جست‌وجوی استاد و پژوهشگر

Richard M. Sternمشاهده پروفایل

استاد

• Speech Recognition
• Audio Signal Processing
• Music Information Retrieval
• +۳ مورد دیگر

Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.

Kevin Evansمشاهده پروفایل

دانشیار

• American Presidency
• Interbranch Relations
• Institutional Development of the Presidency
• +۱ مورد دیگر

Kevin Evans is an Associate Professor in the Department of Politics and International Relations at Florida International University's Steven J. Green School of International and Public Affairs. He specializes in American presidency, interbranch relations, and the institutional development of the presidency. His research examines conflict and cooperation between branches of American government, with work published in leading political science journals. Education includes: Ph.D. in Political Science, University of California, Davis (2011) B.A. in Political Science, University of California, Irvine (2005) His research focuses on presidential-congressional dynamics, constitutional challenges in executive actions, and strategic communication through signing statements. Recent work explores how presidents claim credit for legislative accomplishments and navigate institutional boundaries. Evans's publications consistently analyze presidential power dynamics across administrations, with frequent examination of signing statements as tools for credit-claiming and constitutional positioning. His articles demonstrate sustained interest in historical case studies of executive-legislative relations. Awards and honors include: FIU Faculty Award for Excellence in Teaching (2018) Morris and Anita Broad Research Fellowship (2015-2016, 2012-2013) SPSA Artinian Travel Award (2016) SIPA Faculty-Student Research Award (2015) He has advised graduate students including Maria I. Gabryszewska (Ph.D.) and Melissa Burgess (M.A.), and serves in departmental leadership roles including Graduate Program Director. Current research examines racial violence in Northern Louisiana through intergenerational family networks.

Giorgio Ascoliمشاهده پروفایل

استاد

• Computational Neuroanatomy
• Neuroinformatics
• Neuronal Morphology
• +۵ مورد دیگر

Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.

Kenneth Pearceمشاهده پروفایل

استاد

• Early Modern Philosophy
• Philosophy of Religion
• Metaphysics
• +۳ مورد دیگر

Dr. Kenneth Pearce is a Professor of Philosophy and Academic Unit Head at the Department of Philosophy & Religion, James Madison University (since 2022). He previously served as Head of the Philosophy Department at Trinity College Dublin (2020–2022) and held roles including Ussher Assistant Professor in Berkeley Studies (2016–2020). His research focuses on early modern philosophy, particularly George Berkeley, and philosophy of religion, exploring metaphysical questions about divine power, free will, and religious epistemology. He has published extensively, including books like *Is There a God? A Debate* (2022) and *Language and the Structure of Berkeley's World* (2017). His work bridges historical and contemporary philosophy, addressing themes like grounding theory, divine attributes, and early modern critiques of materialism. Awards include the Sanders Prize (2016) and a Royal Institute of Philosophy Grant (2018). He teaches courses on early modern philosophy, philosophy of religion, and metaphysics, and actively advises graduate students. His media engagements include podcasts and radio discussions on religious debates and philosophical topics. Education: B.A./B.A.S., University of Pennsylvania (2007) Ph.D., University of Southern California (2014) Research Interests: Early modern philosophy (Berkeley, Locke, Arnauld) Philosophy of religion (omnipotence, divine freedom) Metaphysics (grounding, free will) Grants & Service: Directed MPhil in Philosophy programs at Trinity College Dublin Organized conferences like *Irish Philosophy in the Age of Berkeley* (2019) Editorial roles at *PhilPapers* and *The Prosblogion* blog Labs/Teams: Engages in digital humanities projects, including exhibits on Berkeley's manuscripts.

Roxane DE LA SABLONNIÈREمشاهده پروفایل

استاد

• Social Psychology
• Cultural Identity
• Intercultural Relations
• +۵ مورد دیگر

Roxane de la Sablonnière is a Full Professor in the Department of Psychology at the University of Montreal, affiliated with the Faculty of Arts and Sciences. She directs the CSI—Laboratory on Social Change and Identity and is a member of the CÉCD—Centre for the Study of Democratic Citizenship and the CIRCA—Interdisciplinary Research Centre on the Brain and Learning. Her research focuses on social change, cultural identities, intercultural relations, and the psychological impacts of crises like the COVID-19 pandemic. She has advised over 20 graduate students and led numerous research projects funded by agencies such as the Fonds de recherche du Québec (FRQ) and the Social Sciences and Humanities Research Council (SSHRC). Education & Research Focus : Her work examines how rapid societal changes affect individuals, particularly in contexts of immigration, policy shifts (e.g., multiculturalism, secularism), and Indigenous communities. Recent studies include longitudinal analyses of pandemic-related behaviors, identity integration processes, and the role of cognitive strategies in managing conflicting identities. Grants & Collaborations : She leads projects like "S'engager pour mieux aller!" (FRQS-funded) and contributes to strategic networks such as the Centre pour l'Étude de la Citoyenneté Démocratique (CÉCD). Her research integrates psychology with sociology, leveraging mixed methods (e.g., text mining, longitudinal tracking) to address societal challenges. Labs & Teams : As CSI director, she oversees projects on social change dynamics and identity reconstruction. Her work bridges academic and applied domains, influencing policy debates on integration, public health, and community resilience.

Sarah Wiegreffeمشاهده پروفایل

استادیار

• Language Models
• Neural Networks
• Mechanistic Interpretability
• +۳ مورد دیگر

Sarah Wiegreffe is an Assistant Professor in the Department of Computer Science at the University of Maryland, affiliated with the CLIP laboratory. She holds a PhD from Georgia Institute of Technology (2022), advised by Prof. Mark Riedl. Previously, she was a postdoctoral researcher at the Allen Institute for AI (Ai2) and a member of H2Lab at the University of Washington, advised by Ashish Sabharwal and Hannaneh Hajishirzi. Her research focuses on mechanistic interpretability and transparency of language models, aiming to improve reliability, safety, and performance through natural language explanations. Notable contributions include the Mechanistic Interpretability Benchmark (MIB) and work on explaining model noncompliance. Awards include the Ai2 Outstanding Intern Award, Rising Star in Machine Learning/EECS/Generative AI, and recognition as an Outstanding Area Chair (ACL 2023, NeurIPS 2023). She has advised students like Alec Bunn and contributed to workshops like the Actionable Interpretability at ICML 2025. Education: PhD in Computer Science, Georgia Tech (2022); Master’s/Undergraduate degrees not explicitly stated. Active in organizing conferences (e.g., NAACL 2021 Publicity Co-Chair) and teaching (e.g., graduate seminar CMSC848R on LM interpretability).

Karen Panettaمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Simulation Modeling
• +۶ مورد دیگر

Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.

Tim Aistropeمشاهده پروفایل

مدرس ارشد

• IR Theory
• Security Studies
• US & Australian Foreign Policy
• +۵ مورد دیگر

Dr Tim Aistrope is a Senior Lecturer in International Relations and Director of Graduate Studies (Taught) at the University of Kent's School of Politics and International Relations. Based in Canterbury, his research focuses on the intersection of political culture, security studies, and international relations theory. Key themes include the role of imagination, conspiracy theories in foreign policy, and the embodiment of political narratives. His notable work includes the book Conspiracy Theory and American Foreign Policy (2016), analyzing post-9/11 secrecy and power dynamics. He teaches courses on IR theory and security, supervising PhD students exploring topics like securitization and translation. Active in professional associations, he contributes to journals like International Studies Quarterly and International Affairs . His recent publications address pandemic governance, apocalyptic narratives, and cybersecurity. Research interests span IR theory, US/Australian foreign policy, international ethics, and cyber security. His work often bridges cultural studies and security, examining how narratives shape policy and public perception. He has advised on counter-radicalization strategies and explored the role of social media in counterterrorism. His academic contributions highlight interdisciplinary approaches to understanding global politics through lenses of culture, ethics, and critical theory.

Christoph Stadtfeldمشاهده پروفایل

دانشیار

• Social Network Analysis
• Dynamics of Social Networks
• Statistical Methods for Network Data
• +۲ مورد دیگر

Christoph Stadtfeld is Associate Professor of Social Networks at ETH Zurich's Department of Humanities, Social and Political Sciences and co-director of the ETH Social Networks Lab. His research examines social network dynamics, focusing on tie formation processes, network effects on individuals, and advanced statistical methodologies for longitudinal network analysis. Education: PhD from Karlsruhe Institute of Technology (2011) Postdoctoral researcher and Marie-Curie fellow at University of Groningen, University of Lugano, and MIT Media Lab (2011-2014) His work bridges sociology, statistics, and computer science to address fundamental questions about how social structures evolve and influence behavior. Key interests include relational event modeling, co-evolution of networks and attributes, and applications in mental health, political polarization, and scientific collaboration. He develops innovative methods for analyzing dynamic networks using cutting-edge computational approaches. Recent publications reveal strong emphasis on methodological rigor in temporal network analysis, with significant contributions to relational event modeling and dynamic network actor frameworks. His work increasingly addresses societal challenges including political polarization, mental health impacts of social isolation, and innovation dynamics in healthcare. Scientific awards: Raymond Boudon Award of the European Academy of Sociology (2017) Freeman Award of the International Network for Social Network Analysis (2021) As co-director of the ETH Social Networks Lab, Stadtfeld leads interdisciplinary research teams developing novel network methodologies. His work has been supported by prestigious fellowships including Marie-Curie funding, and he actively mentors graduate students in network science methodology and applications across diverse domains. The ETH Social Networks Lab serves as a hub for advancing network theory and methodology, with ongoing projects examining student networks during crises, scientific collaboration dynamics, and innovation ecosystems through the lens of network science.

Brian Calderمشاهده پروفایل

استاد پژوهشی

• Seafloor Characterization
• Seabed Mapping
• Bathymetric Data Processing
• +۱۲ مورد دیگر

Brian Calder is a Research Professor at the Center for Coastal and Ocean Mapping, University of New Hampshire, with a strong affiliation in Ocean Engineering and Earth Sciences. He holds a Ph.D. and M.S. in Image Analysis and Electronics Communications Engineering from Heriot-Watt University. His academic work is centered on advanced methods in seafloor characterization and hydrographic data processing. Ph.D., Image Analysis, Heriot-Watt University M.S., Electronics Communications Eng, Heriot-Watt University His research focuses on the development and application of computational techniques for seabed mapping, bathymetric uncertainty modeling, and autonomous ocean sensing. He integrates machine learning, signal processing, and remote sensing to improve the accuracy and reliability of marine geospatial data. His work supports navigation safety, coastal zone management, and deep-ocean exploration. Recent publications highlight trends in automated nautical chart generalization, trusted community bathymetry systems, and wireless ocean-of-things networks for volunteer data collection. His article portfolio reveals a strong emphasis on data quality, uncertainty quantification, and algorithmic innovation in hydrography and marine geodesy. Brian Calder has received multiple research grants, primarily from NOAA and the U.S. Navy, supporting projects such as IT support for NOAA personnel at UNH, development of bathymetric uncertainty models, and autonomous mapping using Saildrone technology. These grants reflect sustained funding and recognition in the field of hydrographic science. He teaches graduate courses including Seafloor Characterization , Seabed Mapping , and Doctoral Research , indicating active mentorship and academic leadership. His work is conducted within the Center for Coastal and Ocean Mapping, a leading institution in hydrographic research, where he collaborates extensively with experts like Yuri Rzhanov, Larry Mayer, and Christos Kastrisios.

Petter N. Kolmمشاهده پروفایل

استاد بالینی

• Quantitative Trading Strategies
• Delegated Portfolio Management
• Financial Econometrics
• +۵ مورد دیگر

Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.

Xia Ben Huمشاهده پروفایل

استاد

• Machine Learning
• Automated Machine Learning
• Deep Learning
• +۷ مورد دیگر

Xia Ben Hu is a Professor in the Department of Computer Science at Rice University's Brown School of Engineering. He leads research in automated and interpretable machine learning algorithms with applications across social informatics, health informatics, and information security. His work has resulted in widely adopted systems including AutoKeras, TODS, and RLCard. Education: PhD from Arizona State University (supervised by Dr. Huan Liu) Master and Bachelor degrees from Beihang University Prof. Hu's research focuses on developing automated and interpretable machine learning algorithms for large-scale, networked, dynamic and sparse data. His work spans automated machine learning (AutoML), deep learning, fairness in AI, time series analysis, and interpretable AI. He has made significant contributions to neural architecture search, collaborative filtering, anomaly detection, and reinforcement learning in imperfect information games. His publication record shows a clear progression from foundational work in network embedding and collaborative filtering (2017) to more recent work on LLM optimization, quantization, and extending context windows (2024). A consistent thread throughout his research is the focus on making complex machine learning systems more accessible, efficient, and interpretable. Selected Awards: ACM SIGKDD Rising Star Award (2021) NSF CAREER Award (2018) Teaching + Research Excellence Award, Rice University (2023) Multiple Best Paper Awards at top venues including ICML, CIKM, and AMIA Prof. Hu has successfully mentored numerous graduate students, with recent graduates securing tenure-track positions at major universities. His research is generously supported by federal agencies including DARPA (XAI, D3M, NGS2), NSF (CAREER, III, SaTC), NIH, and industrial sponsors such as Adobe, Apple, Google, LinkedIn, and JP Morgan. He has served as General Co-Chair for WSDM 2020 and ICHI 2023, and Program Chair for AIHC 2024. He leads the DATA Lab at Rice University, which develops open-source systems for automated machine learning and reinforcement learning. The lab's AutoKeras system has over 8,000 GitHub stars and 1,000 forks, and their work has been integrated into TensorFlow, Apple production systems, and Bing production systems.

Kassem Fawazمشاهده پروفایل

استادیار

• Security & Privacy
• Machine Learning
• Mobile Computing
• +۵ مورد دیگر

Kassem Fawaz is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His research focuses on security, privacy, and mobile computing, with applications in social robotics, generative AI, and adversarial machine learning. He teaches graduate-level courses including Advanced Computer Security, Master's Research, and Independent Study in Electrical & Computer Engineering. Education: PhD (2017) and MS (2011) from the University of Michigan, BE (2009) from the American University of Beirut His work addresses challenges in privacy-preserving analytics, model robustness, and ethical AI, leveraging commodity devices for secure systems. Recent publications explore social media algorithms, black-box attacks, and family dynamics in generative AI use. Key scientific awards include the NSF CAREER Award (2020), Caspar Bowden Award (2019), and multiple student travel grants from ACM, PETS, and USENIX. He has supervised graduate research projects and taught core security courses since 2023.

Immanuel Trummerمشاهده پروفایل

استاد

• Database Systems
• Query Optimization
• Large Language Models
• +۳ مورد دیگر

Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.

Victoria Lemieuxمشاهده پروفایل

استاد

• Blockchain Technology
• Trustworthy Records
• Records and Information Management
• +۸ مورد دیگر

Victoria Lemieux is a Professor at the University of British Columbia (UBC) Faculty of Arts, School of Information, and Cluster Lead for Blockchain@UBC, Canada’s largest research cluster focused on blockchain technology. Her research centers on risks to trustworthy records in blockchain systems and their impact on transparency, financial stability, and human rights. She has pioneered Canada’s first research-oriented graduate blockchain training program and organized multiple interdisciplinary summer institutes. Education: Ph.D. in Archival Studies from University College London (2002), Certified Information Systems Security Professional (CISSP, 2005). Affiliated with UBC’s Peter Wall Institute for Advanced Studies, Sauder School of Business, and Institute for Computers, Information and Cognitive Systems (ICICS). Research interests span blockchain technology , trustworthy records , risk management , information governance , and visual analytics , with recent work addressing healthcare data frameworks, Web3 AI integration, and socio-cultural dynamics of decentralized systems. She has published extensively on blockchain applications in archives, land transactions, and privacy-preserving technologies. Scientific Awards : 2015 Emmett Leahy Award 2015 World Bank Big Data Innovation Award 2016 Emerald Literati Award 2016 Emerald Literati Outstanding Paper Award Supervision: Currently accepts doctoral students in Computational Archival Science and blockchain-related archival research. Affiliated with the Blockchain@UBC cluster and multidisciplinary research teams exploring decentralized systems for social good.