جست‌وجوی استاد و پژوهشگر

Behrooz Mansouriمشاهده پروفایل

استادیار

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Behrooz Mansouri is an Assistant Professor of Computer Science at the University of Southern Maine. He holds a Ph.D. (2022) in Computer Science from the Rochester Institute of Technology (Rochester, NY), an M.Sc. (2017) in Software Engineering from the University of Tehran, and a B.Sc. (2013) in Software Engineering from Shahid Beheshti University. His roles include Director of the Michael E. Dubyak Center, Director of the Artificial Intelligence and Information Retrieval (AIIR) Lab, and Graduate Program Director. He previously worked on the Parsijoo Persian search engine project and co-organized the ARQMath lab during his Ph.D. Education: Ph.D., Computer Science, Rochester Institute of Technology, USA (2022) M.Sc., Software Engineering, University of Tehran, Iran (2017) B.Sc., Software Engineering, Shahid Beheshti University, Iran (2013) His research focuses on Information Retrieval (IR) , Natural Language Processing (NLP) , and Mathematical Information Retrieval (MIR) . He leads the AIIR Lab, which develops systems like MathMex (a conversational math search engine) and explores cross-lingual math retrieval, legal question classification, and cloud detection in satellite imagery. Recent projects include NSF-funded work on conversational math search and participation in CLEF SimpleText labs. His lab's work has been recognized, including a Best Lab Paper at SimpleText'24 and an NSF CRII Grant (2024). He advises students on projects like legal case search, math definition extraction, and AI ethics in STEM education. Grants & Awards: NSF CRII Grant: 'Towards Conversational Search Systems for Math' (2024) Best Lab Paper at SimpleText'24 (2024) Outstanding Reviewer at ECIR 2024 Doctoral Consortium Co-chair at SIGIR 2025 The AIIR Lab collaborates on interdisciplinary projects, including MathMex (math definition search), Cloud Detection in Satellite Images , and Tree Classification . Prospective students are encouraged to align with these research themes and apply via specific guidelines outlined on his website.

Gokhan Kulمشاهده پروفایل

استادیار

• Cybersecurity
• Computer Systems
• Database Systems
• +۳ مورد دیگر

Dr. Gokhan Kul is an Assistant Professor at Delaware State University (DSU), affiliated with the Division of Physical and Computational Sciences. He holds a Ph.D. in Computer Science from the University at Buffalo, SUNY (2018), and multiple advanced degrees in Computer Engineering and Business Administration. His research focuses on cybersecurity, database systems, and computer systems, with particular emphasis on query clustering, data leakage prevention, and insider threat analysis. Dr. Kul’s professional memberships include the IEEE, ACM, and AAAS. He teaches courses such as Operating Systems (CSCI 320), Introduction to Information Security (INFO 340), and Advanced Computer Networking (CSCI 520). His work has been published in top venues like IEEE Transactions on Knowledge and Data Engineering and the ACM CCS MIST workshops. He has served on program committees for conferences like VLDB 2018, SIGMOD 2017, and IEEE Security and Privacy 2017, contributing to reproducibility review processes. His research addresses critical challenges in database security, mobile app vulnerabilities, and blockchain-enabled network operability.

Wendy Powleyمشاهده پروفایل

دانشیار

• Database Management Systems
• Autonomic Computing
• Cloud Computing
• +۳ مورد دیگر

Wendy Powley is an Associate Professor at Queen’s University’s School of Computing within the Faculty of Arts and Science. She holds a B.A. in Psychology (1984), B.Ed. in Primary Junior Education (1985), and M.Sc. in Computer Science (1990), all from Queen’s University. Her research focuses on database systems, autonomic computing, workload management, and cloud computing. She co-founded the Women in the School of Computing (WISC) group and pioneered the Ontario Celebration of Women in Computing, promoting gender equity in computing education and careers. Education History: B.A., Psychology, Queen’s University (1984) B.Ed., Primary Junior Education, Queen’s University (1985) M.Sc., Computer Science, Queen’s University (1990) Research Interests: Database Management Systems (DBMS) Autonomic Computing and Self-Managing Systems Workload Control and Resource Management Policy-Based Management for Complex Systems Cloud Computing and QoS-aware Applications Publications reflect expertise in DBMS performance optimization, autonomic frameworks, and workload adaptation strategies. Her work has been published in leading conferences/journals since 1993. Scientific Awards: Queen’s University Equity Award (for advancing gender equity in computing) Professional Contributions: First-year course and internship program coordinator at Queen’s School of Computing Co-chair of ACM-W (Association for Computing Machinery-Women in Computing) Organized CAN-CWiC (Canadian Celebration of Women in Computing) events Labs/Teams: Active member of the Database Systems Lab (DSL) led by Dr. Pat Martin (1992–2016). Currently involved in ACM-W initiatives promoting women in computing globally.

John Ousterhoutمشاهده پروفایل

استاد

• Distributed Systems
• Operating Systems
• Storage Systems
• +۵ مورد دیگر

John Ousterhout is the Leonard Bosack and Sandy K. Lerner Professor of Engineering and Professor of Computer Science at Stanford University. He also holds a courtesy position in Electrical Engineering. His research focuses on infrastructure for software systems, including distributed systems, operating systems, storage systems, development frameworks, and programming languages. He is renowned for creating the Tcl scripting language and pioneering work in distributed operating systems and file systems. Education: Ousterhout earned a BS in Physics from Yale University (1975) and a PhD in Computer Science from Carnegie Mellon University (1980). He spent 14 years at UC Berkeley before moving to industry, founding Scriptics and Electric Cloud. He returned to academia at Stanford in 2010. Research Interests: Current projects emphasize granular computing—optimizing datacenter task execution at microsecond scales. This includes innovations in thread management, network communication, and logging systems. His past work includes RAMCloud (a low-latency storage system) and foundational contributions to distributed systems and file systems like Sawmill and Zebra. Scientific Awards: He is a member of the National Academy of Engineering and has received prestigious honors such as the ACM Software System Award, ACM Grace Murray Hopper Award, NSF Presidential Young Investigator Award, and U.C. Berkeley’s Distinguished Teaching Award. Industry Impact: As a serial entrepreneur, he co-founded Scriptics (Tcl/Tk) and Electric Cloud. His work on Tcl revolutionized scripting in software development and web frameworks. He has also contributed to AJAX-driven web components and program build architectures.

Raluca Ada Popaمشاهده پروفایل

دانشیار

• Computer Security
• Systems Security
• Applied Cryptography
• +۴ مورد دیگر

Raluca Ada Popa is an associate professor in the Department of Electrical Engineering and Computer Science at UC Berkeley, holding the Robert E. and Beverly A. Brooks Endowed Chair. She leads the Frontier Security team at Google DeepMind and co-founded the RISELab, SkyLab, and DARE diversity program. Her research focuses on systems security, cryptographic computing, and privacy-preserving machine learning. She received her PhD from MIT in 2014, alongside BS and MS degrees in Computer Science and Mathematics from MIT. Education: PhD in Computer Science, MIT (2014) MS in Computer Science, MIT (2010) BS in Computer Science and Mathematics, MIT (2009) Research Interests: Popa designs systems that compute securely on encrypted data, enabling confidentiality in cloud computing, AI, and collaborative analytics. Her work bridges cryptography and systems, with contributions to secure multi-party computation, hardware enclaves, and decentralized trust mechanisms. Key innovations include CryptDB, Opaque, and Helen, which have influenced industry standards like Microsoft SQL's Always Encrypted service. Publications & Awards: Over 50+ peer-reviewed papers, including influential work at top venues like USENIX Security, SOSP, and IEEE S&P. Notable awards include the ACM Grace Murray Hopper Award (2021), Sloan Research Fellowship (2018), and MIT Technology Review's TR35 (2019). Her work has been recognized for advancing secure computing and privacy-preserving technologies. Advising & Grants: Advisor to over 20 PhD students and postdocs, many of whom hold faculty positions or lead industry research. Recipient of major grants including NSF CAREER Award (2020), Bakar Fellowship (2019), and a $10M NSF Expedition for RISELab. Co-founded Opaque Systems (2021) and PreVeil (2015), with significant industry impact. Labs & Teams: Co-director of RISELab (Real-time, Intelligent, Secure Systems) and SkyLab (secure cloud and edge computing). Founded DARE to promote diversity in EECS research, connecting undergraduates with faculty mentors.

Monica Lestari Paramitaمشاهده پروفایل

مدرس

• Bias and transparency in information retrieval
• Multilingual information access
• Cross-lingual similarity in Wikipedia
• +۳ مورد دیگر

Dr. Monica Lestari Paramita is a Lecturer in Data Science at the School of Information, Journalism and Communication , University of Sheffield. She obtained her BSc in Computer Science (2006) from the University of Indonesia, followed by an MSc (2008) and PhD (2019) in Information Management from the University of Sheffield. BSc in Computer Science, University of Indonesia (2006) MSc in Information Management, University of Sheffield (2008) PhD in Information Management, University of Sheffield (2019) Dr. Paramita's research focuses on bias and transparency in information retrieval , with a particular interest in designing bias-aware search engines and advancing multilingual information access . Her work explores cross-lingual similarity in Wikipedia, aiming to develop methods for measuring such similarities and understanding their implications for diverse user groups. Her recent publications address topics like algorithmic bias , user awareness , and multilingual NLP . These works span areas including AI ethics , digital scholarship , and cultural heritage access , often integrating participatory design and search engine evaluation . Dr. Paramita contributes to teaching modules such as INF113 - Data-Driven Organisations , INF214 - Using Data for Responsible Decision Making , and postgraduate courses on information retrieval and data science. She also co-leads the Shef.AI Interest Group on Fairness, Accountability, Transparency, and Ethics (FATE) in AI.

Kazimierz Chorośمشاهده پروفایل

استاد

• digital image and video processing
• content-based video indexing and retrieval
• computer animations
• +۴ مورد دیگر

Kazimierz Choroś is a Professor at the Department of Applied Informatics , Faculty of Information and Communication Technology , Wrocław University of Science and Technology. He held leadership roles as Deputy Director of the Institute of Informatics (2008-2014) and Deputy Dean of the Faculty of Computer Science and Management (2016-2020). Research interests: digital image/video processing, content-based video indexing, computer animations, multimedia systems, web systems analysis, and information systems design. Organizer and Chair of the International Conference on Multimedia & Network Information Systems (MISSI 2022) Chair of Special Session WebSys 2020 at ICCCI 2020 Other activities: Since 1993, member and former President (1993-2002) of the SAGE Association (Polish Graduates of French Grandes Ecoles). Since 1982, member of the Polish Numismatic Society, author of dozens of numismatic publications, and Editor-in-Chief of Wrocławskie Zapiski Numizmatyczne (2003-present). Contact: Email: kazimierz.choros@pwr.edu.pl Phone: +48-71.320.3799 Office: Building D2, Room 201/1 Address: Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Heiko Mausمشاهده پروفایل

پژوهشگر

• Knowledge Management
• Semantic Technologies
• Knowledge Graphs
• +۷ مورد دیگر

Dr. Heiko Maus is a researcher and department leader at the German Research Center for Artificial Intelligence (DFKI) GmbH in Kaiserslautern, Germany. As (co-)author of over 20 publications since 2005, he specializes in knowledge management, semantic technologies, and context-aware systems. Second Deputy Head of Smart Data & Knowledge Services Department (2022–) Team Leader of Topic Field Knowledge Work (2017–) Key projects: CoMem, EPOS, ForgetIT, myRPA His research focuses on knowledge graph applications for corporate memory systems, combining context modeling with semantic desktop environments . He develops personal knowledge assistants that integrate information silos through evolving knowledge graphs , with recent work exploring LLM-based adaptive relevance prediction for entity recommendation systems. Scientific contributions include the LDK 2019 Best Research Paper Award and multiple ACM/IEEE/ESWC publications . His 2024–2025 articles examine context-aware recommendation systems for knowledge workers, cyber mapping financial systems , and real-life knowledge work datasets .

• Process Mining
• Software Process Mining
• Object-Oriented Programming
• +۷ مورد دیگر

Sergey Shershakov is an Associate Professor at the Faculty of Computer Science, Department of Software Engineering at the National Research University Higher School of Economics (HSE University). He serves as the Academic Director of the Systems and Software Engineering educational program and has been with HSE University since 2010. His academic journey includes a PhD from HSE University (2020) and a Master's degree in Software Engineering from the same institution (2012). Dr. Shershakov's research focuses primarily on Process Mining and Software Engineering, with specific interests in: Process Mining methodologies and tools Software Process Mining and analysis Object-Oriented and Component-Oriented Programming Information Systems Architecture Verification of Algorithms Embedded Systems development His scholarly contributions demonstrate a consistent focus on developing practical tools and methodologies for process mining, with particular emphasis on creating graphical interfaces (like VTMine for Visio), improving performance of process mining algorithms, and applying these techniques to real-world systems. His work bridges theoretical computer science with practical software engineering applications, especially in the domain of business process analysis and optimization. Dr. Shershakov has received numerous recognitions for his academic work: Best Teacher award (2014, 2017-2021) Best Academic Supervisor in the category 'Admission of Foreign Students' (2024) Multiple Gratitude awards from HSE University leadership (2017-2024) Academic Work Allowance (2016-2021) Member of the 'New Researchers' category in the High Professional Potential Group (2014-2015) As an educator, Dr. Shershakov teaches multiple courses in C++ programming, Algorithms and Data Structures, and Introduction to Programming across different departments at HSE University. He leads research projects including an RFBR grant focused on 'Development of methods for efficient implementation of algorithms of process mining based on large event logs' (2018). He is also an active member of the IEEE Task Force on Process Mining. Dr. Shershakov is affiliated with the Laboratory of Process-Aware Information Systems (PAIS Lab) at HSE University, where he conducts research on process mining techniques and their applications. His work has significant implications for business process optimization, system verification, and software engineering practices.

• Machine Learning Theory
• Game Theory
• Information Theory
• +۵ مورد دیگر

Prof. Wouter M. Koolen serves as Professor of Mathematical Machine Learning in the Statistics group at the University of Twente and as Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI). He maintains active research affiliations with INRIA-CWI associate teams 6PAC (with Inria Lille) and 4TUNE (with Inria Paris and Grenoble), and holds the distinction of ELLIS Scholar. Dr. Koolen earned both his MSc and PhD cum laude from the Institute of Logic, Language and Computation at the University of Amsterdam, completing his doctoral work titled 'Combining Strategies Efficiently: High-quality Decisions from Conflicting Advice' in January 2011. His academic journey includes being designated a Master of Logic. Prof. Koolen's research spans theoretical machine learning with deep connections to game theory, information theory, statistics, and optimization. His current work focuses on pure exploration in multi-armed bandit models, game tree search algorithms, and provably accelerated learning methods in statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His theoretical contributions consistently demonstrate practical relevance in sequential decision making and statistical inference. Analysis of his recent publications reveals three dominant research threads: martingale-based methods for anytime-valid statistical inference using e-values, adaptive optimization algorithms with provable guarantees, and theoretical foundations of multi-armed bandit problems. His work increasingly bridges theoretical computer science with modern statistical methodology, particularly in sequential analysis and adaptive experimentation. His notable achievements include: NWO VENI grant for innovative research QUT Vice-Chancellor's postdoctoral research fellowship Designation as ELLIS Scholar recognizing European research excellence cum laude distinctions for both master's and doctoral degrees Prof. Koolen actively mentors the next generation of researchers, having supervised multiple PhD students to completion including Hongwei Wen, Clément Lezane, and Tyron Lardy with defenses scheduled for 2025. His research program is supported by competitive grants focusing on theoretical machine learning and statistical methodology. He maintains an active presence in the international research community through conference presentations, workshop organization, and collaborations across European institutions. Within the Machine Learning group at CWI and Statistics group at the University of Twente, Prof. Koolen contributes to a dynamic research environment focused on theoretical foundations with practical applications. His work often intersects with colleagues investigating sequential decision processes, game-theoretic approaches to learning, and robust statistical inference methods.

Brent T. Langhalsمشاهده پروفایل

دانشیار

• Data Analytics
• Database Systems
• Human-Computer Interaction
• +۳ مورد دیگر

Dr. Brent T. Langhals serves as Associate Professor of Information Resource Management at the Air Force Institute of Technology (AFIT), Department of Systems and Engineering Management, Wright-Patterson AFB, OH. He directs the AFIT Data Analytics Program and chairs the Information Systems specialization curriculum, actively contributing to defense-focused research and graduate education. Education: Ph.D. in Management - Management Information Systems Concentration (minor: Systems and Industrial Engineering), University of Arizona, Eller College of Management (2011). Dissertation: "Using Eye and Head Based Psychophysiological Cues to Enhance Screener Vigilance" M.S. in Information Resource Management, Air Force Institute of Technology (2001). Thesis: "The Effect of Varying Arousal Methods upon Vigilance and Error Detection in an Automated Command and Control Environment" B.S. in History, United States Air Force Academy (1995) Research Focus: Dr. Langhals' work integrates data analytics , database systems , and human-computer interaction with systems engineering principles. His investigations into psychophysiological cues and operator vigilance bridge cognitive science with defense applications, yielding practical solutions for security screening, aviation interfaces, and military decision-making. Current projects emphasize machine learning for predictive analytics in high-stakes environments. Publication Trends: Recent work demonstrates interdisciplinary reach across defense logistics (F-22 sortie cancellations, construction contracts), public health (marijuana use, food insecurity), and emerging technologies (UAS applications, NoSQL databases). His scholarship consistently applies data science to military challenges while advancing fundamental research in human factors and database engineering. Scientific Recognition: Two-time Sigma Iota Epsilon Instructor of the Year (2024, 2018) Department Educator of the Year (2013) Multiple Air Force commendations including Field Grade Officer of the Quarter (2008, 2006) Mentorship & Leadership: Dr. Langhals has graduated 59 Master's/PhD students (38 residential, 21 distance learning) while teaching core courses in Database Systems, Data Analytics, and Strategic Information Management. His research leadership spans contracts in data management, systems engineering, and human performance optimization for Department of Defense applications. Research Infrastructure: As Data Analytics Program Director, he oversees AFIT's research ecosystem for defense data science, coordinating faculty and resources across the Department of Systems and Engineering Management to develop next-generation analytical capabilities for Air Force missions.

Lingxiao Jiangمشاهده پروفایل

دانشیار

• Software Engineering
• Program Analysis
• Software Testing & Debugging
• +۵ مورد دیگر

Lingxiao Jiang is an Associate Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU) in Singapore. His research and teaching interests focus on software engineering, program analysis, testing & debugging, code search & reuse, and deep learning & analysis of code. He is actively involved in multiple research tracks across major software engineering conferences including ASE, ICSE, ESEC/FSE, and MSR. His research interests encompass practical techniques and tools that enhance software quality, increase development productivity, reduce maintenance cost, and improve user experience. He also explores programming languages, systems, security & privacy, data mining, and machine learning to solve software engineering and security problems. His work focuses on context-aware software mining, analysis, and deep learning, with applications in code clone detection, code query processing, bug detection, search for bug fixes, refactoring, and testing & debugging techniques. Professor Jiang's research involves large-scale, diverse, contextual data sources beyond program code, including code change histories, bug databases, test suites, developer activities, user feedbacks, and socio-technical information. His methodology leverages static & dynamic program analysis, code representations, software engineering methodologies, natural language processing, information retrieval, data mining, deep learning, and distributed computing techniques. His research has resulted in numerous publications with significant impact, including the DECKARD tool which received the ACM SIGSOFT Impact Paper Award in 2018. His work spans various areas including scalable code clone detection, deep learning of code, and analysis of software repositories. Scientific Awards: ACM SIGSOFT Impact Paper Award 2018 for DECKARD paper ICSE '18 Distinguished Paper Award for Hierarchical learning of cross-language mappings Professor Jiang actively advises graduate students and has mentored numerous PhD and Master's students to completion. He also leads the Centre for Research on Intelligent Software Engineering (RISE) at SMU, which focuses on high-quality and impactful research in intelligent software engineering. His research has practical applications in various domains including Android security, smart contracts, drone security, and compiler testing.

John Staskoمشاهده پروفایل

استاد

• Data Visualization
• Information Visualization
• Human-Computer Interaction (HCI)
• +۳ مورد دیگر

John Stasko is a Regents Professor in the School of Interactive Computing at the Georgia Institute of Technology, where he has been on the faculty since 1989. He also serves as an Adjunct Professor in the School of Computing Instruction. His primary research focuses on data visualization , information visualization , and visual analytics from a human-computer interaction (HCI) perspective. He has held leadership roles such as Interim School Chair of Interactive Computing (2021–2022) and General Chair of IEEE VIS 2013. Education: B.S. in Mathematics, Bucknell University (1983) M.Sc. and Ph.D. in Computer Science, Brown University (1985, 1989) Research Interests: John leads the Information Interfaces Research Group , emphasizing tools for data exploration and sense-making. His work includes visual analytics systems, peripheral awareness interfaces, and flexible interaction paradigms. Notable contributions include the Jigsaw system for document analysis and ROBIN for migration data visualization. Awards & Recognition: 2023: IEEE VGTC Visualization Lifetime Achievement Award 2014: IEEE Fellow 2012: IEEE VGTC Visualization Technical Achievement Award ACM Distinguished Scientist (2011) ACM CHI Academy & IEEE Visualization Academy memberships Advising & Teaching: John has advised over 20 PhD students and teaches courses like CS 6730: Data Visualization and CS 1331: Intro to Object-Oriented Programming . He emphasizes accessible, user-centered design in both research and education. Affiliations: GVU Center Institute for People and Technology (IPaT) Adjunct Faculty at School of Computing Instruction Member, University of St. Andrews Honorary Faculty (2013)

• Mathematical Foundations of Machine Learning
• Online Convex Optimization
• Statistical Learning Theory
• +۵ مورد دیگر

Dr. Tim van Erven is an Associate Professor at the University of Amsterdam , affiliated with the Korteweg-de Vries Institute for Mathematics . His research focuses on the mathematical foundations of machine learning , particularly adaptive methods in online convex optimization, PAC-Bayesian concentration inequalities, and robust learning strategies. Research Interests : Mathematical machine learning, explainable AI, online learning, statistical learning theory, and adaptive optimization. Grants & Awards : VICI (2025), VIDI (2019), TOP grant (2016), NIPS Outstanding Reviewer (2014), Rubicon (2011). Teaching : Involved in graduate-level machine learning and statistical learning theory courses. His recent work explores explainable machine learning and robust optimization , with a focus on algorithmic recourse, gradient filtering, and theoretical guarantees. His publications span top venues like NeurIPS, COLT, and JMLR, often addressing the intersection of statistics and machine learning. He co-organized workshops such as the AI & Mathematics (AIM) initiative and contributed to the COLT program committee. His group develops MetaGrad and other adaptive algorithms for online learning.

Claire Cardieمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Information Extraction
• +۳ مورد دیگر

Claire Cardie is a Professor in the Departments of Computer Science and Information Science at Cornell University, and the inaugural Associate Dean for Education in the Ann S. Bowers College of Computing and Information Science. She holds the John C. Ford Professorship of Engineering. Her research focuses on natural language processing (NLP), including information extraction, opinion analysis, and machine learning methods. Cardie has pioneered Cornell’s Information Science programs and served as Department Chair. She is a Fellow of ACL (2015), ACM (2019), and AAAS (2021), reflecting her seminal contributions to NLP. Her teaching includes courses like CS4740/5740 (Intro to NLP) and CS6740/INFO6300 (Advanced Language Technologies). She has led major conference roles (e.g., ACL 2018 General Chair) and contributed to datasets like Fashionpedia and GRIT. Education: B.S. in Computer Science (Yale), M.S. and Ph.D. in Computer Science (University of Massachusetts) Research Interests: Cardie’s group develops NLP systems and machine learning techniques for large-scale text analysis, balancing theoretical advances with real-world applications. Current work emphasizes reasoning, robustness in retrieval-augmented generation, and ethical AI evaluation. Publications: Over 100+ articles since 2016, with recent focus on multi-hop reasoning, adversarial attacks on NLP systems, and AI ethics. Notable contributions include the FAIRY dataset for commonsense reasoning and GRIT for event extraction. Awards: AAAI Fellow (2021), ACL Fellow (2015), ACM Fellow (2019) Advising & Service: Mentor for numerous students and researchers. Currently oversees educational initiatives as Associate Dean, emphasizing interdisciplinary computing education and student success.