Priyanka Garg
@priyankagarg
Goal-oriented AI and ML graduate with strong problem-solving skills.
What I'm looking for
I am a Computer Science Engineering graduate specializing in Artificial Intelligence and Machine Learning, with a strong focus on practical applications in robotics. My recent internship at the Indian Institute of Technology Dhanbad allowed me to develop a rock segmentation model using advanced computer vision techniques, showcasing my expertise in Python and deep learning. I thrive in team environments and am driven by a passion for solving complex problems.
During my time as a Developer Intern at Salesforce, I enhanced my skills in the Lightning Component Framework and worked on real-world scenarios, which further solidified my technical foundation. I have also contributed to significant research, including a machine learning-based prediction of liver disease, presented at an international conference. My academic achievements, including a CGPA of 9.03/10, reflect my dedication and commitment to excellence in the field.
Experience
Work history, roles, and key accomplishments
AI Research Intern
Indian Institute of Technology Dhanbad
Jun 2023 - Aug 2023 (2 months)
Developed a rock segmentation model using Computer Vision techniques such as object detection, edge detection with DexiNed, and semantic segmentation, accurately predicting rock sizes (small, medium, large) in mining dumps. The tech stack used included OpenCV, Python, and Deep Learning.
Developer Intern
Salesforce
May 2023 - Jul 2023 (2 months)
Enhanced proficiency in Lightning Component Framework by working on real-world scenarios, focusing on Trailhead Community with Apex. This role involved practical application of development skills within a professional environment.
Education
Degrees, certifications, and relevant coursework
DIT University
B. Tech, Computer Science with Specialization in AI & Robotics
Grade: 9.03/10.0
Pursued a Bachelor of Technology in Computer Science with a specialization in AI & Robotics. Achieved a CGPA of 9.03/10.0, demonstrating strong academic performance in the field.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
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