Adithya Shankar Email & Phone Number

United States

University Park, Pennsylvania, United States

- Developed an advanced ANN achieving 99% accuracy in multiclass classification for graphene-based Chemisensor systems, outperforming traditional methods.- Enhanced sensor accuracy by 19%, elevating it from 80% to 99%, through the integration of LSTM layers and nonlinear optimization techniques, resulting in more reliable and precise sensor data.- Implemented LSTM layers and non-linear optimizations to reduce sensor array errors, nullify drift, and produce highly accurate results.- Collaborated with a multidisciplinary team, leading to innovative solutions in Chemisensor data analysis.

University Park, Pennsylvania, United States

Graduate Grader for Data Structures and Algorithms (CSE465) at Penn State.Evaluated assignments to assess students' understanding and application of core algorithms.Provided constructive feedback on complex topics like algorithm design and data organization.

University Park, Pennsylvania, United States

Developed a traffic monitoring system using STM32 microcontroller and integrated model zoo applications for computer vision tasks.- Implemented real-time vehicle detection and traffic flow analysis using computer vision models.- Optimized neural network models for deployment on the STM32 platform to ensure efficient processing and low power consumption.- Developed and tested algorithms for vehicle counting, speed estimation, and traffic pattern recognition.

- Developed a microcontroller-based 4-wheel autonomous system with environmental sensors and computer vision, implementing SLAM with YDLidar and Raspberry Pi.- Achieved a 200% cost reduction in sensor arrays by optimizing hardware and software integration.- Implemented a SLAM robot with Nvidia Jetson Nano and Arduino using ROS2 that can navigate and move autonomously.-Integrated a real-time Computer Vision module for FPV using Raspberry Pi, with real-time sensor status over cloud using Thingspeak API.

- Built a RESNET-based CNN for plant disease detection on Nvidia Jetson and Raspberry Pi. Created an autonomous drone for real-time agricultural surveillance.- Managed the development of a Computer Vision model for plant health assessment.- Automated Environmental Monitoring: Utilized sensors to track critical parameters like temperature, pH, dissolved oxygen, and water levels in real-time.- Edge Computing and Cloud Integration: Implemented edge computing for on-site decision-making and cloud connectivity for remote monitoring and data analysis.- Innovative Control Systems: Developed automated systems, such as smart aerators and flush-fill mechanisms, to maintain optimal conditions for fish farming.- Underwater Camera Integration: Incorporated a 2-axis gimbal camera for panoramic underwater monitoring, enabling detailed analysis of fish behaviour.- Applied Computer Vision and Machine Learning for Semantic Segmentation.- Achieved 95% accuracy on a dataset of 4000 images featuring local produce from Manipal's fields.

Bengaluru, Karnataka, India

- Designed and implemented a temperature-responsive control system using Arduino, ESP8266, and USB-powered fans, enhancing energy efficiency in environmental control systems.- Acquired hands-on experience in embedded system development, refining skills in energy-efficient design and user-centric product development.

Master'S Degree, Electrical Engineering

Relevant Coursework: -EE556 Graphs, Algorithms, and Neural Networks -EE559 Wireless and Mobile Sensing in the Age of IoT -STAT501.

Bachelor Of Technology - Btech, Electronics And Instrumentation