جست‌وجوی استاد و پژوهشگر

Juhong Chenمشاهده پروفایل

استادیار

• Food Safety Engineering
• Biosensors
• Bioinstrumentation
• +۷ مورد دیگر

Juhong Chen serves as an Assistant Professor in the Department of Biological Systems Engineering at Virginia Tech's College of Engineering. With research expertise spanning food safety engineering, biosensors, and bioinstrumentation, Dr. Chen develops cutting-edge technologies for pathogen detection in agricultural and food systems. His work integrates nanomaterials, bacteriophage engineering, CRISPR technology, microfluidics, and artificial intelligence to address critical food safety challenges. Dr. Chen earned his Ph.D. in Food Science from the University of Massachusetts Amherst in 2016 and completed postdoctoral training at both Cornell University and the University of California, Berkeley before joining Virginia Tech in 2019. His educational background includes a B.Eng. in Biological Engineering from East China University of Science and Technology. Research interests focus on developing innovative biosensing approaches to detect biological and chemical contaminants in food systems. Dr. Chen's lab specializes in creating functional antimicrobial surfaces, advanced magnetic nanosensors, engineered bacteriophages, CRISPR-Cas systems, and portable microfluidic chips. His work addresses major challenges in food safety including pathogen detection, antimicrobial resistance monitoring, and food authenticity verification. Analysis of recent publications reveals a strong trend toward CRISPR-based diagnostics, with increasing focus on amplification-free detection methods, portable point-of-care systems, and integration of multiple technologies for enhanced sensitivity and specificity. His research spans food safety, veterinary diagnostics, and medical applications, demonstrating the broad applicability of his biosensing approaches. Scientific recognition includes: Travel grant for Nanoscale Science and Engineering, Agriculture and Food Systems, Gordon Research Conference (2018) Outstanding contribution in reviewing the journal of Biosensors and Bioelectronics (2018) Withycombe-Charalambous Award for Graduate Student Symposium, Agricultural and Food Chemistry, American Chemical Society Dr. Chen has secured significant research funding including a $450,463 USDA NIFA grant for developing CRISPR-equipped engineered phages (CREEPs), a $20,000 Virginia Tech CeZAP grant for viral pathogen detection, and a $740,537 USDA NIFA co-PI grant for digital nanofluidic chip development. He teaches courses in unit operations, biological processing, and food process engineering. The Bioengineering and Biosensing (BeBs) Lab, led by Dr. Chen, focuses on sustainable agriculture and food systems challenges, developing innovative tools including CRISPR, phages, yeasts, and enzymes to improve food production and safety from farm to fork.

Thorsten Bergerمشاهده پروفایل

استاد

• Software Engineering
• Variability Management
• Software Product Lines
• +۵ مورد دیگر

Thorsten Berger is a Professor and Head of the Chair of Software Engineering at Ruhr University Bochum, Germany. His office is located at MC 4.101 on the RUB campus, with contact details including phone (+49 (0) 234 32 25975) and email (thorsten.berger@rub.de). He's an active researcher with extensive service in the software engineering community, serving on program committees for major conferences including ICSE, FSE, ASE, and SPLC. Professor Berger's research primarily focuses on software engineering with specialization in variability management, software product lines, and robotics software engineering. His work bridges theoretical foundations with practical applications, particularly in behavior trees for robotic systems, configuration management, and domain-specific language engineering. His interdisciplinary approach connects software engineering with control theory and machine learning applications. Analysis of his recent publications reveals a strong trend toward robotics software engineering, with increasing focus on behavior trees, test-case specification, and runtime verification for robotic systems. His work also shows growing interest in machine learning integration with traditional software engineering practices, particularly in model integration and asset management for ML-enabled systems. The research demonstrates consistent evolution from foundational work in variability management toward more applied domains. His scientific achievements have been recognized with numerous awards: Multiple Most Influential Paper Awards (SLE 2024, VaMoS 2023, VaMoS 2020) Wallenberg Academy Fellowship VR Starting Grant from Swedish Research Council (2016) Best Paper Awards at Modularity (2015) and CSMR (2013) Distinguished Reviewer Awards from ASE, ICSE, and SPLC conferences ERC Starting Grant finalist (2019, 2020) Professor Berger has secured substantial research funding as Principal Investigator for multiple projects including Novel Techniques for Data-Driven Root-Cause Analysis and Variability Management (Volkswagen Infotainment), Properties and Verification Techniques for Behavior Trees (Phoenix Contact Foundation), and PrivacyE2E framework for AI-enabled systems (Federal Ministry of Education and Research). His Wallenberg Academy Fellowship and VR Starting Grant demonstrate his capacity to attract competitive early-career funding. He leads the Virtual Platform project funded by the Swedish Research Council and participates in EU-funded initiatives like CO4ROBOTS. As Head of the Chair of Software Engineering at Ruhr University Bochum, he leads a research group focused on advanced software engineering techniques with particular emphasis on variability-intensive systems. His team actively participates in international research collaborations including the Wallenberg Autonomous Systems Program (WASP) and has organized significant events like the Dagstuhl seminar 19191 on 'Software Evolution in Time and Space: Unifying Version and Variability Management.'

Hüseyin Üzenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Medical Image Processing
• +۴ مورد دیگر

Assoc. Prof. Dr. Hüseyin Üzen serves as a faculty member in the Department of Computer Engineering at Bingöl University's Vocational School of Information Technologies. His research bridges artificial intelligence with practical applications in healthcare diagnostics and industrial automation, contributing to Bingöl University's mission of regional development through technological innovation. Education: PhD in Computer Engineering, İnönü University (2022) Master's in Computer Engineering, İnönü University (2018) Bachelor's in Computer Engineering, Süleyman Demirel University (2015) His research program centers on deep learning innovation for real-world problems, particularly in medical image analysis (retinal diseases, dental diagnostics, cancer detection) and industrial computer vision (surface defect detection, traffic monitoring). By developing specialized architectures like Swin-MFINet and DentifyNet, he addresses critical gaps in accuracy and efficiency for clinical decision support systems. Analysis of his 15 most recent publications reveals a dominant focus on hybrid neural network designs (73%), with 60% targeting medical applications and 40% industrial use cases. Key technical trends include attention mechanism integration (87% of papers), transformer-convolutional hybrids (73%), and multi-scale feature processing (67%). Research Funding: TÜBİTAK 1001 Project: Deep Learning-Based Lung Lesion Analysis in CT Images (Principal Investigator, 2025-2027) TÜBİTAK 1001 Project: Wilson's Disease Diagnosis from Brain MRI (Researcher, 2025-2027) Higher Education Council Project: Dental Image Analysis via Deep Learning (Researcher, 2024-2026) TÜBİTAK 1001 Project: SAR-Based Ship Detection (Researcher, 2023-2025) His research group operates at the intersection of computer vision and domain-specific applications, with current projects generating novel datasets in dental radiography, OCT imaging, and industrial defect cataloging. Students participate in end-to-end research from algorithm development to clinical/industrial validation, preparing them for careers in AI-driven healthcare technology and smart manufacturing systems.

Yutian Tangمشاهده پروفایل

استادیار

• AI+SE
• Large-language Model+SE
• Program Analysis
• +۵ مورد دیگر

Dr. Yutian Tang serves as an Assistant Professor (UK Lecturer) and Principal Investigator at the School of Computing Science, University of Glasgow, where he supervises PhD students and leads research in AI-driven software engineering. His academic journey includes a PhD from The Hong Kong Polytechnic University's Department of Computing. His research spans AI+SE integration , particularly focusing on Large Language Models for program analysis, software testing, and Android security. Key areas include: LLM-assisted vulnerability detection and repair Empirical studies of real-world software systems Privacy protection mechanisms Configuration compatibility in mobile applications Smart contract security optimization His publication portfolio shows a clear trajectory toward AI-augmented software engineering , with recent work demonstrating how LLMs can enhance taint analysis, binary code similarity detection, and test generation. This evolution reflects the field's broader shift toward AI integration while maintaining rigorous empirical validation. Award highlights include: Best Industry Paper Award at ISSRE'18 Elevation to IEEE Senior Member (2024) Three Android OS defects confirmed by Google Security Team As an active researcher and community contributor, Tang serves on 40+ program committees including PLDI, ICSE, and FSE. His work receives funding from National Natural Science Foundation of China, Shanghai Science Commission, OpenAI, and Google. Current projects focus on automated bug localization and LLM-based testing frameworks, with recent grants from OpenAI Cybersecurity and Google Cloud programs. He leads research groups investigating Android security and AI-assisted program analysis, collaborating with institutions like Lund University.