NVIDIA Jetson
Local AI classification and policy enforcement combined with cloud LLM reasoning for more complex requests.
The Jetson architecture performs preprocessing, classification, safety checks, and policy decisions locally. Only requests requiring advanced reasoning are routed to cloud-based LLM services.
Architecture 02
TinyML Responsible AI — ESP32
A lightweight embedded architecture that runs local TinyML inference on an ESP32 while integrating with the cloud platform.
The ESP32 version explores how compact embedded hardware can perform feature processing and TinyML inference locally, while the backend handles authentication, policy decisions, analytics, and cloud LLM communication.
Research
Published and applied research.
Published Research · JCSTS
Vision Transformers vs. CNNs
Systematic analysis comparing Vision Transformers and convolutional neural networks across image classification tasks.
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Published Research · JCSTS
Student Stress Classification
Machine learning model for student stress-level classification with personalized recommendation outputs.
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Biomedical AI Research
ACM ChemoCraft
Performed medical image preprocessing, reducing noise and converting DWI to DTI scans to support AI-driven oncology research.
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Projects
Technical projects and experiments.
AI Email Automation Agent
Built an AI-powered Gmail automation agent using FastAPI, Gmail API, Auth0 Token Vault, and scheduled workflows.
Machine Learning Portfolio
Projects spanning computer vision, neural networks, data analysis, and applied AI research.
Photography + AI Gallery
A creative section for photography, future image tagging, and computer vision experiments.
Experience
Research and learning timeline.
UT High School Research Academy
Research experience at UT Austin.
UT Austin Data Science Research Academy
Explored data science and research methods.
UT Dallas, Depart of Computer Science
Research Internship in Quantum Computing
Asklena AI Internship
Worked on AI/software projects and data-driven systems.
ACM Research
Biomedical AI and medical imaging research.
Certificates
Certifications
UT HSRA Computation Meterial Science
UT Austin
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UT Data Science reserach program
UT Austin
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Summer Research Internship in QUANTUM COMPUTING
UT Dallas Depart of Computer Science
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Course Autonomous Cognitive Assistant
MIT Beaver Works Summer Institute course
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Course for Quantum Software
MIT Beaver Works Summer Institute course
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Lumiere High Honors Award
Lumiere Education
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AP Scholar with Distinction
College Board
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Machine Learning Specialization
Stanford University / DeepLearning.AI
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PCAP – Certified Associate Python Programmer
Python Institute
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PCEP – Certified Entry-Level Python Programmer
Python Institute
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Awards
Recognition and honors.
Battle of the Brains — 1st Place Winner
UIL Computer Science — Regional Achievement
Lumiere High Honors Award
AP Scholar with Distinction
National Recognition Program Awardee
RCM Flute Level 6 Certification
Leadership
Beyond academics.
AI/CS Leadership
Led workshops, mentored students, and promoted collaboration in AI and programming.
Community Service
American Red Cross volunteer for 3.5 years and City of Frisco volunteer for 1 year.
Music
Completed Royal Conservatory of Music flute certification through Level 6.
Competitive Swimming
Compete year-round while helping lead practices and mentor younger swimmers.
Middle School Technology Journey
From FLL State-Level Selection to Independent Experimentation
After my FIRST LEGO League team advanced to the state level, I continued exploring robotics independently. Using LEGO SPIKE Prime, I designed and recorded an experiment examining resistance during arm movement. The project strengthened my understanding of sensors, programming, testing, and the connection between hardware and scientific inquiry.
What I Learned
- ✓ Advanced to the state level in FIRST LEGO League
- ✓ Continued robotics exploration beyond the competition
- ✓ Designed an independent LEGO SPIKE Prime experiment
- ✓ Programmed sensors and examined resistance during arm movement
- ✓ Developed skills in testing, troubleshooting, and scientific inquiry
My Journey
Growing Through Technology
A visual timeline of how my curiosity for technology evolved through school — from early exploration to AI research, publications, and real-world projects.
Elementary School
First Curiosity
I began exploring how technology worked through simple games, puzzles, and early coding curiosity.
Middle School
Building Foundations
I started learning programming, robotics, and problem-solving, realizing I could build instead of just use technology.
Freshman Year
Discovering CS
I began taking computer science seriously, strengthening my coding skills and exploring AI concepts.
Sophomore Year
Entering Research
I started applying machine learning to real problems and became interested in research-driven technology.
Junior Year
Research & Impact
Junior year marked a shift from solving problems to exploring deeper questions. Experiences across competitions, HSRA, data science, and quantum research challenged me to learn beyond familiar areas and strengthened my curiosity for research and emerging technologies.
Senior Year
Looking Ahead
I am continuing my journey by exploring the intersection of Computer Engineering and AI, with a growing interest in embedded systems, edge AI, intelligent hardware, and building systems where hardware and software work together to solve real-world problems.