جست‌وجوی استاد و پژوهشگر

Matthew Louis Maurielloمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Ubiquitous Computing
• Sustainability
• +۷ مورد دیگر

Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.

Celestine Mendler-Dünnerمشاهده پروفایل

استاد

• Machine Learning in Social Context
• Algorithmic Collective Action
• Performative Prediction
• +۶ مورد دیگر

Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Julian McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. His research spans recommender systems, machine learning, natural language processing, music information retrieval, and multimodal learning. He maintains an active research group with numerous PhD students and postdocs working on cutting-edge AI problems. His research interests focus on developing advanced algorithms for personalized recommendation systems, with particular emphasis on sequential recommendation, multimodal learning, and integrating large language models with traditional recommendation approaches. His work bridges the gap between theoretical machine learning and practical applications across multiple domains including e-commerce, music, and healthcare. McAuley has published extensively in top-tier conferences including NeurIPS, ICML, KDD, SIGIR, and ACL, with his most recent work exploring the intersection of large language models and recommendation systems. His publications reveal a strong trend toward multimodal approaches that combine text, vision, and audio for more comprehensive understanding and recommendation. He has received significant research funding from major technology companies including Google, Amazon, Facebook, Adobe, and Samsung, as well as government agencies like the National Science Foundation and Department of Defense. His work has practical applications across multiple industries, with a focus on improving user experience through better personalization. McAuley advises numerous PhD students who have gone on to successful careers at leading technology companies and academic institutions. His former students include Wang-Cheng Kang and Jianmo Ni at Google DeepMind, Chris Donahue and Zachary Lipton as assistant professors at CMU, and Ruining He at Google Deepmind.

Alexandra Papoutsakiمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Eye Tracking
• Shared Gaze for Remote Collaboration
• +۵ مورد دیگر

Alexandra Papoutsaki is an Associate Professor of Computer Science at Pomona College since 2017. Her research focuses on Human-Computer Interaction , particularly in webcam-based eye tracking and shared gaze for remote collaboration . Ph.D. in Computer Science from Brown University M.Sc. in Computer Science from Brown University B.Sc. in Computer Science from Athens University of Economics and Business Her work spans personal informatics , crowdsourcing methodologies , and remote usability testing , with recent projects examining collaborative drawing interfaces, baby tracking reflection, and digital health communities. Earlier contributions include computational biology research in genome-wide survival analysis and pan-cancer mutation networks. Key trends in her publications include: Remote collaboration tools enhanced by eye tracking Personal data systems for health and creative domains Foundational work in crowdsourcing quality control Interdisciplinary approaches combining computer science with psychology and medicine Scientific recognition includes: NSF CRII Grant (2020-2022) Wig Distinguished Professor Award (2020) Best Paper at RECOMB (2013) She has developed multiple open-source platforms including WebGazer for scalable eye tracking and Remotion for mobile usability testing. Her research team has published extensively in top venues like CHI, IMWUT, and IJCAI.

George Musgraveمشاهده پروفایل

مدرس ارشد

• Mental Health in Music Industry
• Cultural Sociology
• Creative Industries
• +۴ مورد دیگر

George Musgrave is a Senior Lecturer in Cultural Sociology and Creative Industries at the Institute for Creative and Cultural Entrepreneurship (ICCE) at Goldsmiths, University of London. An interdisciplinary sociologist of culture, he specializes in researching musicians' psychosocial working lives with a focus on mental health and wellbeing in the music industry. His work has directly influenced industry practices and government policy, including the establishment of the Music Minds Matter helpline in 2017. Dr. Musgrave holds a PhD from the Centre for Competition Policy (UEA), an MA in Politics, Philosophy and Economics, and a Cambridge MA (Cantab) in Social and Political Science. His academic journey reflects his interdisciplinary approach that bridges sociology, psychology, and economics in understanding creative work. His research interests center on the psychological experiences and working conditions of creative careers, particularly examining how the music industry impacts mental health. Musgrave investigates the paradox where music-making can be therapeutic while building a music career often proves detrimental to wellbeing. His work explores the intersections of identity, class, gender, and economic precarity in creative labor, with particular attention to how social media, streaming platforms, and industry structures affect musicians' mental health. Analyzing his recent publications reveals a consistent focus on mental health in the music industry across multiple dimensions: epidemiological studies of anxiety and depression among musicians, interventions and policy solutions, the impact of digital platforms, and the cultural narratives surrounding musical work. His research spans methodological approaches from quantitative surveys to qualitative interviews and critical policy analysis, consistently highlighting the need for structural changes in the music industry to support artist wellbeing. Fellow of the Royal Society for Public Health (FRSPH) Fellow of the Royal Society of Arts (FRSA) Fellow of the Higher Education Academy (FHEA) Editorial Board member of Cultural Trends journal Royal Musical Association Music and Mental Health Group Research Coordinator Dr. Musgrave has supervised 68 MA dissertations to completion and seven doctoral projects, including Dr. Steven Sparling (2021). His current PhD supervision spans interdisciplinary topics connecting music, psychology, and sociology. He has secured significant research funding from diverse sources including UKRI (ESRC), the Mayor of London, Help Musicians UK, and the Danish Partnership for Sustainable Development in Music. His major projects include 'Can Music Make You Sick?' and 'When Music Speaks,' which represent the largest studies to date on musicians' mental health in the UK and Scandinavia respectively. Beyond academia, Musgrave maintains an active music career, having signed with EMI/Sony/ATV and performed at major festivals including Reading and Leeds.

Sanjeev Dewanمشاهده پروفایل

استاد

• Information Systems
• Economics of Information Technology
• Digital Platforms
• +۷ مورد دیگر

Sanjeev Dewan is a Professor of Information Systems and Associate Dean of Masters Programs at the Paul Merage School of Business, University of California, Irvine. He also serves as Faculty Director of the Master of Science in Business Analytics program. Prior to joining UCI in 2001, he held faculty positions at the University of Washington and George Mason University. PhD, University of Rochester MS, University of Rochester Bachelor of Technology, Indian Institute of Technology, Delhi His research focuses on the economics of digital platforms , social and mobile analytics , and the valuation of technology investments . He investigates how information technology creates business value, impacts consumer behavior, and influences firm performance. His work spans electronic markets, Web 2.0 technologies, IT productivity, and the digital divide. The most recent publications reveal a strong emphasis on empirical analysis of digital platforms , including studies on gender bias in open source communities, quality certification in the sharing economy (e.g., Airbnb), personalized ranking in app stores, and mobile health applications. His research frequently uses large-scale datasets to examine behavioral patterns, market dynamics, and the economic implications of IT innovations. Faculty Service Award for 2023-24, UCI Paul Merage School of Business Best Paper Award, INFORMS 2019 e-Business Cluster Faculty Service Award for 2016-17, UCI Paul Merage School of Business Best Paper Award, INFORMS Conference on Information Systems and Technology (2009) INFORMS Service Award (2009) INFORMS Certificate of Appreciation (2008) Beta Gamma Sigma Honor Society (1991) University of Rochester Fellowship (1985–1990) Sanjeev Dewan has advised numerous PhD students who have secured faculty positions at leading institutions such as the University of Wisconsin-Madison, HKUST, Penn State, and the University of Hong Kong. His editorial service includes senior editor roles at Information Systems Research and associate editor at Management Science . He has also chaired tracks at ICIS and served as program co-chair for PACIS. There is no indication of external grant funding in the provided text, but his sustained publication record and leadership roles suggest significant research activity and institutional support. He is actively involved in academic leadership and research dissemination, contributing to major conferences and editorial boards. His work bridges theory and practice, particularly in digital platform ecosystems and analytics-driven decision-making.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +۷ مورد دیگر

Golnoosh Farnadi is an Associate Professor at the Department of Computer Science and Operational Research at the University of Montreal and an Assistant Professor at the School of Computer Science at McGill University. She holds a Canada-CIFAR Chair in Artificial Intelligence and serves as a Senior Academic Member at Mila - Quebec Institute for Artificial Intelligence. Her interdisciplinary work bridges computer science, operations research, and ethical AI considerations. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), followed by postdoctoral positions at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). Her research focuses on algorithmic fairness, responsible AI, deep learning, and probabilistic models, with applications spanning healthcare, recommender systems, and public policy. Farnadi's recent publications demonstrate a strong emphasis on addressing fairness in machine learning systems, with particular attention to cultural diversity in recommender systems, fairness in healthcare optimization (particularly kidney exchange programs), and mitigating hallucinations in large language models. Her work consistently combines theoretical rigor with practical applications, often employing novel mathematical frameworks to tackle complex ethical challenges in AI. Among her notable recognitions are the Google Scholar Award (2021), Facebook Research Award (2021), Google Award for Inclusion Research (2023), and being named one of the 100 Brilliant Women in AI Ethics (2023). She was also recognized as a Rising Star in AI Ethics in 2021. Farnadi supervises numerous graduate students through her EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on developing AI systems that promote fairness and equity. Her teaching includes courses on Responsible AI, Machine Learning, and Trustworthy Machine Learning at both McGill University and HEC Montreal.

• Platform studies
• Critical algorithm studies
• Queer media
• +۳ مورد دیگر

Shuaishuai Wang is a Lecturer in Digital Media and Culture at the University of Manchester, specializing in platform studies, critical algorithm studies, and queer media. His research explores queer communities' digital presence across platforms like Douyin, TikTok, and Blued, with a focus on algorithmic governance, data-driven social media operations, and geopolitical-economic contexts. He holds a PhD in Media Studies from the University of Amsterdam and previously served as Assistant Professor at Xi’an Jiaotong-Liverpool University and Lecturer at the University of Amsterdam. Shuaishuai leads two university-funded projects: 'Douyin Data Economies' (100,000 CNY) and 'Algorithmic Configurations of Sexuality' (30,000 Euros). His work appears in journals like Big Data & Society , Television & New Media , and Social Media + Society , and he contributes to media outlets including The Conversation and Sixth Tone. His research interests emphasize the interplay between algorithms, queer identities, and platform economies. Recent studies analyze Douyin's shift to e-commerce, LGBTQ+ visibility challenges in algorithmic systems, and the political dimensions of TikTok. He has also examined business models of LGBTQ platforms and the impact of censorship on digital queer spaces. Key grants include projects investigating data economies and algorithmic configurations of sexuality, reflecting his commitment to understanding technology's societal implications. Collaborations span global institutions, with notable work on misinformation, playful political performances, and transgressive digital cultures.

Jason Smithمشاهده پروفایل

پژوهشگر ارشد

• Human-AI Collaboration
• Interactive Music
• Artificial Intelligence in Creativity
• +۱۲ مورد دیگر

Jason Smith is a Postdoctoral Scholar at Northwestern University , affiliated with the Interactive Audio Lab . He earned his PhD in Music Technology from the Georgia Institute of Technology . Research Focus Human-AI collaboration in creative domains Interactive music systems AI-driven accessibility solutions Creative autonomy and neural audio generation Recommender systems for music libraries Publication Trends His work spans 2019–2025, emphasizing AI applications in music education, accessibility (especially for blind/visually impaired users), and immersive environments like AI holodecks. Key methodologies include co-design, hybrid recommendation algorithms, and automated creativity assessment. Lab Affiliation He contributes to the Interactive Audio Lab, exploring intersections of sound, code, and AI.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

Mislav Balkovićمشاهده پروفایل

دانشیار

• Data Science
• Education Policy
• Labor Market Analysis
• +۷ مورد دیگر

Mislav Balković is an Associate Professor at Algebra University of Applied Sciences, where he has served as Director and Dean since 2000 and 2009, respectively. He drives strategic development for the Algebra Group and contributes to national education policy through roles in expert bodies like the National Council for Science, Higher Education and Technology, and the Accreditation Council of the Agency for Science and Higher Education. Education: PhD (2016) from Faculty of Electrical Engineering and Computing, University of Zagreb Bachelor’s and Master’s degrees from Faculty of Electrical Engineering and Computing, University of Zagreb Research Interests: Mislav’s work bridges Data Science with applications in labor market dynamics , education policy , qualification frameworks , and energy systems . His projects focus on smart grid optimization, digital identity protection, and AI-driven educational reforms. Publications Trends: His recent articles emphasize cross-disciplinary applications , including brain-computer interfaces for image generation, EU labor market analysis , and smart grid resilience . Earlier works explore blockchain-based academic credentials and data analytics in auditing . Grants and Projects: He has led ~50 EU and domestic projects , spanning smart tourist management , lean methodologies for screen creators , and ICT services for alternative communication . His policy work includes drafting laws on adult education and higher education reform. Leadership: Vice President of the Croatian Employers' Association in Education (HUP-UPO) and past President of the Sectoral Council for Electrical Engineering and Computing.

Patrik Wikstromمشاهده پروفایل

استاد

• Computational Communication
• Music Industries
• Algorithmic Culture
• +۳ مورد دیگر

Professor Patrik Wikstrom is a computational communication scholar at Queensland University of Technology (QUT), leading the School of Communication. He serves as Chief Investigator in QUT's Digital Media Research Centre (DMRC) and Associate Investigator in the Australian Research Council's ADM+S Centre. His expertise spans digital media's impact on music and meme cultures, algorithmic systems, and cultural economics. Education: PhD in Media and Communication Studies (Karlstad University), MScEng (Chalmers University). Former roles include Director of DMRC, Associate Dean (Research) at QUT's Creative Industries Faculty, and academic leadership at Northeastern University, Jönköping International Business School, and Karlstad University. Research focuses on digital technologies' societal impacts, including recommender systems, platform governance, and music industry dynamics. Key publications include TikTok: Creativity and Culture in Short Video and The Music Industry: Music in the Cloud . Active in grants like the Australian Cultural and Creative Activity Analysis project (LP160101724). Supervised over 10 doctoral and master's students on topics ranging from platform governance to decolonizing copyright. Current projects explore algorithmic culture, AI ethics, and fair payment models for artists. Labs/Teams: DMRC and ADM+S collaborate on automated decision-making's societal implications. Advocates for responsible AI and interdisciplinary computational methods in social sciences.

David R. Kauchakمشاهده پروفایل

استاد

• Natural Language Processing (NLP)
• Text Simplification
• Machine Learning
• +۳ مورد دیگر

David R. Kauchak is a Professor of Computer Science at Pomona College, part of The Claremont Colleges. He has been affiliated with the institution since 2014. His academic background includes a Ph.D. (2006) and M.A. (2006) in Computer Science from the University of California, San Diego, and a B.S. (2005) in Computer Science from the University of Utah. His research focuses on Natural Language Processing (NLP) , particularly text simplification , aiming to reduce text complexity while preserving content. Applications include improving health literacy through simplified medical text and enhancing accessibility of technical documents. He has also contributed to machine learning methodologies and information retrieval systems. Key awards include the Distinguished Poster Award at AMIA 2016 and the Best Undergraduate Paper Award at SocalNLP 2018. His work frequently addresses interdisciplinary challenges in healthcare communication, education technology, and software tool development. Teaching highlights include courses in Artificial Intelligence , Algorithms , Natural Language Processing , and foundational computer science modules. He has advised numerous projects in computational linguistics and health informatics, though specific student names are not explicitly listed in the provided materials. His collaborations span academic and medical domains, including joint research with institutions like the University of Utah and projects funded through initiatives like the German Climate Modeling Initiative (though this appears unrelated to his primary work). He maintains an active presence in academic conferences, publishing in venues such as ACL, AMIA, and IEEE journals.

• Marketing Analytics
• Artificial Intelligence in Marketing
• Algorithmic Pricing
• +۵ مورد دیگر

Kannan Srinivasan is the H.J. Heinz II Professor of Management, Marketing and Business Technology at Carnegie Mellon University's Tepper School of Business, a position he has held since 1999. Prior to joining CMU, he taught at the business schools of the University of Chicago and Stanford University. His academic career spans over three decades with significant contributions to marketing science and data analytics. His educational background includes: Ph.D. in Management from University of California Los Angeles (1986) MBA in Marketing/Finance from Xavier School of Management, Jamshedpur, India (1980) BA in Engineering from University of Madras, Chennai, India (1978) Srinivasan's research focuses on advanced data analytics models applied to marketing problems, with particular expertise in internet-generated large-scale data analysis. His work bridges the gap between theoretical marketing models and practical business applications, especially in the areas of algorithmic pricing, consumer behavior analysis, and AI-driven marketing strategies. He has pioneered research in dynamic pricing systems, location-aware marketing technologies, and the economic implications of AI in consumer markets. Analysis of his recent publications reveals a strong trend toward examining the intersection of artificial intelligence, consumer welfare, and market dynamics. His work increasingly focuses on ethical implications of AI in marketing, algorithmic bias, and the socioeconomic impacts of digital platforms across various sectors including real estate, social media, and e-commerce. His scientific achievements include: Elected Fellow of the Informs Society of Marketing Science (2013) for lifetime contribution to the field Served as President of the Informs Society of Marketing Science Holds multiple patents related to time and location aware dynamic push content, dynamic pricing, and online advertising Srinivasan has advised numerous doctoral students whose careers have led them to faculty positions at top institutions including Duke, Harvard, Columbia, Yale, University of Chicago, Wharton, University of Michigan, and Indian Institute of Management Bangalore. He has extensive consulting experience with large firms and startups, translating academic research into practical business applications. His professional service includes editorial roles at prestigious journals including Management Science, Marketing Science, and Quantitative Marketing and Economics, as well as significant committee service within CMU including the Elliott D. Smith Award Committee and various Dean's Advisory committees. His research is organized around several key initiatives focused on applying advanced analytics to solve complex marketing problems, with particular emphasis on developing interpretable AI models that balance business objectives with consumer welfare considerations.

• AI Algorithms
• Economic Inequality
• Pricing Algorithms
• +۳ مورد دیگر

Shunyuan Zhang is an Assistant Professor at Harvard Business School with research focusing on AI algorithms, economic inequality, and computer vision applications in business contexts. His work examines how algorithmic systems impact economic outcomes, particularly in sharing economy platforms like Airbnb. His research interests include AI algorithms, economic inequality, pricing algorithms, machine learning, computer vision, and the sharing economy. Zhang's work often combines technical computer vision approaches with economic analysis to understand platform dynamics. Zhang's recent publications demonstrate a strong focus on the intersection of AI, fairness, and economic outcomes. His work analyzes how algorithmic pricing affects racial disparities on platforms like Airbnb, and how visual content impacts demand in the sharing economy. His research employs sophisticated methodologies including deep learning, structural modeling, and causal inference. He has published in top journals and working paper series, with notable work including 'Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb' and 'What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features.' Zhang collaborates extensively with leading researchers at Carnegie Mellon University and University of Toronto, particularly on topics related to algorithmic fairness and platform economics. His work has significant implications for both academic understanding and practical policy recommendations regarding algorithmic systems in marketplace contexts.