Saad Shafiq PhD | Senior Software Consultant

Saad Shafiq, Ph.D., is a Senior Software Consultant at Quandary Peak Research who supports software-related litigation through technical research and analysis. Dr. Shafiq reviews source code, system architecture, technical documentation, data, and AI and machine-learning systems to help litigation teams evaluate complex software questions.

Combining academic research with hands-on engineering, Dr. Shafiq has experience across software development, artificial intelligence, vulnerability detection, data analysis, cloud systems, healthcare software, and distributed applications. His research has examined deep-learning-based vulnerability detection, neural-network modularization, out-of-distribution detection, natural language processing, and the use of large language models and reinforcement learning in software modernization.

Dr. Shafiq holds a Ph.D. in Computer Science and an M.S. in Software Engineering. He has authored or co-authored 19 publications and preprints, including three papers published at the International Conference on Software Engineering, and co-authored a paper recognized with the 2024 SANER Distinguished Paper Award. This combination of research and engineering experience enables him to investigate complex software issues rigorously and explain technical findings clearly to attorneys and legal teams.

Summary of Qualifications

Advanced Education: Holds a Ph.D. in Computer Science and an M.S. in Software Engineering.
Research and Engineering Background: Experience spanning postdoctoral research, academic research, software engineering, blockchain development, and full-stack application development.
Scholarly Publication Record: Author or co-author of 19 publications and preprints, including three papers published at ICSE.
Recognized Research: Co-authored a paper that received the 2024 SANER Distinguished Paper Award.
Research Leadership: Leads and supervises applied machine-learning research involving software engineering, security, medical imaging, and predictive modeling.
Academic Service and Communication: Serves on conference program committees, reviews journal and conference submissions, and has presented research internationally.

Areas of Expertise

Software Engineering and Architecture: Experience designing software modules and APIs, reviewing code, managing releases, and developing prototypes across the software lifecycle.
Artificial Intelligence and Machine Learning: Research and applied experience involving classification, clustering, regression, optimization, deep neural networks, and out-of-distribution detection.
AI System Modernization and Modularization: Published research on deep-learning and reinforcement-learning methods for improving the interpretability, maintainability, and scalability of AI-based systems.
Software Security and Vulnerability Detection: Published research on deep-learning-based vulnerability detection and frameworks for evaluating AI-based detection methods.
Natural Language Processing and Large Language Models: Experience applying topic extraction, latent Dirichlet allocation, sentiment analysis, recommendation methods, and LLMs to software engineering problems.
Data Analysis and Statistical Modeling: Experience analyzing large structured and unstructured datasets using statistical analysis, multivariate analysis, anomaly detection, and machine learning.
Cloud, Distributed Systems, and Blockchain: Hands-on experience with Microsoft Azure, Kubernetes, Hyperledger Fabric, Ethereum smart contracts, and distributed application development.
Healthcare Software and Data Standards: Experience developing electronic health record systems and cloud services involving healthcare data, FHIR, HL7, ICD, and HIPAA requirements.
Programming Languages and Frameworks: Experience using Python, R, SQL, JavaScript, C#, Go, Solidity, .NET, ASP.NET, TensorFlow, PyTorch, Keras, and scikit-learn.