Dr. Lemos’ research spans multiple interdisciplinary fields, combining digital forensics, cybersecurity, forensic linguistics, forensic education, forensic signal processing, site reliability engineering (SRE), DevOps, and advanced sensing and telemetry. His work integrates expertise from forensic science, computer engineering, and electronic engineering, bridging the gap between these domains to develop solutions that enhance both security and operational efficiency.

In digital forensics and cybersecurity, his research focuses on network security, forensic memory analysis, and the processing of digital evidence. He investigates cyber threat detection in critical infrastructure, software-defined networking (SDN) security, and the application of artificial intelligence in forensic investigations. His work also extends to suspect identification from surveillance footage, forensic location analysis, and forensic audio enhancement, exploring image and video forensics, forensic signal processing, and methodologies to enhance the reliability of digital evidence.

Another aspect of his research is forensic linguistics, where he examines textual evidence analysis, authorship attribution, and language use in cybercrime. By integrating natural language processing (NLP) with forensic methodologies, he develops techniques that assist in investigative procedures. Dr. Lemos is also deeply involved in forensic education, creating curricula, training methodologies, and hands-on forensic programs that incorporate AI-driven forensic tools, computational techniques, and real-world case studies.

Beyond forensic science, Dr. Lemos researches cloud-native architectures, automation, and observability in large-scale distributed systems. His work in DevOps and Site Reliability Engineering (SRE) emphasizes Infrastructure as Code (IaC), CI/CD automation, and container orchestration, ensuring fault-tolerant, scalable, and resilient systems. He applies Kubernetes, Docker, Terraform, and GitOps methodologies to enhance the deployment, security, and maintainability of complex infrastructures. His research also focuses on observability in distributed environments, developing strategies for centralized logging, distributed tracing, and real-time monitoring to improve system reliability, security, and incident response.

Dr. Lemos also contributes to advanced sensing and telemetry research, developing sensor networks, IoT-based monitoring systems, and real-time data acquisition technologies for industries such as mining, petroleum, and gas. His work explores AI-driven data extraction from industrial equipment, enabling predictive maintenance, anomaly detection, and optimization of operational efficiency. By integrating machine learning with industrial telemetry, he enhances data processing and decision-making capabilities in critical environments. His research extends to cloud-based infrastructures for telemetry, applying distributed computing, high-throughput data pipelines, and real-time analytics to create scalable and secure industrial monitoring solutions.

Through his multidisciplinary research, Dr. Lemos contributes to advancements in forensic science, cybersecurity, cloud infrastructure, and industrial automation, bringing together electronic engineering, AI, and digital forensics to develop innovative solutions for both security and large-scale operational challenges.