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PhD in Computer Science and Engineering

• Thesis: Dynamic Bayesian Networks - Estimation, Inference and Applications
• Advisor: Prof. Arindam Banerjee

MS in Computer Science

BS in Computer Science

Distinguished Applied Researcher

Research Staff Member

Research Assistant

Data Scientist

Software Developer

Sequential Tabular Data

• Modeling and forecasting sequential data with a focus on event sequence modeling, anomaly and pattern detection, and graph-based representations of customer interactions.

Generative Models

• A broad class of machine learning algorithms that focuses on understanding the underlying data distribution and generate new samples with statistical properties similar to the original dataset.

Knowledge Graphs

• Automatic extraction of structured representations from text data, involving identifying entities, disambiguating their meanings, and mapping them to a graph-based representation for efficient storage, retrieval, and reasoning.

Protein Design

• Forward protein design predicts the structure and function of a protein based on its amino acid sequence, while the inverse design estimates a protein sequence with a desired structure and function.

Information Theory

• Information theoretic view of neural networks analyzing their properties to gain understanding on how they represent and process information, as well as for designing efficient architectures.

Image Captioning

• The task of analyzing the visual content of an image and generating a coherent and textual description that accurately describes the content of an image.