جست‌وجوی استاد و پژوهشگر

• Cryptography
• Security
• Blockchain Technology
• +۴ مورد دیگر

Elaine Shi is a Professor at Carnegie Mellon University's Computer Science Department and Electrical and Computer Engineering Department, with an Adjunct Professor appointment at the University of Maryland. Her research spans cryptography, security, blockchain technology, algorithms, and privacy-enhancing techniques. Co-founder of Oblivious Labs, Inc. Co-developer of cryptographic protocols adopted by Signal, Meta, and Google Co-founder of CyLab's crypto seminar series Her work has been recognized with prestigious awards including the Packard Fellowship, Sloan Research Fellowship, ACM Fellow, and IACR Fellow. She has advised numerous PhD students and postdocs, many of whom now hold academic or industry positions. 2023 ACM CCS Test of Time Award 2020 CyLab Distinguished Alumni Award 2016 ONR YIP Award Recent publications focus on advancing cryptographic protocols, privacy-preserving algorithms, and blockchain security, with key contributions in garbled RAM, oblivious computation, and differentially private mechanisms.

• Operations Research
• Artificial Intelligence
• Decision-Making
• +۱۰ مورد دیگر

Duong Nguyen serves as an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. His research integrates operations research, artificial intelligence, economics, and engineering to develop mathematical models for decision-making in large-scale networked systems including cloud/edge computing, smart grids, and crowdsourcing. He directs the NEMO research group focused on building intelligent multi-agent platforms through optimization and market design. His educational credentials include: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2020) M.Sc. in Telecommunications from INRS, University of Quebec (2014) B.Sc. in Electronic and Telecommunications from Hanoi University of Science and Technology (2011) Dr. Nguyen's research spans Operations Research, Artificial Intelligence, Decision-Making, Market Design, and Optimization with applications in edge computing, power systems, and network economics. His work emphasizes robust algorithms for uncertain environments and secure multi-agent platforms, recently expanding into quantum machine learning and privacy-preserving distributed systems. Current projects address decentralized federated learning, dynamic pricing, and EV charging network design. Analysis of his publication record reveals consistent focus on distributed optimization techniques for edge/cloud systems, with increasing integration of game theory and quantum computing. His work demonstrates strong methodological innovation in handling spatio-temporal uncertainty while addressing practical challenges in energy flexibility and secure genomic computation. His scientific recognition includes: Finalist for Best Student Paper Award at American Control Conference (ACC) 2024 Finalist for Best Paper Award at International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) 2023 Dr. Nguyen actively mentors Ph.D. students including Jiaming Cheng and Long Vu, with student-led research achieving significant recognition. His NEMO group collaborates with institutions including ETH Zurich on projects spanning autonomous driving, edge AI, and quantum optimization. Current research directions emphasize fair resource allocation, privacy-preserving learning, and dynamic pricing frameworks for next-generation networked systems.

• Complex Systems Science
• Artificial Life
• Morphological Computation
• +۳ مورد دیگر

Dr. Dandolo Flumini is a Researcher at the Zurich University of Applied Sciences (ZHAW), School of Engineering, specializing in Applied Complex Systems Science. His research focuses on artificial life, morphological computation, blockchain applications, and computational modeling. He serves as team member or project lead in multiple interdisciplinary initiatives including Bio-HhOST (bio-hybrid tissues), Agroforestry Carbon Token System, and blockchain-based voting solutions. His primary research interests include: Complex Systems Science : Emergent behaviors in biological and artificial systems Morphological Computation : Physical systems performing computational tasks Artificial Chemistry : Programmable chemical systems using droplet networks Blockchain Applications : Decentralized finance and voting systems Computational Ethics : Responsible implementation of AI and modeling Flumini's recent publications (2019-2023) demonstrate strong focus on microfluidic systems, droplet agglomeration physics, programmable chemistry, and ethical AI. His work frequently appears in artificial life and computational modeling venues, with increasing emphasis on real-world applications in sustainability and decentralized systems. He maintains active collaborations through the Applied Complex Systems Science research group at ZHAW, contributing to projects involving microfluidic device design, blockchain architectures, and bio-hybrid tissue engineering.

Yongkai Wuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Dr. Yongkai Wu is an Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University, where he focuses on advancing Responsible AI , Causal Inference , and Machine Learning . His research addresses fairness, trustworthiness, and transparency in AI systems through causal modeling and has been published in top-tier venues like AAAI, NeurIPS, and KDD. Education: Ph.D. in Computer Science (2020) and M.S. in Computer Science (2018) from the University of Arkansas; B.Eng. in Electronic Engineering (2014) from Tsinghua University. Dr. Wu’s research spans Responsible AI and Causal Inference , with applications in healthcare, computer vision, and cybersecurity. He explores Causal Fairness in non-IID settings, Responsible LLMs , and Robust Learning via hyperspectral data. His work integrates ethics into AI/ML systems, ensuring equitable outcomes in dynamic environments. His recent articles highlight trends in Fairness through causal inference, Explainable AI in healthcare, and Efficient LLMs . Collaborations with institutions like the University of Maryland and Prisma Health underscore real-world impact. Scientific Awards: Best Paper Award (SIGKDD'25), travel awards from SBP-BRiMS, IJCAI, KDD, and NeurIPS. Dr. Wu’s grants include NSF , SC EPSCoR , Prisma Health , and United States Army CCDC funding for projects on Responsible AI in Healthcare , Hyperspectral AI , and Robust Learning . He mentors students through summer programs and directed research, emphasizing hands-on experience with Python, PyTorch, and ethical AI frameworks.

Merve Hickokمشاهده پروفایل

مدرس

• AI policy
• AI ethics
• AI governance
• +۱۰ مورد دیگر

Merve Hickok is a Lecturer at the University of Michigan's School of Information, where she teaches data ethics. She also serves as the Responsible Data and AI Advisor at the Michigan Institute for Data Science (MIDAS) and is an affiliated faculty member at the Gerald R. Ford School of Public Policy. Additionally, she is the President and Research Director at the Center for AI and Digital Policy (CAIDP), advising governments and international organizations on AI policy and regulation. Her research focuses on AI policy, ethics, and governance, with particular emphasis on fundamental rights, democratic values, and social justice. She examines the impact of AI systems on individuals, society, and organizations, exploring issues like algorithmic bias, privacy concerns, and the societal implications of emerging AI technologies. Her work bridges technical understanding with social and ethical considerations to promote responsible AI development and deployment, with special attention to employment applications, government procurement, and international policy frameworks. 100 Brilliant Women in AI Ethics™ – 2021 Runner-up for Responsible AI Leader of the Year – 2022 (Women in AI) Lifetime Achievement Award - Women in AI of the Year - 2023 Top AI Leaders in Retail (Ethics & Compliance) - 2024 Hickok provides consultancy to C-suite leaders and training services to public and private organizations on Responsible AI development, due diligence, and governance. She has provided testimony to the US Congress, State of California Civil Rights Office, New York City Department of Consumer and Worker Protection, Detroit City Council, and numerous global organizations. She founded AIethicist.org to help professionals navigate the complex landscape of AI ethics and serves on the founding editorial board of Springer Nature's AI & Ethics journal.

• Artificial Intelligence in Organizations
• Algorithmic Management
• Digital Labor Platforms
• +۳ مورد دیگر

Hatim Rahman serves as Associate Professor of Management and Organizations and Sociology (by courtesy) at Northwestern University, holding the prestigious PepsiCo Chair in International Management. His research fundamentally examines how artificial intelligence and algorithms reshape work structures, employment relationships, and labor market dynamics, with particular focus on digital platform ecosystems and algorithmic management systems. Professor Rahman's academic credentials include a PhD in Management Science & Engineering (2019) and MS in Organizations, Technology, Entrepreneurship (2014) from Stanford University, complemented by a BA in Business Process Management with Minor in Technology and Management from the University of Illinois-Urbana Champaign (2009). His scholarly work centers on the sociotechnical transformation of work through algorithmic systems, investigating worker reactivity to opaque evaluations, platform power dynamics, and emergent resistance movements in digital labor markets. Key contributions explore the "invisible cage" of algorithmic control, the experimental nature of platform management, and the sociological implications of AI-driven workplace restructuring. His research bridges management theory, sociology of work, and technology studies to address critical questions about fairness, autonomy, and accountability in the algorithmic age. Analysis of his publication trajectory reveals a clear evolution from creative occupations research (2017) toward concentrated investigation of algorithmic labor platforms (2021-2025). His work consistently demonstrates how digital platforms reconfigure worker autonomy through experimental management practices, opaque evaluation systems, and novel control mechanisms, while simultaneously generating new forms of worker resistance and activism. The 2024 book "Inside the Invisible Cage" synthesizes these insights into a comprehensive framework for understanding algorithmic worker control. Professor Rahman's exceptional contributions have earned him significant recognition: National Science Foundation CAREER Award recipient for bridging STEM skills and employment gaps George R. Terry Book Award winner from the Academy of Management Named among Poets & Quants' Best 40 Business School Professors Under 40 (2023) Thinkers50 Radar listing for influential management thinking Multiple Academy of Management best paper awards across divisions Microsoft Research AI and Society Fellowship International Labour and Employment Relations Association prizes His research program includes substantial grant funding from the NSF CAREER award, while his academic service encompasses editorial board membership at Administrative Science Quarterly and review work for top management journals. As an educator, he develops innovative courses like "Artificial Intelligence and the Future of Work" that prepare MBA students for technology-driven organizational transformation, emphasizing practical integration of AI systems while addressing ethical implications for workers and organizations.

Hua Xuمشاهده پروفایل

استاد

• Clinical Natural Language Processing
• Biomedical Informatics
• Large Language Models for Biomedicine
• +۳ مورد دیگر

Dr. Hua Xu is the Robert T. McCluskey Professor of Biomedical Informatics and Data Science at Yale School of Medicine. He serves as Vice Chair for Research and Development in the Department of Biomedical Informatics and Data Science and as Assistant Dean for Biomedical Informatics at Yale School of Medicine. Dr. Xu leads the Clinical NLP Lab and is Chair of the NLP working group at the Observational Health Data Sciences and Informatics (OHDSI) program. Dr. Xu received his PhD in Biomedical Informatics from Columbia University, an MS in Computer Science from New Jersey Institute of Technology, and a BS in Biochemistry from Nanjing University. Dr. Xu is a renowned researcher in clinical natural language processing (NLP), having developed novel algorithms for important clinical NLP tasks such as entity recognition and relation extraction. His work has been top-ranked in over a dozen international biomedical NLP challenges. He has developed CLAMP, a comprehensive clinical NLP toolkit that has been successfully commercialized and adopted by hundreds of healthcare organizations worldwide. His research focuses on applying NLP technologies to diverse clinical and translational studies to accelerate clinical evidence generation using electronic health records data. Recently, he has been utilizing NLP to harmonize metadata of biomedical digital objects to promote FAIR principles in biomedicine, and his lab is actively working on developing large language models (LLMs) for diverse biomedical applications. Dr. Xu's recent publications demonstrate a clear trend toward leveraging large language models for biomedical applications. His work spans from benchmarking LLMs for clinical NLP tasks to developing specialized architectures like BiomedRAG (retrieval augmented LLMs for biomedicine). His research addresses critical healthcare challenges including adverse event extraction, oncology clinical trial analysis, and EHR-based association studies, showing how NLP can bridge the gap between unstructured clinical text and actionable medical insights. Dr. Xu's lab has achieved top rankings in numerous NLP challenges, including multiple #1 positions in i2b2 Temporal information extraction, SemEval Disease-modifier extraction, BioCREATIVE Chemical-induced disease extraction, and other prestigious competitions. His contributions to clinical NLP have significantly advanced the field's ability to extract meaningful information from complex medical texts. As the leader of the Clinical NLP Lab at Yale, Dr. Xu oversees research that forms a complete ecosystem: developing novel NLP methods, building robust software tools, and applying these technologies to clinical and translational research. His lab's work closes the loop between methodological innovation and practical healthcare applications, ensuring that advances in NLP directly benefit patient care and medical research.

Chiara Longoniمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Consumer Behavior
• Sustainability
• +۵ مورد دیگر

Chiara Longoni is an Associate Professor in the Department of Marketing at Bocconi University in Milan, Italy. Her interdisciplinary research examines consumer responses to artificial intelligence applications in healthcare, government services, and sustainability, as well as drivers of societal well-being. She has received multiple scientific awards including the 2023 MSI Young Scholar award and 2022 Journal of Consumer Research Best Article Award. Ph.D. in Marketing from New York University’s Stern School of Business M.S. (summa cum laude) from Bocconi University M.A. (Honors) in Psychology from NYU M.Phil. in Marketing from NYU Stern Her research spans two primary domains: AI Adoption: Investigates psychological responses to artificial intelligence in healthcare, recommendation systems, and governance. Sustainability: Analyzes behavior change mechanisms and climate action drivers through global interventions involving 63 countries. Key publication venues include Journal of Consumer Research , Journal of Marketing , Nature Human Behavior , and Science Advances . Current editorial roles as Associate Editor for the Journal of Consumer Research and reviewer for top journals across marketing, management, and medicine.

Kathryn Stoleeمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Software Engineering Education
• +۷ مورد دیگر

Kathryn Stolee is an Associate Professor in the Department of Computer Science at North Carolina State University. She received her Ph.D. in Computer Science from the University of Nebraska-Lincoln under Sebastian Elbaum after graduating from the Jeffrey S. Raikes School of Computer Science and Management. Her research spans multiple perspectives in software engineering: technical (program analysis), human (human aspects of software engineering, software product management), and educational (comparative comprehension of algorithms). Notable contributions include work on regular expression refactoring for improved comprehension, constraint solvers for code reuse identification, and cross-language code-to-code search to aid developers learning new languages. Dr. Stolee has secured over $2,000,000 in federal grants, including an NSF CAREER award. Her research combines analysis techniques (refactoring, semantic code search, code-to-code search) with human factors (comprehension, reuse, learning). Her scholarly contributions have been recognized with a National Science Foundation Faculty Early CAREER Award (2018) and a Best Paper Award at the International Symposium on Empirical Software Engineering and Measurement (ESEM, 2011). Actively engaged in the software engineering research community, Dr. Stolee serves as an author, reviewer, and organizer at top conferences. She is committed to mentoring the next generation of computer scientists and has developed educational interventions focused on software testing and code comprehension.

Saverio Mascoloمشاهده پروفایل

استاد

• Network Congestion Control
• Real-time Communication Systems
• Adaptive Streaming Protocols
• +۴ مورد دیگر

Saverio Mascolo is a Full Professor at the Polytechnic University of Bari , Department of Electrical and Information Engineering. He leads the Control of Computing and Communication Systems Lab (C3Lab) and contributes to IEEE/ACM Transactions on Networking as an Associate Editor. His research focuses on Future Internet, network congestion control, and real-time communication systems. Laurea in Electronic Engineering, Politecnico di Bari (1991) PhD in Electronic and Automatic Control, Politecnico di Bari (1995) Visiting researcher roles at UCLA (1995, 1999), INRIA (2004), and FTW (2004) Research spans network congestion control , adaptive video streaming , and real-time communication . His lab develops protocols like TCP Westwood+ and contributes to WebRTC standards. Current projects include low-delay protocols for immersive video streaming and autonomous systems control. Recent publications emphasize adaptive threshold mechanisms in congestion control, millimeter-wave radar datasets , and immersive teleoperation systems . Projects integrate nonlinear control , time-delay analysis , and cloud-based multimedia delivery . Scientific recognition includes: Elevated to IEEE Fellow (2018) for congestion control contributions Google Faculty Award (2014) for WebRTC research Cisco Research Award (2013) for video streaming control Best paper awards at MMSYS (2025, 2024, 2016) Grants fund research in cloud-based video platforms (MISE, 2017-2020), WebRTC optimization (MIUR, 2012-2015), and network control algorithms. His lab collaborates with institutions like Uppsala University (since 2001) and industry partners.

Md Mobashir Hasan Shandhiمشاهده پروفایل

استادیار

• Digital health technologies
• Wearable sensors
• Machine learning algorithms
• +۱۰ مورد دیگر

Md Mobashir Hasan Shandhi is a tenure-track Assistant Professor at Arizona State University , jointly appointed in the School of Electrical, Computer and Energy Engineering and the Biodesign Institute Center for Bioelectronics and Biosensors . His work focuses on developing equitable digital health technologies—wearable sensors and AI/ML algorithms—for personalized care and remote monitoring of chronic and infectious diseases. Education PhD, Electrical and Computer Engineering, Georgia Institute of Technology, 2020 Postdoc, Biomedical Engineering, Duke University, 2021–2024 MS, Electrical and Computer Engineering, University of Utah, 2016 BSc, Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2011 Research Interests Dr. Shandhi’s lab designs low-cost, reliable wearable sensors and machine-learning models to enable remote monitoring of cardiovascular, respiratory, and infectious disease patients. His goal is to reduce healthcare disparities by translating these technologies into resource-limited settings. Scientific Awards & Grants American Heart Association Career Development Award Mayo Clinic–ASU Alliance Faculty Summer Residency Fellowship AHA Postdoctoral Fellowship Duke Heart Center & Translating Duke Health cardio-oncology grant NIH mHealth Training Institute Scholarship Best Paper, Runner-up Best Paper, First Place Research Awards Distinguished Poster Nominee Advising & Funding Dr. Shandhi is currently recruiting PhD students with backgrounds in electrical/biomedical engineering or computer science. He also welcomes postdocs and MS/undergraduate researchers to join the SHANDHI Lab. Interested candidates should email him directly with a CV and statement of interest. Labs & Teams He directs the SHANDHI Lab at ASU, where interdisciplinary teams of engineers, clinicians, and data scientists collaborate on translating wearable health technologies from bench to bedside.

Liyuan Weiمشاهده پروفایل

دانشیار

• Digital Marketing
• Consumer Psychology
• Technology and Wellbeing
• +۲ مورد دیگر

Dr Liyuan Wei is an Associate Professor in Marketing at Southampton Business School, University of Southampton. She joined the university in 2024 after academic positions at Brunel University London and City University of Hong Kong. Her research explores intersections between digital strategies, consumer psychology, technology, wellbeing, and quantitative methods. PhD: Rotman School of Management, University of Toronto Her research focuses on responsible consumption, ethical marketing practices, and consumer wellbeing. She employs quantitative methodologies to examine digital marketing impacts and develops frameworks for healthy aging interventions through marketing strategies. Recent publications demonstrate expertise in tourism marketing, film industry analytics, healthcare interventions, and sustainability research. Key themes include mindfulness in ethical consumption, pandemic response strategies, and digital loyalty systems. Articles appear in journals like Journal of Policy Research in Tourism , International Journal of Research in Marketing , and Journal of Business Research . Teaching responsibilities include Marketing Research Methods, Marketing Analytics, Digital Marketing, and Strategic Marketing. She actively supervises PhD students in marketing-related topics and welcomes new applicants.

Katharina Eggenspergerمشاهده پروفایل

پژوهشگر

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +۱ مورد دیگر

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

• Machine Learning
• Graph Neural Networks
• Data Attribution
• +۴ مورد دیگر

Jiaqi Ma is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding dual appointments in the School of Information Sciences and the Siebel School of Computing and Data Science. Their research focuses on machine learning, graph neural networks, and data attribution, with particular emphasis on fairness in AI, large language models, and scalable algorithms. Ma has contributed to frameworks like dattri for efficient data attribution and GraSS for scalable influence functions. Research interests include graph learning (e.g., structural information analysis in text-attributed graphs), fairness in ML models (e.g., mitigating disparities in unlearning processes), and ethical AI applications. Collaborations span topics like data curation (DCA-Bench benchmark) and reinforcement learning transparency (A Snapshot of Influence). Key contributions include improving data removal while maintaining fairness (Fair Machine Unlearning), analyzing LLMs' graph structural utilization, and developing open-source tools like OpenHexAI for explainable ML evaluation. No scientific awards are explicitly listed, but their work reflects significant contributions to trustworthy AI and machine learning systems. Advising and grants: While specific students/grants are unlisted, Ma leads research teams advancing graph learning (e.g., Graph Learning Indexer platform) and safety-critical AI (e.g., unsafe data detection via attribution). Their work bridges theoretical foundations (e.g., influence functions) with practical applications (e.g., MNL model rankings).

Joshua D. Blankمشاهده پروفایل

استاد

• Tax Law
• Administrative Law
• Tax Administration and Compliance
• +۴ مورد دیگر

Joshua D. Blank is a Professor of Law and Director of the UCI Law in NYC Program at the University of California, Irvine School of Law. His expertise spans tax administration, administrative agency communication, taxpayer privacy, and automated legal guidance. He previously held roles at NYU School of Law, including Vice Dean for Technology-Enhanced Education and Professor of Tax Law. Blank’s research focuses on modernizing tax compliance systems and addressing inequities in legal guidance accessibility. He has authored multiple influential books, including *Untaxed: The Rich, the IRS, and a New Approach to Tax Compliance* (2024) and *Automated Agencies: The Transformation of Government Guidance* (forthcoming 2025). Education: B.A. (summa cum laude) from NYU College of Arts and Science; J.D. (cum laude) from Harvard Law School; LL.M. in Taxation from NYU Law. Key achievements include the 2020 AALS Teacher of the Year Award and leadership roles in the American Bar Association Tax Section. He co-led a landmark study for the Administrative Conference of the U.S. on automated agency guidance tools, resulting in federal policy recommendations. His work bridges tax law, administrative law, and technology, emphasizing equitable legal frameworks. Recent activities include presenting at Columbia Tax Workshop, Duke Law School, and international conferences. Blank’s advocacy for progressive tax procedure and transparency has influenced both academic discourse and public policy.