جست‌وجوی استاد و پژوهشگر

• Speech Recognition
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Dr. Michael Mason is a Research Fellow at the Queensland University of Technology (QUT), affiliated with the School of Computer Science . His work bridges advanced signal processing, machine learning, and automotive speech systems, focusing on noise-robust speech recognition and speaker verification frameworks. Education: PhD in Hybrid Coding of Speech and Audio Signals (QUT, 2001) Research Interests span speech recognition, acoustic engineering, machine learning, and human factors in automotive environments. His projects address challenges in in-car speech enhancement, voice activity detection, and dialect mismatch compensation. Key Trends in Publications highlight innovations in i-vector modeling , FPGA-based audio processing , and likelihood-maximizing frameworks . Collaborations with experts like Sridharan and Vogt underscore his role in advancing speaker verification systems. Technical Contributions include developing the QUT-NOISE-TIMIT corpus and pioneering FPGA implementations for real-time speech enhancement.

• Neuromorphic Engineering
• Edge AI
• Computer Architecture
• +۴ مورد دیگر

Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.

Xiaoxuan Yangمشاهده پروفایل

استادیار

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +۳ مورد دیگر

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

Thidapat Chantemمشاهده پروفایل

دانشیار

• Real-time systems
• Embedded systems
• Cyber-physical systems
• +۷ مورد دیگر

Thidapat (Tam) Chantem is an Associate Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He is affiliated with the Virginia Tech Research Center - Arlington and leads the RTX Lab. His research focuses on real-time systems, cyber-physical systems (CPS), and embedded systems security. He holds a Ph.D. from the University of Notre Dame (2011), M.S. from the same institution (2008), and a B.S. from Iowa State University (2005). His work integrates hardware-software co-design principles to address challenges in low-power computing, energy-aware system design, and secure embedded systems. Key projects include developing scheduling frameworks for CPS security, GPU-based real-time DNN inference, and vehicular edge computing solutions. He has secured grants from NSF and industry partners to advance time-sensitive CPS design and emergency response systems. His publications span ~150 articles since 2006, emphasizing real-time scheduling, embedded security, and traffic optimization. Notable contributions include DARIS (GPU spatio-temporal scheduling) and SGPRS (periodic deep learning workload management). He actively collaborates on NSF-funded projects like the Semi-Automated Emergency Response System (CPS Synergy grant). Professional service includes reviewing for top-tier journals/conferences and organizing workshops on real-time systems security. His RTX Lab develops solutions for connected vehicle systems, CPS trustworthiness, and energy-efficient multiprocessor architectures.

Md Maruf Hossain Shuvoمشاهده پروفایل

استادیار

• Edge AI
• Applied Machine Learning
• Deep Learning
• +۳ مورد دیگر

Md Maruf Hossain Shuvo is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). His research focuses on artificial intelligence (AI) and edge computing, particularly in healthcare applications such as AI-driven medical decision support tools, biomedical signal/image analysis, and edge intelligence. He integrates engineering, data science, and medicine to develop hardware designs and experimental validations for cutting-edge AI algorithms. Dr. Shuvo's expertise spans AI-enabled biomedical instrumentation, deep learning for sensors and integrated circuits, and application-specific integrated circuits. His interdisciplinary work addresses challenges in computationally intensive AI techniques, emphasizing real-time healthcare solutions. He is affiliated with UTEP's IDRE (Interdisciplinary Research and Education) and AAI (Applied Artificial Intelligence) initiatives. His publications highlight advancements in explainable AI for hypoglycemia detection, wearable sensor data analysis, neuromorphic computing, and semiconductor device modeling. While no explicit awards are mentioned, his contributions to AI in healthcare and edge computing reflect significant academic impact. His research narrative includes collaborations on predictive analytics for glycemic control and hardware optimization for spiking neural networks. Dr. Shuvo's work also explores energy-efficient AI inference on edge devices and sensor-based plant health monitoring using VOC analysis.

Simon Peterمشاهده پروفایل

دانشیار

• Operating Systems
• Networking
• Data Center Systems
• +۵ مورد دیگر

Simon Peter is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington since 2022. His research focuses on operating systems, networking, and energy-efficient data center systems. Previously, he held positions as an Assistant Professor at UT Austin (2016–2022) and earned his Ph.D. (Dr. sc. ETH) from ETH Zurich in 2012, with a Dipl.-Inf. from Carl-von-Ossietzky University of Oldenburg in 2006. Education: Ph.D. in Computer Science, ETH Zurich, 2012 Dipl.-Inf. (M.Sc.), Computer Science, Oldenburg University, 2006 Research Interests: Design of low-latency, scalable, and energy-efficient data center systems Virtual machine and OS design (QoS in memory management, zero-copy I/O) Programmable NICs/switches (RDMA offloading, in-network ML preprocessing) Power control planes and disaggregated storage systems Teaching: Recent courses include CSE481A Operating Systems Capstone and Data Center Systems Extensive teaching history at UW and UT Austin in operating systems and data center topics Awards & Recognition: Not explicitly listed in provided materials Advising & Grants: Actively recruiting Ph.D. students via UW’s program No grant details provided in current text Labs & Teams: Part of UW’s Distributed Systems Lab Collaborates with industry on data center innovations Personal: Music enthusiast with background in violin, electronic music production, and DJing Outdoor activities include skiing and hiking Former Demoscene participant in Germany

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Julian J. McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. His research focuses on advancing the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing, with a particular emphasis on Recommender Systems and Large Language Models (LLMs). His work addresses challenges in ethical AI, generative models, and the integration of knowledge-driven approaches into recommendation systems. Key research interests include responsible AI, lifelong learning, and the development of agentic recommender systems. He explores techniques such as retrieval-augmented generation, causal reasoning, and multi-objective alignment in LLMs. His contributions span foundational theories and practical applications, including the design of scalable systems and frameworks for evaluating generative models. McAuley’s publications highlight advancements in areas like music generation, chain-of-thought reasoning, and knowledge transfer in recommendation systems. His work often bridges theory and practice, addressing real-world challenges in personalized systems and ethical AI deployment.

Minlan Yuمشاهده پروفایل

استاد

• Computer Science
• Networking
• Distributed Systems
• +۹ مورد دیگر

Minlan Yu is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She leads the Harvard Theory and Systems group and co-leads the Harvard Power and AI initiative. Her research focuses on data networking, distributed systems, and software-defined networking, with recent emphasis on sustainable computing and AI-driven network management. She is also Assistant Director of the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing. Key research interests include network optimization, edge AI serving, large-scale resource allocation, and fault tolerance in distributed systems. Recent work highlights include innovations in energy-efficient data centers, homomorphic encryption acceleration, and real-time network telemetry using FPGA coprocessors. Teaching responsibilities include advanced courses on networking (CS 145/243) and systems programming. She actively advises PhD students and postdocs across systems and networking domains. Her lab's work has led to impactful contributions in both academia and industry, with a focus on bridging theory and practical system implementations. Current research initiatives include the Harvard Power and AI initiative exploring energy-efficient AI workflows and the development of evolvable computing infrastructure through the ACE Center.

Emily Dolsonمشاهده پروفایل

استادیار

• Artificial Life
• Evolutionary Computation
• Eco-Evolutionary Dynamics
• +۵ مورد دیگر

Emily Dolson is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at Michigan State University's College of Engineering, and core faculty in the Ecology, Evolution, and Behavior program. Her research focuses on developing algorithmic approaches to control evolutionary trajectories in complex ecological communities through computational experiments, spanning simple models to artificial life systems. This work bridges evolutionary medicine, computational theory, and empirical biology through collaborations with wet-lab scientists. Education: Ph.D., Computer Science & Engineering and Ecology, Evolutionary Biology, & Behavior, Michigan State University (2019) B.A., Computer Science and Biology, Swarthmore College (2013) Her research interests emphasize computational tools for evolutionary dynamics, including phylogenetic tracking, open-ended evolution measurement (via the MODES toolbox), and ecological theory integration into evolutionary algorithms. Recent work explores fitness seascapes, counterdiabatic driving in evolutionary systems, and scalable phylogenetic analysis. Awards: International Society for Artificial Life Outstanding Service Award (2019) International Society for Artificial Life Outstanding Student Research Award (2019) Publications highlight innovations in evolutionary computation techniques, phylogenetic informatics, and interdisciplinary applications of artificial life. Ongoing efforts include developing high-performance tools for tracking evolutionary processes at scale and exploring evolutionary mechanisms relevant to medical and environmental contexts.

Nektarios Tsoutsosمشاهده پروفایل

استادیار

• Information Security
• Applied Cryptography
• Computer Architecture
• +۳ مورد دیگر

Nektarios Tsoutsos is an Assistant Professor at the University of Delaware, specializing in information security, applied cryptography, and hardware security. His research focuses on encrypted computation, homomorphic encryption, and secure hardware architectures. He holds a PhD from New York University, where he was previously a postdoc in the Modern Microprocessor Architectures laboratory. Education : PhD in Computer Science, New York University Research Interests : Homomorphic encryption frameworks and their acceleration Hardware-software co-design for secure computing Privacy-preserving technologies in additive manufacturing and distributed systems Cryptography for machine learning and IoT Awards : New York University Ph.D. Student Fellowship Pearl Brownstein Doctoral Research Award (NYU) Grants & Projects : CAREER Grant: Accelerating Encrypted Computation on Diverse Hardware (2023) CCRI Grant: Data-Driven Cybersecurity for Smart Manufacturing (2023) His work includes developing secure processors (e.g., Juliet), accelerating FHE algorithms (e.g., Ripple), and creating datasets for 3D printing security. He collaborates on frameworks like HELM and Mastic, addressing challenges in privacy, performance, and scalability.

Josep Torrellasمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Processing
• Hardware Reliability
• +۱ مورد دیگر

Josep Torrellas is the Saburo Muroga Professor of Computer Science at the University of Illinois at Urbana-Champaign. He directs the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing and leads research in computer architecture, with focus on parallel processing, energy efficiency, and hardware reliability. Dr. Torrellas has received major awards including the IEEE Harry H. Goode Memorial Award, UIUC Campus Mentoring Award, and IEEE Technical Achievement Award. He is a Fellow of IEEE, ACM, and AAAS. His recent publications demonstrate strong focus on cloud security, serverless computing, and AI acceleration. Research consistently addresses hardware/software co-design for emerging computing paradigms. He has graduated 48 PhD students, many now faculty at top institutions. Dr. Torrellas teaches courses in parallel computer architecture and advises students in the ACE Center for Evolvable Computing.

Anita Say Chanمشاهده پروفایل

استاد

• Science and Technology Studies
• Globalization and Innovation Economies
• Critical Data Studies
• +۷ مورد دیگر

Anita Say Chan, Professor at the University of Illinois Urbana-Champaign, is a leading scholar at the intersection of science and technology studies, critical data analysis, and decolonial frameworks. She holds appointments in the College of Media and the School of Information Sciences (iSchool), directing the Community Data Clinic and co-leading the Just Infrastructures Initiative . Her research examines datafication, ethical technology design, and historical parallels between eugenics and Big Tech, as detailed in her acclaimed book Predatory Data: Eugenics in Big Tech . Education: PhD in History and Anthropology of Science and Technology Studies, MIT Chan’s work spans globalization and innovation economies , feminist data studies , and digital humanities , with a focus on marginalized communities and data sovereignty. Her publications address query optimization , graph-based AI , and reinforcement learning , reflecting a technical and critical lens. Recent awards include the Fiddler Innovation Faculty Fellow and support from the National Science Foundation . She also serves as a Fulbright Specialist , advancing feminist data collectives in Latin America. Key Contributions: Founding the Community Data Clinic at NCSA Co-leadership in the Just Infrastructures Initiative Collaboration on the Feminist Data Manifest-NO project

Fan Laiمشاهده پروفایل

استادیار

• Machine Learning Systems
• Cloud Computing
• Networked Systems
• +۳ مورد دیگر

Dr. Fan Lai is an Assistant Professor at the Department of Computer Science, University of Illinois Urbana-Champaign (UIUC) , where he leads research at the intersection of Machine Learning, Systems, and Computer Networking . His work focuses on building practical systems for efficient GenAI applications across cloud and planetary scales, with real-world adoption by companies like Meta, LinkedIn, and Cisco . Education : Ph.D. in Computer Science from the University of Michigan (2023) Previous Roles : Systems Research@Google, Meta AI Research areas include: Cloud ML Systems (LLMs, Image/VideoGen, DLRMs) Collaborative Learning (Federated Learning, Edge-Cloud ML, Hyperscale Training) ML for Systems (Resource Management, Network Optimization) Recent publications span top conferences like SOSP'25, MICRO'25, MLSys'25, SIGMOD'25 , covering topics from LLM serving to distributed training compression . Awards include ACM SIGOPS Honorable Mention , David J. Kuck Dissertation Prize , and ML and Systems Rising Stars . Teaching includes CS 498: Machine Learning Systems and CS 598: Systems for GenAI . Actively mentoring PhD and MS students in ML systems research, with prior mentees transitioning to institutions like Princeton and UW .

• Artificial Intelligence
• Programming Languages
• Formal Methods
• +۳ مورد دیگر

Gagandeep Singh is an Assistant Professor in the Department of Computer Science at the University of Illinois. His research focuses on trustworthy AI, programming languages, and formal methods, with a particular emphasis on ensuring safety, robustness, and ethical alignment in machine learning systems. He holds a Ph.D. from ETH Zurich and has received multiple awards, including the ACM SIGPLAN Doctoral Dissertation Award. His work spans neural network verification, adversarial robustness, and federated learning, with applications in security, reliability, and ethical AI. He has contributed to journal editorships, conferences, and has taught courses on formal methods and trustworthy AI systems. Education: Ph.D. in Computer Science, ETH Zurich (2014–2020) Masters in Computer Science, ETH Zurich (2012–2014) Bachelor's in Computer Science and Engineering, IIT Patna (2008–2012) Research Interests: Artificial Intelligence Safety & Ethics Formal Verification of Neural Networks Adversarial Machine Learning Federated Learning Programming Languages & Static Analysis Recent Articles: His most recent work addresses challenges in AI alignment, robustness certification, and federated learning optimization, reflecting a focus on both theoretical foundations and practical applications. Awards & Honors: ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award (2021) ETH Medal for Best Master's Thesis (2014) Teaching & Advising: He has taught courses such as 'Formal Software Development Methods' and 'Trustworthy AI Systems', and his research group explores topics like LLM robustness and certified neural networks.

Marco Levoratoمشاهده پروفایل

دانشیار

• Distributed Computing
• Wireless Systems
• Autonomous Vehicles
• +۵ مورد دیگر

Marco Levorato is an Associate Professor of Computer Science at the Donald Bren School of Information and Computer Sciences, University of California, Irvine. He holds a Ph.D. in Electrical Engineering from the University of Padova (2009) and has held postdoctoral roles at Stanford University and the University of Southern California. His research focuses on distributed computing, wireless systems, AI for autonomous systems, and healthcare applications. He has led multiple NSF-funded projects, including collaborations with Intel and Cisco, and chairs/co-chairs several IEEE and ACM conferences. His work has been recognized with awards such as the 2024 Emerging Innovator of the Year Award and the 2016 UC Hellman Fellowship. Key research themes include edge computing, split computing for real-time systems, and security in autonomous UAV networks. Levorato advises a team of graduate students and has pioneered technologies for resilient distributed systems, as seen in his keynote at IEEE HealthCom (2022) and leadership in the NSF/Intel MLWiNS initiative. Recent contributions include frameworks for 6G-enabled offloading in autonomous vehicles, semantic communications for sensor fusion, and context-aware stress monitoring using wearable devices. His interdisciplinary approach bridges machine learning, networking, and IoT to address challenges in smart cities and healthcare.