جستجوی استاد راهنمای دانشگاهی

Geoffrey Wodtkeمشاهده پروفایل

استاد

• Neighborhood effects and urban poverty
• Group conflict and racial attitudes
• Class structure and income inequality
• +۵ مورد دیگر

Geoffrey Wodtke is a Professor in the Department of Sociology at the University of Chicago, where he also serves as Associate Director of the Stone Center for Research on Wealth Inequality and Mobility. He holds multiple committee appointments including the Committee on Quantitative Methods in the Social, Behavioral, and Health Sciences, the Committee on Environment, Geography, and Urbanization, and the Committee on Education. Additionally, he is a Research Associate at the Population Research Center and a Faculty Affiliate with the Program in Computational Social Science. Wodtke earned his Ph.D. in Sociology from the University of Michigan in 2014, where he also completed an M.A. in Statistics in 2011. His undergraduate education began at the University of Wisconsin-Milwaukee before transferring to the University of Wisconsin-Madison, where he received his B.A. in Sociology with a concentration in analysis and research. His research program spans four interconnected areas: neighborhood effects and urban poverty, group conflict and racial attitudes, class structure and income inequality, and methods of causal inference in observational research. Wodtke's work on neighborhood effects has particularly focused on the temporal and developmental dimensions of how neighborhood poverty impacts child development, with findings suggesting more severe effects than previously documented, especially during adolescence for children from poor families. His recent publications reveal a clear trajectory from substantive neighborhood effects research toward increasingly sophisticated causal methodology development. This evolution includes integrating machine learning approaches with traditional causal inference frameworks, as seen in his forthcoming work on "Deep Learning with DAGs." His methodological contributions focus on handling treatment-induced confounding and developing regression-with-residuals approaches for causal mediation analysis. Leo Goodman Award for contributions to sociological methodology (2020) Reviewer Award, Sociology of Education (2016) Mark Chesler Award for best graduate student paper (2014) Student Paper Award honorable mention (2011) Jane Addams Award for best article (2011) Wodtke has secured significant research funding including a $300,264 NSF grant for "Why Neighborhoods Matter" (2020-2023) and an $87,819 SSHRC Canada grant for "Neighbourhoods, Schools, and Environmental Health Hazards" (2018-2022). He has advised numerous graduate students and developed specialized courses on causal mediation analysis. Wodtke co-hosts The Inequality Podcast produced by the Stone Center and has developed several software packages for causal inference including MedFlow, RcGNF, cGNF, and RWRMED.

Pierre Marionمشاهده پروفایل

پژوهشگر

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +۲ مورد دیگر

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

Francisco Camara Pereiraمشاهده پروفایل

استاد

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
• +۳ مورد دیگر

Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.

Yu Mengمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Data Mining
• +۳ مورد دیگر

Yu Meng is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), part of the School of Engineering and Applied Science. He joined UVA in 2024 as a tenure-track faculty member. His research focuses on machine learning, natural language processing (NLP), and data mining, with recent emphasis on large language models (LLMs), alignment, reliability, and ethical AI development. Educated at the University of Illinois Urbana-Champaign (UIUC), Meng earned his Ph.D. in 2023 under advisor Jiawei Han. His doctoral thesis, Efficient and Effective Learning of Text Representations , received the ACM SIGKDD 2024 Dissertation Award. He also held a visiting researcher position at Princeton University under Danqi Chen and was a Google PhD Fellow. His work has been recognized with awards including the Superalignment Fast Grant from OpenAI and notable publications at venues like NeurIPS, ICLR, and ACL. Meng’s research explores topics such as preference optimization (SimPO), retrieval-augmented generation (InstructRAG), and zero-shot learning. He actively serves on program committees for top conferences (ICLR, ICML, NeurIPS) and as an action editor for Transactions of Machine Learning Research (TMLR) . He teaches graduate-level courses on NLP, emphasizing cutting-edge LLM topics like architecture design, instruction tuning, and ethical considerations. Key achievements include contributions to LLM alignment via retrieval optimization, efficient pretraining techniques, and foundational work on weakly supervised learning. His research bridges theory and practice, addressing both technical challenges and societal impacts of AI systems.

Tengfei Maمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Biomedical Informatics
• +۶ مورد دیگر

Tengfei Ma is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliations to Computer Science and Applied Mathematics & Statistics. He holds a Ph.D. from The University of Tokyo, M.S. from Peking University, and B.E. from Tsinghua University. Previously, he was a Research Scientist at IBM T.J. Watson Research Center. His research focuses on machine learning, natural language processing (NLP), and biomedical informatics, particularly deep graph learning, scalable graph methods, and healthcare applications. He has contributed to frameworks like EvolveGCN for dynamic graphs and IGB datasets for graph benchmarks. Key awards include ISWC 2021 Best Paper (Research Track) and IBM Outstanding Research Accomplishments (2019, 2022). His work bridges theory and practice, addressing challenges like over-dilution in GNNs and interpretable time series analysis. Collaborations span interdisciplinary areas, such as AI for wound monitoring and code summarization. He teaches BMI530: Software Development for Biomedical Informatics and is open to graduate students from CS, BMI, and AMS departments. Research highlights include: Deep Graph Learning: Scalability (FastGCN, IGB), dynamic graphs (EvolveGCN), and topology-enhanced GNNs. Healthcare: Models for EHR analysis, medication recommendation (GAMENet), and wearable wound monitoring. NLP: Document summarization, code summarization (CP-BCS), and commonsense generation via knowledge graph compression. Recent projects include AI tools like Influencer for promotional content creation and neural-symbolic models for interpretable time series analysis. His lab explores foundational AI for healthcare, code analysis, and graph systems.

Jimmy Huangمشاهده پروفایل

استاد

• Information Retrieval
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Jimmy Huang is a Full Professor and Tier 1 York Research Chair in Big Data Analytics at the School of Information Technology, York University. His research focuses on information retrieval, AI, NLP, and big data analytics in healthcare and web systems. He has published 360+ papers in top venues like SIGIR and ACL, and leads grants totaling $4M+. Huang chairs IEEE's Technical Community on Intelligent Informatics and serves on numerous conference committees. Education: PhD in Information Science (City, University of London), M.Eng and B.Eng in Computer Science Roles: Chair of IEEE TCII, General Chair of SIGIR 2020 and CIKM 2008 Research interests span task-oriented IR, conversational search, healthcare analytics, and graph-based models. His work on hypergraph collaborative filtering (SIGIR 2022) was named a top influential paper. Current projects include NSERC Discovery Grants ($384K) and ADERSIM CREATE ($1.65M). Award highlights include Fellowships from ACM, IEEE, and Canadian Academy of Engineering. Supervised over 90 students, currently mentoring 12 PhD/MSc candidates and 3 postdocs. Active in surgical safety checklist research and medical data analytics. Labs include the IRLab focused on IR and NLP innovations. Major grants include ORF-RE ($3.5M), NSERC CREATE, and multiple CRD partnerships with industry.

Dimitris Samarasمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dimitris Samaras is a SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He leads the Computer Vision Lab and holds adjunct roles in Biomedical Informatics and Radiology. His research focuses on computer vision, machine learning, medical imaging, and computational behavioral sciences, with interdisciplinary collaborations in psychology and neuroscience. Education: Ph.D. in Computer Science (University of Pennsylvania, 2001), M.S. in Computer Science (Northeastern University, 1994), Diploma in Computer Engineering (University of Patras, Greece, 1992). Research Interests: Modeling 3D shape and illumination interactions, facial expression analysis, medical image analysis, and applying machine learning to brain imaging. Current funded projects include NIH/NIDA grants, NSF initiatives, and collaborations with institutions like Brookhaven National Lab and Adobe. Publications: Over 150 peer-reviewed papers in top venues like ICCV, CVPR, and MICCAI, with impactful work on shadow removal, face relighting, and digital pathology. Recent trends emphasize medical AI, generative models, and multimodal interactions. Awards: SUNY Chancellor’s Award (2018), Dean’s Millionaire’s Club (2016), and multiple NIH/NSF grants. Recognized for contributions to scholarship and creative activities in academia. Grants & Teams: Leads over $10M in active grants, including projects on AI for penguin population tracking, histopathology image analysis, and robotic assistance. Collaborates with interdisciplinary teams in medicine, engineering, and cognitive science. Labs & Initiatives: Directs the Computer Vision Lab, contributes to the ColdSteel/NSF CVDI-NY SPIR consortium, and co-leads the Sensor and Transportation Security Center with Farmingdale State College.

Mahsa Baktashmotlaghمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Domain Adaptation
• +۳ مورد دیگر

Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.

Timothy Harrisمشاهده پروفایل

مدرس

• Distributed Systems
• Operating Systems
• Machine Learning
• +۵ مورد دیگر

Timothy Harris is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology, where he jointly teaches courses on multicore semantics and programming. Currently, he works at OpenAI, focusing on performance optimization for GPU inference of large language models, including the Azure OpenAI Service. Previously, he held roles at Microsoft, AWS, Oracle Labs, and was a faculty member at the University of Cambridge (2000–2004). His research spans distributed systems, runtime systems, operating systems, and high-performance computing, with an emphasis on scalability and performance. He contributed to projects like the Xen hypervisor and the Barrelfish research OS. Key research interests include distributed training of PyTorch models in the ONNX runtime, large-scale storage performance with Amazon S3, and runtime systems for in-memory graph analytics. His work often bridges 'big data' and high-performance computing techniques. Notable contributions include the book Transactional Memory (2010) and the Barrelfish OS, alongside numerous publications in top-tier conferences like SOSP, ASPLOS, and EuroSys. He has served as PC chair for ISMM 2025, VEE 2017, and EuroSys 2015, reflecting his leadership in the systems research community. His awards include a Best Paper Award at PACT 2010. Beyond academia, Harris is an avid hiker, aiming to complete the UK coastline, and maintains a photography portfolio at tlhphotography.uk .

Karin Verspoorمشاهده پروفایل

استاد

• Artificial Intelligence in Medicine
• Biomedical Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.

James S. Duncanمشاهده پروفایل

استاد

• Biomedical Image Processing
• Quantitative Image Analysis
• Cardiac Function Analysis
• +۴ مورد دیگر

James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University, with additional appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. His research focuses on biomedical image processing, quantitative image analysis using geometrical models, and applications in cardiac function and neuro-structure analysis. He has pioneered image-guided interventions and developed computational frameworks for medical imaging challenges. He holds a Ph.D. from the University of Southern California. His work integrates AI, deep learning, and statistical decision-making to advance medical imaging technologies. Notable contributions include advancements in 3D image segmentation, deformable models, and MRI-based tumor response assessment. Dr. Duncan has received prestigious awards, including IEEE Fellow (2001) and induction into the American Institute for Medical and Biological Engineering (2000). His recent research spans AI-driven hemodynamics modeling, trustworthy healthcare AI guidelines, and molecular MRI innovations in immunotherapy monitoring. He collaborates across disciplines to address challenges in cardiovascular, neuroimaging, and oncological applications.

Lionel Levineمشاهده پروفایل

استاد

• Applied Mathematics
• Combinatorics and Discrete Geometry
• Probability and Statistics
• +۴ مورد دیگر

Lionel Levine is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His academic research focuses on abelian networks, interacting particle systems, and the emergence of complex patterns from simple rules. He has held prestigious fellowships, including the Simons Fellowship and Sloan Research Fellowship, and has been honored with an endowed professorship. Levine's work bridges probability theory, combinatorics, and statistical physics, with notable contributions to the study of sandpile models and internal diffusion-limited aggregation (IDLA). Education: Ph.D. in Mathematics (2007), University of California, Berkeley. Research Interests: Applied Mathematics, Combinatorics, Probability, Abelian Networks, Sandpile Models, and their intersections with computer science and statistical physics. His research explores how local rules generate large-scale structures, such as in abelian networks and sandpile models. Awards and Honors: Simons Fellowship, Sloan Research Fellowship, Endowed Professorship in the College of Arts and Sciences. Teaching: Courses include Probability Theory (MATH 6710/6720), Topics in Probability: Math for AI Safety (MATH 7710), and undergraduate mathematics courses like Strategy, Cooperation, and Conflict (MATH 1340). Grants and Funding: Supported by the National Science Foundation (NSF), Simons Foundation, Sloan Foundation, and Institute for Advanced Study. Collaborations: Collaborates with prominent researchers such as Yuval Peres, Cris Moore, and Jim Propp. His work has been published in leading journals like the Annals of Probability and Duke Mathematical Journal. Future Work: Continues investigating AI safety, causal models, and multi-agent learning, including research on mathematical frameworks for transformer circuits and hidden incentives in AI systems.

Sheng Yangمشاهده پروفایل

استادیار

• Additive Manufacturing
• Digital Twin Technology
• Smart Manufacturing
• +۷ مورد دیگر

Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.

Guo Pingمشاهده پروفایل

دانشیار

• Precision Engineering
• Intelligent Metrology
• Advanced Manufacturing Technologies
• +۵ مورد دیگر

Guo Ping is an Associate Professor of Mechanical Engineering at Northwestern University, leading the Advanced Intelligent Manufacturing Laboratory (AIM). His research focuses on precision manufacturing, intelligent metrology via deep learning, and advanced manufacturing applications. He holds a Ph.D. from Northwestern University and a B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D. in Mechanical Engineering, Northwestern University, Evanston, IL B.S. in Automotive Engineering, Tsinghua University, Beijing, China Research Interests: Dr. Guo’s work emphasizes innovations in precision engineering, including ductile-regime machining, smart metrology systems, and robotics-driven manufacturing. Key areas include structural coloration, additive manufacturing, and human-robot collaboration in industrial settings. His lab explores cutting-edge techniques like ultrasonic vibration machining and machine learning for defect detection and process optimization. Publications Trends: Recent work spans AI-driven quality control (e.g., photometric stereo networks), robotic swarm patterning, and wearable fatigue monitoring systems. His research bridges machine learning, robotics, and traditional manufacturing to address scalability and precision challenges. Awards: F.W. Taylor Medal (CIRP, 2023) ASME Kornel F. Ehman Manufacturing Medal (2021) SME Outstanding Young Manufacturing Engineer Award (2020) Professional Service: Associate Editor of the Journal of Manufacturing Processes (2017–present). Active in organizing conferences and reviewing for top journals. Labs & Teams: Directs the AIM Lab, which integrates robotics, AI, and advanced materials to solve problems in precision fabrication and smart manufacturing. Current projects include structural coloration for anti-counterfeiting and fatigue prediction in industrial workers.

Sonny Phamمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Data Mining
• +۳ مورد دیگر

Associate Professor Sonny Pham leads research in artificial intelligence at Curtin University's School of EECMS. His work balances theoretical foundations with practical applications in computer vision, data mining, and deep learning. As head of the IAMAI research group, he collaborates with industry partners on security systems, healthcare AI, and sustainable technologies. His research explores: Computationally efficient deep learning architectures Compressed sensing for high-dimensional data Robust statistical methods for real-world problems Applications in computer vision and industrial automation Recent publications demonstrate a focus on medical imaging interpretation and efficient neural networks, with applications spanning radiology report generation, semantic segmentation for autonomous systems, and cybersecurity. His team's work consistently bridges theoretical AI advancements with industrial applications. Honors include: Multiple WANMA Awards (2021-2024) for industry-impactful research INCITE Award for social impact technology (2024) IEEE Young Author Best Paper Award (2010) Over $5M in competitive research funding including MRFF and DFAT grants He leads the IAMAI research group with 12+ graduate students and coordinates Curtin's Master of Artificial Intelligence program. Industry collaborations include Alcoa Australia, iCetana, and HyprFire.