جست‌وجوی استاد و پژوهشگر

David Ryan Koesمشاهده پروفایل

دانشیار

• Computational Drug Discovery
• Deep Learning
• Discrete Algorithms
• +۳ مورد دیگر

David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.

Ayşe Başarمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Decision Making under Uncertainty
• +۷ مورد دیگر

Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.

Asta Halkjær Fromمشاهده پروفایل

پژوهشگر

• Formal Logic
• Automated Reasoning
• Theorem Proving
• +۱۳ مورد دیگر

Asta Halkjær From is a postdoctoral researcher in the Department of Computer Science at the University of Copenhagen, affiliated with the Software, Data, People & Society (SDPS) section under Dmitriy Traytel. She previously completed her PhD at DTU Compute from 2020 to 2023, focusing on formalized deduction methods in computational logic. She holds a Master’s and Bachelor’s degree from DTU in Computer Science and Software Technology, respectively, with a specialization in Artificial Intelligence and Algorithms. Her research lies at the intersection of formal logic and computer science, particularly in automated reasoning, proof assistants (Isabelle/HOL and Lean), and mechanized metatheory. She has contributed extensively to synthetic completeness proofs, tableau systems, and verified theorem provers. Her work emphasizes formal verification of logical systems, including epistemic logic, hybrid logic, and first-order logic, with a focus on soundness and completeness. The recent publications highlight a consistent trend: the mechanization of logical foundations in proof assistants. Her work bridges theoretical logic with practical verification tools, enabling reliable automation in theorem proving. She has developed and verified provers, explored axiomatic systems, and advanced the methodology of synthetic completeness, often leveraging Isabelle/HOL’s framework. Distinguished Paper Award, CPP 2023 DTU Young Researcher Award DTU Travel Grant (Executive Board Recognition) Otto Mønsted Fonden Travel Grant She has supervised BSc and MSc theses, special courses, and research projects at DTU, and currently teaches Software Development for Digital Health . She has served on program committees for CPP, ITP, and Dalí workshops and has reviewed for journals including Journal of Automated Reasoning and Journal of Logic and Computation . She has also participated in international research visits, including at VU Amsterdam. She is actively involved in building tools for formal reasoning and maintains a personal website with resources, including an Isabelle snippets generator and bibliography tools. Her work continues to advance the foundations of formal logic through mechanized proofs and practical automation.

• Parallel Programming
• Dependently Typed Languages
• Concurrent Systems
• +۳ مورد دیگر

Dr Adam Barwell is a Lecturer and Researcher at the School of Computer Science, University of St Andrews. His research focuses on parallel programming, dependently typed languages, and formal methods in concurrent systems. He advises PhD student Leonid Nosovitskiy and has contributed to projects involving session types, distributed systems, and type-safe refactoring techniques. His educational background includes a PhD (not explicitly detailed here), and he actively engages in academic activities such as organizing events like Doors Open @ Computer Science 2025. Key research themes include concurrency control, functional programming paradigms, and improving code security through dependent types. Recent publications emphasize advancements in parallel language design, fault-tolerant protocols, and type-driven development. Collaborations span international conferences and institutions, reflecting his contributions to both theoretical and applied computer science domains.

• Software Engineering
• Software Testing
• Program Analysis
• +۴ مورد دیگر

Alessandro Orso is the Dean and Professor of the University of Georgia College of Engineering. Previously, he served as a professor at Georgia Institute of Technology, where he directed the Scientific Software Engineering Center and held leadership roles, including interim dean of the College of Computing (2023–2024). He earned his Ph.D. and master’s in computer science and electrical engineering from Politecnico di Milano, Italy. Orso’s research focuses on software testing, program analysis, and debugging, with applications in improving software reliability and security. His work includes developing automated testing tools (e.g., AutoRestTest), methodologies for scientific software engineering, and techniques for program debloating. His research has secured funding from agencies and industry partners like Google, IBM, and Microsoft, including a $11M grant from Schmidt Futures. His scientific awards include being a Distinguished Member of the ACM and IEEE Fellow. He advocates for innovative education, such as Georgia Tech’s Online Master of Science in Computer Science and his virtual software engineering course. His publications span automated testing, API analysis, and security, reflecting his commitment to advancing both academic research and practical software solutions. Orso’s leadership includes directing the Scientific Software Engineering Center, which pioneers methodologies for scientific software improvement and trains a new generation of engineers. His career blends academic excellence with impactful industry collaborations, driving advancements in software engineering practices.

Marco Kuhrmannمشاهده پروفایل

استاد

• Software Engineering
• Agile Software Development
• Process Improvement
• +۳ مورد دیگر

Marco Kuhrmann is a Professor of Agile System Development at the Faculty of Informatics at Reutlingen University. He holds positions including Chair of the Examination Board for the Medical Technology Informatics program. His research focuses on software engineering methodologies, particularly agile development, process improvement, and hybrid methods. He has held academic roles at institutions like the Technical University of Munich, University of Southern Denmark, and the University of Passau. Education: PhD (Dr. rer. nat.) in Computer Science, Technical University of Munich (2008) Habilitation (Dr. rer. nat. habil.), Technical University of Munich (2014) Diploma in Computer Science, University of Potsdam (2004) His research interests include software process improvement, hybrid development approaches combining agile and traditional methods, and software engineering education. He leads projects like ANUKI and contributes to initiatives such as the HELENA survey on hybrid methodologies. His work addresses challenges in regulated domains, global software engineering, and the application of AI techniques in requirements engineering. Publications highlight themes like process transparency, compliance automation, and data augmentation. He is active in academic communities through workshops (e.g., PROFES, ICSSP) and has authored textbooks like Einführung in die Softwaretechnik (Springer, 2021). His teaching emphasizes hands-on experiments, such as simulating distributed teams and process adaptation in stressful scenarios. He collaborates with labs like the Virtual Reality Lab (VRehago) and contributes to research infrastructure like the IPSSE (Goslar) and software process ecosystems. Current projects explore AI-driven requirements evaluation and measurable artifact models in regulated industries.

Zheng Zhangمشاهده پروفایل

پژوهشگر

• Human-AI Interaction
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Zheng Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Notre Dame, where he focuses on human-AI interaction. He also works as an Applied Scientist in Adobe's GenAI team, developing interactive AI systems for user experience enhancement. He has held internships at Apple, AWS AI, and Meta Reality Labs. Education: Ph.D. (in progress) at University of Notre Dame M.S. in Computer Science from University of Rochester and University of Minnesota B.Eng. in Software Engineering from Shaanxi Normal University His research explores the intersection of human-computer interaction (HCI) and machine learning (ML), emphasizing systems that provide adaptive, context-sensitive support for complex cognitive tasks. Key areas include collaborative tools for team ideation, incremental learning from user demonstrations, and multimodal context-aware interfaces. Recent publications demonstrate a focus on AI-augmented collaboration through large shared displays (LADICA), audio-visual annotation (PEANUT), and interactive qualitative coding (PaTAT). His work increasingly integrates generative AI with real-time human input, particularly for co-located teams and educational applications. He serves as a Program Committee member and reviewer for top HCI/ML conferences including CHI, UIST, IUI, and ACL. Teaching experience spans courses in human-AI systems, algorithms, and data structures.

• Software Reusability
• Reverse Inheritance
• E-Learning
• +۵ مورد دیگر

Ciprian Bogdan Chirila is a Senior Lecturer at the University Politehnica of Timisoara (Faculty of Automation and Computer Science, Computing and Information Technology Department) and also teaches at Ioan Slavici University . His career spans over two decades, with roles in teaching, research, and software development. Education : PhD in Computer Science (2010, University Politehnica Timisoara & University Nice-Sophia Antipolis) Master's in Computer Science & Software Engineering (2002) Engineer Diploma in Automation & Computer Science (2001) Analyst-Programmer Diploma (1996) Research Interests : His work focuses on reverse inheritance for software reusability, generative learning objects for education, and software quality assessment using code metrics. He explores interdisciplinary applications in healthcare, mathematics education, and industrial IT services. Article Trends : Recent publications emphasize adaptive e-learning frameworks , gamification in education , and model-driven development . Key themes include automated assessment , dynamic content generation , and integration of standards like SCORM and xAPI . Scientific Awards : Microsoft Award, Student Scientific Communications Session (2001) Award, Student Scientific Communications Session (2002) Third Place, Mechanics and Robotics Contest (1998) Fourth Place, Traian Lalescu Mathematics Contest (1998) Teaching & Development : He has led laboratory sessions and lectures in Data Structures , Compiler Design , Operating Systems , and Heuristic Methods . He organizes programming contests and develops centralized materials management software as a volunteer engineer.

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +۳ مورد دیگر

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

Maxime Gobertمشاهده پروفایل

پژوهشگر

• Database Systems
• Hybrid Polystores
• Database Schema Evolution
• +۴ مورد دیگر

Maxime Gobert is a researcher at the University of Namur's Faculty of Computer Science, specializing in database systems and software engineering. Having completed his PhD in March 2023 titled 'Design, Manipulation and Evolution of Hybrid Polystores,' Gobert has established himself as an expert in hybrid database systems, particularly focusing on the HyDRa framework for modeling and evolving polystores. His research interests span database systems, hybrid polystores, database schema evolution, software engineering, data-intensive systems, and static program analysis. Gobert's work bridges theoretical database concepts with practical applications, particularly in NoSQL databases like MongoDB and complex hybrid data storage environments. His publication record demonstrates a clear trajectory from his 2013 Master's thesis on database reverse engineering through to his recent work on database testing best practices and sign language processing applications. His research shows strong collaboration with colleagues at the University of Namur, particularly with Professor Cleve A., and extends to international collaborations as evidenced by his 2016 guest researcher position at the University of Geneva. Best New Idea and Emerging Results (NIER) Paper Award at the 20th IEEE Working Conference on Source Code Analysis and Manipulation (SCAM 2020) Jean Fichefet 2013 award Gobert has contributed significantly to the development of the HyDRa framework for hybrid polystore management and has extended his research into sign language processing through collaborative projects creating bilingual sign language dictionaries and parallel corpora. His work demonstrates both technical depth in database systems and a commitment to applying this expertise to accessibility-focused applications.

• Software Engineering
• Program Comprehension
• Eye Tracking in Software Development
• +۴ مورد دیگر

Professor Jonathan I. Maletic is affiliated with the University of Kent, UK, and is a leading researcher in software engineering with a particular focus on program comprehension and eye tracking in software development. He has published extensively in top venues such as Empirical Software Engineering, IEEE Transactions on Software Engineering, and the International Conference on Program Comprehension.

• Software Engineering
• Intelligent Systems
• Model-Based Systems Engineering
• +۳ مورد دیگر

Rüdiger Lunde is a Professor at Technische Hochschule Ulm , currently serving as Dean of Studies for Information Systems since September 2019. He previously chaired the Examination Board for programs including INF, CTS, TI, ICS, DSM, MD, and IS from 2014 to 2019. His primary research areas include Software Engineering , Intelligent Systems , and Model-Based Systems Engineering , with a focus on automated safety analysis and technical system simulation. Current Role: Dean of Studies for Information Systems Prior Role: Chair of Examination Board (2014–2019) His research leverages the smartIflow framework for safety tasks such as Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA). He has contributed to formal verification methods using Computation Tree Logic (CTL) and explored temporal reasoning across varying time scales. Recent trends in his work include: Automated safety analysis in technical systems Integration of SysML for artifact generation Model decomposition techniques for complex systems Scientific awards: Best Paper Award at International Workshop on Applications in Information Technology (IWAIT-2015) Contact: Ruediger.Lunde@thu.de | Room: A306a | Consultation hours: By appointment via email.

Shurui Zhouمشاهده پروفایل

استادیار

• Software Engineering
• Open-Source Collaboration
• Fork-Based Development
• +۴ مورد دیگر

Shurui Zhou is an Assistant Professor at the University of Toronto , with primary appointment in the Department of Electrical & Computer Engineering and cross-appointments in the Department of Computer Science and the Department of Mechanical & Industrial Engineering . She is also affiliated with the Schwartz Reisman Institute and a member of the Data Sciences Institute . Zhou directs the FORCOLAB , focusing on enhancing collaboration in distributed software teams, especially in open-source and AI-enabled systems development. Her research integrates software engineering principles with human collaboration insights from organizational behavior, aiming to improve collaboration efficiency through tooling and interdisciplinary methods. Recent publications (2023-2025) explore LLM integration in software development , sustainability in scientific open-source communities , and collaborative challenges in computer-aided design . Education: PhD (2020) from Carnegie Mellon University's Software and Societal Systems Department , MSc from Peking University , BSc from Xi'an Jiaotong University Scientific Awards: Gordon Slemon Teaching of Design Award (2022) Grants: Collaborator on NSERC Alliance-Mitacs Accelerate Grant (2025) for LLM analysis in political data Her lab, FORCOLAB, develops tools like forks-insight.com to analyze GitHub forks and Redundant PR detection bots . Zhou organizes events like the Responsible LLM-Human Collaboration Hackathon (2024) and actively serves on conference committees including ICSE 2025 and ICSME 2024 .

Kinan Dak Albabمشاهده پروفایل

استادیار

• Data Privacy
• Systems Security
• Secure Computation
• +۷ مورد دیگر

Kinan Dak Albab is an Assistant Professor at the Faculty of Computing and Data Sciences (CDS) at Boston University, where he joined in summer 2024. His research spans systems, cryptography, and programming languages, focusing on building practical tools for data privacy and compliance-by-construction. He has developed influential systems such as Sesame, K9db, and DP-PIR, which have been published in top venues like SOSP, OSDI, and USENIX Security. PhD in Computer Science, Brown University MS in Computer Science, Boston University (2020) BS in Computer Science, American University of Beirut (2015) Kinan's research centers on data privacy , secure computation , and systems security . He designs tools that allow developers to build applications that are privacy-compliant by construction. His work leverages systems design, cryptographic protocols, and language-level enforcement to reduce developer burden while ensuring strong privacy guarantees. He is particularly interested in GDPR compliance, private information retrieval, and usability of secure systems. His publications span high-impact areas including end-to-end privacy enforcement (Sesame), database systems with built-in privacy (K9db), and efficient private information retrieval (DP-PIR). These works combine systems performance with strong theoretical foundations, enabling real-world deployment in domains like wage gap analysis and public policy. Presidential Award for Excellence in Teaching, Brown University (2025) Teaching Excellence Award, Boston University (2020) Vivli and Microsoft datatheon Outstanding Graduate Submission (2019) Hariri Institute Graduate Fellow (2017–2020) Mark Sawaya Excellence Award, AUB (2015) 1st Place, ACM Lebanese Collegiate Programming Contest (2015) Kinan has advised and collaborated on real-world secure computation deployments, including a project with the Boston Women's Workforce Council and the Greater Boston Chamber of Commerce to measure wage gaps across over 100 companies. His work contributed to the formation of the startup nthparty and has been cited in the White House’s National Strategy on Privacy-Preserving Data Sharing, the UN Handbook on Privacy-Preserving Computation, and the European Commission’s report on Technological Enablers for Privacy. He is the lead developer of the JIFF framework for secure multi-party computation on the web and has created tools like Carousels for resource estimation in secure programs. He leads the ETOS group and is affiliated with multiparty.org , an initiative focused on advancing privacy-preserving technologies through open-source tools and real-world applications.

Xiaoguang Wangمشاهده پروفایل

استادیار

• Systems and Software Security
• Heterogeneous CPU Architectures
• Secure Software Systems Design
• +۴ مورد دیگر

Xiaoguang Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research spans systems and software security, focusing on heterogeneous CPU architectures, secure software systems, and virtualization-based security frameworks. He actively mentors PhD and Master’s students and offers funded research opportunities for UIC students. University of Illinois Chicago, Department of Computer Science Research: Systems & Software Security, Heterogeneous Architectures, Virtualization His work includes projects like sMVX (multi-variant execution), Dapper (live program rewriting), and DynaCut (dynamic program customization). Recent publications address cross-architecture process migration, Linux kernel security, and using large language models (LLMs) for software security. He teaches advanced courses such as CS 487: Building Secure Computer Systems and CS 594/561: Adv. Linux Kernel Programming , emphasizing hands-on kernel development and security techniques. Grants from the U.S. Office of Naval Research and NSF support his work on secure systems and cross-architecture security solutions.