جستجوی استاد راهنمای دانشگاهی

• Infectious Disease Epidemiology
• Vaccine-Preventable Diseases
• Sexually Transmitted Infections
• +۷ مورد دیگر

Linda Niccolai is a Professor in the Department of Epidemiology of Microbial Diseases at Yale School of Public Health and serves as Senior Associate Dean for Education at Yale School of Medicine. She holds multiple leadership roles including Associate Dean for Academic Affairs at Yale School of Public Health, Director of the CT Emerging Infections Program at Yale, and Director of the HPV Working Group at Yale. Dr. Niccolai is also an Affiliated Faculty member of the Yale Institute for Global Health and contributes to the Cancer Prevention and Control Program at Yale Cancer Center. Dr. Niccolai received her Sc.M. in Epidemiology from Harvard School of Public Health and her Ph.D. in Epidemiology from Tulane University. She has been teaching epidemiology methods courses to MPH, MS, and PhD students for over 20 years at Yale. As an internationally recognized scholar in infectious disease epidemiology, Dr. Niccolai's research focuses on vaccine-preventable diseases and sexually transmitted infections. Her work includes: Understanding uptake, impact, and effectiveness of HPV and other adolescent vaccines Addressing health disparities in vaccine coverage Studying social and behavioral aspects of STI transmission and prevention Conducting public health surveillance on respiratory viruses (including SARS-CoV-2) Monitoring foodborne illness and healthcare-associated infections Dr. Niccolai's research program is interdisciplinary by design and includes behavioral and clinical epidemiology, surveillance, and qualitative approaches. Her work is funded by a diverse portfolio from NIH, CDC, and private foundations. Dr. Niccolai has received numerous honors including: Fellow in the Hedwig van Ameringen Executive Leadership in Academic Medicine Program (2023-2024) Immunization Champion for State of Connecticut from the CDC Population Science Research Prize from Yale Cancer Center Award for Excellence in Teaching at Yale School of Public Health Election to the Connecticut Academy of Science and Engineering Through her leadership of the HPV Working Group, Dr. Niccolai brings together researchers and stakeholders committed to addressing low HPV vaccine uptake. The group develops and implements evidence-based strategies to improve HPV vaccination coverage and decrease the burden of HPV-related diseases in Connecticut.

Zhongxin Liuمشاهده پروفایل

استادیار

• Intelligent Software Engineering
• AI for Software Engineering
• Code Intelligence
• +۵ مورد دیگر

Zhongxin Liu is a Distinguished Research Fellow (Assistant Professor) and Doctoral Supervisor at the College of Computer Science and Technology, Zhejiang University. He received his Ph.D. from Zhejiang University under the supervision of Prof. Shanping Li. His research focuses on intelligent software engineering (AI4SE), particularly using AI to help developers understand, write, change, and test code through learning from software "big data". His educational background includes: Ph.D. in Computer Science, Zhejiang University (2016-2021) Dr. Liu's research interests center around intelligent software engineering with emphasis on code intelligence, program analysis, and software testing. His work leverages machine learning, particularly large language models, to solve challenging problems in software development including code generation, bug localization, and test case generation. He has made significant methodological contributions to the field through his innovative approaches to understanding and improving the software development lifecycle. His recent publications (2024-2026) demonstrate a clear trend toward leveraging large language models for software engineering tasks, with particular focus on code generation, fault localization, and program analysis. The research spans multiple domains including vulnerability detection, smart contract development, and cross-domain code search, showing how AI can enhance traditional software engineering practices. Dr. Liu has received numerous prestigious awards for his contributions: ACM SIGSOFT Distinguished Paper Award (ISSTA 2025, ASE 2018-2020) Distinguished Paper Award (APSEC 2023) Zhejiang University Education Foundation Qizhen Scholar (2021) Distinguished Doctoral Thesis of Zhejiang University (2021) As a Doctoral Supervisor at Zhejiang University, Dr. Liu mentors graduate students in intelligent software engineering. He serves on program committees for major conferences including ICSE, ASE, and FSE. His research group actively recruits undergraduate interns, graduate students, and postdocs to work on code intelligence projects. Dr. Liu is also scheduled to be a Visiting Professor at the University of Stuttgart from October 2024 to January 2025. Dr. Liu leads a vibrant research group at Zhejiang University focused on advancing AI-powered tools for software development. His work bridges theoretical advances in machine learning with practical applications in software engineering, creating novel solutions that help developers be more productive and produce higher quality code.

Luciano Baresiمشاهده پروفایل

استاد

• Software Engineering
• Self-Adaptive Systems
• Edge Computing
• +۵ مورد دیگر

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

Roustiam Chakirovمشاهده پروفایل

استاد

• Control Engineering
• Electromobility
• Mechatronic Vehicle Systems
• +۴ مورد دیگر

Professor Roustiam Chakirov serves as a faculty member in the Department of Engineering and Communication at Bonn-Rhein-Sieg University of Applied Sciences (H-BRS), specializing in control engineering for automotive systems. His work bridges theoretical research and industrial applications in sustainable mobility and energy systems. Education: Doctorate in Electrical Drives/Control Engineering, Nizhny Novgorod University of Technology, Russia Studies in Automation Engineering Research Focus: Chakirov's work centers on electromobility , mechatronic vehicle systems , and renewable energy integration . His expertise spans rapid control prototyping , neural networks for photovoltaic systems , and energy efficiency optimization through adaptive control. Current projects address vehicle electrical system management, wind-to-heat conversion, and smart microgrid integration for electric vehicles. Publication Trends: His 2006-2015 publications reveal a consistent trajectory from foundational control theory toward applied sustainable mobility solutions. Key themes include photovoltaic optimization (2015), multi-bus vehicle power management (2014), and electric vehicle charging coordination (2013), demonstrating interdisciplinary convergence of control engineering, renewable energy, and automotive systems. Scientific Recognition: Honorary Doctorate from Chernihiv National University of Technology (2016) for German-Ukrainian research cooperation in Renewable Energies and Electromobility Research Leadership: Chakirov directs third-party funded projects including the TREE-Energy Lab (TRE³L) partnership with GKN Driveline and GKN Sinter Metals. His work receives support from the German Academic Exchange Service and involves Horizon 2020 collaborations, focusing on hydrogen technology, powder metallurgy, and eco-mobility solutions. Laboratory Infrastructure: He operates within H-BRS's TREE-Energy Lab complex comprising Powder Fabrication-Lab, Mobility-Lab, Hydrogen-Lab, and Simulation-Lab, enabling integrated research from component design to system-level energy management.

Michael Scharfمشاهده پروفایل

استاد

• Communication Networks
• TCP/IP Protocols
• Network Management
• +۶ مورد دیگر

Professor Michael Scharf is a faculty member at Esslingen University of Applied Sciences, where he serves as Professor for Communication Networks since September 2018. His expertise lies in communication networks, with a particular focus on TCP/IP protocols, network management, and optimization techniques. Esslingen University of Applied Sciences, School of Computer Science and Information Technology Professor for Communication Networks (2018-present) Laboratory Management of Communication Technology Laboratory Dr. Scharf's research interests span applied communication networks, with special emphasis on modern TCP/IP applications for IoT and Automotive Ethernet, as well as network management and data modeling using NETCONF/YANG frameworks. His work bridges theoretical networking concepts with practical implementations, particularly in the areas of congestion control, multipath transport, and application-layer traffic optimization. His publication portfolio demonstrates consistent contributions to networking research, with recent focus on YANG data modeling for TCP, IoT networking standards, and ALTO protocol extensions. The research trajectory shows evolution from congestion control mechanisms to modern network data modeling and IoT applications. Best presentation/demo award for 'Monitoring and Abstraction for Networked Clouds', Proc. ICIN, Oct. 2012 Chair of the TCPM working group in the IETF from 2011 to 2022 Reviewer for TCP/UDP port numbers allocation by IANA since 2022 Dr. Scharf has supervised numerous student works including Bachelor's and Master's theses, study projects, and research projects. His industry experience prior to academia included significant roles at Alcatel-Lucent Bell Labs and Nokia, where he worked on network management systems and was recognized as a Distinguished Member of Technical Staff. He leads the Communication Technology Laboratory (KTlab) at Esslingen University, which focuses on practical implementations and testing of communication network technologies, particularly in the areas of cyber-physical networks, embedded systems communication, and automotive communication networks.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.