Key Responsibilities:
Develop and implement machine learning algorithms on the Azure platform to address Microsoft client-specific business challenges.
Leverage Microsoft Azure's cloud services to deploy and manage machine learning models at scale.
Optimize model performance by utilizing Microsoft cloud-based resources efficiently.
Stay updated on the latest developments in ML, and cloud computing for innovative solutions tailored to Microsoft's needs.
Assist and mentor colleagues by sharing expertise in machine learning and Azure, fostering a collaborative and knowledgeable team environment.
Key Responsibilities:
Designing and implementing Machine Learning models to analyze large datasets.
Developing and maintaining databases, data pipelines, and other data infrastructure to support analytics efforts.
Presenting findings and insights to technical and non-technical stakeholders, including senior executives.
Participating in peer review of research and code.
Key Responsibilities:
Understand the day-to-day business issues through data.
Compile and analyze data related to business issues.
Develop clear visualizations to convey complicated data in a straightforward fashion.
Key Responsibilities:
Investigated various machine learning algorithms and models for covid-19 medical image classification.
Collected and preprocessed covid-19 medical images for use in training and testing models.
Collaborated with team members to discuss research findings and ideas.
Key Responsibilities:
Execute research programs applying machine learning to real domains.
Investigate machine learning algorithms and approaches to solve AI problems.
Write code in python to support research.
Write research papers and gave presentations at academic conferences.
Key Responsibilities:
. Assisting in AI and Probability courses with lectures, tutorials, and classroom discussions.
Preparing and grading assignments and exams.
Providing additional support to students during office hours.
Key Responsibilities
Conducted research on information retrieval systems using deep learning.
Explored data mining techniques for healthcare decision-making.
Analyzed experimental data and contributed to research papers.
Collaborated with other researchers and professors to achieve research goals.
Participate in Projects:
Titanic: Machine Learning from Disaster.
Minist Dataset: Digit Recognizer.
House Prices: Advanced Regression Techniques.
Real or Not? NLP with Disaster Tweets.
Categorical Feature Encoding Challenge II.
Santander Customer Transaction Prediction.
Key Responsibilities
Published chapters related to machine learning and deep learning techniques.
Contributed to the dissemination of knowledge through research publications.
Worked collaboratively with a research team.