Faye Taxman | Data-Driven Decision Making | Best Researcher Award

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Prof. Faye Taxman | Data-Driven Decision Making | Best Researcher Award

University Professor at George Mason University, United States

Dr. Faye S. Taxman is a distinguished University Professor at George Mason University, where she serves as the Director of the Center for Advancing Correctional Excellence! (ACE!). Her work has had a profound impact on criminal justice policy, implementation science, and evidence-based practices in correctional settings. With decades of experience in criminology, she has contributed significantly to improving interventions for justice-involved populations, particularly in the areas of rehabilitation, health services, and community corrections. A widely cited scholar, Dr. Taxman has received numerous accolades for her groundbreaking research and dedication to the field.

Profile

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Education

Dr. Taxman earned her Ph.D. in Criminal Justice from Rutgers University in 1982, following an M.A. in the same field in 1981. Prior to that, she completed her undergraduate studies in Political Science and Criminal Justice at the University of Tulsa, where she graduated with honors in 1977. Her academic training laid a strong foundation for her career in research, policy development, and the advancement of evidence-based practices in criminology and public policy.

Professional Experience

Dr. Taxman has held numerous academic and research positions throughout her career. Since 2020, she has been a University Professor at George Mason University’s Scholar School of Policy and Government. She has also served as Director of the Center for Advancing Correctional Excellence! (ACE!) since 2009. Her academic affiliations extend to institutions such as Griffith University, Howard University School of Medicine, and Florida State University. Before joining George Mason University, she held key positions at the University of Maryland, Virginia Commonwealth University, and the Institute for Law and Justice, among others. Her career has been marked by extensive involvement in research projects aimed at improving correctional systems, public safety outcomes, and evidence-based policy applications.

Research Interests

Dr. Taxman’s research focuses on criminal justice policy, correctional rehabilitation, implementation science, and behavioral health interventions for justice-involved individuals. She has been instrumental in developing and evaluating strategies to enhance community corrections, improve substance use disorder treatments, and implement evidence-based practices in justice systems. Her work has emphasized the integration of public health and justice systems, aiming to improve rehabilitation outcomes and reduce recidivism. Her recent projects include studies on supervision conditions, digital interventions for justice-involved individuals, and the development of translational research strategies for policy implementation.

Awards and Recognitions

Dr. Taxman has received numerous prestigious awards for her contributions to criminology and public policy. In 2023, she was honored with the Vollmer Award from the American Society of Criminology for her outstanding scholarship. She has also been recognized with the Scholar School Award for Outstanding Scholarship, the Society for Implementation Research Collaboration Mission Award, and the Joan McCord Award for experimental criminology. Additionally, she was named a Fellow of the American Society of Criminology and the Academy of Experimental Criminology. Her lifetime achievements in sentencing and corrections research have been recognized by the Division of Sentencing and Corrections of the American Society of Criminology. Her scholarship continues to shape the field and influence justice reform initiatives.

Selected Publications

Taxman, F. S., & Pattavina, A. (2021). “Simulation Modeling for Criminal Justice.” Criminology & Public Policy. Cited by 85 articles.

Taxman, F. S., Henderson, C. E., & Young, D. (2019). “Behavioral Health Services and Probation: Evidence-Based Practices.” Journal of Offender Rehabilitation. Cited by 120 articles.

Taxman, F. S., Caudy, M. S., & Rhodes, A. (2018). “Translational Criminology: Applying Research to Justice Practices.” Justice Quarterly. Cited by 97 articles.

Taxman, F. S., & Perdoni, M. (2017). “The Role of Implementation Science in Correctional Settings.” Journal of Criminal Justice Education. Cited by 75 articles.

Taxman, F. S., & Bouffard, J. (2016). “Community Corrections and Risk-Needs Assessment Tools.” Criminal Justice and Behavior. Cited by 140 articles.

Taxman, F. S., & Belenko, S. (2015). “Substance Abuse Treatment in the Criminal Justice System: Implementation and Impact.” Health & Justice. Cited by 130 articles.

Taxman, F. S. (2014). “The Role of Supervision in Reducing Recidivism: Lessons from Evidence-Based Practices.” Corrections Today. Cited by 110 articles.

Conclusion

Dr. Faye S. Taxman is a leading figure in criminology, recognized for her extensive research and commitment to improving the criminal justice system through evidence-based interventions. Her work has influenced policy decisions, program implementations, and research methodologies in the field of criminal justice. Through her leadership, scholarship, and dedication to mentorship, she continues to shape the future of criminal justice and public policy research. Her contributions have left an enduring impact on the advancement of effective correctional practices and justice system improvements.

Muhammad Dilshad | Data Privacy and Security | AI & Machine Learning Award

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Mr. Muhammad Dilshad | Data Privacy and Security | AI & Machine Learning Award

Student at Quaid e Azam University Islamabad, Pakistan

Muhammad Dilshad is a dedicated and driven professional in the field of Computer and Information Technology. Holding a Master’s degree in Computer and Information Technology (MCIT) from Quaid-i-Azam University, Islamabad, he specializes in Cybersecurity, Networking, Machine Learning, and Blockchain. With practical experience in network performance monitoring and troubleshooting, he has contributed significantly to optimizing infrastructure security. His research interests revolve around enhancing Internet of Vehicles (IoV) security, employing Federated Learning, and integrating Blockchain technology to build decentralized, tamper-resistant frameworks. Proficient in various programming languages and analytical tools, he continually strives to apply emerging technologies for solving real-world security challenges.

Profile

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Education

Muhammad Dilshad began his academic journey with a strong foundation in science and mathematics, completing his Matriculation from BISE DG Khan Board. He then pursued an Intermediate of Computer Science (ICS) from the same board, gaining expertise in programming and computational concepts. His passion for technology led him to obtain a Bachelor of Science in Information Technology (BSIT) from Bahauddin Zakariya University, Multan, where he honed his skills in web development, networking, and database management. He further advanced his knowledge by earning a Master of Science in Information Technology (MSIT) from Quaid-i-Azam University, Islamabad, specializing in Machine Learning, Federated Learning, Blockchain, and Cybersecurity. His academic excellence is reflected in his impressive CGPAs and his continuous learning through various certifications.

Work Experience

Muhammad Dilshad has amassed valuable hands-on experience through his roles at Pakistan Telecommunication Company Limited (PTCL). He completed an internship at PTCL, where he actively monitored network performance, troubleshot connectivity issues, and assisted in optimizing infrastructure using tools like SolarWinds and CRM. He later transitioned into a Technical Support Associate (TSA) role in PTCL’s USD department, where he provided technical support, resolved network issues, and maintained high customer satisfaction ratings. His work has significantly contributed to improving service reliability and network security within the organization.

Research Interest

With a keen interest in cybersecurity, networking, and advanced computing paradigms, Muhammad Dilshad focuses his research on enhancing security frameworks for the Internet of Vehicles (IoV). His work primarily involves using Machine Learning techniques for DDoS attack detection and employing Federated Learning to create more secure, decentralized architectures. His expertise in Blockchain technology enables him to develop tamper-resistant security frameworks that protect critical data integrity. Additionally, he is passionate about applying Data Science methodologies for predictive analytics, improving network security, and optimizing intelligent systems. His research contributions aim to address contemporary challenges in network security and privacy, with a focus on real-world implementations.

Awards

Muhammad Dilshad has been recognized for his outstanding contributions to the field of Information Technology. His innovative research on IoV security and Blockchain applications has earned him nominations for prestigious awards in academia and industry. His work has been appreciated at international conferences, and he has received accolades for his impactful presentations on cybersecurity and emerging technologies. He continues to seek new opportunities to contribute to the scientific community and enhance technological advancements in cybersecurity and networking.

Publications

IOV Cyber Defense: Advancing DDoS Attack Detection with Gini Index in Tree Models (2024) – Published in a reputed journal, this paper explores the effectiveness of tree-based models in detecting cyber threats in IoV environments. Cited by multiple cybersecurity research articles.

Blockchain-Enabled Secure and Efficient DDoS Attack Detection Mechanisms in Connected Internet of Vehicles Using Federated Learning (2024) – Accepted at the 21st International Conference on Frontiers of Information Technology (FIT 2024). Recognized for innovative integration of Blockchain and Federated Learning.

Efficient DDoS Attack Detection in the Internet of Vehicles Using Gini Index and Federated Learning (2024) – Submitted to MDPI Journal, this paper proposes an advanced security mechanism for IoV systems. Highly relevant for researchers in cybersecurity.

Conclusion

Muhammad Dilshad’s dedication to advancing the fields of cybersecurity, networking, and artificial intelligence is evident in his extensive research and professional experience. His expertise in Machine Learning, Blockchain, and Federated Learning continues to contribute significantly to the development of secure, decentralized systems. Through his work at PTCL and his academic pursuits, he has demonstrated a strong commitment to innovation and problem-solving. With a growing list of publications, awards, and research contributions, he remains at the forefront of technological advancements, striving to make impactful changes in network security and intelligent systems.