About
Engineering Faculty
Bio
Prof. Pragati Dharmale is a Computer Science faculty member and researcher with expertise in artificial intelligence, machine learning, deep learning, EEG signal analysis, database systems, and software development. My teaching responsibilities include the design and delivery of hands-on, upper- and lower-division computer science courses in Python, Java, web development, database systems, systems analysis, and applied artificial intelligence, with an emphasis on project-based learning and real-world problem solving.
She serves as a Senior Research Advisor, mentoring high school, undergraduate, and graduate students on interdisciplinary AI-focused research projects. These efforts have resulted in multiple publications in IEEE peer-reviewed conferences. She also contributes to the scholarly community as an IEEE conference paper reviewer.
She is the author of the academic book Database Architecture and Implementation: From Modeling to Performance Tuning, which presents a comprehensive framework for database design, optimization, and performance engineering relevant to both academic instruction and professional practice.
Degree & Academic Institution:
- MS, Information Technology
Southern New Hampshire University - MS, Digital Electronics
Sipna College of Engineering & Technology
Courses Taught:
- CS 457 - Data Modeling and Implementation Techniques
- CS 457L - Data Modeling Lab
- CS 526 - Advanced Web Development
- CS 547 - Advanced Database Management
Publications:
Conference
- Multi-core GPU–Fast parallel SAR image generation
- Accelerated SWT based de-noising technique for EEG to correct the Ocular Artifact
- Enhanced Human Computer Interaction using hand gesture analysis on GPU
- Deep Learning-Based Sign Language Recognition for Enhanced Communication
- Low Power Logic Design Schemes Wearable Technology
- Enhancing Autism Spectrum Disorder Recognition in EEG Data through Filtering-Driven CNN Approach
- Automated Recognition of Autism Spectrum Disorder from EEG Signals Using a CNN-LSTM Hybrid Model
- Enhancing Human-Computer Interaction: An Accurate Video-Based American Sign Language Translation Model Using Machine Learning Techniques
- Exploration of New Models to Enhance Treadmill Usage and Analyze Optimal Use Cases for All Age Groups
- Fusion of Convolutional and Recurrent Networks for Autism Detection from EEG Signals.
Books
Database Architecture and Implementation: From Modeling to Performance Tuning
Patents
- IOT based camera for healthcare management
- System and Method for Multimodal Attention Analysis Using EEG Signals and Adaptive Deep Learning Framework
National Conference
- Innovative Models for Enhancing Treadmill Efficiency and Identifying Optimal Use Cases Across Diverse Age Groups
- Classification of Autism Spectrum Disorder from EEG Signals Using a CNN-LSTM Hybrid Model
- EEG-Based Epileptic Seizure Detection with Python and Machine Learning
- Combining LLM-Derived Features with ML for Explainable Diabetes Risk Assessment
Bio
Prof. Pragati Dharmale is a Computer Science faculty member and researcher with expertise in artificial intelligence, machine learning, deep learning, EEG signal analysis, database systems, and software development. My teaching responsibilities include the design and delivery of hands-on, upper- and lower-division computer science courses in Python, Java, web development, database systems, systems analysis, and applied artificial intelligence, with an emphasis on project-based learning and real-world problem solving.
She serves as a Senior Research Advisor, mentoring high school, undergraduate, and graduate students on interdisciplinary AI-focused research projects. These efforts have resulted in multiple publications in IEEE peer-reviewed conferences. She also contributes to the scholarly community as an IEEE conference paper reviewer.
She is the author of the academic book Database Architecture and Implementation: From Modeling to Performance Tuning, which presents a comprehensive framework for database design, optimization, and performance engineering relevant to both academic instruction and professional practice.
Degree & Academic Institution:
- MS, Information Technology
Southern New Hampshire University - MS, Digital Electronics
Sipna College of Engineering & Technology
Courses Taught:
- CS 457 - Data Modeling and Implementation Techniques
- CS 457L - Data Modeling Lab
- CS 526 - Advanced Web Development
- CS 547 - Advanced Database Management
Publications:
Conference
- Multi-core GPU–Fast parallel SAR image generation
- Accelerated SWT based de-noising technique for EEG to correct the Ocular Artifact
- Enhanced Human Computer Interaction using hand gesture analysis on GPU
- Deep Learning-Based Sign Language Recognition for Enhanced Communication
- Low Power Logic Design Schemes Wearable Technology
- Enhancing Autism Spectrum Disorder Recognition in EEG Data through Filtering-Driven CNN Approach
- Automated Recognition of Autism Spectrum Disorder from EEG Signals Using a CNN-LSTM Hybrid Model
- Enhancing Human-Computer Interaction: An Accurate Video-Based American Sign Language Translation Model Using Machine Learning Techniques
- Exploration of New Models to Enhance Treadmill Usage and Analyze Optimal Use Cases for All Age Groups
- Fusion of Convolutional and Recurrent Networks for Autism Detection from EEG Signals.
Books
Database Architecture and Implementation: From Modeling to Performance Tuning
Patents
- IOT based camera for healthcare management
- System and Method for Multimodal Attention Analysis Using EEG Signals and Adaptive Deep Learning Framework
National Conference
- Innovative Models for Enhancing Treadmill Efficiency and Identifying Optimal Use Cases Across Diverse Age Groups
- Classification of Autism Spectrum Disorder from EEG Signals Using a CNN-LSTM Hybrid Model
- EEG-Based Epileptic Seizure Detection with Python and Machine Learning
- Combining LLM-Derived Features with ML for Explainable Diabetes Risk Assessment