جست‌وجوی استاد و پژوهشگر

Christine Bauerمشاهده پروفایل

استاد

• Interactive Intelligent Systems
• Recommender Systems
• Human-Computer Interaction
• +۹ مورد دیگر

Christine Bauer is a Professor of Interactive Intelligent Systems at the Department of Artificial Intelligence and Human Interfaces (AIHI), University of Salzburg, Austria. She is also Co-Lead of the interdisciplinary focus area InterMediation. Music—Effect—Analysis at the inter-university organization Wissenschaft & Kunst. Her research lies at the intersection of artificial intelligence, human-computer interaction, and human-centered computing, with a strong emphasis on fairness, context-awareness, and multi-method evaluation in recommender systems. Doctoral degree in Social and Economic Sciences (Business Informatics), 2009, University of Vienna MSc in Business Informatics, 2011, TU Wien Diploma in International Business Administration, 2002, University of Vienna Study of Jazz Saxophone, Konservatorium der Stadt Wien Her research interests center on interactive intelligent systems , particularly recommender systems in music and media, with a human-centered approach. She investigates how technology can align with societal and individual needs, focusing on fairness in algorithms , culture-aware recommendation , and multi-stakeholder evaluation . Her work often integrates interdisciplinary methods and real-world impact. The recent publications highlight a consistent trend in fairness and diversity in music and news recommendation, cross-cultural user behavior , and evaluation frameworks . Her research bridges technical rigor with ethical considerations, particularly in algorithmic bias and artist equity. She has made significant contributions to understanding gender imbalance , cultural granularity , and user conformity in digital music ecosystems. Elise Richter laureate (FWF) 6 best paper awards, 5 nominations Women in RecSys Journal Paper of the Year Award (2023, 2024) 5 awards and 6 recognitions for reviewing excellence Christine Bauer has supervised over 70 theses and taught at 17 institutions worldwide. She is deeply involved in academic service, including editorial roles (AE at TOIS, TORS, JITT), conference organization (RecSys, CHI, CIKM), and mentoring initiatives (WiMIR, LEA, Allyship at CHI). She leads impactful projects such as FairRecKit and SpART: Spotlight on Artists in Recommender Systems , aiming to create more equitable and transparent recommendation technologies. She frequently engages in public outreach through keynotes, media appearances, and policy discussions. She is affiliated with the University of Salzburg as her primary institution and was previously associated with JKU Linz and the University of Klagenfurt. Her labs and research teams focus on interactive intelligent systems , music recommenders , and fair AI , often in collaboration with interdisciplinary partners in arts and social sciences.

Navid Rekab-sazمشاهده پروفایل

استادیار

• Natural Language Processing
• Information Retrieval
• Fairness and Bias in AI
• +۷ مورد دیگر

Navid Rekab-saz is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University Linz (JKU), Austria. He is actively involved in research and teaching, offering courses such as Natural Language Processing and Natural Language Processing with Deep Learning . He maintains regular office hours and is accessible via email and a dedicated booking system for meetings. His research focuses on natural language processing , information retrieval , fairness and bias in AI , and recommender systems , with applications in humanitarian action and ethical AI. He employs deep learning and machine learning techniques to address challenges in bias mitigation, explainability, and domain adaptation. His work often bridges technical innovation with societal impact, especially in developing inclusive and fair AI systems. The recent publications of Navid Rekab-saz reflect a strong trend in debiasing strategies , parameter-efficient learning , and evaluation of societal biases in search and recommendation systems. His research spans from foundational work on word embeddings and retrieval models to applied studies in humanitarian NLP and gender bias in user queries. He frequently collaborates with a broad network of researchers and contributes to the development of datasets and benchmarks. Scientific Awards: Best Student Paper Award at ISMIR 2022 for 'Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms' Advising and Grants: Navid Rekab-saz has advised and collaborated with numerous students and researchers, many of whom are co-authors on his publications. While specific grant details are not listed in the provided text, his extensive publication record in top-tier venues suggests active involvement in funded research projects, likely supported by national or European funding bodies. He is also engaged in interdisciplinary research, particularly at the intersection of technical AI and legal or social implications. Labs and Teams: He is a core member of the Institute of Computational Perception at JKU, where he contributes to research projects in computational linguistics and AI. He collaborates closely with the team led by Prof. Markus Schedl and participates in initiatives related to music information retrieval, fairness in AI, and humanitarian applications of NLP.

Li Chenمشاهده پروفایل

استاد

• Conversational AI
• Explainable AI
• Recommender Systems
• +۷ مورد دیگر

Professor Li Chen is a full Professor and Associate Head (Research) in the Department of Computer Science at Hong Kong Baptist University (HKBU), with an affiliate appointment at the Academy of Wellness and Human Development. She leads the Positive Intelligence Lab , focusing on intelligent technologies for human well-being. Her research spans conversational AI, explainable AI, recommender systems, and human-computer interaction. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (Nominee for Best PhD Thesis Award) Master in Computer Software and Theory, Peking University, China Bachelor in Computer Science, Peking University, China Her research interests revolve around personalized conversational and explainable AI, with applications in entertainment, education, e-commerce, and mental well-being. She has published over 150 papers in top venues including ACM TOIS, IJHCS, CHI, SIGIR, AAAI, RecSys, and UMAP . Her work has been recognized with awards such as the RecSys Best Student Paper Award (2024), CHI Honourable Mention (2022), and multiple best paper awards at UMAP and UMUAI. The most recent publications reflect a strong trend toward fair, explainable, and user-centric recommender systems , with increasing integration of large language models , mental health applications , and conversational agents . Her research emphasizes user feedback, negative sampling techniques, and evaluation frameworks grounded in real user behavior. Scientific Awards & Recognition: President’s Award for Outstanding Performance in Teaching (Individual), HKBU (2024/25) President’s Award for Outstanding Performance in Research Supervision (2022/23) World’s Top 2% Most-Cited Scientists, Stanford University (2021–2024) ACM Senior Member (2015) RecSys’24 Best Student Paper Award CHI’22 Honourable Mention Award UMAP’20 Best Student Paper Award UMUAI 2018 Best Paper Award THE Awards Asia 2021 Excellence and Innovation in the Arts (Co-I) Professor Chen is actively involved in mentoring PhD and Master’s students such as Wanling Cai and Yuhan Zhao, who have co-authored award-winning papers. She has secured research funding through grants like the HKBU IRCMS Project. Her editorial leadership includes serving as Co-Editor-in-Chief of ACM Transactions on Recommender Systems (TORS) , Associate Editor for ACM TiiS , and Editorial Board Member for UMUAI . She has chaired major conferences including ACM RecSys’23 (General Co-Chair), RecSys’20 (Program Co-Chair), and UMAP’18 (Program Co-Chair). She leads the Positive Intelligence Lab , which conducts interdisciplinary research on AI for well-being. The lab has developed datasets like the Intent Annotation of Recommendation Dialogue (IARD) and focuses on user-centric AI design, mental health chatbots, and personalized recommendation interfaces.

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Olga Russakovsky is an Associate Professor in the Computer Science Department at Princeton University. She serves as Associate Director of the Princeton AI Lab and Chair of the Board of Directors at AI4ALL, a nonprofit dedicated to diversity in AI leadership. Her research focuses on computer vision, machine learning, human-computer interaction, and fairness in AI. She specializes in developing AI systems that reason about the visual world, emphasizing fairness, accountability, and transparency. Her work integrates computer vision with ethical AI frameworks, and she is affiliated with Princeton’s Center for Statistics and Machine Learning and Center for Information Technology Policy. Her publications address biases in datasets, explainable AI, and generative models. Her recent research trends include: Bias detection in datasets (e.g., CelebA, ImageNet) Interactive and explainable AI systems Generative models like diffusion and vision-language integration Deepfake detection and AI forensics Conceptual learning and few-shot training Scientific awards: NSF CAREER Award for fairer computer vision systems Co-founder of AI4ALL and Stanford AI4ALL outreach programs She advises students through AI4ALL initiatives and leads the Visual AI Lab, which focuses on robust, inclusive AI development. Her work bridges technical innovation with societal impact, particularly in diversity-focused education.

Jean F. Honorio Carrilloمشاهده پروفایل

استاد مدعو

• Machine Learning
• Artificial Intelligence
• Optimization Theory
• +۵ مورد دیگر

Jean F. Honorio Carrillo is an Adjunct Professor at Purdue University's Department of Computer Science and a Senior Lecturer at the University of Melbourne's School of Computing and Information Systems. He specializes in machine learning theory, optimization, and their applications to combinatorial and non-convex problems. His research focuses on developing algorithms with theoretical guarantees for structured prediction, robustness, fairness, and federated learning. He has advised numerous students across multiple institutions and holds adjunct roles at Purdue's Statistics Department and MIT CSAIL. Roles: Senior Lecturer (Melbourne), Adjunct Professor (Purdue), Adjunct at MIT CSAIL Research Areas: ML Theory, Non-Convex Optimization, Fairness, Federated Learning Key contributions include breakthroughs in exact inference for structured prediction, optimization frameworks for NP-hard problems, and theoretical foundations for modern ML challenges. His work has been published in top venues like NeurIPS, ICML, and JMLR. He has secured grants from NSF and industry partners, including a 2021 NSF DMS grant for deep learning research. His students have gone on to postdoctoral roles at NUS and UChicago/CMU.

D. James Greinerمشاهده پروفایل

استاد

• Access to Justice
• Legal Empiricism
• Randomized Control Trials
• +۴ مورد دیگر

D. James Greiner is the Honorable S. William Green Professor of Public Law and Faculty Director of the Access to Justice Lab at Harvard Law School. His research focuses on randomized control trials in legal contexts, particularly in civil and criminal justice systems. Greiner holds a Ph.D. in statistics from Harvard University and previously practiced law, focusing on employment discrimination and voting rights. He teaches courses on civil procedure, expert witnesses, and access to justice. Greiner's work emphasizes empirical methods to evaluate legal interventions, including studies on unbundled legal assistance, the impact of representation in court, and algorithm-assisted decision-making in pretrial risk assessments. His Access to Justice Lab designs and implements experiments to assess interventions aimed at improving justice outcomes for underserved populations. Key projects include evaluating online dispute resolution systems, analyzing criminal justice record-clearing policies, and studying the effects of counsel at first court appearances. His research has been published in leading legal and statistical journals, including the Harvard Law Review and the Yale Law Journal.

Yun Fuمشاهده پروفایل

استاد

• Urban Design
• Architecture
• Sustainability
• +۳ مورد دیگر

Yun Fu is a Professor at the Harvard Graduate School of Design (GSD), specializing in urban design and architecture. He co-founded SEMESTER, a design and research studio, and has held roles such as coordinator of the Design Discovery program and faculty affiliate at the Joint Center for Housing Studies. His work bridges theory and practice, focusing on risk, resilience, and sustainability in urban contexts. Education: Ph.D. from Harvard University (2020), Master of Architecture I (Advanced Placement) from Harvard GSD (with AIA Henry Adams Medal), and Bachelor of Architectural Studies from UNSW Sydney (with multiple awards). Research interests include seismic design, modern architecture in Asia Pacific, and the philosophical underpinnings of design intelligence. Recent projects explore suburban densification in New Zealand and interdisciplinary approaches to urban challenges. His books, such as Thinking and Building On Shaky Ground , have won major awards like the DAM Architecture Book Prize and Graham Foundation Grant. Teaching includes core studios like Elements of Urban Design and advanced seminars on urban theory. He has led interdisciplinary studios addressing real-world issues in the Asia Pacific, with student work featured in the NZ Listener and presented to government agencies. Awards include the Rome Prize Fellowship, Sinclair Kennedy Travelling Fellowship, and multiple design accolades from Dezeen and international furniture fairs. His studio, SEMESTER, has won global recognition for projects like the Temporary Storage Garden (2024 Dezeen Award).

Doaa Abu-Elyounesمشاهده پروفایل

استاد مدعو

• AI Ethics
• Algorithmic Governance
• Public Policy
• +۳ مورد دیگر

Doaa Abu-Elyounes is an Adjunct Professor at Ecole Normale Superieure Paris Saclay and holds the title of Affiliate at the Berkman Klein Center for Internet and Society, Harvard University. She currently serves as a Program Specialist for the Ethics of Artificial Intelligence at UNESCO, where she leads global implementation of the UNESCO Recommendation on AI Ethics. Previously, she worked as a law clerk at the Supreme Court of Israel and as an advisor to the Director General of the Israeli Ministry of Justice. She earned her S.J.D. (Doctor of Juridical Science) from Harvard Law School and has consulted the OECD on AI regulation. Her research emphasizes algorithmic governance in public sectors, focusing on translating ethical principles like fairness, transparency, and accountability into actionable policy frameworks. She collaborates with interdisciplinary stakeholders, including legal experts, computer scientists, and policymakers, to address challenges in AI regulation. Her work bridges law, technology, and public policy, advocating for robust regulatory standards and ethical oversight of AI systems. While no formal awards are listed, her contributions to international AI ethics initiatives reflect her impactful scholarly and professional engagement. In advising roles, she has mentored legal and policy professionals through her advisory work and academic affiliations. Her affiliation with the Berkman Klein Center and UNESCO highlights collaborative research environments focused on societal implications of technology.

Vijayaraghavan Aravindanمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Machine Learning
• Quantum Information
• +۳ مورد دیگر

Dr. Vijayaraghavan Aravindan is an Associate Professor in the Department of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He is a core member of the Theory CS Group and serves as the Site Director for the NSF-funded Institute for Data, Economics, Algorithms, and Learning (IDEAL). His research focuses on theoretical computer science, particularly algorithmic foundations of machine learning, quantum information, and beyond worst-case analysis. Education: Ph.D. and M.A. in Computer Science, Princeton University B.Tech. in Computer Science and Engineering, Indian Institute of Technology Madras Research Interests: His work bridges theoretical computer science and machine learning, emphasizing efficient algorithms for high-dimensional data, quantum entanglement certification, and adversarial robustness. He explores paradigms such as smoothed analysis and stability-based approaches to provide practical algorithmic guarantees. Awards & Grants: NSF CAREER Award Google Research Scholar NSF AITF Grants (CCF-1637585, CCF-2154100) Advising & Teaching: He advises PhD students on topics like quantum computing, robust machine learning, and optimization. Courses taught include graduate algorithms, theoretical foundations of data science, and quantum computation. His lab collaborates on projects at IDEAL and with institutions like Carnegie Mellon University and TTI Chicago. Professional Leadership: Served as FOCS 2024 General Chair and organizes the McCormick Theory Workshops. Active on program committees for ICML, COLT, and NeurIPS.

Lauren Kleinمشاهده پروفایل

استاد پژوهشی

• Digital Humanities
• Data Science
• Gender Studies
• +۳ مورد دیگر

Lauren Klein is the Winship Distinguished Research Professor and Associate Professor in the Departments of Quantitative Theory & Methods and English at Emory University. She directs the Emory Digital Humanities Lab and leads the Mellon-funded Atlanta Interdisciplinary AI Network. Previously, she taught at Georgia Tech's School of Literature, Media, and Communication. Her research integrates computational methods with critical approaches to explore gender, race, and justice, producing influential works like Data Feminism (2020) and An Archive of Taste: Race and Eating in the Early United States (2020). She co-edits the hybrid print-digital series Debates in the Digital Humanities . Education: PhD in English and American Studies from CUNY Graduate Center; AB in Literature (English and French) from Harvard University. Awards include ACLS, NEH, and Mellon Foundation grants. Her work bridges data science and humanities, emphasizing ethical frameworks for AI and social justice. Director of Emory Digital Humanities Lab PI of Atlanta Interdisciplinary AI Network Editor, Debates in the Digital Humanities

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +۶ مورد دیگر

Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.

Diptarka Chakrabortyمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +۵ مورد دیگر

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +۵ مورد دیگر

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

Aleksandra Slavkovicمشاهده پروفایل

استاد

• Statistical Data Privacy
• Differential Privacy
• Algebraic Statistics
• +۵ مورد دیگر

Aleksandra Slavković is a Professor of Statistics and Associate Dean for Graduate Education at Pennsylvania State University's Eberly College of Science. She holds a PhD in Statistics from Carnegie Mellon University (2004) and has held academic roles since 2004, including appointments at the Institute for Computational and Data Sciences and Penn State College of Medicine. Her research focuses on statistical data privacy, differential privacy, algebraic statistics, and applications in social and health sciences. She has authored over 50 peer-reviewed publications and serves on editorial boards of top journals like Journal of Privacy and Confidentiality and Annals of Applied Statistics . Slavković has received major honors including Fellowships from the Institute of Mathematical Statistics (2021) and American Statistical Association (2018). She leads initiatives to enhance graduate education, including the Science Achievement Graduate Fellows Program, and actively promotes diversity in STEM through her leadership roles. Her recent work emphasizes privacy-preserving techniques for genomic, healthcare, and network data, with contributions to synthetic data generation and secure multiparty computation protocols. Her academic service includes chairing ASA committees and advising at the National Academy of Sciences. She maintains collaborative ties with institutions like Cornell University and UC Berkeley through visiting scholar programs, and her research bridges statistics, computer science, and applied mathematics.

• Data Management
• Peer Review
• Research Evaluation
• +۳ مورد دیگر

Professor Kalpana Shankar is a Full Professor at the School of Information and Communication Studies at University College Dublin (UCD). She holds roles such as former Head of School (2015-2018) and Co-Director of the UCD Centre for Digital Policy. Her research focuses on data management, peer review processes, research evaluation, and digital policy, with notable projects funded by Science Foundation Ireland, the Irish Research Council, and the U.S. National Science Foundation. Key contributions include studies on the sustainability of social science data archives and peer review at SFI. Education: PhD in Library and Information Science from UCLA, postdoctoral fellowships at UCLA (Center for Embedded Networked Sensing) and NIH (Science Policy). Professional Roles: Editor-in-Chief of Online Information Review , member of the Royal Irish Academy’s Equity Diversity and Inclusion committee, and representative on the National Open Research Forum (NORF). Teaching: Coordinator for UCD’s MSc/Diploma in Digital Policy and PhD supervision in critical data studies, open data, and research evaluation. Research Interests: Sociotechnical imaginaries of data, peer review fairness, digital policy frameworks, and the impact of pandemic responses on academia. Awards include the 2020 UCD College of Social Sciences and Law Teaching Excellence Award.