جست‌وجوی استاد و پژوهشگر

Konstantin Baumanمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Recommender Systems
• +۳ مورد دیگر

Dr. Konstantin Bauman is an Associate Professor in the Department of Management Information Systems at Temple University's Fox School of Business. He holds a PhD in Mathematics (Geometry and Topology) from Moscow State University and dual Master’s degrees in Mathematics and Machine Learning from prestigious Russian institutions. His research focuses on machine learning, data science, and context-aware recommender systems, emphasizing novel methods for predicting customer preferences and designing personalized recommendation frameworks. Education: PhD in Mathematics (Geometry and Topology), Moscow State University MS in Mathematics, Moscow State University MS in Machine Learning, Moscow Institute of Physics and Technology/Yandex School of Data Analysis Research Interests: Data Science and Analytics Machine Learning and Recommender Systems Context-Aware Systems and Text Mining Technology-Enhanced Learning Recent Work Trends: His publications emphasize context-aware recommendation algorithms, privacy concerns in personalized systems, and applications of hyperbolic embeddings. He also explores device impact on employee feedback and cryptocurrency investor behavior using multimodal data analysis. Awards: None explicitly listed in the provided materials. Advising/Grants: No formal advisees listed; his work at Yandex and NYU involved leading machine learning teams and tackling large-scale data science challenges. Labs/Teams: Active in the MIS department at Temple, contributing to research on adaptive learning systems and enterprise machine learning applications.

Xuan Liangمشاهده پروفایل

مدرس

• Spatial Modelling
• Panel Data
• Nonparametric Statistics
• +۳ مورد دیگر

Xuan Liang is a Lecturer in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), Australian National University. With a PhD from Peking University and postdoctoral experience at Monash University, his research focuses on spatial statistics, nonparametric modeling, and environmental data analysis. Education: PhD in Statistics (Peking University, 2017), BSc in Statistics (Zhejiang University, 2012) His work addresses methodological challenges in spatial panel data analysis, network modeling, and air pollution quantification. He has developed novel techniques for meteorological confounder adjustment in air quality assessments and contributed to distributed data analysis methods. Recent research trends include: Advancing quasi-score matching for spatial econometric models Improving subbagging algorithms for big data Creating robust distributed data aggregation frameworks Refining spatial autoregressive panel data methodologies Scientific contributions include: ANU Vice-Chancellor’s Citation for Outstanding Contribution to Student Learning (Early Career), 2022 CBE Teaching Commendation for Outstanding Teaching, 2020 Co-development of the ggmatplot R package for matrix visualization Co-inventor of Chinese patent 201811183512.0 for air quality assessment He teaches advanced courses in time series analysis, regression modeling, and mathematical statistics at ANU, while maintaining active research collaborations in econometrics and environmental statistics.

• Marketing Analytics
• Electronic Commerce
• Online Advertising
• +۱۱ مورد دیگر

Prof. Dr. Bernd Skiera holds the first chair for electronic commerce in Germany at Goethe University Frankfurt am Main since 1999. He serves on the board of efl - The Data Science Institute and the Schmalenbach Society, while representing Germany at the European Marketing Community (EMAC). Chair of Marketing, Goethe University Frankfurt am Main Board member, efl - The Data Science Institute National representative, European Marketing Community (EMAC) His research focuses on MarTech/SalesTech integration, customer value management, online advertising analytics, data-driven pricing models, and the economic implications of internet privacy regulations. He leads the ERC Advanced Grant project on cookie usage restrictions' economic consequences. Recent publications examine dynamic pricing in digital markets, competition visualization using big data, and AI applications in marketing analytics. The ERC Advanced Grant research has produced empirical analyses of GDPR impacts on advertising ecosystems. 2015 Journal of Marketing Best Paper Award 2013 International Journal of Research in Marketing Best Paper Award Multiple MSI/H. Paul Root Award recognitions He has mentored 17 doctoral students who became professors globally, including at London Business School and LMU Munich. His work bridges marketing analytics with financial valuation through customer equity modeling.

• Computer Science
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.

Irem Dikmenمشاهده پروفایل

استاد

• Construction Project Risk Management
• Climate Risk and Resilience in Built Environment
• Digital Construction Technologies
• +۵ مورد دیگر

Professor Irem Dikmen is a leading academic in Construction Engineering and Management at the University of Reading, where she serves as School Director of Internationalisation in the Chancellor's Building. Her research integrates engineering, management, and information sciences to advance construction project risk management, particularly focusing on climate resilience, digital technologies, and social value in infrastructure systems. PhD, MSc, and BSc in Civil Engineering from Middle East Technical University Her work leverages systems thinking, artificial intelligence, and digital tools to develop decision-support frameworks for megaprojects and climate adaptation. Recent publications highlight innovations in NLP contract analysis, energy performance ontologies, and risk visualization techniques. She supervises students on topics spanning IoT lifecycle management, ESG risks, and NLP defect detection. Key collaborations include the Climate and Finance Research Cluster and Walker Institute , with contributions to digital construction technologies and sustainability risk assessment. Teaching modules include Construction Risk Management, Economics, and Business Organisation.

Kamran Sedigمشاهده پروفایل

استاد

• Human-Computer Interaction
• Information Visualization
• Cognitive Technologies
• +۳ مورد دیگر

Dr. Kamran Sedig serves as a Professor in the Department of Computer Science and the Faculty of Information and Media Studies at Western University, where he directs the Insight Lab. His research focuses on designing interactive technologies to enhance human cognitive activities involving data and information, including decision making, problem solving, and learning across domains like healthcare, finance, and scientific discovery. His academic credentials include: Ph.D. in Computer Science (Human-Computer Interaction) from The University of British Columbia under Prof. Maria Klawe, with dissertation nominated for the Governor General’s Gold Medal M.Sc. in Computer Science (Artificial Intelligence) from McGill University under Prof. Renato De Mori B.Sc. in Computer Engineering and Science from Concordia University as Valedictorian with The Most Great Distinction Sedig’s research synthesizes computer science, information science, cognition theory, and game studies to develop frameworks for interactive visual tools (IVTs). He investigates human-data interaction, visual reasoning, and interactivity design to support complex cognitive tasks like medical diagnosis, financial analysis, and scientific exploration. His human-centered approach emphasizes how computational tools and humans form coordinated cognitive systems for optimal task execution. Analysis of his recent publications reveals dominant trends in health informatics applications (drug safety analytics, electronic health records) and foundational work on human-information interaction frameworks. His visual analytics systems consistently bridge theoretical models with practical tools for ontology exploration, document triage, and explainable AI, demonstrating strong interdisciplinary collaboration across medical and computational domains. Key recognitions include: Governor General’s Gold Medal nomination for doctoral research Valedictorian honors at Concordia University As Insight Lab director, Sedig mentors graduate students through courses like Human-Computer Interaction, Information Visualization, and Design of Digital Cognitive Games. His teaching philosophy emphasizes how cognitive technologies mediate human thinking processes in professional and private contexts. While specific grant details aren’t provided, his lab’s sustained output in health analytics and visual interfaces indicates robust research funding. The Insight Lab operates as a collaborative hub for developing and evaluating IVTs, with current projects including VICTORIOUS for document scoping reviews and VISEMURE for multimorbidity analysis. Sedig’s team prioritizes empirical validation of how interaction design affects cognitive load and task efficiency in real-world data-intensive environments.

Luc Anselinمشاهده پروفایل

استاد

• Spatial data science
• spatial econometrics
• computational social science
• +۶ مورد دیگر

Luc Anselin is the Stein-Freiler Distinguished Service Professor of Sociology and the College at the University of Chicago. He serves as Director of the Center for Spatial Data Science and Senior Fellow at NORC, with affiliations in the Department of Sociology and the Committee on Quantitative Research Methods in Social, Behavioral, and Health Sciences. B.S., Vrije Universiteit Brussel, 1975 M.S., Vrije Universiteit Brussel, 1976 M.A., Cornell University, 1979 Ph.D., Cornell University, 1980 Anselin is a pioneer in spatial data science, spatial econometrics, and computational social science. His work bridges quantitative geography, regional science, and computer science, with applications in urban studies, economic analysis, and health research. His recent publications focus on spatial regimes, software ecosystems like GeoDa and PySAL, stigma analysis in public health, and computational methods for urban equity. These reflect interdisciplinary trends in spatial statistical modeling, open-source development, and geospatial policy evaluation. Scientific recognition includes: Fellow, Regional Science Association International (2004) Walter Isard Prize (2005) William Alonso Memorial Prize (2006) National Academy of Sciences (2008) American Academy of Arts and Sciences (2011) Anselin has led major grants and directed institutions like the GeoDa Center for Geospatial Analysis and Computation at Arizona State University. He has mentored interdisciplinary collaborations across sociology, economics, and data science. His laboratories and teams include the Center for Spatial Data Science at the University of Chicago and the GeoDa Center at Arizona State University, fostering innovation in geospatial computation and open-source spatial analytics.

Matloob Khushiمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.

• AI Governance
• Privacy and Security
• Responsible AI
• +۸ مورد دیگر

Yang Wang is a Professor of Information Science and Computer Science (courtesy) at the University of Illinois at Urbana-Champaign (UIUC), where he co-directs the SALT lab and is part of the Interactive Computing group and Security and Privacy Research at Illinois (SPR@I). He holds affiliations with the Institute of Software Research (ISR) at UC Irvine and advises Conviva. His research spans AI governance, privacy, security, and inclusive technologies, focusing on marginalized populations such as people with disabilities and non-Western communities. Education: Ph.D. from the University of California, Irvine (UCI), under advisors Dr. Alfred Kobsa, Dr. André van der Hoek, and Dr. Gene Tsudik. Previous roles include Assistant Professor at Syracuse University and Research Scientist at Carnegie Mellon University’s CyLab. He has collaborated with institutions like Alcatel-Lucent Bell Labs, Intel Research, and Tsinghua University. Research interests include AI safety for children (aisafety4kids.org), inclusive privacy mechanisms, accessible authentication for visually impaired users, and policy implications of AI. Notable projects include Inclusive.AI (funded by OpenAI), privacy in smart homes, and drone privacy studies. Awards and grants include NSF SaTC awards, NSF CAREER, and industry partnerships with Meta/Facebook, Google, and OpenAI. His work has been featured in outlets like the New York Times and Wall Street Journal. Current PhD advisees include Smirity Kaushik and Yaman Yu, with notable alumni such as Dr. Yaxing Yao (now at Johns Hopkins) and Dr. Tanusree Sharma (Penn State). Labs/Teams: Co-director of the SALT lab, part of SPR@I and the Interactive Computing group at UIUC. Collaborates with industry and policy bodies, including the FTC.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

• Artificial Intelligence
• Deep Learning
• Recurrent Neural Networks
• +۱۲ مورد دیگر

Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)

Sam Kirshnerمشاهده پروفایل

دانشیار

• Behavioral Decision Making
• Operations Management
• Technology Management
• +۶ مورد دیگر

Associate Professor Sam Kirshner is a faculty member at the University of New South Wales within the School of Information Systems and Technology Management . His research focuses on behavioral decision making , algorithmic impact on operations , and artificial intelligence applications in business contexts. PhD in Management Science from Queen’s University, Canada Teaching expertise in data visualization, predictive analytics, and AI ethics Co-author of Business Analytics: A Management Approach Member of the Ethical AI Advisory His research explores how psychological distance and construal level theory influence decisions in supply chains, technology management, and consumer behavior. Recent work examines ChatGPT's decision biases , algorithm aversion , and sustainable operations under financial constraints. Key publication trends show focus areas: AI ethics and human-AI collaboration Behavioral supply chain analysis Temporal/spatial psychological distance effects CO2 forecasting with sparse data Consumer behavior in digital platforms Virtual reality and cognitive processing Supervision roles include mentoring 2 PhD students and 6 honors students, contributing to the next generation of scholars in business analytics and technology management.

Shrirang Mareمشاهده پروفایل

استادیار

• Security and Privacy
• Usable Security
• Human-Computer Interaction
• +۳ مورد دیگر

Shrirang Mare is an Assistant Professor in the Computer Science Department at Western Washington University (WWU), focusing on computer security and privacy with emphasis on usable security solutions for diverse user groups including low-literate individuals, the elderly, and cross-cultural populations. His research bridges human-computer interaction and cyber-physical systems to create accessible security mechanisms for emerging technologies. His educational background includes a PhD in Computer Science from Dartmouth College (2016), where his dissertation on seamless authentication for ubiquitous devices established his research trajectory. Dr. Mare's research interests encompass security and privacy, human-computer interaction, and cyber-physical systems, with specialized focus on security for low-literate users and developing regions. His work consistently addresses real-world applicability through field studies in countries like India and Pakistan, examining mobile payment systems, SMS fraud, and smart home privacy challenges. Analysis of his 15 most recent publications (2018-2024) reveals a dominant trend in wearable-based authentication systems (wristbands for desktops/smartphones) and privacy solutions for consumer IoT, particularly smart homes. His research uniquely combines technical security mechanisms with deep user-centered design principles, often targeting underserved populations in developing regions. No scientific awards or fellowships were mentioned in the provided documentation. Teaching responsibilities include Privacy Enhancing Technologies, Object Oriented Design, and Data Structures at WWU, with prior guest lectures at UC Berkeley and the University of Washington; no current advisees or grant funding details were specified. Dr. Mare operates within the Computer Science Department at WWU but no dedicated research lab or team structure was referenced in the source materials.

Sara Chatfieldمشاهده پروفایل

دانشیار

• American Politics
• American Political Development
• Gender and Politics
• +۴ مورد دیگر

Sara Chatfield is an Associate Professor of Political Science at the University of Denver's College of Arts, Humanities and Social Sciences, with additional affiliations in Gender and Women's Studies. Her work bridges American political development, gender politics, and methodological innovation in political science research. Dr. Chatfield earned her Ph.D. and MA in Political Science from the University of California, Berkeley (2014 and 2007 respectively), and her BA in Politics from Oberlin College (2006). Her research focuses on American political development with particular emphasis on gender and politics, public law, and political behavior. American Political Development Gender and Politics Women's Rights History Public Law Political Behavior Methodology and Causal Inference Her recent publications demonstrate a consistent focus on how gender shapes political institutions and processes across historical and contemporary contexts. Early work examined 19th century married women's economic rights reform, while more recent research investigates contemporary issues like bathroom access politics and gender disparities in political campaign staffing. Her scholarship often employs innovative methodological approaches to historical questions, bridging qualitative and quantitative traditions in political science. Dr. Chatfield teaches courses on American politics and law, including Constitutional Law I, Judicial Politics, and a freshman seminar on the politics of bathrooms. Her book In Her Own Name: The Politics of Women's Rights Before Suffrage (Columbia University Press, 2023) and her Cambridge Element The Politics of Bathroom Access and Exclusion in the United States (2024) represent significant contributions to understanding how gender shapes political institutions across historical periods.

Camille Landaisمشاهده پروفایل

استاد

• Public Finance
• Labour Economics
• Applied Microeconomics
• +۵ مورد دیگر

Camille Landais is a Professor of Economics at the London School of Economics and Political Science (LSE), where she leads the Department of Economics. She serves as Director of STICERD (Suntory and Toyota International Centres for Economics and Related Disciplines) and Co-Director of the LSE-Gates Hub for Equal Representation in the Economy (H.E.R.). Additionally, she is a member of the French Council of Economic Advisers (CAE). Her research expertise spans Public Finance, Labour Economics, Applied Microeconomics, and Microeconometrics, with a focus on gender inequality, taxation policies, and child penalties. Landais holds a PhD in Economics from the Paris School of Economics. Her research interests include optimizing public spending efficiency, analyzing the economic impacts of family policies, and addressing wealth inequality through tax reforms. Recent work emphasizes the 'child penalty' phenomenon, migration responses to wealth taxation, and strategies for achieving full employment. She has contributed to high-impact publications such as the Review of Economic Studies and collaborates internationally on projects like the Child Penalty Atlas. Landais teaches advanced courses in public economics and actively engages in policy advising. She directs research centers that prioritize interdisciplinary approaches to economic challenges and inequality reduction. Her work frequently involves large-scale data analysis, including banking data and microsimulation techniques, to evaluate policy outcomes. Education: PhD in Economics, Paris School of Economics Research Centers: Director of STICERD, Co-Director of H.E.R., International Inequalities Institute Associate Teaching: EC325/EC426/EC534 (Public Economics modules) Professional Roles: Member of French Council of Economic Advisers, Editor of LSE Public Policy Review