جست‌وجوی استاد و پژوهشگر

• Machine Learning for Music
• Music Modeling and Generation
• Ethics of AI in Music
• +۴ مورد دیگر

Bobby Lee Townsend Sturm JR is an Associate Professor at KTH Royal Institute of Technology, leading the MUSAiC project (ERC-2019-COG). He holds a PhD in Electrical and Computer Engineering from UC Santa Barbara (2009), followed by postdoctoral research at LAM, Paris 6, and academic roles at Aalborg University and Queen Mary University of London. His research focuses on AI ethics in music, generative AI for music, and folk music preservation. Current roles at KTH include teaching and supervising in Machine Learning, Music Informatics, and AI Ethics. He has pioneered AI music generation challenges (e.g., 2020 Double Jigs Challenge) and investigates societal impacts of AI on traditional music cultures. His work bridges technical innovation with cultural and ethical considerations, addressing issues like data colonialism, algorithmic bias, and human-AI collaboration in creative contexts. Education: PhD (UCSB, 2009), Postdoc (Paris 6), Academic appointments at Aalborg University (2010–2014) and Queen Mary University (2014–2018) Key Projects: MUSAiC (ERC), Virtual Session System for Irish Music, Traditional Music Dataset Analysis Teaching: Courses in Machine Learning, Music Acoustics, and ICT Innovation Publications span peer-reviewed journals and conferences, emphasizing ethical AI, music generation, and interdisciplinary research in MIR (Music Information Retrieval). He actively collaborates with musicians, anthropologists, and technologists to ensure culturally informed AI development.

Devery J. Rodgersمشاهده پروفایل

استادیار

• Educational Leadership
• Tech Equity
• Education Technology in PK-12
• +۱۰ مورد دیگر

Dr. Devery J. Rodgers is an Assistant Professor of Educational Leadership in the College of Education at California State University, Long Beach. Her work centers on equity, educational technology leadership, and culturally responsive practices in PK–12 and higher education. With over 20 years of experience as a K–12 administrator and teacher, she brings deep practical insight to her academic role. Education: Doctor of Education in Educational Leadership – University of Southern California Master of Arts in Education – Curriculum & Instruction – California State University, Dominguez Hills Bachelor of Arts in English and African-American Studies – University of California, Los Angeles Research Interests: Dr. Rodgers’ scholarship focuses on tech equity, leadership in education technology, culturally responsive leadership in STEM, and racial justice in education. She explores how digital tools can be leveraged equitably and how identity, particularly of Black students and educators, shapes educational outcomes. Publications and Research Trends: Her recent work spans pandemic pedagogy, AI bias, digital leadership, and mentorship. She frequently publishes in top journals and presents at AERA, ISTE, and NCORE. Her research consistently emphasizes equity, identity, and systemic change in education. Scientific Awards and Recognition: Recipient of the President’s/Provost’s EDI Grant Her graduate students have won 2nd place at the university-wide Grad SLAM competition (Melanie Gerner and Jaclyn Caballero) Advising and Grants: Dr. Rodgers actively mentors graduate students and leads research teams. She has secured institutional funding to study the experiences of Black graduate students and leads a data science team exploring algorithmic fairness in image diversity. She co-advises the Black/Pan-African Graduate Student Association, fostering community and support. Labs and Teams: She leads a data science research team focused on image diversity and algorithmic fairness, which has presented at the CSULB College of Natural Sciences and Mathematics Symposium. Her collaborative work integrates education, technology, and social justice.

Michael J. Sandelمشاهده پروفایل

استاد

• Political Philosophy
• Ethics
• Democracy
• +۶ مورد دیگر

Michael J. Sandel is a Professor of Government at Harvard University, renowned for his work in political philosophy and public ethics. His scholarship explores justice, democracy, market morality, and the ethical implications of biotechnology, reaching global audiences through his televised course Justice and BBC series The Public Philosopher . B.A., Brandeis University (1975) D.Phil., Oxford University (1981) as a Rhodes Scholar His research interrogates the moral limits of markets, genetic engineering, and the erosion of civic virtue in modern democracies. Notable works include What Money Can’t Buy (2012), which critiques market society, and The Case Against Perfection (2007), addressing biotechnology’s ethical challenges. Princess of Asturias Award in Social Sciences (2018) Member, American Academy of Arts and Sciences Rhodes Scholarship As a public intellectual, Sandel has lectured globally at venues like St. Paul’s Cathedral and Seoul’s Olympic Stadium, engaging debates on corruption, climate ethics, and intergenerational justice. His Tech Ethics course examines AI and social media’s moral dimensions.

Yanjun Zhangمشاهده پروفایل

پژوهشگر ارشد

• Cybersecurity
• Privacy-Preserving Technologies
• Federated Learning
• +۳ مورد دیگر

Dr. Yanjun Zhang is an Honorary Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on privacy-preserving technologies, federated learning, cybersecurity in IoT systems, and machine learning security. He holds a PhD in Privacy-Preserving Sharing for Genome-Wide Analysis from The University of Queensland (2021). Education: PhD in Information Technology, School of Information Technology and Electrical Engineering, The University of Queensland (2021) Research Interests: Designing secure collaborative machine learning frameworks Defending against adversarial attacks in cyber-physical systems Privacy preservation in distributed genomic and medical data analysis Compliance and ethics in virtual personal assistant applications Key Contributions: Developed privacy-preserving federated learning frameworks (AgrAmplifier, PrivColl) Conducted foundational studies on evasion attacks in IoT systems Created datasets for analyzing malicious browser extensions and Alexa skills Labs/Teams: Active contributor to UQ Cyber initiatives, including the 2021-2022 Seed Funding project on federated deep learning for medical imaging.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +۵ مورد دیگر

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

• Algorithms
• Combinatorics
• Parameterized Algorithms
• +۱۲ مورد دیگر

Fedor V. Fomin is a Professor in the Department of Informatics at the University of Bergen, Norway, where he leads the Algorithms Research Group. His work is central to theoretical computer science and combinatorics, with significant contributions to algorithm design and analysis. His primary research interests include: Parameterized Algorithms and Kernelization Exact (Exponential Time) Algorithms Graph Algorithms and Graph Minors Approximation Algorithms and Treewidth Matroid Algorithms and Metric Embedding Algorithmic Fairness and Pursuit-Evasion Problems The selected publications reflect a strong trend in foundational algorithmic techniques, particularly in parameterized complexity, kernelization, and exact algorithms. His work often bridges theoretical depth with practical applicability, especially in graph-theoretic problems and preprocessing methods. His scientific recognition includes: EATCS Nerode Prize 2015 EATCS Nerode Prize 2017 Fedor V. Fomin has made substantial contributions through major textbooks such as Parameterized Algorithms (2015) and Kernelization (2019), which have become essential resources in the field. He has collaborated with leading researchers including Daniel Lokshtanov, Saket Saurabh, and Dieter Kratsch. While specific advising roles are not listed, his publications and books suggest extensive mentorship and collaboration. He is actively involved in organizing academic events like FPT Fest and GRASTA, indicating leadership in the research community. He is affiliated with the Algorithms Research Group at the University of Bergen, contributing to a vibrant research environment focused on discrete algorithms and complexity.

Vassilis Christophidesمشاهده پروفایل

استاد

• Databases
• Web Information Systems
• Big Data Processing
• +۹ مورد دیگر

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Afra Alishahiمشاهده پروفایل

استاد

• Computational Linguistics
• Language Acquisition
• Neural Models of Language
• +۵ مورد دیگر

Afra Alishahi is a Full Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. Her research focuses on computational models of human language acquisition and grounded language learning, leveraging neural models to explore how language processing and acquisition occur. She has held roles including Assistant Professor at Tilburg University (since 2011) and Postdoctoral Fellow at Saarland University (2008-2011). Her work bridges computational linguistics, cognitive science, and artificial intelligence, with contributions to understanding language learning mechanisms through models that integrate visual, auditory, and linguistic data. Education: PhD (university unspecified), with prior academic roles in Iran and Germany. Awards: CoNLL 2017 Best Paper Award, 2023 Outstanding Paper Award, NWO Aspasia Grant (2015), and NWO Natural Artificial Intelligence Grant (2015). Her research has been supported by grants such as the Dutch National Research Agenda-funded project on interpreting deep learning models for text and sound. Research Interests: Grounded language learning, interaction effects in language acquisition, and neural model interpretability. Key areas include multi-modal learning (e.g., linking speech to visual scenes), computational modeling of child language learning, and probing neural networks for linguistic knowledge. She co-organized workshops like BlackboxNLP (2018-2020) and has authored over 60 publications, including influential works on phonology encoding in neural models and gender disambiguation in machine translation. Teaching: Courses include Cognitive Models of Language Learning , Computational Linguistics , and Language, Cognition & Computation . She advises master's theses and leads projects in data science and AI. Lab/Team: Leads research on computational modeling, collaboration with interdisciplinary teams (e.g., with Grzegorz Chrupała, Afsaneh Fazly), and involvement in initiatives like the Interpreting Deep Learning Models for Text and Sound project.

Jop Briëtمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Quantum Information Theory
• Combinatorics
• +۳ مورد دیگر

Jop Briët is a Researcher at the Department of Algorithms and Complexity at Centrum Wiskunde & Informatica (CWI) in the Netherlands. His work focuses on theoretical computer science, quantum information theory, combinatorics, and tensor analysis. He has held grants including the Veni Innovational Research Grant from NWO and a Rubicon fellowship. He has authored over 50 publications in leading venues, exploring topics such as Grothendieck inequalities, quantum computing, and additive combinatorics. His research interests span the interplay between combinatorics and computational complexity, with particular emphasis on tensor analysis, probabilistic methods, and algorithm design. Recent work includes studies on Szemerédi’s theorem with random differences and the application of quantum query algorithms to entanglement-based problems. Awards: Outstanding paper award TQC (2020), Andreas Bonn medal (2013), Stieltjesprijs (2011). Professional Activities: Editor for ERCIM News, Board Member of Koninklijk Wiskundig Genootschap, and frequent invited speaker at workshops on quantum computing and combinatorics. Grants: Veni Grant (2014), Rubicon Fellowship (2012). Current teaching includes courses on Additive Combinatorics and Quantum Information Processing, reflecting his commitment to bridging foundational theory with advanced applications in computing and mathematics.

• Smart Energy Systems
• Multi-agent Systems
• Computational Intelligence
• +۷ مورد دیگر

Han La Poutré is a Professor at Delft University of Technology and a Senior Researcher/Manager at CWI's Intelligent and Autonomous Systems group. His research focuses on multi-agent systems, computational intelligence, and optimization techniques for Smart Energy Systems (SES), integrating electrical engineering and economics frameworks. M.Sc. in Mathematics (1986, TU Eindhoven) Ph.D. in Computer Science (1991, Utrecht University) His work spans game theory , reinforcement learning , and market mechanism design for energy grid optimization, addressing challenges in congestion management, demand-side bidding, and AI-driven media sector innovation. Recent projects include AI, Media & Democracy Lab and RESCUE (Cyber-Physical Energy Security). Key trends in his 2020-2025 publications include: Hybrid fairness/welfare frameworks for grid congestion Double-sided auction mechanisms for heat/power markets Deep reinforcement learning in discrete action spaces Game-theoretical cybersecurity modeling Multi-temporal demand response coordination Scientific Awards 2015: Best paper award at GECCO-2015 He has led NWO-funded projects like Computational Capacity Planning in Electricity Networks and served as Vice President of ERCIM. Current affiliations include ACM Transactions on Intelligent Systems editorial board.

Uriel Feigeمشاهده پروفایل

استاد

• Algorithms
• Computational Complexity
• Algorithmic Game Theory
• +۴ مورد دیگر

Uriel Feige is a Professor in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science. Born in Jerusalem, he earned his BSc from the Technion and his MSc and PhD from the Weizmann Institute under the guidance of Adi Shamir. After postdoctoral research at Princeton University and IBM T.J. Watson Research Center, he joined the Weizmann Institute in 1992. From 2004-2007, he worked with Microsoft Research's Theory Group and continues to serve as a visiting researcher there. Education: BSc: Technion MSc: Weizmann Institute of Science (under Adi Shamir) PhD: Weizmann Institute of Science (under Adi Shamir) Professor Feige's research focuses on algorithms, computational complexity, and algorithmic game theory. His work spans approximation algorithms, hardness results, and beyond worst-case analysis of combinatorial optimization problems. He has made significant contributions to the understanding of random walks on graphs, particularly in analyzing cover times and developing approximation algorithms. His research also extends to allocation problems and fairness in algorithmic game theory, while previously working on cryptography and other theoretical topics. Professor Feige's publications reveal consistent contributions to theoretical computer science with a strong emphasis on fundamental algorithmic problems. His work bridges theoretical foundations with practical implications, particularly in approximation algorithms where he has established both upper and lower bounds for various problems. Many papers focus on the interplay between graph structure and algorithmic performance. Scientific Awards: 2001 Godel Prize 2005 SIAM Outstanding Paper Prize Professor Feige has advised numerous students documented in technical reports on his website. His research has been supported by various grants enabling his theoretical contributions. He actively teaches courses including algorithms, linear programming, combinatorial optimization, approximation algorithms, and algorithmic game theory through 2024-2025. Professor Feige maintains an active research program within the Department of Computer Science and Applied Mathematics at the Weizmann Institute, where his work intersects with multiple research groups focusing on theoretical computer science and algorithms.

Spencer Williamsمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Business Law
• Contract Law
• +۴ مورد دیگر

Spencer Williams is an Associate Professor of Law at California Western School of Law and Golden Gate University School of Law , where he researches and teaches at the intersection of business law and technological innovation . His work focuses on AI applications in legal practice , complex systems theory , and venture capital financing . He holds a JD from Stanford Law School and a BS in Economics from MIT . Prior to his academic career, Professor Williams practiced corporate and securities law at Wilson Sonsini Goodrich & Rosati and served as General Counsel for a Y Combinator-backed technology startup . Current Research Interests: AI-driven contract modeling Algorithmic price regulation Network theory in legal systems Smart contract frameworks Venture capital term optimization Legal informatics His recent publications analyze generative AI in contract design (2024), LLM benchmarking (2023), and edge computing contracts (2023). He contributes to the CodeX - Center for Legal Informatics and Stanford Legal Design Lab . Contact: swilliams@ggu.edu | Office: 415-442-6651

Michael Oberstمشاهده پروفایل

استادیار

• Causal inference
• Machine learning
• Healthcare decision-making
• +۳ مورد دیگر

Michael Oberst is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering, affiliated with the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on developing reliable machine learning systems for healthcare decision-making, emphasizing causal inference and robust performance across diverse clinical settings. Key research themes include: Ensuring ML system reliability comparable to FDA-approved medical tools Causal reasoning in observational healthcare data Robustness to dataset shifts across hospitals Algorithmic fairness under unobserved confounding Medical adaptation of large language models Recent publications (2025-2024) demonstrate trends in prediction-powered inference, clinical validation frameworks, and robustness evaluation methods. His work appears in top ML venues (NeurIPS, ICML, UAI, EMNLP) and translational medicine journals. Michael holds a BS in Statistics from Harvard University and a PhD in Computer Science from MIT, with postdoctoral training at Carnegie Mellon University's Machine Learning Department. His group actively seeks PhD students and postdocs for developing trustworthy AI solutions in healthcare.

Zhenke Wuمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Precision Medicine
• Latent Variable Models
• +۵ مورد دیگر

Zhenke Wu is an Associate Professor (with tenure) in the Department of Biostatistics at the University of Michigan School of Public Health. He holds affiliate appointments at the Michigan Institute for Data and AI in Society (MIDAS) and leads the Michigan Statistics for Individualized-healthcare Lab (MiSIL). His research bridges statistical methodology and public health applications, with particular focus on precision medicine. Educational background includes: PhD in Biostatistics from Johns Hopkins University (2014) BS in Mathematics from Fudan University (2009) Dr. Wu's methodological research focuses on: Structured Bayesian latent variable models for disease subtyping and clustering Causal inference methods for sequential interventions in mobile health studies Reinforcement learning frameworks for personalized health interventions Scalable computation for high-dimensional biomedical data His applied work spans infectious diseases, mental health, autoimmune disorders, and cancer through collaborations with multiple research consortia including the Intern Health Study and UZIMA-DS project in Africa. Recent publications demonstrate strong focus on Bayesian methods, causal inference, and reinforcement learning applications in digital health. Article themes include mobile health interventions, synthetic EHR development, fair machine learning algorithms, and novel approaches for longitudinal and survival data analysis. Methodological innovations consistently address challenges in precision medicine and individualized health decision-making. Dr. Wu leads several collaborative initiatives including the Precision Health Use Case for mental health treatment (PROMPT) and partners with the Rogel Cancer Center. He advises multiple PhD students and postdoctoral researchers in statistical methodology development and health applications.

Carola Leichtمشاهده پروفایل

دانشیار

• Organisational Behavior
• Diversity in the Workplace
• Social Psychology
• +۴ مورد دیگر

Dr. Carola Leicht is a Reader in HRM/Organisational Behaviour at Kent Business School, University of Kent. Her research focuses on social psychological processes influencing workplace decisions, particularly around diversity, stereotypes, and leadership evaluations. She applies these insights to areas such as women and minorities in leadership and the cultural context of science-religion interactions. Current affiliation: University of Kent, Kent Business School Prior role: Post-Doctoral Researcher at Coventry University Her research combines social cognition, stereotype analysis, and intergroup dynamics to examine how diversity experiences and implicit biases shape organisational behavior. She has secured significant funding, including a £2 million Templeton Religion Trust grant as Co-Investigator for a public perceptions study on science and religion. Recent publications highlight her interdisciplinary approach, blending organisational behavior with sociology and psychology to analyze leadership prototypicality, science-religion narratives, and stereotype mitigation. Her awards include a Professional Fellowship from the Advanced HE Academy . Carola is active in academic societies, including the Society for Personality and Social Psychology and the European Association of Social Psychology. She serves on the advisory board of the International Research Network for the Study of Science and Belief in Society.