As a data science and ML consultant, I collaborate with companies across different industries to transform complex data into actionable insights that improve operational efficiency. Some of my current projects include:• Maritime Industry: I work as the data intelligence lead for a maritime software company, partnering with clients in vessel operations and port call management to craft data strategies and implement analytics that enhance decision-making and proactively address operational bottlenecks. My work involves using Agile frameworks to manage projects efficiently, incorporating tools such as Jira and GitHub for streamlined collaboration and version control. I leverage machine learning algorithms, data wrangling, and ETL pipelines to extract and analyze vessel movements, identify identical vessels, track port visits, and monitor berth changes within ports.• Sensor Data Analysis: Consulting with an early-stage startup focused on analyzing time series sensor data to detect early signs of mechanical issues in heavy equipment. I develop Python-based ETL processes, notebooks, and classes for processing and analyzing large-scale sensor data, complete with data visualizations and in-depth analyses. By examining sensor patterns and trends, I help the team identify potential anomalies, supporting improved equipment reliability and reducing downtime.

- Redesigned a series of data science courses such as Statistical Analysis of Data, Statistical Modeling, and Its Applications and Statistical Methods in Machine Learning. - Incorporated heavy R coding into lectures, assessments, and projects- Actively participated in data science initiatives of Hamilton College where one such initiative resulted in a data science major. – Successfully engaged students in class Machine Learning projects in which we designed and developed state-of-the-art predictive Classification and Regression models using Machine Learning – Supervised algorithms and performed an in-depth analysis of the structure of models.– Extensively used open-source tools – Python and R Studio (R) for statistical analysis and building machine learning algorithms.- Also teaching Calculus courses- Doing research in combination of applied mathematics and machine learning

Proved the existence of theoretical neural network solutions for differential equations, and designed and implemented numerical algorithms in Python to solve partial differential equations. This was a teamwork consisting of two research scientists, me, and one of my students.Research in the combination of applied mathematics and machine learning.

Research in combination of applied mathematics and machine learning.

Teaching Statistical Analysis of Data (MAT 152).

Coordinating various math service courses (particularly calculus but also college algebra, precalculus, etc.), working on the math placement test and student success, and coordinating minor in applied mathematics.

– Promoted data analysis certificate– Delivered technical lectures in Applied Probability – Delivered technical lectures in Regression– Delivered technical lectures in Time Series Analysis – Both at undergraduate and graduate levelsTeaching: Calculus 1 (Differential Calculus), Calculus 2 (Integral Calculus), Calculus 3 (Vector Calculus), Differential Equations with Modeling Applications, Applied Probability (with R), Linear Algebra, Introduction to Probability (with R), Mathematical Modeling (with Python), Complex Variables and Their Applications, Partial Differential Equations, Nonlinear Partial Differential Equations, Practical Time Series Analysis

Teaching, Recitation, Grading