جست‌وجوی استاد و پژوهشگر

• Operations Management
• Supply Chain Management
• Sharing Economy
• +۶ مورد دیگر

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

Susan Davidsonمشاهده پروفایل

استاد

• Databases
• Bioinformatics
• Data Management
• +۴ مورد دیگر

Susan Davidson is the Weiss Professor in the Department of Computer and Information Science at the University of Pennsylvania, where she co-directs the Data Science Program. She currently serves as Deputy Dean of the School of Engineering and Applied Science and chairs the Computing Research Association's Board of Directors. Her research focuses on databases, bioinformatics, data management, and provenance-based systems. Co-founder, Greater Philadelphia Bioinformatics Alliance Founding co-director, Center for Bioinformatics Fulbright Scholar and Hitachi Chair, INRIA-GEMO Key research areas include data citation, trust management in collaborative systems, workflow provenance, and privacy in data analysis. Her recent publications explore explainability frameworks, sub-table selection for data exploration, and security in distributed training systems. 2023 Lindback Award for Distinguished Teaching 2021 VLDB Women in Databases Award 2021 AAAS Fellow 2020 Spira Award for Teaching & Mentoring 2017 IEEE TCDE Impact Award She has advised numerous PhD students and postdocs, including Sudeepa Roy (Duke University) and Julia Stoyanovich (NYU). Courses taught recently include CIS550 (Database Systems) and CIS545 (Big Data Analytics).

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +۳ مورد دیگر

Prof. Dr. Gülşen Eryiğit is a Professor at Istanbul Technical University within the Faculty of Computer and Informatics , Department of Artificial Intelligence and Data Engineering . She founded and directs the ITU Natural Language Processing Group , Turkey's leading team in Turkish-language NLP, and serves as Senior Action Editor for ACL RR , Director of ITU TÖMER (Turkish Language Teaching Center), and Co-Chair of the EU UniDive Cost Action WG3. Education: PhD in Computer Engineering from ITU (2007), MSc and BSc from ITU and Marmara University Research: Focuses on Natural Language Processing for Turkish, including dependency parsing , coreference resolution , multiword expressions , and language education technology Her recent work involves multilingual transfer learning , LLM applications for Turkish text simplification, and gamification for morphology education. She has received prestigious awards like the Siemens Excellence Award and TÜBİTAK's Above Threshold Award . Her research has produced 65+ publications and 29+ projects funded by EU, TÜBİTAK, and industry partners. Scientific Awards: Siemens Excellence Award (2007) Above Threshold Award (TÜBİTAK, 2015) Certificate of Appreciation (EU 7th Framework, 2012) Thank You Plaque (ITU, 2017) The ITU NLP Group under her leadership has developed Turkey's first licensed NLP software exported internationally. She collaborates with European institutions through COST actions and participates in ACL, CoNLL, and LREC conferences. Her lab focuses on language technology for Turkish , including sign language processing and social media normalization.

Siva Balakrishnanمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Causal Inference
• Public Policy & Social Sciences
• +۵ مورد دیگر

Siva Balakrishnan is an Associate Professor at Carnegie Mellon University with a joint appointment in the Department of Statistics and Data Science and the Machine Learning Department . He holds an affiliation with the Dietrich College of Humanities and Social Sciences. Previously, he was a postdoctoral researcher at UC Berkeley's Department of Statistics, advised by Martin Wainwright and Bin Yu, and earned his Ph.D. in Computer Science from CMU's Language Technologies Institute under Jaime Carbonell. His research focuses on statistical machine learning, causal inference, and high-dimensional statistics, with notable contributions to domain adaptation, optimal transport, and robust statistics. Education: Ph.D. in Computer Science, Carnegie Mellon University (Language Technologies Institute) Postdoctoral Researcher, University of California, Berkeley (Department of Statistics) Research Interests: His work bridges theoretical foundations and algorithmic development, emphasizing robust statistical methods and their applications in causal inference, public policy, and machine learning. Key areas include nonparametric methods, optimization, and high-dimensional data analysis. He has pioneered techniques in domain adaptation, such as the RLSbench framework for relaxed label shift scenarios. Awards & Grants: Amazon Research Award (2021) Google Research Scholar Award (2021) NVIDIA Pioneer Award (2018) IMS Lawrence D. Brown Student Award (2020, 2022) National Science Foundation Grants (CCF-1763734, DMS-1713003, etc.) Professional Activities: He serves as an Associate Editor for JASA and on the editorial boards of Foundations and Trends in Statistics . His work has been featured in top venues like NeurIPS, ICML, and the Annals of Statistics. He currently holds a sabbatical at UC Berkeley's Department of Statistics (Spring 2025). Labs & Collaborations: He actively participates in the Statistics and Machine Learning Reading Group and the Causal Inference Working Group , fostering interdisciplinary research in CMU's vibrant academic community.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +۱۳ مورد دیگر

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

• Speech Processing
• Language Assessment
• Natural Language Processing
• +۳ مورد دیگر

Mark Gales is Professor of Information Engineering at the University of Cambridge and an Official Fellow at Emmanuel College. He is currently on sabbatical leave for the 2024/25 academic year. Prior to his academic career, he worked as a consultant at Roke Manor Research Ltd, developing radar systems, before transitioning to speech and language processing. PhD in 'Model-Based Techniques for Robust Speech Recognition' (University of Cambridge, 1995) BA in Electrical and Information Sciences (University of Cambridge, 1988) His research focuses on speech and language processing , particularly in automated language assessment and low-resource speech technology . He leads the Automated Language Teaching and Assessment (ALTA) Institute , which collaborates with Cambridge University Press & Assessment (CUP&A) to develop commercial tools like Linguaskill and Speak & Improve . These platforms provide automated spoken/written assessment for millions of users globally. Recent publications highlight his work in LLM-driven speech processing , including adversarial attacks on foundation models, end-to-end spoken error correction, and uncertainty estimation frameworks. His team's research spans multilingual capabilities, with deployments in languages ranging from Dholuo to Tok Pisin . Awards : IEEE Fellow, ISCA Fellow Leadership : Fellows' Steward at Emmanuel College Mark has contributed extensively to Hidden Markov Model (HMM) applications in speech recognition, which underpinned early automatic speech systems. His work now bridges LLM-based language assessment with cross-lingual transfer learning and robustness testing for real-world deployments.

• Computational Social Science
• Political Participation
• Big Data
• +۳ مورد دیگر

Dr. Jonathan Bright is a Senior Research Fellow and Associate Professor at the Oxford Internet Institute, University of Oxford , specializing in computational approaches to the social and political sciences. His research explores how digital technologies reshape political participation and how new data sources enhance governmental decision-making. Research Interests: Focused on social media, political behavior, computational social science, and big data , Bright’s work addresses digital governance, algorithmic bureaucracy, and the dynamics of political fragmentation online. His empirical studies span open government data usage, crowdsourced geographical information, and the impact of neighborhood effects on data completeness. Scientific Contributions: Leading studies on algorithmic governance in UK local authorities (2020) Investigating geodemographic biases in crowdsourced platforms like OpenStreetMap (2018) Mapping political fragmentation in social media ecosystems (2018) Grants & Collaborations: Funded by UK taxpayers, ESRC, and Google, his work intersects with projects on vaccine misinformation, hate speech monitoring, and digital economies. Formerly held positions as Research Fellow and Research Associate at OII from 2013 to present.

• Digital Strategy
• Innovation Management
• Open Innovation
• +۷ مورد دیگر

Christopher L. Tucci is a Professor of Digital Strategy & Innovation at Imperial College London's Business School and currently serves as Interim Vice Dean of Education. He is also the Founding Dean of NEOM's College of Business & Innovation in Saudi Arabia. Previously, he held the Chair in Corporate Strategy & Innovation at École Polytechnique Fédérale de Lausanne (EPFL), where he served as Dean of the College of Management from 2013 to 2018. His academic qualifications include a PhD in Management from MIT and multiple degrees from Stanford University: an AB in Music, BS in Mathematical Sciences, and MS in Computer Science and Computer Science. His research focuses on digital strategy, innovation management, open innovation, and business model innovation, with particular emphasis on how firms transition to new technologies and organizational forms. He has pioneered work on crowdsourcing, inter-networking, and AI-driven innovation. His award-winning article 'Crowdsourcing as a Solution to Distant Search' (co-authored with Allan Afuah) has received multiple accolades, including the AMR Decade Award in 2022. Professor Tucci teaches courses such as AI Ventures, Co-Creation in AI & Robotics, and Design Thinking. He actively contributes to academic leadership roles, including serving on the Academy of Management's Board of Governors and leading initiatives like Imperial's I-X (Artificial Intelligence and Digital Innovation hub). His work integrates technology, strategy, and organizational dynamics to address modern business challenges. Educations: PhD in Management, MIT (1992–1997) SM in Technology & Policy/EECS, MIT (1989–1991) BS in Mathematical Sciences, Stanford (1978–1982) AB in Music, Stanford (1979–1983) MS in Computer Science, Stanford (1982–1984) Labs/Teams: Co-Director of Imperial's I-X initiative, focusing on AI, data science, and digital innovation.

Arvind Karunakaranمشاهده پروفایل

استادیار

• Sociology of Work and Occupations/Professions
• Organization Theory
• Technological/Organizational Change
• +۴ مورد دیگر

Arvind Karunakaran is an Assistant Professor at Stanford University in the Department of Management Science and Engineering. He holds affiliations with the Center for Work, Technology, and Organization (WTO) , Stanford Technology Ventures Program (STVP) , Stanford Institute for Human-centered Artificial Intelligence (HAI) , and the Digital Economy Lab (DEL) . His research focuses on authority and accountability in the workplace , particularly under technological change and human-AI augmentation. Education: Ph.D. from MIT Sloan School of Management (2018) His work employs ethnographic and field-based methods to study empirical puzzles in workplace dynamics, complemented by comparative-historical analysis and quantitative/textual data analysis . Key research areas include social/algorithmic evaluation of workers , human-AI reskilling , and conflicts in symmetrical relations . Recent publications analyze AI's impact on workplace inequality , platform governance , and crowd-based accountability . His findings are published in top journals like Administrative Science Quarterly and Organization Science . Scientific Awards & Recognitions: Best Published Paper Award (ASA, 2025) Gerard J. Tellis Best Junior Faculty Paper Award (AIM Conference, 2025) W. Richard Scott Article Award (ASA, 2024) Responsible Research in Management Award (RRBM, 2024) Future of Organizations Fellowship (2024) Professor Karunakaran advises doctoral and master's students in areas related to technology and organizations , and teaches courses on organizational behavior , organizational theory , and innovation management .

Peter A. Flachمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Explainable AI (XAI)
• +۳ مورد دیگر

Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.

Pamela Hindsمشاهده پروفایل

استاد

• Organizational Behavior
• Human-Computer Interaction
• Cross-Cultural Collaboration
• +۳ مورد دیگر

Pamela J. Hinds is the Rodney H. Adams Professor and Fortinet Founders Chair in Stanford University's School of Engineering, holding a joint appointment in the Department of Management Science and Engineering. She co-directs the Center on Work, Technology, and Organization and serves on the Director's Council for the Hasso Plattner Institute of Design. Her research focuses on technology's impact on teams, collaboration, and innovation in global and cross-cultural contexts. She earned her Ph.D. in Organizational Science and Management from Carnegie Mellon University (1997). Research Interests: Dr. Hinds explores cross-boundary work teams, particularly those spanning national borders, examining cultural dynamics, language barriers, and site visits' role in knowledge sharing. She investigates human-robot interaction in workplaces and emerging technologies' effects on work practices, including AI, 3D printing, and open innovation systems. Her work bridges organizational behavior, human-computer interaction, and global work dynamics. Awards & Recognition: Lasting Impact Award (2018) Senior Editor of Organization Science Co-editor of Distributed Work (MIT Press) Advising & Leadership: As department chair, Hinds shapes educational and research agendas. Her grants include NSF-funded studies on digital technologies in agriculture and crowd-based innovation. She advises initiatives at the intersection of technology, work design, and global collaboration. Labs & Teams: Co-leads the Center on Work, Technology, and Organization, fostering interdisciplinary research on modern work environments. Engages in Stanford's Design School (d.school) to integrate human-centered design principles into workplace innovation.

Sainyam Galhotraمشاهده پروفایل

استادیار

• Artificial Intelligence
• Causal Inference
• Database Systems
• +۴ مورد دیگر

Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for trustworthy analytics, integrating causal inference, data management, and machine learning to enhance robustness, explainability, and fairness in algorithmic systems. PhD: University of Massachusetts Amherst (2020), advised by Barna Saha B.Tech: Indian Institute of Technology Delhi (2014), advised by Amitabha Bagchi Postdoctoral Research: University of Chicago (Computing Innovation Fellow) His work spans artificial intelligence, causal inference, and responsible data science, emphasizing ethical algorithm design and reliable data integration. Recent publications highlight advancements in fair clustering, causal feature selection, and entity resolution frameworks. His research trends from 2023–2024 include contributions to spatio-temporal data correlation, community detection in geometric graphs, and distribution-aware dataset search. Key themes are fairness in machine learning, causal modeling, and scalable data management solutions. Computing Innovation Fellowship (2021) DAAD AInet Fellow (2021) ACM SIGMOD Entity Resolution Programming Contest Finalist (2021) Krithi Ramamritham Computer Science Scholarship (2019) BEST Paper Award in SIGSOFT FSE 2017 He actively seeks PhD or Master’s students interested in data science and trustworthy AI. Contact via email: sg@cs.cornell.edu .

Emma Piersonمشاهده پروفایل

استادیار

• Artificial Intelligence
• Healthcare
• Health Equity
• +۳ مورد دیگر

Emma Pierson is an Assistant Professor of Computer Science at the University of California, Berkeley, affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) , Computational Precision Health , and the Center for Human-Compatible AI . She focuses on developing data science and machine learning methods to address issues in healthcare equity and social inequality . Her work includes studies on race adjustments in clinical algorithms, migration patterns, and leveraging LLMs for health equity. Education: Ph.D. in Computer Science from Stanford University (2020), Master’s in Statistics from the University of Oxford. Prior roles include Assistant Professor at Cornell Tech, Senior Researcher at Microsoft Research, and data scientist at 23andMe and Coursera. Research Interests: Her research spans fair clinical prediction , sparse autoencoders , health disparities , and algorithmic fairness . Notable projects include the MIGRATE dataset for granular migration analysis and studies on policing disparities. Awards: NSF CAREER Award, Rhodes Scholarship, Hertz Fellowship, MIT Technology Review 35 Innovators Under 35, and Samsung AI Researcher of the Year. She writes a statistics blog ( Obsession with Regression ) and contributes to media outlets like The New York Times and FiveThirtyEight . Labs/Teams: Leads the MIGRATE project, a collaboration to analyze fine-grained migration data. Engages in interdisciplinary work across AI, healthcare, and social science.

Ming Yinمشاهده پروفایل

دانشیار

• Social Computing
• Crowdsourcing
• Human-AI Interaction
• +۶ مورد دیگر

Ming Yin is an Associate Professor in the Department of Computer Science at Purdue University. Her research bridges human-computer interaction, applied artificial intelligence, computational social science, and behavioral sciences. She focuses on leveraging human behavior data to design intelligent systems that balance machine efficiency with human understanding, trust, and engagement. Education : PhD in Computer Science (Harvard University, 2017), B.E. in Computer Software (Tsinghua University, 2011) Previous Roles : Postdoctoral researcher at Microsoft Research New York City (2017–2018) Teaching : Courses on AI, Human-AI Interaction, Data Mining, and Human-Centered Computing Research Interests center on social computing, crowdsourcing, human-AI interaction, and ethical AI. She employs experimental and computational methods to study how human behavior can improve AI systems' design, fairness, and user trust. Her work has significant implications for gig economy platforms, decision support systems, and algorithmic accountability. Scientific Contributions include over 15 recent articles in top venues like CHI, IJCAI, and ACL. These works explore topics such as LLM-driven trust calibration, adversarial social influence, and ethical AI design. Her research has been recognized with the NSF CAREER Award Siebel Scholar (Class of 2017) Multiple Best Paper and Honorable Mention Awards at CHI, CSCW, and HCOMP Teaching Expertise spans courses like Introduction to Artificial Intelligence (CS 471), Human-AI Interaction (CS 592-HAI), and Data Mining (CS 573). She emphasizes project-based learning and designing systems for real-world problems, such as "learning in a new era" in her 2025 HCI course.

William Yang Wangمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۹ مورد دیگر

William Yang Wang serves as the Mellichamp Professor of Artificial Intelligence at the University of California, Santa Barbara (2019-present). He directs the UCSB Center for Responsible Machine Learning, the Mind and Machine Intelligence Initiative, and the UCSB NLP Group. His research focuses on theoretical foundations and practical algorithms for AI, particularly in NLP, LLMs, and neuro-symbolic reasoning. PhD in Computer Science from Carnegie Mellon University Active in AI theory and applications (2016-present) Research interests span multiple AI domains, with special emphasis on NLP and responsible machine learning. He has pioneered datasets like HybridQA, TabFact, and VaTeX, enabling advancements in multi-hop QA, fact verification, and video-language tasks. His work combines statistical relational learning with modern deep learning paradigms. Recent publications center around multimodal reasoning, knowledge graph integration, and responsible AI development. He has received numerous accolades including the IEEE SPS Pierre-Simon Laplace Award (2024) and NSF CAREER Award (2021). Karen Sparck Jones Award (2022) DARPA Young Faculty Award (2018) IBM Faculty Award Mentoring 15+ PhD and postdoc researchers who now hold positions at Microsoft Research, Amazon, Meta GenAI, and academic institutions like Arizona and Rutgers. His lab maintains active collaborations with industry partners through initiatives like ChipAgents.ai, which he founded as CEO.