AI Robotics & Autonomous Systems Engineer

GANESH PALADUGULA

******.**********@*******.*** +1-214-***-**** United States LinkedIn GitHub Portfolio SUMMARY

Computer Science graduate student specializing in AI and Robotics with experience building machine learning, computer vision, and autonomous navigation systems using Python, ROS 2, TensorFlow/PyTorch, and OpenCV. Skilled in developing end-to-end ML pipelines, SLAM-based robotic systems, and scalable analytics solutions through industry experience and research-driven projects. Seeking AI/ML, robotics, computer vision, or software engineering internships to build intelligent real-world systems. EDUCATION

Indiana Wesley University Jan 2026 – Present

Master of Science in Computer Science – AI Specialization University at Buffalo, The State University of New York Jan 2024 – Dec 2025 Master of Science in Engineering Science – Robotics SKILLS

Programming: Python, SQL, Bash, C/C++, MATLAB, OOP, Data Structures & Algorithms Data & Analytics: Pandas, NumPy, Scikit-learn, Power BI, DAX, Power Query, FAISS, FastAPI, Flask ML & AI: XGBoost, PyTorch/TensorFlow, Feature Engineering, Model Evaluation, Sentence-Transformers, Deep Learning, Computer Vision Robotics & Vision: ROS, OpenCV, SLAM, Sensor Fusion, Path Planning, Robotic Perception, LiDAR/camera Integration, Autonomous Navigation Tools & Platforms: HubSpot, Git/GitHub, Docker, CI/CD (GitHub Actions), Jupyter, Streamlit, REST APIs EXPERIENCE

Graduate Student Assistant University at Buffalo May 15, 2024 - Sep 4, 2024

• Tuned and characterized a bionic tactile sensing system using servo motors, linear actuators, and sensor-feedback control, achieving a 0.76 ms response time under varying load conditions.

• Developed manipulation and motion-control algorithms for a 16-servo robotic hand and Universal Robots UR5e using NI-DAQ, Arduino, and real-time data acquisition workflows.

• Designed and prototyped custom robotic components in Autodesk Fusion 360 while optimizing electromechanical integration, actuator performance, and experimental test setups.

Data Science Associate dOS Solutions, India July 24, 2023 - Nov 9, 2023

• Engineered and deployed supervised machine learning models using scikit-learn and XGBoost to analyze large-scale business datasets and identify key operational and revenue-driving factors.

• Developed scalable end-to-end ML pipelines in Python encompassing data preprocessing, feature engineering, model training, validation, and predictive analytics workflow automation.

• Translated complex stakeholder requirements into data-driven analytical solutions by delivering executive dashboards, performance insights, and actionable business intelligence across cross-functional teams. Data Analytics Intern LTI MindTree, India Feb 27, 2023 - July 7, 2023

• Designed and deployed 10+ interactive Power BI dashboards and automated reporting pipelines, reducing manual reporting effort by approximately 40%.

• Engineered scalable ETL workflows and optimized data models using Power Query and DAX, enabling efficient KPI tracking and self-service analytics.

• Collaborated with stakeholders to translate business requirements into actionable dashboards and strategic insights supporting executive decision-making.

PROJECTS

Face Matching & Recognition System Python, OpenCV, TensorFlow, PyTorch Jan 2026 – Apr 2026

• Engineered a deep learning–based face recognition pipeline using OpenCV and CNN-based facial embeddings for high-accuracy identity verification.

• Built preprocessing and feature extraction workflows including face detection, alignment, normalization, and embedding generation for robust recognition.

• Implemented vector similarity search using cosine distance and embedding-space comparisons to improve facial matching across varying lighting and pose conditions.

• Optimized real-time inference workflows using Python, NumPy, and TensorFlow/PyTorch for scalable face matching across image datasets. Autonomous Indoor Delivery Robot ROS 2, RTAB-Map, Nav2, Gazebo, RealSense, RPLidar Feb 2025 – Dec 2025

• Built an autonomous indoor delivery robot using RealSense D435i, RPLidar A2, and IMU sensors for real-time perception and localization.

• Implemented RTAB-Map SLAM with fused visual and wheel odometry, achieving localization accuracy within 5 cm in dynamic indoor environments.

• Developed Nav2-based path planning and obstacle avoidance pipelines, achieving a 98% mission success rate across 50+ autonomous delivery runs and validated navigation performance through Gazebo simulation, CI regression testing, and system benchmarking.

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