Automating Scanning Probe Microscopy: Techniques and Applications

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Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Covers data representations and processing techniques essential for effective machine learning algorithms.

Details an internship project focused on improving code generation accuracy using large language models and a new framework called EPIGEN.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Explores robotic generalization, tactile perception, pose estimation, and active learning in uncertain environments.

Explores predicting query execution time for JIT compiled database engines using active learning and experimental results.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Delves into the ICAP Hypothesis, concept maps, learning methods, and Cohen's d.