CV

Adjunct Professor (part time)

Data Scientist

Physical Scientist

Associate (part time)

Assistant Professor of Mechanical Engineering

Founding Engineer

Mechanical Engineering Instructor (part time)

CAE Engineer Intern

Doctor of Philosophy in Mechanical Engineering

• Focus on Design and Manufacturing
• Minor in Energy Technology
• Thesis: 'Guided Wave Simulation in Long Pipes using Hybrid Modeling'

Master of Science in Mechanical Engineering

• Thesis: 'Damage Identification in Beam-Type Structures via Modal Analysis'

Bachelor of Science in Mechanical Engineering

Certified Scrum Developer (CSD)

• Focusing on Scrum principles and specialized agile engineering techniques. Acquired essential tools and methodologies for building high-quality products in an iterative and incremental manner within a Scrum environment.

Designing and Implementing a Microsoft Azure AI Solution

• Building AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI.

Designing and Implementing a Data Science Solution on Azure

• Implementing Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

MongoDB Essential Training

• The training covered: setting up MongoDB databases, CRUD operations in MongoDB, aggregation pipelines, data modelling, and accessing MongoDB using APIs.

Leveraging Deep Learning for Natural Language Processing

• Topics covered: text pre-processing, word embeddings, sentiment classification, Named Entity Recognition, various neural networks such as RNN, GRU, and LSTM, and advanced techniques like Attention Models and Transformers.

Components of a Big Data and AI Solution

• Courses included: The course covers: Data and the Enterprise, Storing and Querying Data, HDFS, Spark, and Kafka, NoSQL Databases, Analyzing and Interpreting Data, Neural Networks, Data Visualization

NSF I-Corps Hub Northeast Region

• Led technical aspects, conducted customer interviews, identified market opportunities, and refined the commercialization strategy for an innovative gravity-based energy storage system.

Master's Certificate in Applied Mathematics - Data Analytics

• Coursework included: Computational Methods for Analytics, Machine Learning, Probability and Statistics for Analytics, Database Methods for Analytics, Data Mining, and Computing in R.

Deep Learning Specialization

• Coursework included: Neural Networks and Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models.

IBM Data Science

• Coursework included: What is Data Science?, Tools for Data Science, Data Science Methodology, Python for Data Science, AI & Development, Databases and SQL for Data Science with Python, Data Analysis with Python, Data Visualization with Python, Machine Learning with Python, and Applied Data Science Capstone.

Alan Alda Center for Communicating Science

• Coursework included: Distilling Your Message, Improvisation for Scientists, Writing to be Understood, and Engaging Key Audiences.

Currents to Current: Converting River Energy to Electricity (20,000 USD)

Development of a Physics-Informed Model for a Fluid-Structure Interaction Environment: Gearing Toward a Simulation of a Hydrokinetic Energy Harvesting System (3000 USD)

Piezoelectric Based River Current Energy Harvester (20,000 USD)

Wave Characteristics Prediction Using Machine Learning Algorithms (30,000 JPY)

Ocean Wave Energy Converter Module for Observation and Navigation Buoys (20,000 USD)

• Top ten finalist for the Federal Aviation Administration Data Challenge (Link)
• Winner of the Connection Creation Contest at 2023 Marine Energy Collegiate Competition (Link)