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Lorenzo FarinaView profile

Professor

• Network Medicine
• Bioinformatics
• Systems Biology
• +6 more

Lorenzo Farina is a Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, specializing in Electronic and Computer Bioengineering (ING-INF/06). With over 25 years of academic leadership, he co-founded Italy's first Bioinformatics degree program and established key oncology precision medicine initiatives, maintaining active collaborations with Harvard Medical School's network medicine division. His educational background includes a cum laude Electronic Engineering degree and PhD in Systems Engineering, both from Sapienza University. These foundational studies evolved into pioneering work in positive linear systems theory, evidenced by his highly-cited Wiley textbook Positive Linear Systems: Theory and Applications (2000). Farina's research centers on network medicine – applying complex network science to molecular medicine since his 2004 breakthrough. His work spans cancer mechanisms (breast, glioblastoma, lung), drug repositioning (including COVID-19 applications), and liquid biopsy biomarker development. Current projects focus on miRNA-based network biomarkers for cancer diagnostics and immunotherapy response prediction, integrating multi-omics data through advanced computational frameworks. Analysis of his 15 most recent publications (2024-2025) reveals dominant themes: sexual dimorphism in cancer networks (MIRROR platform), immunotherapy response signatures, and critical examinations of AI's role in precision medicine. His work consistently bridges computational innovation with clinical applications, particularly in oncology diagnostics and therapeutic optimization. His scientific recognition includes: 2001 Guillemin-Cauer Award for best IEEE Transactions on Circuits and Systems article 2014 SysBio Award for annual best publication Farina actively mentors through interdisciplinary programs he established, including the Network Oncology doctoral program. His laboratory collaborations span Sapienza's Oncogenomics and Immunology Laboratories, Harvard's Channing Division of Network Medicine, and clinical departments in oncology and radiology, driving translational research from computational models to patient applications. He leads multiple research teams focused on network-based diagnostics, including the MIRROR platform for cancer disparity analysis and liquid biopsy development teams investigating circulating miRNA networks for early cancer detection across multiple malignancies.

• Software Engineering
• Model Checking
• Formal Verification
• +4 more

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

• Extreme wind loads on low-rise buildings
• Post-disaster field investigations
• Performance-based wind engineering
• +2 more

David Roueche serves as the Gottlieb Associate Professor of Structural Engineering within the Department of Civil and Environmental Engineering at Auburn University's Samuel Ginn College of Engineering. His research focuses on structural performance under extreme wind events, forensic engineering methodologies, and improving building resilience against hurricanes and tornadoes through interdisciplinary approaches. Dr. Roueche's academic foundation includes advanced degrees from the University of Florida, with complementary physics training: Ph.D. in Structural Engineering, University of Florida M.S. in Civil Engineering, University of Florida B.S. in Civil Engineering, University of Florida B.S. in Engineering Physics, Jacksonville University His primary research explores extreme wind loads on low-rise buildings , post-disaster field investigations , and performance-based wind engineering , with specialized expertise in light wood-frame structures and surge/flood modeling. He integrates engineering analysis with social science through survivor interviews to reconstruct tornado events and identify vulnerabilities in residential construction, particularly for mobile and manufactured housing in the Southeastern United States. Analysis of his recent publications reveals a dominant focus on post-disaster assessment frameworks, field data collection protocols, and performance-based evaluation methods for wind-affected structures. His work increasingly emphasizes interdisciplinary collaboration—combining engineering, social science, and geospatial technologies—to develop comprehensive disaster response systems and improve building codes. Key trends include standardization of forensic engineering practices through organizations like StEER and application of computational modeling to predict structural failures. Dr. Roueche's significant recognitions include: Ginn Faculty Achievement Fellow designation NSF CAREER Award (2020) for advancing post-windstorm assessment methodologies He directs substantial research funding including a $500,000 USDA grant for timber-steel composite research and leads the Auburn Mass Timber Collaborative—an interdisciplinary initiative uniting forestry, architecture, and engineering faculty. Through the Structural Engineering Emergency Response (StEER) network, he coordinates Field Assessment Structural Teams for disasters like Hurricane Ian and the 2022 Arabi tornado, developing standardized protocols adopted nationally for post-disaster evaluations. His mentoring extends to doctoral students in civil engineering, with recent success in securing competitive fellowships for advisees. As a core member of StEER, Dr. Roueche develops and implements field assessment protocols used in rapid disaster response. He leads FAST teams deploying UAVs, LiDAR, and ground surveys to document structural performance after hurricanes and tornadoes, with datasets informing FEMA guidelines and building code revisions. His work with the Auburn Mass Timber Collaborative advances sustainable construction methods through experimental testing of innovative structural systems.

Vincent HellendoornView profile

Assistant Professor

• Artificial Intelligence
• Software Engineering
• Programming Languages
• +4 more

Vincent Hellendoorn is a Research Scientist at Google DeepMind and an Assistant Professor at Carnegie Mellon University (currently on leave). He works in the School of Computer Science 's Software and Societal Systems Department , developing intelligent tools that leverage AI to democratize programming expertise through code modeling and LLM research. His research focuses on AI applications in software engineering Code language model analysis and training Multi-modal whiteboard-to-code systems Open-source model releases like PolyCoder Current work examines how to make programming more accessible through LLMs, with recent ICSE’25 research exploring whiteboard sketch translation. He advises PhD students including Nikitha Rao (7 papers, Spring 2025 PhD graduate) Luís F. Gomes (ICSE’25 paper lead) and collaborates with researchers like Jonathan Aldrich and Claire Le Goues. Contact: vhellendoorn@cmu.edu vhellendoorn@google.com GitHub: @VHellendoorn

Julia LawallView profile

Researcher

• Operating Systems
• Domain-Specific Languages
• Program Transformation
• +4 more

Julia Lawall is a Senior Research Scientist (Directrice de Recherche) at Inria-Paris, where she leads research in the Whisper group. She has made significant contributions to the fields of programming languages, operating systems, and software engineering, with a particular focus on program transformation and Linux kernel development. Her work bridges theoretical computer science with practical software engineering challenges. Dr. Lawall's research primarily centers on the design and implementation of domain-specific languages for operating system problems, program transformation techniques, and automated software evolution. Her most notable contribution is the Coccinelle framework, which has been instrumental in automating the evolution of Linux device drivers for over a decade. Her work spans from theoretical foundations in optimal reduction of the lambda calculus to practical tools that address real-world software maintenance challenges in large-scale systems like the Linux kernel. Her publication record demonstrates consistent contributions across multiple domains, with recent work focusing on Android API evolution, Linux kernel bug detection, and program transformation techniques. The trajectory of her research shows a progression from theoretical programming language concepts to increasingly practical applications in system software maintenance and evolution. EuroSys Test of time award for 'Documenting and Automating Collateral Evolutions in Linux Device Drivers' at EuroSys 2008 Best paper award for 'Diagnosys: Automatic Generation of a Debugging Interface to the Linux kernel' at ASE 2012 Most Influential ICFP Paper Award for foundational work on lambda calculus Best Reviewer at GPCE 2020 and Distinguished Reviewer at ASE 2020 Dr. Lawall has been actively involved in the academic community, serving as program co-chair for numerous prestigious conferences including ASE 2019, FSE 2026, and EuroSys 2025. She has also contributed to community initiatives as the Linux kernel coordinator for Outreachy (2015-2018) and as a member of the advisory board for Software Heritage. Her leadership extends to editorial roles, including associate editor for Higher-Order and Symbolic Computation and membership on the editorial board of Science of Computer Programming. She leads the Whisper research group at Inria-Paris, which focuses on program transformation techniques and their applications to system software. The group has developed several influential tools including Coccinelle, Coccinelle4J, LiLiput, Prequel, and JMake, which have had substantial impact on both academic research and industrial practice in software maintenance and evolution.

Sumon BiswasView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Programming Languages
• +5 more

Sumon Biswas is a tenure-track Assistant Professor in the Department of Computer and Data Sciences at Case School of Engineering, Case Western Reserve University. Previously, he was a Postdoctoral Researcher at the Institute for Software Research (ISR) at Carnegie Mellon University, working with Dr. Eunsuk Kang. He received his Ph.D. in Computer Science from Iowa State University under the supervision of Dr. Hridesh Rajan. His research focuses on the intersection of Software Engineering and Artificial Intelligence with particular emphasis on responsible AI engineering. His work spans several key areas: Formal verification and reasoning of fairness in AI systems Designing fair and safe AI systems AI engineering and analysis of machine learning software Long-term risks in machine learning systems Analysis of technical debt in AI/ML systems Dr. Biswas has made significant contributions to understanding and addressing fairness in machine learning pipelines, verification of neural networks, causal reasoning in ML pipelines, and safety assurance of predictive systems. His recent work increasingly focuses on foundation models and large language models (LLMs), with an emphasis on safety and responsible deployment of AI agents. His lab operates the state-of-the-art AISC2 cluster with five HGX H200 servers featuring 40 NVIDIA H200 GPUs. His publications show a consistent trend toward addressing both theoretical and practical challenges in responsible AI, with increasing focus on long-term system behavior, LLMs, and practical deployment challenges. The research spans formal methods, empirical studies, and practical tool development. Dr. Biswas has received several awards including the Research Excellence Award from Iowa State University and has been invited to serve on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology (TOSEM). He serves on the program committees of major software engineering conferences including ICSE, ASE, and ESEC/FSE, and has reviewed for prestigious journals such as IEEE Transactions on Software Engineering. As an educator, he teaches courses on Responsible AI Engineering and Software Engineering, focusing on building high-quality software systems that meet responsible AI principles including fairness, robustness, explainability, and safety.

Ben HermannView profile

Professor

• Programming Languages
• Security
• Static Analysis
• +6 more

Ben Hermann is a Professor for Secure Software Engineering at Technische Universität Dortmund, Germany, with research focusing on the intersection of programming languages and security. His work primarily centers on vulnerability detection using static analysis, risk assessment of software libraries, security guarantees in type systems, and language-based security. His academic journey includes a doctoral degree from Technische Universität Darmstadt in 2016, followed by postdoctoral work in Eric Bodden's Secure Software Engineering Group at the Heinz Nixdorf Institute. Since 2020, he has held a tenure-track professorship at TU Dortmund, after serving as an interim Professor for IT Security at Paderborn University from October 2019 to September 2020. Hermann's research portfolio demonstrates consistent output in static program analysis frameworks, particularly with contributions to Soot and PhASAR. His work spans vulnerability detection, analysis reusability, and research quality in computer science, with emphasis on artifact evaluation. His recent publications show progression from framework development toward security applications and research methodology. Hermann has been actively involved in numerous conferences including ASE, ICSE, ECOOP, and SPLASH, often serving in program committees and as session chair. His research group at TU Dortmund includes students like Maximilian Krebs who presented work on energy consumption prediction of programs. He maintains an active presence in the academic community, advocating for research quality and reproducibility, and serves in various organizational roles including Doctoral Symposium Chair and Diversity & Inclusion Chair for conferences.

Farhad AnsariView profile

Professor

• Structural Health Monitoring
• Smart Sensors
• Fiber Optic Sensors
• +4 more

Farhad Ansari is a UIC Distinguished Professor and Christopher B. and Susan S. Burke Professor of Civil Engineering at the University of Illinois at Chicago. He is a leading specialist in monitoring the structural status of bridges, dams, buildings and tunnels, with expertise in fiber optic sensor technology for structural health monitoring systems. His work spans infrastructure monitoring projects worldwide, including assessments of the Brooklyn Bridge and monitoring systems for bridges in China. Professor Ansari received his Ph.D. in Civil Engineering from the University of Illinois at Chicago (1983), M.S. in Civil Engineering from the University of Colorado (1979), and B.S. in Civil Engineering from the University of Illinois at Urbana-Champaign (1976). Professor Ansari's research focuses on the application of optical fiber sensors for structural health monitoring of civil infrastructure. His expertise includes forensic investigations of bridges, development of monitoring systems using both discrete (FBG) and distributed sensors (BOTDA, Rayleigh, MZ), and nondestructive testing methodologies. He has consulted on structural monitoring systems for bridges worldwide, including New York's Brooklyn Bridge, Lingotto Bridge in Turin, Italy, Manhattan Bridge, and numerous bridges across the United States and China. His recent publications demonstrate a strong focus on distributed fiber optic sensing technologies for bridge monitoring, with particular emphasis on crack detection, deflection monitoring, and vehicle weight detection without traditional influence lines. His work bridges civil engineering, materials science, and optical technology to create innovative solutions for infrastructure monitoring that are fast, accurate, and affordable. Professor Ansari has received numerous honors including the Aftab Mufti Medal for achievements in civil structural health monitoring (2018), Presidential Fellow of the University of Illinois System (2019-2020), and UIC Distinguished Professor designation (2015). He also received the Nova Award from the Construction Innovation Forum in 1997. As Editor-in-Chief of the Journal of Civil Structural Health Monitoring published by Springer, Professor Ansari has shaped the discourse in his field. He has served on numerous panels including as a Panel member for the Research Assessment Exercise (RAE) in Hong Kong and as Oversight Panel member for the IDEA Program of the National Cooperative Highway Research Program. His laboratory at UIC develops strategies for forensic analysis of bridges and other infrastructure, producing graduates with unique expertise in sensors, NDT, and structural health monitoring. Professor Ansari's Structural Health Monitoring Laboratory specializes in developing monitoring systems for civil infrastructure. The lab's capabilities are unique due to their combination of structural engineering expertise with specialized knowledge in fiber optic sensors. They work on the complete lifecycle of monitoring systems from design and installation to real-time data acquisition and analysis.

Xiaoyuan XieView profile

Professor

• software testing
• fault localization
• AI for software engineering
• +6 more

Xiaoyuan Xie is a Professor at the School of Computer Science, Wuhan University, where he leads a research group focused on software engineering, particularly in software testing, fault localization, and the intersection of AI and software engineering. His work bridges theoretical foundations with practical applications in software quality assurance and development processes. His research interests span software testing, fault localization, program slicing and analysis, debugging techniques, search-based software engineering, evolutionary computing, and machine learning applications in software engineering. He has pioneered work in metamorphic testing, particularly for AI systems, and developed innovative approaches for fault localization that consider exception trigger streams and parallel debugging contexts. Xie's publication trend shows a strong focus on applying AI techniques to software engineering problems, with recent work emphasizing metamorphic testing for AI systems, code search, debugging assistance, and fault localization enhanced by deep learning. His research bridges traditional software engineering with modern AI techniques, creating novel solutions for testing and debugging complex software systems. ICST 2025 Most Influential 10-years Journal First Paper Award Multiple ACM SIGSOFT Distinguished Paper Awards (2024, 2022, 2021) CCF Open Source Innovation Competition First Prize 2024 CCF NASAC Software Innovation Award for Young Researchers 2021 ACM SIGEVO HUMIES 2017 Silver Medal Luojia Young Scholar, Wuhan University 2016 Professor Xie has supervised numerous PhD and Master's students who have gone on to prestigious positions at companies like Alibaba, Tencent, and ByteDance, as well as academic institutions including Yale University and the Hong Kong University of Science and Technology. His research is supported by multiple national grants, including NSFC重点项目 (key projects), 国家重点研发计划 (key R&D programs), and the Research Fund for International Excellent Young Scientists from NSFC.

Ning YuView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +6 more

Ning Yu serves as an Associate Professor in the Department of Computing Sciences within the School of Arts & Science at State University of New York Brockport. He earned his Ph.D. in Computer Science from Georgia State University and joined SUNY Brockport in 2017 after serving as a Tenure-Track Assistant Professor at the University of South Carolina Upstate. Georgia State University, Computer Science, Ph.D. Southern Illinois University Carbondale, Computer Science, M.S. Dr. Yu's research spans artificial intelligence, network and information security, big data analytics, deep learning, and cloud computing with significant applications in bioinformatics. His work demonstrates a clear progression from foundational AI and security research toward specialized applications in healthcare and energy systems. The most recent publications show increased focus on graph-based deep learning approaches for biomedical problems, particularly in cancer genomics and drug response prediction. His scholarly impact includes over 40 publications in prestigious venues including ACM/IEEE Transactions, BMC, PLoS, and Information Sciences. The publication trend reveals consistent output with increasing emphasis on interdisciplinary applications, particularly at the intersection of AI and biomedical research. Teacher of The Year, School of Science and Art, SUNY Brockport 2022-2023 Influential Professor 2022, SUNY Brockport, Fall 2023 Provost Post-Tenure Scholarship Award, $3,500, SUNY Brockport, Spring 2024 Google Research Credits Grants (2018, 2020-2021) WORLDWIDE TOP 10 FINALISTS, IBM 2017 Watson Analytics Global Competition As an educator, Dr. Yu has mentored numerous undergraduate researchers who have presented at national conferences including NCUR and SURC. He founded SUNY Brockport's first ACM SIGAI Student Chapter and has secured significant funding including a multi-campus SUNY IITG grant for AI education development. His research group actively recruits students for projects involving cloud development (Azure/GCP/AWS), CI/CD, Docker/K8s, and software architecture. Prior to academia, Dr. Yu accumulated 10 years of professional experience in software development and system networking with certifications from Cisco, Microsoft, and Google.

Reid HolmesView profile

Professor

• Software Engineering
• Software Testing
• Code Search
• +7 more

Reid Holmes is a Professor in the Software Practices Lab at the University of British Columbia's Department of Computer Science, Faculty of Science. With over 15 years of academic experience, he has progressed from Assistant Professor at the University of Waterloo (2010-2015) to Associate Professor (2015-2022) and now full Professor (2022-present) at UBC. His research spans multiple dimensions of software engineering with a strong emphasis on the human aspects of development. Dr. Holmes' research interests focus on understanding the problems software engineers encounter when creating and evolving software systems. His work examines software testing and validation, source code reuse, code search, context-sensitive example location, API understanding, speculative analysis, code review, and team awareness. He believes that by better understanding how people create, explore, evolve, and reason about software systems, we can enhance developers' effectiveness and improve software quality. His research is characterized by its practical application to real-world software development challenges. His recent publications demonstrate a consistent focus on improving developer productivity through better tools and understanding of developer behavior. The publications reveal a trajectory from traditional software engineering topics toward newer areas involving AI-assisted development, with particular attention to how generative AI affects workflow and collaboration. His work consistently bridges theoretical software engineering concepts with practical developer experience considerations. ACM SIGSOFT Distinguished Paper Award (multiple times) FSE 2024 Most Impactful Paper Award 2024 ICSE Most Influential Paper Award Winner of the 2008 MSR Mining Challenge As an educator and mentor, Dr. Holmes has supervised numerous graduate students who have gone on to successful careers in academia and industry. He has served on program committees for major software engineering conferences including ICSE, FSE, and MSR, and has been an Associate Editor for TSE since 2016. His work on developer tools like CodeShovel, Devy, and Baker demonstrates his commitment to creating practical solutions that address real developer pain points.

Luca VizioliView profile

Associate Professor

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• +3 more

Luca Vizioli is an Associate Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research. His work focuses on advancing neuroimaging techniques, particularly at ultrahigh magnetic fields (e.g., 10.5T), to enhance spatial and temporal resolution in functional MRI (fMRI). Research interests include RF coil design, noise reduction algorithms (e.g., NORDIC), and applications of deep learning to imaging challenges. Education details are not explicitly stated, though his PhD is noted. His research spans cutting-edge topics like laminar fMRI at UHF, denoising strategies for submillimeter resolution, and the impact of psychedelic substances on brain networks. He collaborates on tools like fMRIPrep Lifespan for developmental neuroimaging preprocessing. Key technical innovations include high-Quality 0.5mm isotropic fMRI leveraging random matrix theory and physics-driven AI, as well as multi-echo distortion correction frameworks. His work bridges engineering (coil design) and neuroscience (cortical processing mechanisms), with implications for understanding both normal brain function and neurological disorders.

Sanka RasnayakaView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +6 more

Sanka Rasnayaka is a Lecturer at the School of Computing, National University of Singapore , where he has worked since 2022. He previously served as a full-time Teaching Assistant and PhD candidate at the same institution. PhD in Computer Science (2021), National University of Singapore B.Sc. Engineering (Honours) in Computer Science and Engineering (2016), University of Moratuwa, Sri Lanka Research interests focus on Artificial Intelligence and Computer Vision , specifically in biometrics, privacy, and human perception. Current projects explore: Biometrics & Continuous Authentication (gait, keystroke dynamics) Large Language Models for Software Engineering Physics-Informed Generative AI for flood modeling Recent publications demonstrate expertise in: Biometric authentication systems Privacy-preserving AI Transformer architectures for behavioral analysis Generative AI in environmental modeling Teaching includes foundational courses like CS1101S Programming Methodology (co-teaching since 2022) and CS2040 Data Structures and Algorithms (AY2410). He also developed workshops on Generative AI, LLMs, and Computer Vision. Projects supervised: CRISP Platform for classroom management Live Annotation Tool for educators AI Pal for customized LLM assistants Scientific awards include multiple Teaching Excellence Awards (2024-2025) and scholarships from NUS (2017-2021).

• Cybersecurity
• Network Protocols
• Cloud Computing
• +7 more

Damir Regvart is a lecturer at Algebra University of Applied Sciences , specializing in Cybersecurity , Network Protocols , and Cloud Computing . With a background in Electrical Engineering and a Master's in Electrical Engineering from the Faculty of Electrical Engineering and Computing in Zagreb (2002), he focuses on advanced network security protocols, Zero-touch technologies, and cloud infrastructure. Research Highlights : Zero Trust Architecture, Honeypot Deception Strategies, Machine Learning in Threat Detection, Microsoft Azure Security, and Infrastructure as Code hardening. Key Projects : Pan-European and national initiatives in network automation, cloud forensics, and IoT security. Recent Publications (2024-2025) explore cutting-edge topics like AI-driven security testing, forensic capabilities in cloud environments, and the intersection of SDN and cybersecurity. His work addresses vulnerabilities in cryptographic systems and the legal implications of digital signatures. Technology Focus : Zero-touch provisioning, Microsoft Sentinel automation, and RedFish/vSphere API integration.

Rui ChenView profile

Researcher

• Genomics
• Machine Learning
• Cancer Biology
• +3 more

Rui Chen, PhD, is a Researcher in the Department of Molecular Physiology and Biophysics at Vanderbilt University School of Medicine. Their work focuses on integrating multi-omics data with advanced computational methods to uncover genetic mechanisms in complex diseases, including schizophrenia, cancer, and neurodevelopmental disorders. Dr. Chen’s research emphasizes leveraging machine learning for precision medicine applications, such as predicting cancer outcomes and identifying druggable genes. Key areas of expertise include functional genomics, Bayesian statistical frameworks, and computational tool development for genomic data quality control (e.g., the DRAMS tool). Their studies often bridge basic science and clinical translation, using zebrafish models and biobank resources for disease discovery. Dr. Chen has contributed to advancements in understanding gene regulatory networks in plants (e.g., rice stamen development) and has pioneered methods for analyzing non-coding variants using deep learning. Their work consistently addresses translational challenges in genetics, such as improving rare variant association studies and refining GWAS interpretations through multi-omics integration. Contact: rui.chen.1@vanderbilt.edu