جست‌وجوی استاد و پژوهشگر

Sai Qian Zhangمشاهده پروفایل

استادیار

• Efficient AI Algorithm
• AR/VR Computing
• Neural Network Accelerator Design
• +۲ مورد دیگر

Sai Qian Zhang is an Assistant Professor at New York University, holding dual appointments in the Electrical and Computer Engineering Department at NYU Tandon School of Engineering and the Computer Science Department at the Courant Institute of Mathematical Sciences. He earned his Ph.D. from Harvard University in 2021 and completed his B.A.Sc and M.A.Sc at the University of Toronto. Previously, he worked as a Senior Research Scientist at Meta Reality Labs (2022–2024). His research focuses on algorithm and hardware co-design for efficient deep neural network implementation, with a particular emphasis on AR/VR computing. Key areas include neural network accelerator design, efficient AI algorithms, and privacy-preserving techniques for AR/VR systems. He leads the System & Artificial Intelligence (SAI) Lab, which explores intersections between deep learning and hardware systems to optimize AI performance and resource efficiency. Recent research highlights include papers on gaze-tracked foveated rendering, speculative decoding strategies, and parameter-efficient fine-tuning for large models. He has advised numerous students, including recipients of prestigious awards such as the DAC Young Fellow and ECE Myron M. Rosenthal Award. Zhang teaches courses like Efficient AI and Hardware Accelerator Design (Spring 2025), emphasizing model compression, quantization, and accelerator architectures. His work has been recognized through grants and collaborations with industry partners like Meta and Andes Technology.

• Deep Learning
• Hardware Architecture
• Computer Networks
• +۳ مورد دیگر

Brad McDanel is an Assistant Professor of Computer Science specializing in the intersection of deep learning, hardware architecture, and computer networks. His research focuses on optimizing deep neural networks (DNNs) through techniques like systolic array implementations, sparse architectures, and quantization. He has contributed to accelerating DNN inference on edge devices and distributed systems, with recent work addressing challenges in Large Language Models (LLMs) and multi-modal architectures. Education: Ph.D., Computer Science, Harvard University (2019) M.Sc., Computer Science, Wake Forest University (2012) B.Sc., Computer Science, Wake Forest University (2010) Research interests include speculative decoding, transformer optimization, and efficient deployment of AI systems. His work bridges theoretical frameworks and practical implementations, emphasizing computational efficiency and scalability. Notable contributions include innovations in systolic array optimization and distributed DNN architectures. Publications span conferences like ASPLOS, ICS, and ICDCS, with a focus on algorithmic and hardware-level optimizations for AI systems. He has pioneered techniques such as BranchyNet and Embedded Binarized Neural Networks, which enhance inference speed and resource utilization.

Yufei Dingمشاهده پروفایل

دانشیار

• Quantum Computing
• Machine Learning
• Compiler Optimization
• +۳ مورد دیگر

Yufei Ding is an Associate Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD), and founder of the PICASSO Lab. She earned a Ph.D. in Computer Science from North Carolina State University and a B.S. in Physics from the University of Science and Technology of China. Research Interests: Quantum Computing Machine Learning Domain-Specific Languages Compiler Optimization Hardware Acceleration High-Performance Computing Her recent publications focus on quantum compilation, error correction, and machine learning systems, with a particular emphasis on hardware-aware optimizations. She has received multiple prestigious awards, including the NSF CAREER Award (2020) and the IEEE TCHPC Early Career Researchers Award (2019). Scientific Awards: NSF CAREER Award (2020) IEEE Computer Society TCHPC Early Career Researchers Award (2019) Yufei actively advises Ph.D. students and postdoctoral researchers in quantum computing and machine learning systems. Her lab offers openings for both quantum computing and machine learning research.

• Computer Architecture
• Operating Systems
• Memory Management
• +۵ مورد دیگر

Youngjin Kwon is an Associate Professor at the School of Computing, KAIST, and a member of the Computer Architecture and Systems Lab (CASYS). His work bridges systems research with practical applications in memory management, concurrency debugging, and energy-efficient computing. Recipient of Best Paper Awards at ACM SOSP'24, SOSP'21, and USENIX ATC'18 Honored with KAIST's EWON Endowed Chair Professor (2021) and Soo-Young Lee Teaching Innovation Award (2021) Awarded KAIST Breakthrough (2023) and Technology Innovation Award (2023) Research focuses on: Processing-in-Memory (PIM) and DRAM architectures Concurrency bugs in kernel and hypervisor systems Energy-efficient frameworks for large language models Memory disaggregation and tiered memory systems Trusted Execution Environments (TEE) for secure computing SmartNIC offloading and eBPF-based optimizations His recent publications highlight innovations in speculative decoding, memory safety, and system scalability. Awards reflect both technical excellence and pedagogical impact. He actively mentors graduate and undergraduate students in his lab, emphasizing hands-on research in cutting-edge system design.

Amir Gholaminejadمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Neural Networks
• Scientific Machine Learning
• +۴ مورد دیگر

Amir Gholaminejad is a Research Fellow at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab and Sky Lab, and co-director of the Pallas Lab. He earned his PhD from UT Austin, where his dissertation on large-scale bio-physics-based image segmentation won the university's 2018 Outstanding Dissertation Award. His research spans quantized neural networks , neural ODEs , large-scale agentic systems , and scientific machine learning . Melosh Medal Finalist Amazon Machine Learning Research Award (2020) Best Student Paper Award (SC'17) Gold Medal (ACM Student Research Competition) Research trends focus on Transformer optimization , context length extension , memory-efficient AI , and physics-informed neural networks . He mentors PhD, Masters, and undergraduate students, with alumni now at institutions like Microsoft, Apple, and Google. Recent work includes SqueezeLLM and LLMCompiler for ICML'24, and he teaches AI Systems at Berkeley.

Peyman Mirtaheriمشاهده پروفایل

استاد

• Biomedical Engineering
• Neurotechnology
• Sensor Technology
• +۴ مورد دیگر

Peyman Mirtaheri is a full Professor of Biomedical Engineering at Oslo Metropolitan University's Faculty of Technology, Art and Design, leading the NIRS/optical lab established in 2009. He holds an adjunct professorship at Michigan Technological University's Biomedical Engineering department. His research focuses on developing novel sensor technologies for brain activity detection and applying fNIRS/EEG to study motion and balance in the cerebral cortex. As PI, he oversees major projects like PACER (RCN-funded), MgSafe (Horizon 2020), and Future Running Shoes (IP-N collaboration with Gaitline AS). He teaches medical sensors and actuators in the ACIT master's program and leads the ADEPT research platform integrating health intelligence and brain-inspired technologies across multiple faculties. Education details are not explicitly listed in the provided texts, but his academic roles imply advanced credentials in biomedical engineering. His research spans interdisciplinary domains including neurotechnology, wearable sensors, and clinical applications of brain monitoring. Over 70 publications demonstrate expertise in fNIRS signal processing, neuromorphic computing, and user-centered biomedical design. Professional networks include active Twitter (@PMirtaheri) and LinkedIn profiles. Current projects emphasize translational research and ethical design considerations in neurotechnology. His work bridges engineering, health sciences, and arts through collaborative platforms like ADEPT, addressing both technical innovation and societal impact.

Andrea Zanetteمشاهده پروفایل

استادیار

• Reinforcement Learning
• Machine Learning
• Decision Making
• +۲ مورد دیگر

Andrea Zanette is an incoming Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. His research focuses on the theoretical foundations of reinforcement learning, particularly in data-efficient exploration, function approximation, adaptivity, and offline learning. He received his Ph.D. from Stanford University in 2021. Research interests center on: Fundamental limits of reinforcement learning efficiency Robust decision-making under uncertainty Theoretical guarantees for offline learning Efficient exploration strategies Stable learning algorithms His publications establish fundamental results in reinforcement learning theory, including provable efficiency bounds for offline RL, theoretical characterization of exploration challenges, and algorithm stabilization methods. Recent work develops techniques for language model reasoning optimization and evaluation acceleration. He received the Gene Golub Outstanding Dissertation Award for his Ph.D. work on modern RL challenges.

Mohamed Zahranمشاهده پروفایل

استاد بالینی

• Parallel computing
• Heterogeneous architectures
• Hardware/Software interaction
• +۷ مورد دیگر

Mohamed Zahran is a Clinical Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland (2003) and has extensive experience in academia and industry, including roles at CUNY and The George Washington University. His research focuses on computer architecture, heterogeneous systems, parallel computing, and AI-driven hardware optimization. Education: Ph.D., Electrical and Computer Engineering, University of Maryland (2003); M.Sc., Cairo University (1999); B.Sc., Cairo University (1997). Research interests include exascale computing, GPU architectures, cache hierarchy design, and AI support for architecture. He has authored over 40 refereed papers and a book on heterogeneous computing. Awards include ACM Distinguished Speaker (2019–2025) and IEEE Distinguished Contributor (2021). Teaching: Courses on multicore processors, GPUs, parallel computing, and compilers. Advised numerous graduate and undergraduate students, including notable achievements in student research competitions.

Kangwook Leeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Information Theory
• +۵ مورد دیگر

Professor Kangwook Lee is a faculty member in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His research focuses on machine learning, deep learning, information theory, and coding. He holds a PhD from UC Berkeley and has received numerous awards including the NSF CAREER Award and Amazon Research Awards. His work spans theoretical advancements and practical applications in AI, federated learning, and data privacy. Education: PhD 2016, University of California-Berkeley MS 2012, University of California-Berkeley BS 2010, KAIST (Korea Advanced Institute of Science and Technology) Research Interests: Machine Learning: Focuses on improving model efficiency, generalization, and robustness in large language models (LLMs) and neural networks. Information Theory: Explores coding techniques and optimization strategies for distributed systems and federated learning. Federated Learning: Develops privacy-preserving methods and algorithms for decentralized data environments. Recent Research Trends: His publications emphasize LLM capabilities, parameter-efficient fine-tuning, and novel approaches to federated learning. Recent efforts include benchmarking LLM agents in video games and advancing transformer architectures for length generalization. Awards: 2024 NSF CAREER Award 2024 Amazon Research Award 2023 MLSys Best Paper Award Multiple fellowships from Brain Korea 21, GCORE, and KSEA Advising & Teaching: Teaches graduate courses in artificial neural networks, ethics of data engineering, and research methodologies. Supervises students in machine learning and federated learning projects.

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.

• Machine Learning
• Inference Acceleration
• World Models
• +۴ مورد دیگر

Hongyang Zhang is a tenure-track Assistant Professor at the University of Waterloo's David R. Cheriton School of Computer Science and Faculty of Mathematics, affiliated with the Vector Institute for AI. His research bridges theoretical and applied aspects of machine learning, including world modeling, AI inference acceleration, and security. He leads the SafeAI Lab and holds memberships in the AI Institute and Cybersecurity and Privacy Institute. Dr. Zhang completed his Ph.D. at Carnegie Mellon University's Machine Learning Department and conducted postdoctoral research at Toyota Technological Institute at Chicago. His educational background includes a degree from Peking University. Research interests focus on: Developing generative world models for robotics and autonomous systems Creating efficient algorithms for accelerating LLM inference Building System-2 LLMs for enhanced reasoning Advancing AI security against adversarial threats Publications demonstrate strong focus on efficient AI systems and security, with recent work on EAGLE series acceleration techniques and The Matrix world generation framework dominating top conferences like ICML, NeurIPS, and CVPR. Awards and honors: 1st place in CVPR 2021 Security AI Challenger Multiple NeurIPS challenge wins in adversarial vision AAAI New Faculty Highlights awardee IEEE Senior Member Leads SafeAI Lab focusing on trustworthy AI systems and regularly serves as area chair for top machine learning conferences including NeurIPS, ICML, and ICLR.

Dean M. Tullsenمشاهده پروفایل

استاد

• Computer Architecture
• Multithreading
• Multicore Processors
• +۷ مورد دیگر

Dean M. Tullsen is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. He leads the High Performance Processor Architecture and Compilation Lab and focuses on computer architecture, particularly multithreading, multicore processors, secure execution, and energy-efficient design. His research bridges hardware and software for parallel computing, security, and resource optimization. Education: Ph.D. from the University of Washington (1996), B.S. and M.S. from UCLA. Research Interests: Computer architecture, multithreading, speculative execution, hardware security, energy efficiency, and heterogeneous computing. Scientific Awards: Best Paper Awards at USENIX Security Symposium (2024), IEEE S&P (2023), and Distinguished Paper Awards at POPL (2021), ASPLOS (2023), plus the 2020 ACM SIGARCH/IEEE-CS TCCA Influential ISCA Paper Award. Students: Advised 30+ graduate students, including Craig Disselkoen, Mohammadkazem Taram, and Ashish Venkat, who now hold positions at Google, Microsoft, and academia. Lab: Leads the High Performance Processor Architecture and Compilation Lab, advancing techniques in secure execution and parallel computing.

Hong Zhangمشاهده پروفایل

استادیار

• Large-scale data analytics
• Distributed ML training and serving systems
• Application and network scheduling
• +۵ مورد دیگر

Hong Zhang is an Assistant Professor at the University of Waterloo, specializing in large-scale data analytics, distributed machine learning systems, and cloud computing. His research focuses on advancing robust and efficient methods for modern AI systems, including topics like adversarial robustness, zero-knowledge proofs for deep learning, and scalable inference techniques for large language models. He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology and dual bachelor's and master's degrees in Electronics and Communications Engineering from Huazhong University of Science and Technology. Education: Ph.D., Computer Science and Engineering, Hong Kong University of Science and Technology (2019) B.Sc. and M.Sc., Electronics and Communications Engineering, Huazhong University of Science and Technology, China (2013) Research Interests: Hong's work bridges theoretical foundations and practical applications in distributed ML systems, data center networking, and secure AI. Key areas include optimizing large-scale training/serve pipelines, mitigating adversarial vulnerabilities, and developing privacy-preserving techniques like zero-knowledge proofs for deep learning. His contributions span both algorithmic innovations and real-world system implementations. Publications Trends: Recent work emphasizes scalable inference acceleration (e.g., Eagle series), trustworthy AI (e.g., generative model guidelines), and cryptographic safeguards for ML (e.g., zkdl/zkllm frameworks). He frequently explores intersections between security, efficiency, and scalability in modern AI systems. Awards/Grants: No specific awards or grants mentioned in available texts. Advising & Labs: While student names aren't listed, his research group likely focuses on distributed systems and secure ML. Active collaborations likely involve cloud computing and adversarial machine learning domains.

Mihai Surdeanuمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۲ مورد دیگر

Mihai Surdeanu is an Associate Professor in the Department of Computer Science at the University of Arizona. His academic work focuses on advancing natural language processing and machine learning techniques, with a particular emphasis on large language models, information extraction, and model efficiency. He can be reached at msurdeanu@arizona.edu. Research Interests: Natural Language Processing Machine Learning Deep Learning Information Extraction Artificial Intelligence Scientific Trends: His recent work explores critical challenges in large language models, including data contamination detection, adversarial perturbation defense, quantization methods, and reasoning robustness. Publications highlight techniques like prompt chaining, layerwise optimization, and speculative generation to improve model performance and interpretability.

Michael Mahoneyمشاهده پروفایل

استاد

• Machine Learning
• Big Data
• Statistical Analysis
• +۵ مورد دیگر

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.