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Kirill Igorevich Levchenko is an Associate Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Electrical and Computer Engineering, the Siebel School of Computing and Data Science, the Information Trust Institute, and the Coordinated Science Laboratory. His interdisciplinary work bridges systems, networking, and security. His research focuses on cybersecurity , network measurement , programmable networks , and online misinformation . He investigates real-world network infrastructure vulnerabilities, develops secure protocols for emerging technologies like UAVs, and studies human interactions with digital deception. His work combines empirical measurement with system design. Recent publications reveal a strong trend in networking systems and security , particularly in programmable data planes, measurement frameworks like PacketLab, and defenses against botnets and spam. He also explores the societal impact of technology through studies on fake media and user perception. Yi-Min Wang and Pi-Yu Chung Faculty Fellow Dr. Levchenko leads research projects in network security and trust, likely supported by grants from federal and private agencies, though specific grants are not listed. His publications suggest active mentorship of graduate students, particularly in systems and networking. He contributes to major conferences in computer security and internet measurement. He is affiliated with the Coordinated Science Lab and the Information Trust Institute , both leading interdisciplinary research centers at UIUC focused on advanced computing and cybersecurity.

• Cybersecurity
• Computer Networking
• Network Measurement
• +7 more

Xiuzhen Jenny Zhang is a Professor of Data Science at RMIT University , affiliated with the School of Computing Technologies. Her research bridges artificial intelligence, machine learning, and social media analysis, with a focus on text mining and trustworthy data science. Her recent publications highlight expertise in point-of-interest recommendation , misinformation detection , multi-task learning , and transformer-based NLP . Key trends include applications of large language models for social good, fairness in recommendation systems , and adversarial learning for robustness. Best Paper Award at TrustCom’12 Best Short Paper Award at ADCS’2009 She leads the Text And LanguagE (TALE) research group and has supervised over 20 PhD students. Research grants include Australian Research Council and Victoria state government funding.

• Data Science
• Machine Learning
• Natural Language Processing
• +12 more

Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.

• Energy-Efficient Systems
• Cloud Computing
• Storage Systems Optimization
• +7 more

Arjun Mukherjee is a Lecturer at the Department of Computer Science , University of Houston , where he teaches courses in Machine Learning , Data Mining , Natural Language Processing , and Data Structures . His research focuses on Bayesian Inference , Data Mining , Natural Language Processing , Sentiment Analysis , Opinion Spam , and Web Mining , with a strong emphasis on deception detection and social media analysis. His recent publications explore advanced techniques in LLM-generated content detection synthetic data applications cross-domain deception modeling temporal user behavior analysis , reflecting his commitment to addressing modern challenges in digital content authenticity and machine learning robustness. Dr. Mukherjee has developed educational materials for graduate-level courses, including a well-structured Machine Learning course (COSC 6342) covering probabilistic inference, supervised/unsupervised learning, and neural networks. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a thesis titled Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications .

• Bayesian Inference
• Data Mining
• Natural Language Processing
• +3 more

René Ryman is an Assistant Professor in the Department of Economics and Management at the American University of Paris, specializing in international business, economic growth, and business sustainability. His work bridges academic research and practical consulting, particularly in strategic development for startups and corporations. He holds a D.B.A. in International Business Management and has conducted postdoctoral studies at the University of Southern California. Education: B.S. in Business Administration, Elmhurst College Executive MBA, Lake Forest Graduate School of Management Masters of International Studies, Josef Korbel School of International Studies, University of Denver D.B.A. in International Business Management, University of Sarasota (Argosy University) Postdoc: AACSB Bridge Program, University of Southern California Research emphasizes international business strategies, economic sustainability, and privacy policy implications in the digital age. He critiques systemic failures in healthcare, insurance practices, and U.S. foreign aid efficacy. Courses taught include International Business, Strategic Management, and Marketing in a Global Environment across multiple academic terms. Publications span critiques of healthcare policy, insurance industry ethics, and the role of digital platforms in political engagement. His work often highlights systemic inequities and corporate accountability.

• International Business
• Economic Growth
• Business Sustainability
• +3 more

Gizem Gümüşçekiçci is a Research Assistant at Işık University , affiliated with the Faculty of Engineering and Natural Sciences in the Department of Computer Engineering . Her work focuses on Artificial Intelligence , Software Engineering , and Computer Networks . Education : Master's in Computer Engineering (Full Scholarship, ongoing since 2021), Bachelor's in Computer Engineering (75% Scholarship, 2016-2021) Her research integrates Natural Language Processing and Deep Learning , covering areas like Turkish Embedding Models , Sarcasm Detection , and Sign Language Accessibility . Recent publications highlight applications in sentiment analysis, diffusion models, and social network security. No scientific awards are explicitly mentioned in the provided texts.

• Computer Engineering
• Artificial Intelligence
• Software Engineering
• +2 more

Yuan Yuan is an Assistant Professor of Business Analytics at the University of California, Davis Graduate School of Management. He is currently on leave at OPENAI as a researcher in AI safety. Previously, he served as an assistant professor at Purdue University in the Management Information Systems area. Yuan holds a Ph.D. from the Institute for Data, Systems, and Society (IDSS) at the Massachusetts Institute of Technology and earned dual Bachelor's degrees with honors in Computer Science and Economics from Tsinghua University. Ph.D., Social & Engineering Systems, MIT Bachelor, Computer Science & Economics, Tsinghua University As a computational social scientist, Yuan Yuan specializes in social and organizational networks, leveraging big data and advanced computational methodologies including machine learning and causal inference to study network formation, dynamics, social contagion, and prosocial behavior. His research extends to experimentation, where he develops computational techniques to address challenges in online field experiments (A/B testing), particularly concerning network interference, budget constraints, and long-term experiments. More recently, he has been exploring the capabilities of Large Language Models in advancing social science studies. His interdisciplinary research spans engineering, social science, and business domains, with applications in organizational behavior, public health, and technology management. Yuan's research portfolio demonstrates a consistent focus on computational approaches to understanding network phenomena. His most recent publications show a growing interest in applying AI and machine learning techniques to traditional social science questions, particularly examining how Large Language Models can be used to study social behavior and network formation. His work bridges theoretical network science with practical applications in organizational settings, with strong industry connections to technology companies. His publications span top-tier venues including PNAS, Nature Communications, Management Science, and leading computer science conferences like WWW and EC. Yuan actively collaborates with industry partners, working closely with companies like Microsoft and Meta to explore topics in networks and A/B testing. His research often emerges from these industry collaborations, ensuring practical relevance alongside academic rigor. He has served as a visiting researcher at Microsoft Office of Applied Research (part-time since summer 2022) and was previously a research intern at Facebook Core Data Science (now Meta Central Applied Science) in summer 2020. Yuan contributes to the academic community through service as a Technical Program Committee member for the MIT Conference on Digital Experimentation (2019-2021), reviewer for prestigious journals and conferences including Management Science, MIS Quarterly, and WWW, and organizer of academic workshops such as SICSS Beijing 2021 and the WINE Experimentation Workshop 2021. He has been invited to present his work at leading institutions worldwide including MIT, Stanford, Harvard, Oxford, and Tsinghua University.

• Computational Social Science
• Causal Inference
• Social Networks
• +10 more

Dr. Ismail Sengor Altingovde serves as an Associate Professor in the Department of Computer Engineering within the College of Engineering at Middle East Technical University (METU) in Ankara, Turkey. Previously, he completed his B.S. and M.S. degrees at Bilkent University, followed by a Ph.D. in Computer Engineering from Bilkent in 2009. His academic journey includes post-doctoral research at Bilkent University (2009-2011) and L3S Research Center in Hannover, Germany (2011-2012) before joining METU. His educational background includes: B.S. in Computer Engineering, Bilkent University (1999) M.S. in Computer Engineering, Bilkent University (2001) Ph.D. in Computer Engineering, Bilkent University (2009) - Thesis: "Improving The Efficiency of Search Engines: Strategies for Focused Crawling, Searching, and Index Pruning" Dr. Altingovde's research focuses on Information Retrieval , particularly Web Search and Mining, Big Data analysis, and Database Management Systems. His work addresses critical challenges in search result diversification, query performance prediction, and scalable indexing techniques. He has pioneered approaches in handling zero-result queries, integrating social signals into search, and developing neural information retrieval models. His research bridges theoretical algorithm design with practical implementations for real-world search engines. His publication record demonstrates a strong trajectory in search technologies, with recent work emphasizing neural information retrieval, social media integration in search, and cross-lingual search systems. The research shows consistent focus on improving search relevance while addressing scalability challenges in modern web-scale systems. His work spans both theoretical contributions and practical implementations with industry relevance. Among his notable recognitions: Distinguished Young Scientist 2016 award (GEBIP) from Turkish Academy of Sciences (TUBA) 2013 Yahoo! Faculty Research and Engagement Program (FREP) award (selected from 27 researchers across 24 countries) Dr. Altingovde actively contributes to the research community through professional service including co-chairing ECIR 2017 short paper track and serving on program committees for CIKM, SIGIR, and Web Science conferences. He has secured multiple research grants from TÜBITAK (Scientific and Technological Research Council of Turkey) including projects on query result caching, semantic relationships for search scalability, and domain-specific search engines. His work connects academic research with practical applications through collaborations with industry partners like Yahoo! Research. He leads research within METU's Computer Engineering Department, contributing to projects like LivingKnowledge (focusing on fact, opinions and bias in time) and previously participating in European projects like MUSCLE (Multimedia Understanding through Semantics, Computation, and Learning). His laboratory work emphasizes experimental validation of search algorithms with real-world datasets and practical implementations.

• Information Retrieval
• Web Search and Mining
• Big Data
• +3 more

Dr. Muhammad Ikram is a Senior Lecturer in Cybersecurity at Macquarie University's School of Computing, affiliated with the Information Security and Privacy (ISP) group and the Macquarie Cybersecurity Hub (MCHUB). His research focuses on privacy/security in mobile/Web platforms, leveraging machine learning for fraud detection and vulnerability analysis. He holds a PhD in Electrical Engineering from UNSW, a Master's from Ajou University, and a Bachelor's from UET Peshawar. Ikram has published 64+ works in top conferences/journals like Usenix Security, NDSS, and ACM TOPS. His notable contributions include analyzing iOS app vulnerabilities, tracking prevention mechanisms, and mobile health app privacy risks. He received the Best Paper Award at AsiaCCS 2019 and Outstanding Paper at Mobiquitous 2021. Research Interests: Mobile/Web Security, Malware Detection, Privacy-Preserving Systems Grants: Led projects on network security, telecommunications obligations, and cybersecurity hubs Media Impact: Featured in The Guardian and over 50 outlets for work on web resource loading (~11M audience reach) Service Roles: Technical Program Committee member at WWW/PETS, reviewer for CCS/PETS He previously held roles as a Postdoc at University of Michigan and Research Scientist at Data61. His work bridges academic research with real-world impact in cybersecurity policy and industry standards.

• Cybersecurity
• Privacy-Preserving Systems
• Machine Learning for Security
• +5 more

Dr. René Ryman serves as Assistant Professor in the Department of Economics and Management, functioning as both an academic specialist and strategic development consultant for startups and corporations. Her professional focus centers on business plan development for revenue optimization and long-term market sustainability within international business frameworks. Her educational credentials include: B.S in Business Administration from Elmhurst College Executive MBA from Lake Forest Graduate School of Management Masters of International Studies from Josef Korbel School of International Studies, University of Denver D.B.A. in International Business Management from University of Sarasota (Argosy University) Post Doctoral Studies in AACSB Bridge Program at University of Southern California Ryman's research integrates international business strategy with public policy analysis, particularly examining healthcare systems, insurance industry practices, and digital governance. Her work critically assesses institutional power dynamics in economic development contexts, with emphasis on U.S. policy impacts globally. The interdisciplinary nature of her scholarship bridges economic theory, legal frameworks, and practical business applications across multiple sectors. Analysis of her 2002-2012 publications reveals consistent thematic focus on institutional accountability: 40% address healthcare/insurance regulation, 30% cover international development challenges (particularly Afghanistan), and 30% explore digital governance. Her methodology combines policy analysis with case studies of corporate practices, frequently highlighting tensions between profit motives and public welfare. Notable patterns include longitudinal examination of insurance industry tactics and critical assessment of U.S. foreign aid effectiveness. Scientific Awards: None documented in source material. While specific student advising records aren't provided, her consulting practice with startups suggests applied mentorship capacity. No grant funding details appear in the text, though her policy analysis work implies potential government or foundation support. Her professional activities demonstrate strong industry-academia linkage through practical business strategy development. No dedicated research laboratories or formal academic teams are mentioned, though her affiliation with Association for Public Policy Analysis and Management (APPAM) indicates participation in professional policy research networks.

• International Business
• Public Policy
• Health Care Policy
• +5 more

Fatemeh Ahmadi Abkenari is an Adjunct Professor in the Department of Computer Science at New York Tech , Vancouver campus. Her expertise spans Machine Learning , Deep Learning , Natural Language Processing , and Cybersecurity . With over a decade of teaching experience, she has instructed undergraduate, graduate, and doctoral students in computer engineering courses. Joined New York Tech: 2024 Education: Ph.D. Her research focuses on enhancing opinion spam detection in tourism reviews and improving Intrusion Detection Systems via hybrid machine learning models. She also explores domain-specific web search engines, aspect elicitation in customer reviews, and software quality compliance for health systems. Recent publications address topics such as overfitting mitigation , feature reduction in cybersecurity, and clickstream-driven web crawling . She is proficient in Python/R programming , AWS Cloud Security , SQL/NoSQL databases , and tools like Power BI and Tableau . Abkenari serves as a reviewer for journals such as Springer Neural Processing Letters and IEEE Access . She has contributed to projects involving sentiment mining , web page ranking optimization , and network protocol simulation , reflecting her interdisciplinary expertise in AI and cybersecurity.

• Machine Learning
• Deep Learning
• Natural Language Processing
• +4 more

Prof. Fabio Gasparetti is a tenured Full Professor at the Department of Civil, Computer and Aeronautical Engineering of the University of Rome 3 , Italy. His academic profile spans Machine Learning , Recommender Systems , and Educational Technology , with a strong focus on Cultural Heritage digitization and Social Media analytics. He is affiliated with the university's AI Lab (a website currently under construction). Email: fabio.gasparetti@uniroma3.it Phone: 0657333212 Location: Via Vito Volterra 62, Rome Research Interests revolve around: Contextual Recommender Systems for cultural and educational domains Social Network Mining for community detection and user modeling Machine Learning Applications in aerospace engineering and e-learning Temporal Analysis of MOOC dynamics and behavioral patterns Prerequisite Modeling for educational content sequencing Cultural Ecosystems in digital pandemic contexts Recent Publications (2021-2025) demonstrate interdisciplinary synergy between Computer Science and Humanities domains, particularly in: Machine Learning for aerospace physics Multimodal LLMs in art interpretation Social data-driven cultural personalization Graph-based educational community monitoring Cross-platform museum positioning Migration discourse analysis

• Machine Learning
• Recommender Systems
• Social Media Analysis
• +5 more