Paula Lauren earned her Ph.D. in Computer Science from Oakland University in 2018. Prior to her doctoral studies, she spent over 10 years working full-time in the computing industry, with roles ranging from computer programmer to software engineering manager.

At Lawrence Technological University, she teaches both undergraduate and graduate courses in artificial intelligence, machine learning, and pattern recognition, as well as text-based machine learning. She has also taught database systems and foundational computer science courses.

» Education

• Ph.D., Computer Science and Informatics, Oakland University
• M.S., Computer and Information Science, The University of Michigan-Dearborn
• B.S., Business Administration, Wayne State University

» Research Interests

• Machine Learning and Deep Learning
• Artificial Intelligence in Computer Science Education
• Signal Processing and Audio Engineering
• Music Information Retrieval

» Courses and Advising

• MCS7013 Collaboration Research Project 1
• MCS7033 Collaboration Research Project 2
• MCS6623 Natural Language Processing
• MCS5803 Algorithms Design and Analysis
• MCS5623 Machine Learning
• MCS4993 Text Mining and Analytics
• MCS4833 Senior Project 1
• MCS4843 Senior Project 2
• MCS4633 Artificial Intelligence
• MCS4223 Machine Learning with Text
• MCS3543 Database Systems
• MCS2534 Data Structures
• MCS1514 Computer Science 1
• MCS1243 Foundations of Computer Science
• MCS1142 Introduction to C Programming

» Professional Experience

• Associate Professor, Lawrence Technological University
• Research Assistant, Oakland University
• More than 10 years of industry experience in software engineering and project management

» Selected Publications

• P. Lauren, “Improving Subword Embeddings in Large Language Models using Morphological Information,” in Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models, Walter de Gruyter, 2024, p. 333.
• P. Lauren, “Work-in-Progress: Course-based Undergraduate Research Experience (CURE) with Generative AI in a Computer Science Course,” in 2024 IEEE Frontiers in Education Conference (FIE), Washington, DC, USA, 2024, pp. 1-5, doi: 10.1109/FIE61694.2024.10893139.
• P. Lauren, “Reconstructing Word Representations from Pre-trained Subword Embeddings,” in 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2022, pp. 35-40, doi: 10.1109/CSCI58124.2022.00013.
• P. Lauren and P. Watta, “A Conversational User Interface for Stock Analysis,” in 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 5298-5305, doi: 10.1109/BigData47090.2019.9005635.
• P. Lauren et al., “Generating word embeddings from an extreme learning machine for sentiment analysis and sequence labeling tasks,” Cognitive Computation, vol. 10, no. 4, pp. 625-638, 2018, doi: 10.1007/s12559-018-9556-3.