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John J. LucidoView profile

Academic

• Treatment planning for radiation therapy
• Special procedures in radiation therapy
• Quality assurance in radiation therapy
• +5 more

John J. Lucido, III, Ph.D., is a Medical Physicist at the Mayo Clinic in Rochester, Minnesota, specializing in Radiation Oncology . He is affiliated with the Mayo Clinic College of Medicine and Science and actively involved in clinical research, education, and global medical physics initiatives. Education: Ph.D. in Medical Physics (2013) - University of British Columbia M.S. in Mathematical Physics (2008) - Rutgers University B.S.E. in Engineering Physics - Electrical Engineering (2008) - University of Michigan Dr. Lucido's research focuses on advanced radiation therapy techniques , including: Automated treatment planning systems Dosimetric characterization of novel radiation devices Machine learning applications in organ-at-risk segmentation Clinical decision tools for spine stereotactic body radiation therapy (SBRT) Quality assurance protocols for radiation delivery His publications highlight the integration of Monte Carlo simulations , deep learning , and radiobiological modeling to optimize radiation therapy for complex cases such as pregnant patients, ultracentral lung cancers, and skin electron beam therapy. Scientific awards and honors include: Teacher of the Year - Radiation Physics (Mayo Clinic, 2022) International Graduate Student Fellowship (University of British Columbia, 2009) Graduate Aid in Areas of National Need Fellowship (Rutgers University, 2005) Certified Therapeutic Medical Physicist (American Board of Radiology, 2016) Dr. Lucido actively contributes to professional societies as a Voting Member of the Society of Directors of Academic Medical Physics Programs (SDAMPP) , Chair of the Eclipse Scripting and Automation Group , and International Consultant for C/CAN - Kumasi City Radiotherapy Project . He also serves on multiple Mayo Clinic committees related to education and quality assurance. His work emphasizes global radiation therapy standardization and educational infrastructure development , particularly in underserved regions like Ghana. Recent projects include automated testing platforms for treatment planning scripts and customizable radiation shields for skin therapy.

• Systems Engineering
• Control Systems Design
• Thermal Management Systems
• +5 more

James Allison is an Associate Professor in both the Industrial and Enterprise Systems Engineering and Aerospace Engineering departments at the University of Illinois at Urbana-Champaign. He is also affiliated with the Carl R. Woese Institute for Genomic Biology. His research focuses on systems engineering, control systems design, thermal management systems, and optimization methodologies. Dr. Allison has contributed to advancements in fluid-based thermal management systems, floating offshore wind turbine control co-design, and AI-driven design optimization. His work emphasizes interdisciplinary approaches, blending mechanical, aerospace, and computational engineering principles. Key research areas include design automation, graph neural networks for system architecture exploration, and reliability-based co-design of complex systems. He has pioneered methodologies for extracting design knowledge from optimization data and advancing multifunctional structures for attitude control in aerospace systems. Awards: NSF CAREER Award (2017) Labs/Groups: Involved in the development of tools like LGR-MPC and SS-MPC for Model Predictive Control, and the WEIS toolset for offshore wind turbine analysis. Dr. Allison’s recent publications highlight contributions to thermal management system configurations, control strategies for offshore renewable energy systems, and topology optimization techniques. His research bridges theoretical advancements with practical applications in aerospace, energy, and manufacturing sectors.

Mariana SilvaView profile

Academic

• Educational Technology
• Computer Science Education
• Large Language Models (LLMs) in Assessment
• +3 more

Mariana Silva is a Teaching Associate Professor at the Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign, and CEO of PrairieLearn Inc. She holds a Ph.D. in Theoretical and Applied Mechanics from UIUC (2009). Her research focuses on leveraging educational technologies, such as Large Language Models (LLMs), to enhance computer-based assessments and scalable teaching practices. Silva has taught over 11 courses to 9,000+ students, emphasizing innovations in STEM education. She has pioneered adaptive testing tools and randomized question generators to improve equity and accessibility in large-scale courses. Education : Ph.D., Theoretical and Applied Mechanics, UIUC (2009) M.S., Mechanical Engineering, Federal University of Rio de Janeiro (2003) B.S., Mechanical Engineering, Federal University of Rio de Janeiro (2001) Research Interests : Silva’s work centers on technology-driven education , including automated grading using LLMs, design of adaptive assessment systems, and fostering collaborative learning. She has developed tools like PrairieLearn to streamline teaching workflows while maintaining educational rigor. Awards & Recognition : Scott H. Fisher Computer Science Teaching Award (2022) Rose Award for Teaching Excellence (2022) Multiple “List of Teachers Ranked as Excellent” (2009–2017) Engineering Council Outstanding Advising Award (2014–2019) Labs & Teams : Co-founder of PrairieLearn Inc., which provides open-source platforms for STEM education. She collaborates with interdisciplinary teams to advance educational technologies and testing infrastructure.

Alessandro OrsoView profile

Professor

• Software Engineering
• Software Testing
• Program Analysis
• +3 more

Alessandro (Alex) Orso is a Professor in the School of Computer Science and Interim Dean of the College of Computing at Georgia Institute of Technology. He holds an M.S. in Electrical Engineering (1995) and a Ph.D. in Computer Science (1999) from Politecnico di Milano, Italy. Since 2000, he has been a faculty member at Georgia Tech. Affiliations: School of Computer Science, Scientific Software Engineering Center, Center for Experimental Research in Computer Systems (CERCS), and Online Master of Science Computer Science (OMSCS). Research Focus: Software engineering with emphasis on testing, program analysis, and improving software reliability/security through formal methods and tools. His research has been funded by DARPA, NSF, IBM, and Microsoft, among others. He co-founded the Scientific Software Engineering Center to advance methodologies for high-performance scientific software. Orso is a Distinguished Member of the ACM and an IEEE Fellow. Key contributions include developing techniques for automated REST API testing, program debloating, and cross-browser web application testing. His work bridges theory and practice, emphasizing real-world system validation. Awards: Four impact awards: ISSTA (2017, 2021), ASE (2020), IBM Haifa (2013) Editorial roles: ACM TOSEM, IEEE TSE Program chairs: ISSTA 2010, FSE 2014, ICSE 2017 Advising & Grants: Supervised over 40 students (PhD, Master's, undergrad). Secured funding from government/industry partners. Tools developed include AutoRestTest, Barista, and X-PERT. Labs/Teams: Leads the Arktos Research Group, focusing on software testing, analysis, and tool development. Collaborates with industry and government on applied research projects.

Paria ShiraniView profile

Assistant Professor

• Cybersecurity
• IoT Security
• Vulnerability Research
• +4 more

Paria Shirani is an Assistant Professor and Tier 2 Canada Research Chair in Cybersecurity at the School of Electrical Engineering and Computer Science (EECS), University of Ottawa. She holds a PhD in Information Systems Engineering from Concordia University (FRQNT Doctoral Scholarship recipient) and completed an NSERC Postdoctoral Fellowship at Carnegie Mellon University (CMU). Her research focuses on cybersecurity, including IoT security, vulnerability detection, malware analysis, threat intelligence, and AI/ML applications. She leads funded projects across undergraduate, master’s, PhD, and postdoctoral levels, emphasizing equity, diversity, and inclusion (EDI). Research Highlights: Develops AI-driven solutions for IoT security and vulnerability detection. Pioneers binary code fingerprinting and firmware analysis techniques. Advances threat intelligence through machine learning and anomaly detection. Key awards include the NSERC Postdoctoral Fellowship, FRQNT Doctoral Scholarship, and the Tier 2 Canada Research Chair. Collaborations involve institutions like Concordia University, Carnegie Mellon University, and IBM’s Cyber Range. She actively serves on editorial boards (e.g., ACM Computing Surveys) and organizes conferences (e.g., SecureComm, PST).

Matthew B. DwyerView profile

Professor

• Software Verification and Validation
• Software Engineering
• Program Analysis
• +4 more

Matthew B. Dwyer is the Robert Thomson Distinguished Professor of Computer Science at the University of Virginia, leading research in software verification, program analysis, and autonomous systems. His work focuses on formal methods for ensuring dependable software, particularly in safety-critical domains like autonomous vehicles. He has advised numerous PhD and Master’s students, many of whom hold academic and industry positions globally. Education: Ph.D. in Computer Science from the University of Massachusetts Amherst, M.S. from UMass Boston, and BSEE from the University of Rochester. Research Interests: Software Verification & Validation, Program Analysis, Formal Methods, Neural Network Testing, Safety-Critical Systems. His lab, the Laboratory for Engineering Safe Software, develops tools like DNNV for verifying deep neural networks. Awards: ACM Fellow (2019), IEEE Fellow (2013), multiple test-of-time and impact paper awards from ISSTA, ICSE, and SIGSOFT conferences. Service: Program chair for ICSE (2022), FSE (2004), and other top venues; editorial roles at IEEE TSE, ACM TOPLAS, and Springer STTT. Co-developed property specification patterns for model checking, widely used in industry and academia.

Amlan ChatterjeeView profile

Associate Professor

• High-Performance Computing
• Big Data
• Graph Compression
• +5 more

Dr. Amlan Chatterjee is an Associate Professor in the Department of Computer Science at California State University, Dominguez Hills. His work focuses on high-performance computing, big data analytics, and GPU-based graph compression techniques. He has held academic roles since 2015, including Assistant Professor (2015-2021) and currently serves as Associate Professor. His research explores efficient computation on large datasets using multi-core architectures and GPUs, alongside cloud computing optimization and IoT applications in aviation and health monitoring. Education: Ph.D., Computer Science, University of Oklahoma, 2014 M.S., Computer Science, State University of New York, 2009 B.Tech., Computer Science & Engineering, West Bengal University of Technology, 2007 Research Interests: Dr. Chatterjee's research spans graph compression, social network analysis, cloud resource optimization, and IoT-driven solutions for aviation safety and health monitoring. He has mentored students in projects like GPU-based big data processing and cloud computing efficiency. Awards: Graduate Computer Science Scholarship (University of Oklahoma, 2012-13) Computer Science Advisory Board Scholarship (2012) Phillips Petroleum Scholarship (2010-11) Top Undergraduate Rank (1st/68 students) Academic Contributions: He has served on numerous committees, including the Research Chair for Untenured Faculty (2016-17), IEEE conference session chairs, and accreditation boards. His teaching includes courses on data structures, operating systems, and introductory computer science.

Nesime TatbulView profile

Researcher

• Large-scale Data Management Systems
• Time Series Analytics
• Observability Data Management
• +4 more

Nesime Tatbul is a Senior Research Scientist at Intel Labs and MIT's Computer Science and Artificial Intelligence Lab (CSAIL). She leads Intel's Data Systems and AI Lab (DSAIL) and previously held a faculty position at ETH Zurich. She holds a PhD and MS from Brown University and BS/MS from Middle East Technical University (METU). Her research focuses on large-scale data management systems, learned systems, time series analytics, and observability. Key contributions include the Aurora/Borealis and S-Store systems. She has served on program committees for SIGMOD, VLDB, and CIDR, and holds roles as an ACM Distinguished Member and IEEE Senior Member. Her work spans over 70 publications, including influential contributions to stream processing and query optimization. Awards include the PVLDB Distinguished Editor Award (2023), CIDR Test of Time (2025), and ACM SIGMOD Best Paper (2021). Current projects include DSAIL, Exathlon, and Mach, advancing observability and AI-driven data systems. She also contributes to editorial roles at VLDB and PVLDB.

Franz WotawaView profile

Professor

• Model-Based Reasoning
• Qualitative Reasoning
• Theorem Proving
• +6 more

Franz Wotawa is a Professor of Software Engineering at Graz University of Technology. He holds a M.Sc. (1994) and PhD (1996) from Vienna University of Technology. He has served as head of the Institute for Software Technology from 2003–2009 and since 2020. His research focuses on model-based reasoning, software testing, autonomous systems, and diagnosis, with over 390 peer-reviewed publications. He founded Softnet Austria (2006) to bridge research and industry. He leads the Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems since 2017 and has supervised 90+ master and 36+ PhD students. His awards include the 2016 Lifetime Achievement Award from the International Diagnosis Community. He is a member of Academia Europaea, IEEE, and AAAI. **Education**: M.Sc. in Computer Science, Vienna University of Technology, 1994 PhD, Vienna University of Technology, 1996 **Research Interests**: Model-based reasoning, qualitative reasoning, theorem proving, mobile robotics, verification/validation, software testing/debugging, AI, and autonomous systems. **Notable Projects**: A-IQ Ready (2022–2026): Quantum sensing for autonomous systems. ALFA (2024–2027): AI for smart diagnosis in building automation. Bilateral AI (2024–2029): Combining symbolic and sub-symbolic AI. VARCOS (2025–2028): Vehicle-road cooperative systems for autonomous driving. **Awards & Memberships**: Lifetime Achievement Award (2016, International Diagnosis Community) Senior Member, AAAI Member of Academia Europaea, IEEE, ACM, and Austrian Computer Society **Labs/Teams**: Christian Doppler Laboratory for Quality Assurance Methodologies (since 2017). Active in Cluster of Excellence “Bilateral AI” at TU Graz.

• Computer Science
• Cybersecurity
• WebAssembly Security
• +3 more

Weihang Wang is a Volunteer Assistant Professor in the Department of Computer Science and Engineering at the University of Southern California (USC), part of the School of Engineering and Applied Sciences. His research focuses on WebAssembly security, program analysis, and static/dynamic analysis frameworks. He holds a PhD and has published extensively on topics like WebAssembly obfuscation, decompilation techniques, and flaky test mitigation. Research interests include cybersecurity, software engineering, and the application of AI in program analysis. His work addresses challenges in cross-compilation for WebAssembly, decompilation accuracy, and automated detection of security vulnerabilities in web applications. Notable projects include WaSCR (side channel repairer), WBSan (bug detection), and Wefix (flaky test automation). He has received NSF travel grants for IEEE Security Development (SecDev) conferences in 2022 and 2023. His articles span 2010–2025, showing sustained contributions to web security, compiler optimization, and program transformation. His work intersects with practical applications like ad-blocking systems (Adhere) and bird flu outbreak prediction using migration data (2010–2013). Grants and awards include NSF funding for travel and research, reflecting his active role in academic conferences. He is affiliated with USC's computer science department and maintains an active Google Scholar profile with over 30 publications listed.

Don SannellaView profile

Professor

• Computer Science
• Formal Methods
• Algebraic Specification
• +5 more

Don Sannella is a Professor of Computer Science at the University of Edinburgh, affiliated with the Laboratory for Foundations of Computer Science (LFCS) within the School of Informatics. His research focuses on formal methods, algebraic specification (notably the CASL language), security, and theoretical computer science. He has contributed to foundational work in specification languages and their implementation, including the CoFI initiative. Sannella has authored influential books on formal software development and algebraic specifications. He holds the rank of Fellow of the Royal Society of Edinburgh. His work spans academic leadership, editorship of Theoretical Computer Science , and projects like Mobility and Security (MRG) and App Guarden for secure mobile applications. His teaching includes courses on functional programming, computability, and formal methods. He has advised students such as Nikita Samarin and collaborated on research grants, including those in resource analysis and concurrency. Sannella’s contributions integrate rigorous formal techniques with practical software development challenges, emphasizing modularity, correctness, and security.

Cyrus OmarView profile

Assistant Professor

• Programming Languages
• Human-Computer Interaction
• Software Engineering
• +4 more

Cyrus Omar is an Assistant Professor of Computer Science at the University of Michigan, affiliated with the Department of Computer Science and Engineering and the College of Engineering. He leads the Future of Programming Lab, which focuses on creating live programming environments and collaborative tools like Hazel and Grove. His research bridges programming languages, human-computer interaction, and education. Education and Mentorship: Omar redesigned EECS 490 (Programming Languages) and mentors over 150 undergraduates annually, many of whom co-author top-tier conference papers. He emphasizes structured onboarding for students and has trained researchers now in academia and industry. Research Interests: Develops live programming environments (e.g., Hazel) for working with incomplete code, collaborative editing systems (Grove), and AI-enhanced tools. His work emphasizes usability, creativity, and accessibility in coding. Recent projects include bidirectional typing, typed holes, and integrating large language models into IDEs. Awards: NSF CAREER Award (2023), 1938E Award (2025), and multiple Distinguished Paper Awards at OOPSLA, POPL, and SPLASH. His grants include NSF funding for live program sketching environments. Lab & Teams: The Future of Programming Lab explores programming experience design, with projects like Rustviz and structured editing frameworks. Collaborates on tools for test-driven scientific model validation and neural interface design.

Mark LawfordView profile

Professor

• Software engineering
• Safety critical systems
• Digital & Smart Systems
• +3 more

Mark Lawford is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. His expertise spans software engineering, safety critical systems, and model-based development, with a focus on digital & smart systems and transportation research clusters. Contact: Email: lawford@mcmaster.ca Website: www.cas.mcmaster.ca/~lawford His research explores automotive systems , formal verification , and model-driven engineering , particularly addressing challenges in automotive electrification , centralized E/E architectures , and safety assurance cases . Recent publications emphasize test case generation , change impact analysis , and model transformation tools for Simulink, SysML, and AUTOSAR. Key trends in his work include autonomous vehicle safety , real-time control systems , and integration of formal methods with industrial practices across automotive, biomedical, and nuclear domains.

Amro Al-Said AhmadView profile

Lecturer

• Cloud Computing
• Software Engineering
• Software Testing
• +4 more

Dr. Amro Al-Said Ahmad is a Lecturer in Computer Science at the School of Computer Science and Mathematics, Keele University, UK. He holds a Ph.D. in Cloud Computing from Keele University and has served as a teaching fellow and lecturer in Jordan prior to his current role. His research focuses on cloud computing scalability, software testing, and agile methodologies. He has published extensively in top-tier journals and conferences, including Information and Software Technology and Journal of Cloud Computing , and contributed to organizing events like BCS HCI and IEEE SSD. Education: BSc (Software Engineering, Philadelphia University, 2009), MSc (Computer Science, Amman Arab University, 2014), PhD (Cloud Computing, Keele University, 2019). Research interests include cloud scalability, chaos engineering, software testing, and healthcare informatics. His funded projects include AWS credits for scalability analysis and an Erasmus+ grant for international collaboration. He teaches courses like Cloud Computing and Software Engineering Project Management at Keele. Grants: AWS Cloud Credit ($5K), Keele New Starter Award (£2K), Erasmus+ MED2IaH (€58,325 share). Lab/Teams: Active in cloud resilience frameworks and chaos engineering research groups.

Stephen RamseyView profile

Associate Professor

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +6 more

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .