جست‌وجوی استاد و پژوهشگر

• Statistical Modeling
• Bayesian Analysis
• Educational Data Analysis
• +۴ مورد دیگر

Dr. Haiyan Liu is an Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, within the School of Social Sciences, Humanities, and Arts. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2018). Her research focuses on advanced statistical modeling of psychological and educational data, including high-dimensional, longitudinal, and social network data. She develops Bayesian methodologies and machine learning techniques to enhance understanding of human behavior, with recent emphasis on structural equation modeling, network dynamics, and nonparametric growth curves. Her work addresses challenges in survey methodology and behavioral data analysis. Dr. Liu’s educational background includes a Ph.D. in Quantitative Psychology from the University of Notre Dame (2018), complementing her current academic role. Her lab, accessible at https://sites.google.com/view/ucmhaiyanliu , supports her research activities. Her research interests span Bayesian SEM, social network analysis, and applications of machine learning to behavioral data, aiming to bridge methodological innovation with practical psychological inquiry. Her recent articles highlight advancements in Bayesian model selection, longitudinal sentiment analysis, and social network mediation. She emphasizes prior specification rigor in Bayesian frameworks and explores nonlinear relationships in social dynamics. Though no awards are explicitly listed, her contributions to statistical methodologies in psychological research reflect significant scholarly impact. Dr. Liu advises students in quantitative methods and has developed software tools like logistic4p for misclassification correction in logistic regression. Her work integrates computational methods with theoretical advancements, positioning her as a key contributor to modern quantitative psychology.

• Algorithm Theory
• Algorithms and Data Science
• Economics and Computation
• +۶ مورد دیگر

Stefano Leonardi is a Full Professor in the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza Università di Roma. His research focuses on Algorithm Theory, Algorithms and Data Science, and Economics and Computation. He leads the ERC Advanced Grant project AMDROMA, exploring algorithmic mechanisms for online markets. He has held roles as Conference Chair for STOC 2021, WWW 2015, and FUN 2018, and coordinates the Sapienza School of Advanced Studies (2016-2018). His work spans approximation algorithms, online algorithms, and mechanism design. Awards include the ERC Advanced Grant and EATCS Fellowship. His research interests emphasize foundational algorithmic problems in web-based markets, leveraging rigorous design and large-scale data analysis. Recent projects include ALGADIMAR (PRIN 2019-2022) for digital market algorithms. He chairs the Highlights of Algorithms conference series and serves on program committees for top venues like EC, ICALP, and SODA. His lab focuses on web algorithmics and data mining, addressing challenges in online labor markets and fair division. Leonardi's academic contributions include over 100 publications, with recent work on fair algorithms, prophet inequalities, and mechanism design in auctions. He has pioneered methods for submodular optimization, online learning, and multi-agent systems. Grants and awards reflect his leadership in bridging theory with real-world applications, particularly in digital economies. Grants: ERC Advanced Grant (2018-2023), PRIN ALGADIMAR (2019-2022) Leadership: Chair of ACM STOC 2021, WWW 2015, and 9th FUN Conference Labs/Teams: Laboratory on Web Algorithmics and Data Mining Key Projects: AMDROMA (algorithmic mechanisms), ALGADIMAR (digital markets)

Daniele Loiaconoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.

Lin Wangمشاهده پروفایل

مدرس

• Machine Learning
• Audio-Visual Signal Processing
• Robotic Perception
• +۳ مورد دیگر

Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.

Alex Warstadtمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing
• Cognitive Modeling
• +۵ مورد دیگر

Alex Warstadt is an Assistant Professor at the University of California San Diego, holding appointments in the Department of Linguistics and the Halıcıoğlu Data Science Institute (HDSI). His research focuses on computational linguistics, applying advances in Large Language Models (LLMs) to understand human language acquisition, processing, and structure. Key contributions include developing the CoLA and BLiMP benchmarks for evaluating grammatical ability in LLMs, and the BabyLM Challenge to promote data-efficient language models. His work bridges theoretical linguistics, experimental methods, and computational modeling, particularly in pragmatics and discourse structure. Education: He earned B.A.s in Linguistics and Music Theory from Brown University and a Ph.D. in Linguistics from New York University (NYU), with a dissertation on 'Artificial Neural Networks as Models of Human Language Acquisition.' Postdoctoral work at ETH Zürich furthered his interdisciplinary research. He leads the LeM🍋N Lab at UC San Diego, which investigates language learning, meaning representation, and natural language processing through interdisciplinary collaboration. Research Interests: Warstadt’s research emphasizes leveraging machine learning to explore developmental linguistics, computational cognitive modeling, and pragmatic phenomena such as relevance and presupposition. His lab’s work aims to create models that align with human learning processes while advancing efficient NLP techniques. Recent projects include studying multimodal input effects and optimizing models for developmental plausibility. Labs/Teams: Director of the Learning, Meaning, and Natural Language (LeM🍋N) Lab, focusing on interdisciplinary research across linguistics, cognitive science, and data science.

Larry Heckمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Larry Heck is a Professor at the Georgia Institute of Technology with joint appointments in the School of Electrical and Computer Engineering and the School of Interactive Computing. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar. His research focuses on machine learning, deep learning, natural language processing, conversational systems, and speech/speaker recognition. He directs the AI Virtual Assistant (AVA) Lab, advancing next-generation AI assistants. Dr. Heck has held leadership roles in industry, including at Microsoft, Google, Samsung, and Viv Labs, and has over 50 U.S. patents. Education: BSEE from Texas Tech University (1986) MSEE and PhD in Electrical Engineering from Georgia Tech (1991) Research Interests: Dr. Heck’s work bridges machine learning and human-centric AI, with emphasis on conversational systems, multimodal interaction, and real-world applications. His AVA Lab develops AI assistants that integrate visual, auditory, and contextual cues for natural interaction. Recent projects include multimodal sensor integration, dialogue systems for caregiving networks, and embodied AI for avatar animation. Awards: IEEE Fellow (2020) Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Advising & Grants: While primarily focused on industry collaboration, Dr. Heck mentors students through Georgia Tech’s interdisciplinary programs. His research is funded by government agencies and corporate partnerships, including the NSA and DARPA. Labs & Teams: The AVA Lab collaborates with academia and industry to create AI systems that understand context, gestures, and environment. Current initiatives include multimodal dialogue datasets (e.g., OKCV, SensorQA) and reinforcement learning frameworks for real-time systems.

Lu Shijianمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Image and Video Analytics
• +۱۴ مورد دیگر

Lu Shijian is an Associate Professor (tenured) at the School of Computer Science and Engineering , Nanyang Technological University (NTU) , Singapore. He holds a PhD in Electrical and Computer Engineering from the National University of Singapore and leads the Visual Intelligence Lab (VILab) , focusing on humanlike visual perception, understanding, and creation. University: Nanyang Technological University School: School of Computer Science and Engineering Academic Rank: Associate Professor (tenured) Email: Shijian.Lu@ntu.edu.sg Office: N4-02C-101, NTU, Singapore His research spans computer vision, deep learning, image and video analytics, visual intelligence, and machine learning , with key topics including scene text detection, unsupervised domain adaptation, image synthesis, satellite image analytics, and facial expression recognition. His work integrates supervised, semi-supervised, and self-supervised learning across 2D images, 3D point clouds, and multi-spectral data. The recent publications highlight a strong trend in domain adaptation, generative modeling, 3D vision, and multimodal AI . His lab produces high-impact work accepted at top venues like CVPR, ICCV, ECCV, NeurIPS, and TPAMI, with applications in autonomous systems, image editing, and robust AI. Top winner of ICFHR2014 Competition on Word Recognition from Historical Documents Top winner of ICDAR 2013 Robust Reading Competition (scene text segmentation) Top winner of ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) Top winner of H-DIBCO 2010 – Handwritten Document Image Binarization Competition Top winner of ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) Lu advises several PhD students and serves as an Associate Editor for Pattern Recognition and Neurocomputing . He has held leadership roles in top conferences as Senior Program Committee member (IJCAI, AAAI) and Area Chair (ICDAR, WACV). His lab, the Visual Intelligence Lab , is actively recruiting PhD students and conducting cutting-edge research in visual intelligence, with recent work on 3D Gaussian splatting, backdoor attacks, and vision-language models.

Jim Glassمشاهده پروفایل

پژوهشگر

• Speech Processing
• Natural Language Processing
• Self-Supervised Learning
• +۳ مورد دیگر

Jim Glass is a Senior Research Scientist at the Massachusetts Institute of Technology (MIT), where he leads the Spoken Language Systems Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL). He also holds an affiliation with the Harvard University Program in Speech and Hearing Bioscience and Technology. Education: S.M. in Electrical Engineering and Computer Science, MIT Ph.D. in Electrical Engineering and Computer Science, MIT His research spans speech and natural language processing, focusing on self-supervised learning for speech and language processing, cross-modal learning between audio, speech, and vision, using speech and language as biomarkers for health, and conversational interaction. He has published over 400 refereed papers and supervised more than 110 master's and doctoral theses. Scientific Awards: IEEE Fellow Fellow of the International Speech Communication Association Jim Glass serves as an Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence. His work is conducted in collaboration with the Spoken Language Systems Group at CSAIL, where he has been a prominent figure in advancing speech and language technologies.

Catherine Stinsonمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning Ethics
• Computational Psychiatry
• +۴ مورد دیگر

Catherine Stinson is an Assistant Professor and Queen’s National Scholar in Philosophical Implications of Artificial Intelligence at Queen's University. She holds joint appointments in the School of Computing and the Philosophy Department, blending technical and philosophical expertise to address ethical challenges in AI. PhD in History & Philosophy of Science, University of Pittsburgh (2013) MSc in Computer Science, University of Toronto (2013) Dr. Stinson's research focuses on methodological and ethical dimensions of artificial intelligence, including: Bias and explanation in machine learning Computational psychiatry Embodied intelligence Community-led design AI ethics education Her recent publications analyze corporate influence in NLP, privacy implications of large language models, and documentation practices in AI datasets. They explore intersections between technical performance, ethical responsibility, and societal impact. Scientific awards include: Queen’s National Scholar Finalist for Outstanding Certification at Transactions in Machine Learning Research As director of the Ethics and Technology Lab at Queen's, she leads interdisciplinary projects challenging algorithmic bias, investigating AI creativity, and developing tools to resist targeted violence. Current initiatives include: Critical evaluation of chatbots for mental health support Tracking (In)justice project on police-involved deaths in Canada

Vatsal Sharanمشاهده پروفایل

استادیار

• Machine Learning
• Theoretical Computer Science
• Statistics
• +۶ مورد دیگر

Vatsal Sharan is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California's Viterbi School of Engineering. He maintains affiliations with the Theory Group, Machine Learning Center, and the Center for AI in Society at USC. Education: Ph.D. in Computer Science from Stanford University, advised by Greg Valiant Postdoctoral research at MIT, hosted by Ankur Moitra Vatsal Sharan's research centers on the theoretical foundations of machine learning, positioned at the intersection of machine learning, theoretical computer science, and statistics. His work investigates fundamental limits for solving learning and estimation tasks under computational and information-theoretic constraints, with the goal of developing practical algorithms that are efficient, fair, and robust. His research spans memory-efficient learning, algorithmic fairness, robustness in deep learning, and the theoretical underpinnings of transformers and large language models. A significant portion of his work explores how memory constraints affect learning algorithms and whether memory can serve as a distinguishing factor between 'efficient' and 'expensive' techniques in machine learning. His recent publications demonstrate a strong focus on multicalibration, transformer interpretability, and trustworthy AI systems. Scientific Awards: Amazon Research Award (2021 and 2023) SoCal NLP Symposium 2023 Best Paper Award COLT 2022 Best Paper Award Vatsal Sharan advises a diverse group of Ph.D. students including Siddartha Devic, Bhavya Vasudeva, Julian Asilis, Deqing Fu, Devansh Gupta, Spandan Senapati, and Tianyi Zhou. His research is supported by multiple prestigious grants from the NSF, Amazon Research, Google Research, and the Okawa Foundation. He is an active participant in the Learning Theory Alliance (LeT-All), a community-building and mentorship initiative for the learning theory community. His teaching portfolio includes advanced courses on machine learning theory and trustworthy machine learning at USC, where he shapes the next generation of researchers in theoretical aspects of artificial intelligence.

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.

• Social Media Analysis
• Cybersecurity
• Online Disinformation
• +۳ مورد دیگر

Jeremy Blackburn is an Associate Professor in the Department of Computer Science at Binghamton University. He co-founded the International Data-driven Research for Advanced Modeling and Analysis Lab ( iDRAMA Lab ) and leads its Binghamton satellite. His research focuses on large-scale measurement and analysis of social media, particularly the behavior of malicious actors and disinformation campaigns.

• AI in Healthcare
• Electronic Health Records Analysis
• Biomedical Time Series Analysis
• +۶ مورد دیگر

Dr. Kezhi (Ken) Li is an Associate Professor of AI in Healthcare at the Institute of Health Informatics, University College London (UCL). He leads the AI for Health research group and has established himself as a leading expert in applying artificial intelligence to solve complex problems in healthcare, with over 130 publications in leading journals (total impact factor greater than 400). Dr. Li earned his Doctor of Philosophy from Imperial College of Science, Technology and Medicine in 2013, followed by research positions at the Medical Research Council (2015-2017), University of Cambridge (2014-2015), and Royal Institute of Technology (KTH) (2013-2014). His academic journey reflects a consistent trajectory from technical AI research toward increasingly healthcare-focused applications. Dr. Li's research focuses on solving physiological, medical, clinical, and operational problems in healthcare using AI techniques. His specific expertise includes AI in healthcare using electronic health records (EHR), biomedical time series analysis using monitors/wearables, diabetes management, large language models (LLM) in healthcare (especially mental health), patient flow optimization, and digital health with federated learning. His work bridges the gap between cutting-edge AI methodologies and practical healthcare applications, with a strong focus on improving patient outcomes and healthcare system efficiency. Analysis of Dr. Li's publication history reveals a strong emphasis on diabetes management technologies, particularly blood glucose prediction systems using advanced neural network architectures. More recently, his work has expanded into mental health applications of large language models, blockchain-based federated learning for healthcare data, and mortality prediction in critical care settings. His research demonstrates a clear evolution from purely technical AI development toward increasingly clinically impactful applications, with growing emphasis on explainability, privacy preservation, and real-world implementation challenges. Dr. Li has received numerous prestigious awards recognizing his contributions to healthcare AI: Best Application Award of IEEE Global Blockchain Conference (2025) Fellow of British Computer Society (2025) Fellow of the Royal Society for Public Health (2024) Healthcare Partnership of the Year category at the London Higher Awards (2024) ECR Promising Project Award (2023) Gallivan Award finalists (2022) Stylianos Kalaitzis PhD Award Winner (2022) HDR UK Team of the Year (COVID-19) Award (2021) As an educator, Dr. Li serves as the Director of MRes study (AI-enabled Healthcare Systems) at UCL. He leads multiple key modules including Healthcare Artificial Intelligence Journal Club, Dissertation in Artificial Intelligence Enabled Healthcare, and Advanced Machine Learning for Healthcare. His supervision extends across dissertation projects and junior researchers in his AI for Health group. His research has been supported by various grants, including those from HDR UK, focusing on translating AI innovations into practical healthcare solutions. Dr. Li leads the AI for Health research group (https://ai4hucl.github.io/ai4h_webs/), which comprises researchers with diverse expertise in machine learning, healthcare systems, and clinical domains. The group maintains strong collaborations with healthcare providers and industry partners to ensure their research addresses real-world healthcare challenges and can be effectively translated into clinical practice.

Gabriel Wainerمشاهده پروفایل

استاد

• Modelling and Simulation Methodologies
• Discrete Event Systems
• Real-Time Modelling
• +۷ مورد دیگر

Dr. Gabriel Wainer is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He leads the Advanced Real-Time Simulation Lab and specializes in modeling and simulation methodologies, particularly focusing on discrete event systems, real-time modeling, cellular automata, and DEVS formalism. Research Interests: Discrete event systems, DEVS formalism, cellular automata, real-time simulation, IoT applications, and parallel/distributed simulation Affiliation: Carleton University Recent publications highlight his work in advanced simulation frameworks, energy-efficient 5G systems using deep reinforcement learning, and pandemic modeling with cellular automata. His lab develops tools like PROMETHEUS and Devsmap for standardized DEVS model representation, while also exploring applications in wireless communication, building energy systems, and behavioral epidemiology.

Leland Bybeeمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Econometrics
• +۲ مورد دیگر

Leland Bybee is an Assistant Professor of Finance at the University of Chicago Booth School of Business . He leverages machine learning and natural language processing to address economic and financial questions, particularly focusing on belief measurement with applications to asset pricing and behavioral economics. Ph.D. in Financial Economics, Yale School of Management (2024) M.S. in Statistics, University of Michigan (2017) B.A. in Economics, University of Chicago (2013) His research integrates computational methods with economic theory to analyze: Textual analysis of business news for macroeconomic tracking Narrative-driven asset pricing models Memory-based belief formation using kernel methods Macroeconomic determinants of currency returns He has received multiple awards including: Dimension Fund Advisors Distinguished Paper Award BlackRock Applied Research Award HEC Top Finance Graduate Award The Brattle Group PhD Candidates Award EFA Engelbert Dockner Memorial Prize Bybee teaches Machine Learning in Finance and participates in finance seminars, contributing computational tools like regIPCA (Python) and changepointsHD (R) to the research community.