Faculty

Institution

Unit

Venue

Peng Li

Professor

University of California, Santa Cruz

Affiliations

Baskin School of Engineering

Electrical and Computer Engineering

Peng Li is currently serving as a Baskin School Of Engineering Professor at University of California, Santa Cruz.

Honors

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Experiences

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Education

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Peng Li

University of California, Santa Cruz

Professor

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electrical engineering systems engineering machine learning computer science computer engineering computer-aided design power electronics computational neuroscience deep learning parallel computing design automation modeling and simulation cognitive neuroscience neural networks circuit design neural engineering analog design neural computation electronics automatic control hardware acceleration electronic design engineering optimization thermal engineering vlsi technology timing analysis power engineering electronic design automation accelerator technology circuit simulation scientific computing power delivery computer-aided engineering low-power electronics neuroinformatics advanced computing accelerated design numerical simulations gpu computing processor architectures circuit analysis emerging memory technologies neurocomputers yield optimization liquid state machine power delivery networks dynamic stability field programmable gate array vlsi circuit thermal modeling thermal analysis neuromorphic engineering radio frequency recurrent neural networks timefrequency analysis support vector machines parallel processing self-organization power system optimization energy optimization vlsi design nonvolatile memristor memories exponential stabilization memristive neural networks rf circuits on-chip voltage regulation parameter dimension reduction systolic-array acceleration neural processors ic power delivery nonlinear mixed-signal circuits neural accelerators impedance extraction cybernetics information technology bio-inspired computation advanced intelligent system computational intelligence technology neural network (machine learning) intelligent systems brain modeling learning algorithms brain-like computing

Notable Scholarly Works

Notable Federal Grants

Collaborators

Pareto Optimization of Analog Circuits Using Reinforcement Learning. ACM Transactions on Design Automation of Electronic Systems (2024)

Karthik Somayaji NSPeng Li

electronic design automationreinforcement learningelectrical engineeringpareto optimizationengineeringdesign automation

Semi-Supervised Learning of Dynamical Systems With Neural Ordinary Differential Equations: A Teacher-Student Model Approach. Proceedings of the AAAI Conference on Artificial Intelligence (2024)

Yu WangYuxuan YinKarthik Somayaji NSJán DrgoňaMalachi SchramMahantesh HalappanavarFrank LiuPeng Li

machine intelligencesemi-supervised learningartificial intelligencecomputational intelligencemachine learningcomputer science

AUTONF: Automated Architecture Optimization of Normalizing Flows With Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution. Proceedings of the AAAI Conference on Artificial Intelligence (2023)

Yu WangJán DrgoňaJiaxin ZhangKarthik Somayaji Nanjangud SuryanarayanaMalachi SchramFrank LiuPeng Li

systems engineeringartificial intelligencedeep learningoptimal discrete solutionmachine learningunconstrained continuous relaxationautomated architecture optimization

Contrastive Learning With Consistent Representations. arXiv (Cornell University) (2023)

Wang, ZihuWang, YuHu, HanbinLi, Peng

consistent representationscomputer sciencemachine learningcontrastive learning

Domain-Specific Machine Learning Based Minimum Operating Voltage Prediction Using On-Chip Monitor Data. (2023)

Yuxuan YinRebecca ChenChen HePeng Li

systems engineeringon-chip monitor datacomputer engineeringelectrical engineeringmachine learning

Extreme Risk Mitigation in Reinforcement Learning Using Extreme Value Theory. arXiv (Cornell University) (2023)

NS, Karthik SomayajiWang, YuSchram, MalachiDrgona, JanHalappanavar, MahanteshLiu, FrankLi, Peng

extreme value theoryreinforcement learningextreme risk mitigationbehavioral sciences

HOSNN: Adversarially-Robust Homeostatic Spiking Neural Networks With Adaptive Firing Thresholds. arXiv (Cornell University) (2023)

Geng, HejiaLi, Peng

cognitive neuroscienceneural networkscomputational neurosciencecomputer visiondeep learningmachine learningneuroscienceadaptive firing thresholds

Recognizing Wafer Map Patterns Using Semi-Supervised Contrastive Learning With Optimized Latent Representation Learning and Data Augmentation. (2023)

Zihu WangHanbin HuChen HePeng Li

materials sciencesystems engineeringdata augmentationmechanical engineeringsemi-supervised contrastive learningcomputer visionpattern recognitionimage analysiswafer map patternsmachine learning

Uncertainty Aware Machine-Learning-Based Surrogate Models for Particle Accelerators: Study at the Fermilab Booster Accelerator Complex. Physical Review Accelerators and Beams (2023)

Schram, MalachiRajput, KishansinghNS, Karthik SomayajiLi, PengSt. John, JasonSharma, Himanshu

machine learning

UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities. arXiv (Cornell University) (2023)

Geng, HejiaXu, BoxunLi, Peng

machine intelligencesystems engineeringhuman-computer interactioncomputational linguisticskantian-inspired prompting frameworknatural languagenatural language processingprogramming languagesmachine learninglinguisticslarge language models

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Peng Li

Professor

University of California, Santa Cruz

Affiliations

Baskin School of Engineering

Electrical and Computer Engineering

Peng Li is currently serving as a Baskin School Of Engineering Professor at University of California, Santa Cruz.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Peng Li

University of California, Santa Cruz

Professor

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Pareto Optimization of Analog Circuits Using Reinforcement Learning. ACM Transactions on Design Automation of Electronic Systems (2024)

Karthik Somayaji NSPeng Li

electronic design automationreinforcement learningelectrical engineeringpareto optimizationengineeringdesign automation

Semi-Supervised Learning of Dynamical Systems With Neural Ordinary Differential Equations: A Teacher-Student Model Approach. Proceedings of the AAAI Conference on Artificial Intelligence (2024)

Yu WangYuxuan YinKarthik Somayaji NSJán DrgoňaMalachi SchramMahantesh HalappanavarFrank LiuPeng Li

machine intelligencesemi-supervised learningartificial intelligencecomputational intelligencemachine learningcomputer science

Yu WangJán DrgoňaJiaxin ZhangKarthik Somayaji Nanjangud SuryanarayanaMalachi SchramFrank LiuPeng Li

systems engineeringartificial intelligencedeep learningoptimal discrete solutionmachine learningunconstrained continuous relaxationautomated architecture optimization

Contrastive Learning With Consistent Representations. arXiv (Cornell University) (2023)

Wang, ZihuWang, YuHu, HanbinLi, Peng

consistent representationscomputer sciencemachine learningcontrastive learning

Domain-Specific Machine Learning Based Minimum Operating Voltage Prediction Using On-Chip Monitor Data. (2023)

Yuxuan YinRebecca ChenChen HePeng Li

systems engineeringon-chip monitor datacomputer engineeringelectrical engineeringmachine learning

Extreme Risk Mitigation in Reinforcement Learning Using Extreme Value Theory. arXiv (Cornell University) (2023)

NS, Karthik SomayajiWang, YuSchram, MalachiDrgona, JanHalappanavar, MahanteshLiu, FrankLi, Peng

extreme value theoryreinforcement learningextreme risk mitigationbehavioral sciences

HOSNN: Adversarially-Robust Homeostatic Spiking Neural Networks With Adaptive Firing Thresholds. arXiv (Cornell University) (2023)

Geng, HejiaLi, Peng

cognitive neuroscienceneural networkscomputational neurosciencecomputer visiondeep learningmachine learningneuroscienceadaptive firing thresholds

Recognizing Wafer Map Patterns Using Semi-Supervised Contrastive Learning With Optimized Latent Representation Learning and Data Augmentation. (2023)

Zihu WangHanbin HuChen HePeng Li

materials sciencesystems engineeringdata augmentationmechanical engineeringsemi-supervised contrastive learningcomputer visionpattern recognitionimage analysiswafer map patternsmachine learning

Uncertainty Aware Machine-Learning-Based Surrogate Models for Particle Accelerators: Study at the Fermilab Booster Accelerator Complex. Physical Review Accelerators and Beams (2023)

Schram, MalachiRajput, KishansinghNS, Karthik SomayajiLi, PengSt. John, JasonSharma, Himanshu

machine learning

UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities. arXiv (Cornell University) (2023)

Geng, HejiaXu, BoxunLi, Peng

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