جستجوی استاد راهنمای دانشگاهی

Christos Faloutsosمشاهده پروفایل

استاد

• Data Mining
• Graph Mining
• Fractals and Power Laws
• +۳ مورد دیگر

Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds a B.Sc. from the National Technical University of Athens and M.Sc./Ph.D. from the University of Toronto. His research focuses on data mining, graph analysis, fractals, and database systems. Notable contributions include foundational work on R-trees, graph mining laws (e.g., Kronecker graphs), and applications in medical imaging, network security, and fraud detection. Key projects include PEGASUS (petascale graph mining), fraud detection in online auctions (NetProbe), and tools for human trafficking analysis (TrafficVis). He has led NSF-funded projects on tensor mining, network anomaly detection, and bioinformatics. Over 300 refereed publications highlight his contributions across databases, data mining, and networks. Awards include the KDD Best Paper (2005, 2016), SIGMOD Test-of-Time Award, and recognition as a top nurturer in IT. His lab collaborations span the Parallel Data Lab (PDL), Machine Learning Department, and Computational Biology.

Tiancheng Zhaoمشاهده پروفایل

پژوهشگر

• Multimodal Foundation Models
• AI Agents
• Vision-Language Models
• +۳ مورد دیگر

Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.

Fernando De la Torreمشاهده پروفایل

استاد مدعو

• Machine Learning
• Computer Vision
• Augmented Reality
• +۴ مورد دیگر

Fernando De la Torre is a Courtesy Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with an affiliation to the Robotics Institute where he has been a research faculty member since 2005. He holds a Ph.D. in Electronic Engineering from Ramon Llull University (2002). His research focuses on machine learning and computer vision, with applications in human health, augmented/virtual reality, generative models, and data-centric methodologies. He directs the Human Sensing Laboratory, which explores technologies for human behavior analysis and health monitoring. Notable contributions include founding FacioMetrics LLC (acquired by Meta), advancing facial recognition and 3D human digitization, and developing frameworks for robust visual models. His work bridges theory and practice, with over 225 peer-reviewed publications and editorial roles, including Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. Recent research trends emphasize generative AI applications in satellite imagery analysis, VR/AR rendering optimizations (e.g., Gaussian splatting), and clinical motion recognition for healthcare. His projects often intersect with industry, addressing challenges in wearable health monitoring and immersive technologies. His lab collaborations span academia and industry, focusing on scalable solutions for 3D human modeling, adversarial robustness, and multimodal data fusion. Key achievements include pioneering work on 3D face animation from speech and garment reconstruction from single images.

Andrew O. Arnoldمشاهده پروفایل

استاد مدعو

• Machine Learning
• Natural Language Processing
• Quantitative Trading
• +۵ مورد دیگر

Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.

Shixiang Zhuمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Operations Research
• +۷ مورد دیگر

Shixiang (Woody) Zhu is an Assistant Professor in Data Analytics at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University. He holds a PhD in Machine Learning from Georgia Institute of Technology (2022) and B.S./M.S. in Computer Science from Beijing University of Posts and Telecommunications (2017). His research bridges machine learning, operations research, and statistics, focusing on sequential modeling, human-AI collaboration, and energy systems operations. He has received awards including the IEEE Power & Energy Society Best Paper Award (2025) and was a finalist for the INFORMS Wagner Prize (2021). Education : PhD in Machine Learning, Georgia Tech (2017–2022) B.S./M.S. in Computer Science, BUPT (2010–2017) His research emphasizes spatio-temporal data analysis , decision making under uncertainty , and applications to energy systems, healthcare, and public policy. Notable projects include optimizing police zone design (Wagner Prize finalist) and enhancing grid resilience through robust optimization. He actively collaborates with institutions like Argonne National Laboratory and NSF-funded projects. Awards : Best Paper Award, IEEE Power & Energy Society (2025) Gen-AI Fellows (2024) Finalist, INFORMS Wagner Prize (2021) Advising & Grants : Advises PhD students Zekai Fan, Wenbin Zhou, and others Recipient of Block Center Seed Grant (2024), NSF funding (2024) His work spans energy resilience, public policy optimization, and causal inference in social systems. He co-leads the INFORMS Data Mining Society and reviews for top journals like Operations Research and Management Science.

Cameron N. Riviereمشاهده پروفایل

استاد پژوهش

• Medical Robotics
• Surgical Robotics
• Control Systems
• +۵ مورد دیگر

Dr. Cameron N. Riviere is a Research Professor at Carnegie Mellon University (CMU), affiliated with the Robotics Institute, Department of Biomedical Engineering, and Department of Mechanical Engineering. He also holds an Adjunct Professorship at the University of Pittsburgh’s Department of Rehabilitation Science and Technology. His research focuses on surgical robotics, medical devices, and control systems, emphasizing minimally obtrusive robotic systems for microsurgery and minimally invasive surgery. Key projects include the Micron handheld tremor-canceling device and the HeartLander cardiac robot. Education: B.S. in Aerospace and Ocean Engineering from Virginia Tech (1989), Ph.D. in Mechanical Engineering from Johns Hopkins University (1995). Research Interests: Medical robotics, rehabilitation robotics, control systems, human-machine interfaces for biomedical applications, and physiological motion compensation. Recent work includes development of robotic systems for retinal surgery, cardiac therapy, and assistive technologies for individuals with movement disorders. Awards Best Paper of 2016 (IEEE/ASME Transactions on Mechatronics) Finalist for Best Medical Robotics Paper (IEEE ICRA 2016) Best Paper (IEEE MFI 2015, IROS 2014) Advising and Grants: Mentor to PhD students (e.g., Arpita Routray) and masters students (e.g., Aditti Ramsisaria). Projects include the HeartLander epicardial therapy robot and advanced tracking systems for handheld surgical tools. Collaborates with institutions like Mayo Clinic and NIH-funded initiatives. Labs: Director of the Surgical Mechatronics Laboratory at CMU’s Robotics Institute, focusing on interdisciplinary development of robotic systems for clinical applications.

Tze Meng Lowمشاهده پروفایل

استاد پژوهش

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +۳ مورد دیگر

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Haohan Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

Jiashun Jinمشاهده پروفایل

استاد

• Statistics
• Big Data Analytics
• Network Analysis
• +۴ مورد دیگر

Jiashun Jin is a Professor of Statistics and Affiliated Professor of Machine Learning at Carnegie Mellon University, where he leads research in statistical inference for Big Data with a focus on 'Rare and Weak' signal regimes. He received his Ph.D. in Statistics from Stanford University in 2003. His research interests span: Development of novel methods like Higher Criticism, Graphlet Screening, IF-PCA, and SCORE for high-dimensional data Applications in genomics (cancer classification, SNP analysis), cosmology (non-Gaussian detection in CMB), and network science Theoretical frameworks including phase diagrams to characterize statistical detectability boundaries Recent publications (2021-2023) demonstrate strong emphasis on network community detection, text analysis, genetic association studies, and COVID-19 resource allocation, with consistent focus on statistical optimality in high-dimensional settings. Honors and awards include: NSF CAREER Award (2007) IMS Tweedie Award (2009) IMS Fellow (2011) ASA Fellow (2019) ICCM Distinguished Paper Award (2020) He has developed a significant dataset of 83,381 statistical publications for coauthorship/citation network analysis and has delivered invited lectures including the IMS Medallion Lecture (2015).

• Computational Neuroscience
• Systems Neuroscience
• Neural Circuits Characterization
• +۷ مورد دیگر

Matthew Smith is a Professor in Biomedical Engineering and Neuroscience Institute with a focus on computational and systems neuroscience. As Co-Director of the Center for the Neural Basis of Cognition, his research explores neural circuits, motor control, and spatial cognition. His work bridges experimental and computational methods, including studies on neural plasticity, electrophysiological recordings, and non-invasive neurostimulation techniques. Key contributions include advancing methods for estimating intracranial pressure and optimizing brain stimulation protocols. His interdisciplinary approach integrates machine learning, statistical analysis, and neuroimaging to understand complex brain functions. Research interests span neural coding mechanisms, visual perception dynamics, and the interplay between arousal systems and motor planning. Notable projects include investigations into V4 neuronal activity modulation by recent experience and the development of compact deep neural network models for visual cortex analysis. Smith’s lab employs advanced signal processing tools, such as SLEX analysis and latent dynamic factor modeling, to decode high-dimensional neural data. His recent publications highlight breakthroughs in understanding working memory robustness, transcranial ultrasound modulation specificity, and the spatial organization of prefrontal cortical networks. While no formal awards are listed, his extensive grant-funded research includes collaborations on cerebral autoregulation studies and neurovascular impedance modeling. Advising activities focus on training interdisciplinary PhD candidates in systems neuroscience and neural computation.

Carine Pierrette Mukamakuzaمشاهده پروفایل

مدرس

• Digital Healthcare Solutions
• Machine Learning
• Recommender Systems
• +۴ مورد دیگر

Dr. Carine Pierrette Mukamakuza is a Lecturer at Carnegie Mellon University Africa's College of Engineering, specializing in Digital Healthcare Solutions , Machine Learning , and Data Science . Her work bridges Computer Science , Public Health , and Social Network Analysis , with a focus on malaria detection systems and cardiac diagnostics. Ph.D. in Informatics, Technical University of Vienna MS & BS in Computer Science, Central South University, China Research Themes : AI applications in malaria diagnostics (U-Net, YOLOv5, SVM) Deep Learning for ECG/PCG signal analysis Social network impacts on recommender systems Digital health infrastructure for Africa Article Trends : Combines Machine Learning and Public Health to develop digital diagnostic tools for malaria and heart disease, with recent emphasis on system reliability and scalability across Africa. Projects : Developing automated malaria screening tools for Rwanda, integrating AI into UN SDG-aligned health systems.

James H. Garrett Jr.مشاهده پروفایل

استاد

• Civil Engineering
• Data Mining
• Machine Learning
• +۶ مورد دیگر

James H. Garrett Jr. is the Provost and Thomas Lord Professor of Civil and Environmental Engineering at Carnegie Mellon University. He previously served as Dean of the College of Engineering (2013-2018) and Head of the Department of Civil and Environmental Engineering (2006-2012). Garrett joined CMU in 1990 and has held leadership roles including Associate Dean for Academic and Graduate Affairs (2000-2006). Ph.D., Civil Engineering, Carnegie Mellon University (1986) M.S., Civil Engineering, Carnegie Mellon University (1983) B.S., Civil Engineering, Carnegie Mellon University (1982) Garrett’s research focuses on sensor systems, data mining, and machine learning for civil infrastructure condition assessment. His work spans smart cities, transportation engineering, and structural health monitoring, with applications in HVAC systems, water infrastructure, and bridge diagnostics. He has pioneered methods for integrating BIM with maintenance data and developing intelligent decision support systems. Garrett’s publications emphasize sensor networks, data-driven infrastructure management, and machine learning for anomaly detection in transportation and building systems. His work bridges civil engineering with computational methods, including variational autoencoders for rail monitoring and adaptive graph filtering for bridge diagnostics. Alexander von Humboldt Research Prize (2012) Steven J. Fenves Award for Systems Research (2007) ASCE Computing in Civil Engineering Award (2006) As founding co-director of the Pennsylvania Smarter Infrastructure Incubator and a leader in the Wilton E. Scott Institute for Energy Innovation, Garrett has advanced smart infrastructure through sensor networks and data analytics. He also served as co-chief editor of the ASCE Journal of Computing in Civil Engineering (2008-2013).

Jing Leiمشاهده پروفایل

استاد

• Statistical Machine Learning
• High-Dimensional Statistics
• Nonparametric Methods
• +۴ مورد دیگر

Jing Lei is a Professor in the Department of Statistics & Data Science at Carnegie Mellon University , affiliated with the Dietrich College of Humanities and Social Sciences . His research focuses on statistical theory and methodology for high-dimensional, complex data with applications in machine learning, genomics, and network analysis. Ph.D., Department of Statistics, UC Berkeley (2010) Prior: Statistician at Google (2010-2011), Visiting Research Scientist at CMU (2011) Research interests include: Statistical Machine Learning: Conformal Prediction, Cross-Validation, Spectral Clustering High-Dimensional Statistics: Sparse PCA, Dimension Reduction, Network Estimation Nonparametric Methods: Smoothing Techniques, Functional Data Analysis Data Privacy: Differentially Private Estimators, Distribution-Free Inference Applications: Single-cell RNA-seq, Multi-omics Data, Astronomy Recent work involves minimax optimal graphon estimation , discrete argmin inference , and adaptive conformal prediction . Key trends include network data analysis, high-dimensional hypothesis testing, and privacy-preserving statistical methods. Scientific awards: Best Paper Award, AISTATS 2012 Editorial service: Associate Editor, Journal of the Royal Statistical Society: Series B (2024-present) Associate Editor, Annals of Statistics (2019-present) Associate Editor, Journal of the American Statistical Association (2020-present) Currently teaching 36710 Advanced Probability and Statistics (Fall 2024). Affiliated with CMU research labs including CSAFE and DELPHI .

James Sharpnackمشاهده پروفایل

دانشیار مدعو

• Statistical Machine Learning
• Graph Signal Processing
• Anomaly Detection
• +۱ مورد دیگر

James Sharpnack is an Adjunct Associate Professor in the Statistics Department at UC Davis and an AI Research Scientist at Duolingo. His research focuses on statistical machine learning applied to anomaly detection, graph signal processing, and epidemiological modeling. His methodological work includes developing scan statistics for network data, trend filtering techniques, and domain adaptation approaches. Applied projects involve COVID-19 wastewater monitoring, gravitational lens detection in astronomy, and transportation data analysis. Sharpnack contributes to educational AI systems through his industry role.

Christopher Genoveseمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Inference
• Computational Neuroscience
• +۷ مورد دیگر

Christopher Genovese serves as Professor and Department Head of the Department of Statistics within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His academic leadership extends to interdisciplinary collaborations through the Neuroscience Institute, where he contributes to computational neuroscience research initiatives. Dr. Genovese's research spans high-dimensional and nonparametric statistical methodology with applications across multiple scientific domains. His primary focus areas include computational neuroscience, cosmology, evolutionary biology, and educational data science. Specific methodological interests encompass graphical models, spatial statistics, inverse problems, multiple testing procedures, and adaptive function estimation. His recent publication portfolio demonstrates strong interdisciplinary engagement, with computational neuroscience representing the dominant application area (35% of recent work), followed by cosmology (20%), educational technology (15%), and evolutionary biology (10%). Methodologically, high-dimensional statistics and nonparametric inference form the core theoretical contributions across these applications. Current research initiatives include developing systems for inferring student learning states from online educational data and novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. These projects exemplify his commitment to translating statistical theory into practical scientific and medical applications.