جستجوی استاد راهنمای دانشگاهی

Bryan Parnoمشاهده پروفایل

استاد

• Secure Systems
• Formal Verification
• Cryptography
• +۳ مورد دیگر

Bryan Parno is a Professor at Carnegie Mellon University in the College of Engineering, holding the Kavčić-Moura Chair in Electrical & Computer Engineering and Computer Science. His research focuses on designing secure systems with formal verification to ensure rigorous security guarantees in practical implementations. Key Research Areas : Formal verification of distributed/concurrent systems, secure cryptographic protocols, blockchain applications, and hardware-assisted security mechanisms. Awards : Distinguished Artifact Awards at USENIX Security (2025), SOSP (2024), CAV (2024) Jay Lepreau Best Paper Award (OSDI 2025) IEEE Cybersecurity Award for Practice (2024) Sloan Research Fellowship (2018) ACM Doctoral Dissertation Award (2011) Notable Contributions : EverCrypt cryptographic provider (Linux kernel integration 2020) Project Everest (formal verification of HTTPS ecosystem) Verus verification framework IronFleet verified distributed systems Students : Yi Zhou (PhD 2025), Elisaweta Masserova (PhD 2024), Travis Hance (PhD 2024), Abhiram Kothapalli (PhD 2024), Jay Bosamiya (PhD 2024), James Larisch (2023), Antoine Delignat-Lavaud (2021), and others. Collaborations : Microsoft Research, Amazon Web Services (Amazon Scholar 2025), Algorand Foundation (Technical Advisory Committee 2019).

Russell S. Schwartzمشاهده پروفایل

استاد

• Computational Biology
• Cancer Genomics
• Tumor Evolution
• +۶ مورد دیگر

Russell S. Schwartz is a Professor and Department Head of Computational Biology at Carnegie Mellon University's Mellon College of Science. He holds a Ph.D. in Computer Science from MIT and has postdoctoral training in computational biology. His research focuses on algorithmic and machine learning approaches to understand complex biological systems, particularly cancer genomics and tumor evolution. Key areas include clonal lineage inference, tumor phylogenetics, and multi-omics data integration. He leads initiatives in computational cancer research, developing tools like TUSV and MosaicSim for tumor evolution modeling. His lab also addresses challenges in bioinformatics education, contributing to the ISCB Competency Framework. Current projects emphasize spatial tumor modeling, ctDNA dynamics, and precision oncology strategies.

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).

Gregory Gangerمشاهده پروفایل

استاد

• Computer Systems
• Cloud Computing
• Distributed Systems
• +۶ مورد دیگر

Gregory Ganger is the Stephen J. Jatras Professor at Carnegie Mellon University's College of Engineering, holding joint affiliations with the Computer Science Department and CyLab. He has served as Director of the Parallel Data Lab (PDL) since 2000 and teaches Storage Systems (Fall) and Advanced Cloud Computing (Spring) courses. Education: Ph.D. in Computer Science & Engineering (1995, University of Michigan) M.S. in Computer Science & Engineering (1993, University of Michigan) B.S. in Computer Science (1991, University of Michigan) Dr. Ganger's research spans computer systems, focusing on: cloud computing, distributed systems, storage architectures, and machine learning infrastructure. His work addresses challenges in resource scheduling, non-volatile memory optimization, and scalable infrastructure design through projects like BigLearning, CILES, HeART, and Zoned Storage systems. Recent publications demonstrate expertise in: DNN training optimization (GraphPipe, Nonuniform-Tensor-Parallelism), sustainable storage (Storage Emissions, FairyWREN), and cloud efficiency (MACARON Cache). These works intersect machine learning, storage systems, and hardware-software co-design. Scientific Awards: 2021 OSDI Best Paper 2021 SOSP Best Paper 2021 SoCC Test of Time Award 2021 R&D 100 Award As advisor to 8 graduate students, he contributes to training the next generation of systems researchers. His lab (PDL) collaborates with industry partners on cutting-edge storage and cloud infrastructure problems.

Timothy X Brownمشاهده پروفایل

استاد

• Wireless systems
• Networking
• Network security
• +۳ مورد دیگر

Timothy X Brown is a Professor in the Department of Electrical and Computer Engineering at the College of Engineering. His research focuses on wireless systems, networking, network security, machine learning, cognitive radios, and unmanned aircraft systems. His work spans applications ranging from rural education chatbots to drone-based search and rescue systems, with a strong emphasis on developing-world contexts. Research Interests: Unmanned aircraft systems (UAVs) for agriculture, search-and-rescue, and telecommunications Design of AI-driven conversational assistants for educational and community support Spectrum management policies and cognitive radio networks Wireless network security and interference analysis Technology adoption in rural and under-resourced environments Recent Research Trends: Recent publications (2020–2025) highlight growing emphasis on human-centered AI applications in education, including chatbot systems for teacher support in Côte d’Ivoire. Earlier work (2016–2020) focused on UAV airframe design, dynamic spectrum policies, and network performance analysis in heterogeneous cellular systems. Professional Affiliations: Part of the College of Engineering's faculty, contributing to academic programs in electrical and computer engineering across Africa, Silicon Valley, Thailand, and other global locations.

Kun Zhangمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Causal Discovery
• +۶ مورد دیگر

Kun Zhang is a Professor at Carnegie Mellon University's Department of Philosophy and an affiliate faculty member in the Machine Learning Department. His research bridges causal discovery, machine learning, and philosophical foundations of AI, with applications in neuroscience, computational finance, and climate analysis. His work focuses on: Automated causal discovery from heterogeneous data Causality-based learning for transfer and deep learning Philosophical principles of causation and machine learning Recent research trends emphasize causal representation learning, nonparametric modeling, and real-world applications across biomedical, financial, and Earth sciences. He leads the Causal Learning and Reasoning (CLeaR) group at CMU and collaborates with the Center for Integrative AI (CIAI) at MBZUAI.

John Kitchinمشاهده پروفایل

استاد

• Catalysis
• Energy Production
• Machine Learning
• +۴ مورد دیگر

John Kitchin is a Professor of Chemical Engineering at Carnegie Mellon University, affiliated with the College of Engineering. He leads the Kitchin Research Group, focusing on catalysis, energy systems, and the integration of machine learning into scientific discovery. His work bridges computational modeling and experimental validation, with contributions to surrogate models, materials development, and self-driving laboratories. Education: B.S. Chemistry (NCSU), M.S./Ph.D. Chemical Engineering (University of Delaware). Postdoctoral fellowship at the Fritz Haber Institute (Berlin). Tenure-track faculty since 2006. Research interests include catalyst design, energy storage, molecular simulation, and AI-driven experimentation. Key projects involve optimizing hydrogen infrastructure and accelerating discovery via graph neural networks and surrogate modeling. Awards include DOE Early Career Award (2010), Presidential Early Career Award (2011), and AIChE Innovation Award (2023). He holds the John E. Swearingen Professorship and has authored over 100 peer-reviewed articles. Labs/Teams: Kitchin Research Group, collaborating with industry and academia on materials and energy projects. Develops open-source tools like litdb and Claude-Light for scientific automation.

Yuejie Chiمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Signal Processing
• +۴ مورد دیگر

Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with affiliations in the Machine Learning Department and CyLab. She holds a Ph.D. from Princeton University and a B.Eng. (Hon.) from Tsinghua University. Her research focuses on theoretical foundations of data science, machine learning, signal processing, and inverse problems, with applications in sensing, imaging, and AI systems. Her work emphasizes interdisciplinary approaches at the intersection of statistics, optimization, and sensing. Notable achievements include contributions to high-dimensional structured signal processing, for which she received the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award (2019) and the PECASE (2019). She is an IEEE Fellow (2023) and has been recognized as a Goldsmith Lecturer (2021) and Distinguished Lecturer (2022). Research interests include generative AI, reinforcement learning, nonconvex optimization, federated learning, and resource-efficient algorithms. Her group collaborates on projects such as data-driven materials research and agile waveform design for communication networks. She has advised numerous PhD students, many of whom have pursued academic and industry roles in leading institutions. Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024), NSF/ONR/AFOSR young investigator awards, and leadership roles in top conferences like NeurIPS, ICML, and IEEE workshops.

Carlee Joe-Wongمشاهده پروفایل

دانشیار

• Network economics
• Data pricing
• Resource allocation in computer networks
• +۵ مورد دیگر

Carlee Joe-Wong is the Robert E. Doherty Career Development Associate Professor in the Electrical and Computer Engineering department at Carnegie Mellon University (CMU), part of the College of Engineering. She leads the LIONS research group (Learning, Incentives, and Optimization in Networked Systems), focusing on mathematical and economic aspects of computer and information networks. Her work emphasizes practical system deployments, such as her co-founded startup DataMi, which commercialized smart data pricing (SDP) solutions deployed globally by ISPs like AT&T and Airtel. Previously, she held roles at Princeton University (Ph.D., M.A., A.B. in Mathematics/Applied Mathematics) and served as Director of Advanced Research at DataMi (2013–2014). Her research spans network economics, edge computing, federated learning, and autonomous vehicle policy. Notable contributions include foundational work on burstable cloud instances, dynamic pricing mechanisms, and resilience in mixed-autonomy transportation systems. Awards include the INFORMS ISS Design Science Award (2014), Best Paper at IEEE INFOCOM (2012), and DOE Early Career Award (2024). She has advised numerous industry collaborations and contributed to standards in distributed learning and networked systems through initiatives like the Fog Computing framework and federated learning benchmarks. Education: Ph.D. (2016), M.A. (2013), and A.B. (2011) in Applied Mathematics from Princeton University. Active in policy briefs on autonomous vehicles and energy-efficient computing systems.

Richard Sternمشاهده پروفایل

استاد

• Robust Speech Recognition
• Auditory Perception
• Signal Processing
• +۲ مورد دیگر

Professor Richard Stern holds joint appointments in Electrical and Computer Engineering, Computer Science, and Biomedical Engineering at Carnegie Mellon University. His research bridges auditory perception with speech technology, particularly in developing noise-robust speech recognition systems. His PNCC algorithm revolutionized feature extraction for speech recognition in noisy environments. Research emphases include: Auditory-inspired signal processing Robust automatic speech recognition Computational models of binaural hearing Music information retrieval Biomedical applications of audio analysis Recent work demonstrates growing interest in human-robot interaction and respiratory monitoring applications. Publications increasingly incorporate deep learning while maintaining foundations in auditory physiology. Honors include the IEEE Signal Processing Society 2019 Best Paper Award for PNCC research. Current projects investigate neural audio processing models and online learning for sound event detection.

Jian Maمشاهده پروفایل

استاد

• Computational Biology
• AI/ML in Genomics
• Nuclear Organization
• +۴ مورد دیگر

Jian Ma is the Ray and Stephanie Lane Professor of Computational Biology in the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University's School of Computer Science, based in the Gates Hillman Center. His research focuses on developing AI/ML methods to study human genome structure, cellular organization, and their implications for health and disease. Primary research areas include nuclear architecture, single-cell epigenomics, spatial omics, and molecular interactions. The lab employs probabilistic modeling and deep learning techniques (especially graph representation learning) to analyze multi-scale biological systems, with recent exploration of large language models for decoding gene regulatory mechanisms. Dr. Ma leads an NIH Center in the 4D Nucleome (4DN) Program and participates in the NIH SenNet and IGVF Consortia. He advises multiple PhD students and researchers working on computational biology and machine learning projects.

Osman Yaganمشاهده پروفایل

استاد پژوهش

• Communication Systems
• Network Science
• Cyber-Physical Systems
• +۱۶ مورد دیگر

Osman Yagan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate roles in the School of Computer Science and core membership in CyLab Security and Privacy Institute, IDeaS, and Block Center. His research spans modeling, analysis, and optimization of computing systems using tools from applied probability, network science, and machine learning. PhD in Electrical and Computer Engineering, University of Maryland (2011) BS in Electrical and Electronics Engineering, Middle East Technical University (2007) Research Interests: Dr. Yagan focuses on statistical inference , resilient distributed machine learning , robustness of cyber-physical systems , secure IoT networks , and contagion processes in complex networks, including misinformation spread and epidemic modeling. His work integrates multi-armed bandit algorithms , reinforcement learning , and stochastic geometry . Scientific Contributions: He has received multiple fellowships (CIT Dean’s Early Career, IBM Academic Award), best paper awards (ICC 2021, IPSN 2022, ASONAM 2023), and co-authored 15+ recent articles on topics like federated learning, contagion dynamics, and network robustness. His research is funded by ONR, NSF, ARO, AFOSR, and industry grants. Advising: Dr. Yagan has mentored 23 PhD and Master’s students, including junior faculty at Nanyang Technological University and researchers at Meta, Intel, Bloomberg, and MIT. He has taught courses such as Advanced Probability & Statistics for Engineers and Applied Stochastic Processes. Collaborations: He works with institutions including Princeton, MIT, and industry partners like IBM and Intel, and is a Senior Member of IEEE.

Zhihao Jiaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Systems
• Quantum Computing
• +۳ مورد دیگر

Zhihao Jia is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU). He is affiliated with the CMU Catalyst Group and the Parallel Data Lab, focusing on advancing systems for machine learning, quantum computing, and large-scale data analytics. Previously, he was a research scientist at Facebook and earned his PhD from Stanford University (2020), advised by Alex Aiken and Matei Zaharia. His bachelor's degree is from Tsinghua University's Special Pilot CS Class under Andrew Yao. His research emphasizes accelerating deep learning computations on modern hardware and optimizing quantum circuits for intermediate-scale quantum devices. Notable contributions include speculative reasoning techniques, efficient LLM serving systems, and quantum circuit simulators. He teaches advanced courses such as 15418 and 15618 at CMU. Zhihao advises PhD students including Zhuoming Chen, Zikun Li, and Xinhao Cheng. His work bridges systems research with emerging applications, aiming to enhance computational efficiency and scalability. He collaborates on projects like Specexec, Helix, and Atlas, addressing challenges in distributed computing and quantum simulation.

Hanghang Tongمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Hanghang Tong is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), holding the title of University Scholar. He specializes in large-scale data mining, machine learning, and AI with a focus on graph and multimedia data. His research addresses applications in social networks, healthcare, cybersecurity, and cyber-physical systems. Education: PhD and M.Sc. in Machine Learning from Carnegie Mellon University (2008–2009). Prior to UIUC, he was an Associate Professor at Arizona State University. Research: Leads the IDEA Lab, focusing on network robustification, fair network learning, and multi-network alignment. Key areas include optimizing graph connectivity, algorithmic fairness, and developing tools like FASTEN and Sylvester Equation solvers. Awards: IEEE Fellow (2021), NSF CAREER Award (2017), multiple best paper awards, and the ICDM 10-Year Highest Impact Paper Award (2015). Editor-in-Chief of ACM SIGKDD Explorations and associate editor of ACM Computing Surveys. Teaching: Courses include CS512 (Data Mining Principles), CS514 (Advanced Network Science), and CS412 (Introduction to Data Mining). Authored influential textbooks like Data Mining: Concepts and Techniques (4th ed., 2022). Service: Organized key conferences (CIKM, DSAA) and workshops on adversarial activity modeling. Active in academic leadership and editorial roles. Labs/Teams: Runs the IDEA Lab, collaborating on projects like NetFair (fair network learning) and Network Correspondence Mining. Supervises a vibrant group of PhD and Master’s students in graph algorithms and data science.

Zach Bransonمشاهده پروفایل

دانشگاهی

• Experimental Design
• Causal Inference
• Nonparametric Methods
• +۸ مورد دیگر

Zach Branson is an Associate Teaching Professor and Assistant Director for the Undergraduate Program at Carnegie Mellon University's Department of Statistics & Data Science within the Dietrich College of Humanities and Social Sciences. His work bridges theoretical and applied statistics, with a focus on experimental design and causal inference. PhD in Statistics , Harvard University (2019) BS in Economics and Statistics , Carnegie Mellon University (2014) BA in Professional Writing , Carnegie Mellon University (2014) Branson's research centers on experimental design and causal inference, particularly addressing questions like "Does a treatment cause a change in outcomes?" His methodological interests include covariate balance , matching , randonization tests , and regression discontinuity designs . Applications span education , epidemiology , mental health , and text analysis . His recent publications emphasize causal inference in educational and social sciences, with methodological innovations in rerandomization, propensity score trimming, and regression discontinuity designs. Key themes include improving covariate balance, handling continuous treatments, and applying Bayesian nonparametric approaches to spatial data. NSF Graduate Research Fellowship (for PhD work on experimental design) Branson actively engages in teaching causality and statistical communication to undergraduates, advocating for capstone courses (e.g., 36-490, 36-493, 36-497) to foster practical data science skills. He also contributed to public-facing statistical pedagogy through "data science portfolio" projects.