جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Generative Modeling
• Scientific Computing
• +۳ مورد دیگر

Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.

Peter Scupelliمشاهده پروفایل

دانشیار

• Design Futures
• Learning Environments
• Sustainable Design
• +۶ مورد دیگر

Peter Scupelli is an Associate Professor of Design and Director of the Learning Environments Lab at Carnegie Mellon University's School of Design. He previously served as the Nierenberg Professor (2019-2022) and Visiting Professor at Politecnico di Milano (2022-2024). His work bridges learning environments , design futures , and zero-carbon transitions , co-founding the Global Design Futures Network (GDFN) . Education: Ph.D. in Human-Computer Interaction, Carnegie Mellon University (2009) M.S. in Human-Computer Interaction (2006) M.Des. in Interaction Design (2002) Laurea in Architecture, Universitá di Genova (1998) Scupelli’s research focuses on integrating Transition Design into practice, aligning short-term design actions with long-term sustainability goals. His pedagogy combines Design Thinking and Futures Thinking , emphasizing value embedding and socio-ecological contexts. Recent articles explore trends like digital transformation , blended learning , and climate disaster response . Scientific Awards: Nierenberg Professor (2019-2022) Professional Activities: Scupelli teaches undergraduate and doctoral courses, including Design Futures and Zero-Carbon Lifestyles . He leads the Learning Environments Lab and advises on the Sustainability Initiative Advisory Council at CMU.

Bruno Sinopoliمشاهده پروفایل

استاد

• Cyber-Physical Systems (CPS)
• Networked Control Systems
• Smart Infrastructures
• +۷ مورد دیگر

Bruno Sinopoli is a Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University, with courtesy appointments in the Robotics Institute and Mechanical Engineering Department. He holds affiliations with ICES, CyLab, and the Pittsburgh Digital Studio (PDL), and co-directs the Smart Infrastructure Institute (SII). His research focuses on cyber-physical systems, networked control systems, and resilient infrastructure, addressing challenges in smart grids, wireless sensor networks, and cybersecurity. Education: Ph.D. (2005), M.S. (2003), and Laurea (1998) in Electrical Engineering from UC Berkeley, Università di Padova, and University of California, Berkeley, respectively. Notable awards include the George Tallman Ladd Research Award (2010), NSF Career Award (2010), and Eliahu Jury Award (2006). Research Interests include networked embedded control systems, cybersecurity for critical infrastructure, and smart city technologies. He has led projects funded by NSF, NIST, and industry, such as a $3.2M NIST grant for first responder communication systems and NSF CRISP grants for infrastructure resilience. Key contributions include frameworks for secure control systems, resilient sensor networks, and adaptive video streaming algorithms. His work bridges theoretical control systems with practical applications in energy, transportation, and urban infrastructure. Students supervised include doctoral candidates like Dragana Bajovic (A.G. Milnes Award winner) and Yilin Mo, as well as master’s students in localization and control systems. Active collaborations span robotics, mechanical engineering, and interdisciplinary initiatives in smart infrastructure.

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.

John Paul Shenمشاهده پروفایل

استاد

• Modern Processor Design
• Instruction-Level Parallelism
• Dependable Computing
• +۳ مورد دیگر

John Paul Shen is a Distinguished Service Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), based at the Silicon Valley campus. With over three decades of experience in academia and industry, he has held prominent roles including founding director of Nokia Research Center-North America Lab (2007–2012) and director of Intel’s Microarchitecture Research Lab (2000–2006). His academic career includes a tenured professorship at CMU’s ECE Department from 1990–2000 and a return to CMU in 2015 as a tenured professor. Education: Ph.D. in Electrical Engineering, University of Southern California M.S. in Electrical Engineering, University of Southern California B.S. in Electrical Engineering, University of Michigan Research Focus: Modern Processor Design: Specializes in superscalar processors, instruction-level parallelism (ILP), and microarchitecture validation tools like VMW (Visualization-Based Microarchitecture Workbench). Neuromorphic Computing: Develops energy-efficient temporal neural networks (TNNs) and CMOS implementations for sensory processing. Edge AI: Focuses on low-precision accelerators, sparsity exploitation, and real-time data analytics for edge devices. Dependable Computing: Leverages idle processor resources for error-checking and fault tolerance. Awards & Impact: Recipient of multiple teaching awards during his CMU tenure. Supervised 17 Ph.D. students and dozens of M.S. students, with notable contributions to compiler design and parallel computing. Authored two influential textbooks, including Modern Processor Design: Fundamentals of Superscalar Processors , used at Stanford. Generated over 100 patents and 200+ papers at Nokia and Intel, with a focus on mobile computing and hardware architecture innovations. Labs & Collaborations: Founded and led the Nokia Research Center-NAL, advancing mobile Internet and computing. Directed Intel’s Microarchitecture Research Lab, advancing IA32/IA64 processor designs. Current research at CMU Silicon Valley focuses on neuromorphic systems, wearable computing, and real-time data analytics.

Philip Koopmanمشاهده پروفایل

دانشیار

• Autonomous Vehicles
• Safety Engineering
• Embedded Systems
• +۵ مورد دیگر

Philip Koopman is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, specializing in autonomous vehicle safety and dependable embedded systems. His work focuses on ensuring safety in self-driving technologies, including standards development (e.g., ANSI/UL 4600), risk assessment, and fault-tolerant software design. Education & Affiliations: Carnegie Mellon University: Faculty in ECE since [year not explicitly listed, but content suggests 20+ years] Principal author of UL 4600 safety standard for autonomous products Former advisor to graduate students (no longer accepting new advisees) Research Interests: Autonomous vehicle safety validation and assurance Reliability of embedded systems software Cyclic redundancy check (CRC) algorithms and error detection Ethical and legal frameworks for automated systems Standards for vehicle automation (e.g., SAE J3016) Key Contributions: Authored influential books on safety in autonomous systems, CRCs, and embedded software Developed safety case methodologies for autonomous vehicles Testified before U.S. Congress on self-driving car safety Labs/Teams: Leads research initiatives on safety-critical systems, including collaboration with industry and government agencies. Maintains active engagement through blogs (Safe Autonomy, Better Embedded Software) and standards bodies.

Vivian H Shenمشاهده پروفایل

دانشگاهی

• Haptics
• Human-Computer Interaction
• Virtual Reality
• +۴ مورد دیگر

Vivian H Shen is an Assistant Teaching Professor at Carnegie Mellon University's Entertainment Technology Center while completing her PhD in Robotics under Prof. Chris Harrison. Her work bridges embedded systems, sensing technology, and immersive VR/AR experiences with a focus on haptic feedback solutions. Education: Pursuing PhD in Robotics at Carnegie Mellon University Research Interests: Haptics, Human-Computer Interaction, Virtual/Augmented Reality, Embedded Systems Current research explores novel haptic modalities beyond traditional vibrotactile feedback, including synthetic jet mid-air haptics and electroosmotic pump-driven wearable arrays. Her work targets practical implementations for consumer VR/AR while advancing tactile immersion through technical characterizations and extensive user studies. Recent publications demonstrate technical innovations in haptic glove miniaturization, mouth-focused tactile rendering, and extended-range touch sensing systems. These contributions span hardware prototyping, algorithm development, and application case studies across extended reality, medical, and automotive domains. Scientific Awards: Jane Street Graduate Research Fellow, Swartz Entrepreneurial Fellow, NSF GRFP Honorable Mention, Best Paper Awards at TOCHI 2023 and CHI 2022 Technical Expertise: VR/AR Systems, Embedded Sensing, Wearable Actuators, Depth Camera Applications

Yang Zhangمشاهده پروفایل

استادیار

• Human-Centered Computing
• Ambient Computing
• Sensing Technologies
• +۷ مورد دیگر

Yang Zhang is an Assistant Professor in the Electrical and Computer Engineering (ECE) department at UCLA, leading the Human-Centered Computing and Intelligent Sensing Lab (HiLab). His research focuses on enhancing ambient computing through novel sensing technologies and energy-harvesting systems, advancing IoT, assistive technologies, and sustainable intelligence. He earned his PhD from Carnegie Mellon University (CMU), with prior work at Microsoft Research and Disney Research. Research Interests: His work spans wearable computing, ubiquitous sensing, and hardware prototyping. Key areas include self-powered sensors (e.g., Sozu), laser-based environment sensing (Vibrosight), and body-centric interfaces (SkinTrack, ActiTouch). He emphasizes practical, inclusive, and energy-efficient solutions. Awards & Recognition: CHI Best Paper (2020: Wireality) CHI Best Paper (2018: Wall++) Qualcomm Innovation Fellowship (2017) Multiple Honorable Mentions at CHI/UIST Teaching: Courses include Circuit Theory, Engineer Interactive Systems, and Electrical & Electronic Circuits. Labs & Teams: Directs the HiLab, collaborating on projects like OptoSense, MorphingCircuit, and Wireality. His lab emphasizes cross-disciplinary innovation at the intersection of hardware and human-computer interaction.

Lori Levinمشاهده پروفایل

استاد پژوهش

• Computational Linguistics
• Low-Resource Languages
• Machine Translation
• +۶ مورد دیگر

Lori Levin is a Research Professor at Carnegie Mellon University's Language Technologies Institute (LTI) within the School of Computer Science. Her research focuses on computational linguistics, particularly documenting endangered languages, linguistic typology, and developing multilingual NLP systems for low-resource scenarios. She co-founded and co-chairs the North American Computational Linguistics Olympiad (NACLO) and leads the LLab, which explores human language and computational modeling. Levin teaches advanced linguistics courses including Grammar Formalisms and ConLanging. Her work emphasizes hybrid approaches combining human-engineered linguistic knowledge with machine learning to address data scarcity challenges. Notable projects include the Spoken Mapudungun corpus (Chilean minority language) and the Linguistic-Core Approach for low-resource language analysis. Research interests span corpus annotation frameworks, interlinear glossing systems, and the application of typological universals to NLP. She has pioneered methodologies for rapid NLP system development in linguistically under-resourced contexts. Levin collaborates internationally on initiatives like the MILE Corpus for Less Commonly Taught Languages and NESPOLE! multilingual speech communication systems.

Alexandra Ionمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Digital Fabrication
• Metamaterials
• +۹ مورد دیگر

Alexandra Ion serves as the Sara Kiesler Assistant Professor in the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University's School of Computer Science. She directs the Interactive Structures Lab, pioneering research at the intersection of HCI, digital fabrication, metamaterials, and computational design to create accessible tools for novice users. Education PhD from Hasso Plattner Institute, Germany Postdoctoral research at ETH Zurich, Switzerland Research Focus Her work centers on developing interactive design tools for digital fabrication of complex structures, emphasizing material discovery through broadened participation. She investigates how embedding functionality within geometry enables structures to react to simple inputs with complex behaviors—supporting applications in programmable matter, accessibility, and adaptive interfaces. Key themes include making advanced fabrication technologies accessible while advancing high-tech materials through user-centered innovation. Publication Evolution Her scholarly output reveals a trajectory from foundational metamaterial mechanisms (2016) toward integrated physical AI systems (2025). Early work established computational frameworks for mechanical metamaterials, while recent publications demonstrate applied implementations in rehabilitation robotics and unobtrusive adaptive environments. This progression reflects consistent focus on human-centered physical computing, with increasing emphasis on real-world impact in assistive technology and intelligent everyday objects. Scientific Recognition NSF CAREER Award for Material-Changing Interfaces Best paper award at ACM PACM (ISS’23) UIST 2023 Best Demo Award (Jury's choice) Sara Kiesler Junior Development Chair (2023-2024) Rising Stars in EECS 2019 honoree Multiple Cannes Lions awards for interactive media Mentorship and Funding She actively advises graduate students including Yuyu Lin, Violet Han, Jesse Gonzalez, and Yujia Liu, guiding research in physical AI and rehabilitation technology. Her work receives substantial support through the NSF CAREER Award and institutional funding, enabling development of full-stack research prototypes that bridge software systems and physical interfaces. She emphasizes inclusive participation in material innovation through her lab’s design philosophy. Interactive Structures Lab The lab develops computational methods and innovative materials that seamlessly blend computation, fabrication, and robotics. Current projects include unobtrusive physical AI systems for everyday environments, personalized rehabilitation orthoses, and human-scale robotic surfaces. Her team’s work has been exhibited at Ars Electronica Center (Linz) and through international traveling exhibitions, demonstrating real-world viability of metamaterial applications.

Jeffrey Ichnowskiمشاهده پروفایل

استادیار

• Robotics
• Motion Planning
• Grasping Algorithms
• +۴ مورد دیگر

Jeffrey Ichnowski is an Assistant Professor at Carnegie Mellon University's Robotics Institute. He holds a Ph.D. from the University of North Carolina at Chapel Hill and a B.A. from UC Berkeley. His research focuses on robot motion and grasp planning using cloud computing, optimization, and deep learning. He leads the Momentum Robotics Lab and has advised students including Hongyi Chen and Bardienus Duisterhof. Notable work includes developing FogROS2 for cloud robotics and achieving superhuman surgical peg transfer using deep learning. Awards include the 2022 Best Systems Paper for untangling long cables. His lab explores applications in soft robotics, hair care, and deformable object manipulation. Recent projects include MOE-Hair (finalist at HRI 2025) and dynamic pen-spinning by soft robots.

Apurva Gandhiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Apurva Gandhi is a Researcher at Carnegie Mellon University , affiliated with the School of Computer Science and the Computer Science Department. Their research focuses on Artificial Intelligence with applications in program synthesis , agentic tasks , and handwritten content analysis . Research Interests : Artificial Intelligence Machine Learning Natural Language Processing Program Synthesis Publications highlight expertise in web agents , AI-driven database systems , deepfake detection , and sequence modeling for cybersecurity . Contact : apurvag@andrew.cmu.edu

Seth Goldsteinمشاهده پروفایل

دانشیار

• Programming Languages
• Systems
• Computer Architecture
• +۹ مورد دیگر

Seth Goldstein is an Associate Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on ensembles (massively scalable systems), distributed systems, reconfigurable computing, and programmable matter (Claytronics). He leads projects like BoLT (a decentralized lending platform) and Phoenix (computing without traditional processors). He has advised numerous graduate students and teaches courses including Compiler Design, Computer Architecture, and Cloud Computing. Research Interests : Goldstein explores systems with massive interacting components, such as molecular-scale devices, modular robotics, and economic systems. His work spans compilers (e.g., register allocation optimizations), time synchronization protocols for embedded systems, and algorithms for self-reconfiguring robots. Recent projects include BlockGraph (a secure distributed ledger) and peer code review methodologies. Key Contributions : Goldstein's work on Claytronics pioneered programmable matter, enabling shape-shifting materials. His BoLT platform addresses lending market inefficiencies using blockchain principles. He has also developed algorithms for modular robot coordination and energy-efficient self-reconfiguration. Labs/Teams : Goldstein directs research in the CMU Robotics Institute and collaborates with interdisciplinary teams on projects like the PipeRench and Phoenix architectures. His lab explores intersections between nanotechnology, distributed systems, and societal applications like new monetary systems.

Justin Chanمشاهده پروفایل

استادیار

• Digital Health Systems
• Mobile and Wearable Systems
• Wireless Sensing
• +۶ مورد دیگر

Justin Chan is an Assistant Professor at Carnegie Mellon University, affiliated with the School of Computer Science (S3D Department) and the College of Engineering's Electrical and Computer Engineering Department. He leads the Semantic Signals Lab, focusing on creating mobile systems for physiological intelligence and augmented perception. His research spans AI-enabled healthcare systems, real-time mobile intelligence, and acoustic-tactile sensing for robotics and accessibility. Education: Ph.D. (expected 2023) in Computer Science & Engineering from the University of Washington, M.S. (2018) from the same institution, and a B.A. (2015) in Computer Science from Dartmouth College with high honors. Research Interests : His work combines wireless sensing, deep learning, and signal processing to develop technologies like smartphone-based ear infection detection (FDA-listed), newborn hearing screening (TUNE initiative in Kenya), and cardiac arrest detection via smart speakers. These innovations emphasize equitable healthcare access and real-world impact. Awards : SIGMOBILE Doctoral Dissertation Award Runner Up IEEE Pervasive Computing Emerging Rockstar American Academy of Audiology Research Award Grants & Projects : NIH-backed TUNE project, partnerships with NGOs like the Global Foundation for Children with Hearing Loss, and collaborations with startups like Wavely Diagnostics (FDA-listed ear infection tech) and Sound Life Sciences (acquired by Google). Leads development of privacy-preserving protocols for contact tracing (CovidSafe/WA Notify). Labs & Teams : Director of the Semantic Signals Lab, focusing on interdisciplinary projects merging hardware, software, and clinical applications.

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.