جست‌وجوی استاد و پژوهشگر

Mohit Iyyerمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Large Language Models
• +۶ مورد دیگر

Mohit Iyyer is an Associate Professor of Computer Science at the University of Maryland, College Park, with an affiliate appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He leads research in the Computational Linguistics and Information Processing (CLIP) Lab, focusing on natural language processing and machine learning. Previously, he was an associate professor at UMass CS, a Young Investigator at AI2, and completed his PhD at UMD CS. Dr. Iyyer's research spans multiple critical areas in NLP and AI, with particular emphasis on improving instruction following in large language models, evaluating long-form and multilingual text generation, supporting creative writing through human-LLM collaboration systems, and enhancing the robustness of AI-generated text detectors against adversarial attacks. His work addresses fundamental challenges in language model capabilities, including long-context understanding, multilingual processing, and the evaluation of complex language generation tasks. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying large language models in real-world scenarios. Key themes include the development of robust evaluation frameworks for long-form text generation, techniques for improving model instruction following capabilities, methods for detecting AI-generated content, and tools for supporting creative writing tasks. His research bridges theoretical advances with practical applications across multiple domains. Samsung AI Researcher of the Year award (2022) NSF CAREER award (2021) Distinguished Paper award at CCS 2023 Outstanding paper award at EACL 2023 Best long paper at NAACL 2018 (for ELMo) Dr. Iyyer has advised numerous PhD students who have gone on to successful careers in both academia and industry, including positions at Google DeepMind, Microsoft, Virginia Tech, and Cornell. His research has been supported by significant grants, notably the NSF CAREER award for his work on interactive storytelling. He actively collaborates with researchers across institutions and regularly presents his work at leading AI and NLP conferences worldwide. He leads the Computational Linguistics and Information Processing (CLIP) Lab at the University of Maryland, which focuses on cutting-edge research in natural language processing. The lab has developed innovative approaches to language model evaluation, instruction following, creative writing assistance, and AI-generated text detection. Current projects include developing benchmarks for computer-using web agents, improving long-context language model capabilities, and exploring culturally-aware multilingual question answering systems.

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

Kexin Rongمشاهده پروفایل

استادیار

• Database Systems
• Machine Learning
• Data Management
• +۶ مورد دیگر

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Tech, leading the D2I Lab as part of the Georgia Tech Database Group . She also holds an affiliation with VMware Research Group by Broadcom. Education: Ph.D. in Computer Science (2021) from Stanford University, advised by Peter Bailis and Philip Levis B.S. in Computer Science (2015) from California Institute of Technology Research Interests focus on democratizing data science through systems that integrate database management , machine learning , and human-computer interaction . Key projects include SketchQL for sketch-based video retrieval, Inshrinkerator for deep learning checkpoint compression, and Lotus for ML preprocessing pipeline characterization. Scientific Awards include Amazon Research Award (2024) NSF Awards #2335881 (2024) and #2333737 (2023) Catherine M. and James E. Allchin Early Career Professorship (2022) SIGMOD Distinguished PC Award (2024) SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention (2022) Advising includes Ph.D. students: Rajveer Bachkaniwala, Hongbin Zhong Co-advised students: Hantian Zhang (with Xu Chu), Renzhi Wu (with Xu Chu) Former Ph.D. advisee: Peng Li (2023 graduate) Lab & Collaborations : Leads the D2I Lab at Georgia Tech, which has received funding from NSF and Bosch Research. The lab actively collaborates with institutions like Stanford University and contributes to open-source projects including MacroBase and ASAP .

Alexey Tumanovمشاهده پروفایل

استادیار

• Machine Learning
• Distributed Systems
• Resource Management
• +۴ مورد دیگر

Alexey Tumanov is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology, part of the College of Computing. His research focuses on systems for machine learning, resource management, and scheduling in distributed environments. He holds a PhD from Carnegie Mellon University and conducted postdoctoral research at UC Berkeley under Ion Stoica. Previously, he worked at the University of Toronto and in industry on cloud computing and datacenter systems. Education: PhD in Computer Science, Carnegie Mellon University (2019) Postdoc, UC Berkeley RISELab (2019) MSc in Computer Science, University of Toronto (2012) BSc in Computer Science, University of Toronto (2010) Research Interests: Tumanov's work addresses challenges in distributed machine learning, including efficient resource management for inference and training pipelines, scheduling algorithms for soft-real-time systems, and federated learning. His lab (SAIL) develops systems like SARATHI-SERVE and RocketKV to optimize LLM inference and co-scheduling. He emphasizes practical deployments in healthcare (HOLMES) and cloud environments. Key Contributions: Ray distributed framework for AI (OSDI 2018) ESCHER scheduler for ephemeral cloud resources (SoCC 2022) InferLine ML pipeline orchestration (SoCC 2020) Awards: NSERC CGS-D3 Scholarship (2016) Best Paper at EuroSys 2016 (TetriSched) Google PhD Fellowship Nominee (2018) Lab & Teaching: Leads the Systems for Artificial Intelligence Lab (SAIL) at Georgia Tech. Teaches advanced courses on operating systems (CS3210) and systems for ML (CS8803-SMR). Supervises 15+ graduate students across PhD and MS programs.

Tariq Iqbalمشاهده پروفایل

استادیار

• Human-Robot Collaboration
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Tariq Iqbal is an Assistant Professor in the Department of Systems Engineering and Computer Science at the University of Virginia, located at Olsson Hall 263. His research focuses on developing robotic systems capable of fluent collaboration with humans in complex environments, such as factories. His work integrates robotics, artificial intelligence, computer vision, and machine learning to enhance human-robot coordination and trust. Research interests include human-robot interaction , motion prediction , multimodal learning , and assistive robotics . Notable projects involve creating systems like CoHRT for teamwork, FOLD for cloud robotic workload distribution, and VADER for motion prediction using GANs. He has secured a grant from the Air Force Office of Scientific Research to advance trust between humans and robots. His publications span topics such as gesture recognition, trust modeling via biometrics, and embodied question answering. Recent work addresses dementia care robotics and energy optimization in maritime ports. Ongoing efforts emphasize ethical design, accessibility, and safety in human-robot systems.

Beatriz De La Iglesiaمشاهده پروفایل

استاد

• Health Informatics
• Data Mining
• Algorithm Development
• +۷ مورد دیگر

Beatriz De La Iglesia is a Professor and Head of the School of Computing Sciences at the University of East Anglia. Her research focuses on data mining, health informatics, and algorithm development with applications in healthcare and precision agriculture. She leads the Data Science Laboratory and holds roles including Director of the Data Science MSc and former Director of PgT Programmes. Her work spans clinical data analysis (e.g., gastrointestinal cancers, cardiovascular risk), text mining of medical reports, and Twitter-based syndromic surveillance. She has pioneered techniques in clustering heterogeneous data, imputation methods, and deep learning for crop analysis and medical diagnostics. With over 70 publications, she collaborates internationally on projects like the NIHR Health Protection Unit in Gastrointestinal Infections and precision spraying systems. Academic Background: BSc (1st class) and PhD in Computing Science from UEA (1994, 2001), Postgraduate Diploma in Higher Education (2004). Leadership: Advanced leadership training (ILM3, Aurora Programme), promotes women in STEM globally. Key Projects: Includes AI-enabled medical IoT trust-management, automated cognitive assessment, and crop yield prediction using CNNs. Awards: Not explicitly listed, but recognized through high-impact collaborations and academic roles. Her research bridges computational methods with real-world challenges in healthcare and agriculture, emphasizing interdisciplinary approaches. Recent work highlights automated precision spraying evaluation systems and AI-driven phenotypic analysis for wheat crops.

Yanjing Liمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware Security
• Emerging Technologies
• +۴ مورد دیگر

Yanjing Li is an Assistant Professor in the Department of Computer Science (Systems Group) at the University of Chicago. She holds a Ph.D. in Electrical Engineering from Stanford University, an M.S. in Mathematical Sciences (with honors) and a B.S. in Electrical and Computer Engineering (with a double major in Computer Science) from Carnegie Mellon University. Her research focuses on computer architecture, hardware security, emerging technologies, and the intersection of machine learning with robust system design. Education: Ph.D., Electrical Engineering, Stanford University M.S., Mathematical Sciences (Honors), Carnegie Mellon University B.S., Electrical and Computer Engineering (Double Major in Computer Science), Carnegie Mellon University Research Interests: Professor Li explores efficient, intelligent, and secure computer architectures. Key areas include photonic interconnects, fail-secure architectures, resilience in deep learning accelerators, and cross-layer system design. Her work emphasizes energy-efficient computing and hardware security against failures and attacks. Awards & Honors: 2022 DAC Under-40 Innovators Award 2021 Google Research Scholar Award 2015 Intel Labs Gordy Academy Award Outstanding Dissertation Award (European Design and Automation Association) Multiple Best Paper Awards at IEEE/ACM Conferences Advising & Projects: Leads the YLab, mentoring PhD students like Yi He and Zequan Zhou. Active projects include UpDown (graph analytics acceleration), photonic interconnects funded by NSF/E2CDA, and fail-secure architectures. Collaborates on NSF-funded photonic computing research with UIUC and UChicago teams. Labs & Groups: Directs YLab and contributes to the Systems Group, fostering interdisciplinary work in systems, programming languages, and hardware-software co-design.

• Computer Architecture
• Machine Learning
• Edge Computing
• +۷ مورد دیگر

Kostas Siozios is a researcher affiliated with Aristotle University of Thessaloniki (AUTH) in Greece. His work spans computer architecture, embedded systems, and machine learning, with a focus on hardware-aware optimization, energy efficiency, and edge computing. Key research areas: Computer Architecture Machine Learning Edge Computing Approximate Computing Printed Electronics Cyber-Physical Systems Recent publications highlight advancements in federated learning, DNN acceleration, and energy-efficient design for emerging hardware. His work often integrates evolutionary algorithms, genetic optimization, and variability-aware strategies. Collaborations include researchers from institutions like RWTH Aachen, TU Delft, and ETH Zurich. Topics frequently address hardware-software co-design, low-power systems, and reconfigurable architectures.

• Embedded Systems
• Machine Learning
• Internet of Things (IoT)
• +۳ مورد دیگر

Prof. Dr. Gregor Schiele is a Full Professor at the University of Duisburg-Essen , where he leads the Embedded Systems Department of Computer Science since 2014. His research focuses on self-organizing embedded systems in the Internet of Things (IoT), with emphasis on local embedded intelligence using deep learning and neural networks, as well as programming abstractions for adaptive systems . PhD in Computer Science (2007) from the University of Stuttgart Prior affiliations: Insight Centre for Data Analytics (Ireland), National University of Ireland, Galway, University of Mannheim Current research explores Embedded Machine Learning , FPGA-based AI Accelerators , and Energy-Efficient IoT Systems , with recent publications on quantization-aware training , configuration-aware power management , and medical signal processing . His 15 most recent articles highlight expertise in FPGA optimization , soft sensors , and adaptive architectures for time-series forecasting and fluid flow estimation . Key collaboration networks include the Elastic AI Ecosystem , ZaKI.D , and TransfAIr projects. He teaches courses like Programmieren in C , Internet of Things: Protocols and System Software , and Embedded Systems , while supervising research assistants and student teams in the IoT Garage initiative.

Oscar Gustafssonمشاهده پروفایل

دانشیار

• Digital Circuit Design
• Field-Programmable Gate Array (FPGA)
• Application-Specific Integrated Circuit (ASIC)
• +۷ مورد دیگر

Oscar Gustafsson is an Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Division of Electronics and Computer Engineering (ELDA). His research focuses on energy-efficient computations in digital circuits, particularly in ASIC and FPGA architectures, with applications in communication systems and hardware acceleration for AI. The 2024 publications highlight his work on FPGA implementations for dynamic programming in hybrid vehicles, hardware-aware neural network optimizations, FFT architectures for MIMO systems, Python-based finite word-length effect simulations, and CPU-FPGA co-design strategies. These works intersect with Computer Engineering Artificial Intelligence Signal Processing Computer Arithmetic Embedded Systems domains. He leads the Division of Computer Engineering at Linköping University, emphasizing collaborative research including Swedish-Chinese 5G technology partnerships.

Hang Liuمشاهده پروفایل

استادیار

• High-Performance Computing
• Graph Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. Hang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University. He previously held faculty positions at Stevens Institute of Technology (2019-2022) and University of Massachusetts Lowell (2017-2019). His research focuses on high-performance computing systems leveraging GPUs, FPGAs, and SSDs for graph analytics, machine learning, and numerical methods. He holds a Ph.D. from George Washington University (2017) and a B.E. from Huazhong University of Science & Technology (2011). Education: Ph.D. Electrical and Computer Engineering, George Washington University, 2017 B.E. Software Engineering, Huazhong University of Science and Technology, 2011 Research Interests: Dr. Liu's work spans high-performance computing (HPC), graph algorithms, machine learning acceleration, and numerical methods optimization. He develops novel architectures and algorithms for efficient processing of large-scale graphs and ML models on heterogeneous hardware, with applications in scientific computing and data analytics. His systems research emphasizes algorithm-hardware co-design for FPGAs and GPUs to achieve energy-efficient performance. Awards: Notable achievements include the IEEE CS TCHPC Early Career Award (2022), NSF CAREER Award (2021), and multiple best paper awards. He leads projects funded by NSF and industry partnerships, including AWS and Lawrence Berkeley National Lab. Advising & Grants: Advisor for multiple award-winning senior design projects and research grants. His work on AWS GraphChallenge and Berkeley Lab SRP Fellowships demonstrates sustained industry-academia collaboration. Labs/Teams: Active in the Rutgers High-Performance Systems (RHPS) lab, focusing on scalable graph analytics and ML infrastructure. Collaborates with interdisciplinary teams on AI-driven scientific discovery initiatives like DeepSpeed4Science.

Jose Nunez-Yanezمشاهده پروفایل

استاد

• Hardware Architectures
• Heterogeneous Systems
• Machine Learning Hardware
• +۱۲ مورد دیگر

Jose Nunez-Yanez is a Professor in hardware architectures for Machine Learning at Linköping University, affiliated with the Department of Electrical Engineering and the Division of Electronics and Computer Engineering (ELDA). He has over 20 years of experience in high-performance embedded hardware design and is associated with the Wallenberg Autonomous Systems Program (WASP). His research focuses on energy-efficient, adaptive, and parallel hardware systems for machine learning and signal processing. Key areas include FPGA-based acceleration, run-time reconfiguration, and edge AI. He has led significant research projects with industry and academic partners such as ARM, ST Microelectronics, ESA, and Intel. The recent publications highlight a strong trend toward hardware-aware optimization of Graph Neural Networks for edge deployment, emphasizing quantization, dynamic range analysis, and heterogeneous computing. This reflects a broader shift toward efficient deep learning on resource-constrained platforms. Best Paper Award, Adaptive Hardware Systems (AHS) Conference, ESA/NASA, 2014 Best Paper Award, Adaptive Reconfigurable Computing (ARC) Conference, 2016 He has supervised 20 PhD students and served as the program director of the Advanced Microelectronics Systems Engineering (AMSE) program at the University of Bristol, significantly expanding its reach. He has secured substantial funding as Principal Investigator and Co-Investigator with partners including Sensata Systems, DSTL, Qioptiq, and Xilinx. His industry fellowships include Royal Society and Marie Curie awards. He is a member of the Royal Society Grants Board, the EPSRC College, and the Marie Curie Alumni Association. His work bridges academia and industry, with active consultancy in FPGA optimization and lossless data compression.

Anand Raghunathanمشاهده پروفایل

استاد

• Brain-inspired Computing
• Machine Learning
• System-on-Chip Design
• +۷ مورد دیگر

Anand Raghunathan is a Silicon Valley Professor and Chair of the VLSI area at Purdue University's Elmore Family School of Electrical and Computer Engineering. He serves as Associate Director of the $36M SRC/DARPA Center for Brain-inspired Computing (C-BRIC) and Co-Director of the Purdue/TSMC Center for a Secure Microelectronics Ecosystem (CSME). His research focuses on Brain-inspired computing Energy-efficient machine learning System-on-chip design Post-CMOS devices Heterogeneous computing Educated with a B.Tech from IIT Madras, and MA/PhD in Electrical Engineering from Princeton University, he directs Purdue's Integrated Systems Laboratory (ISL) which explores domain-specific architectures and compute-in-memory technologies. His work spans security for embedded systems, battery-efficient computing, and computational imaging commercialized through High Performance Imaging, Inc. Research keywords include: Neural network acceleration Hardware-software co-design Security vulnerabilities Post-CMOS devices Transformer optimization Scientific awards include: IEEE Fellow MIT TR35 innovator 9 Best Paper Awards 2 NEC Technology Commercialization Awards Purdue Faculty Excellence Award Advising and grants: Co-founded High Performance Imaging, Inc. Founded Purdue/TSMC Center for Secured Microelectronics Ecosystem Hold 29 U.S. and 16 international patents Chair of 5 IEEE/ACM conferences Editorial roles in ACM/IEEE journals Labs and teams: Integrated Systems Laboratory (ISL) Collaborations with NEC Labs, Princeton University, and IIT Madras Lead $36M SRC/DARPA C-BRIC Center

Priya Pandaمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Machine Learning
• Energy-Efficient AI
• +۳ مورد دیگر

Priya Panda serves as an Assistant Professor in the Electrical Engineering Department at Yale University, where she directs the Intelligent Computing Lab. Her academic foundation includes a Ph.D. from Purdue University completed in 2019. Educational background: Ph.D. in Electrical Engineering or related field from Purdue University (2019) Her research program centers on neuromorphic computing and the development of robust, energy-efficient machine intelligence systems. She investigates hardware-software co-design approaches to deploy AI on resource-constrained devices while maintaining resilience against adversarial perturbations and optimizing power consumption. This work bridges neuroscience-inspired architectures with practical deep learning applications. Analysis of her publication record reveals a dominant focus on energy efficiency in deep learning, with recurring methodologies including quantization, pruning, and spiking neural networks. Her research consistently targets hardware-aware AI implementation, demonstrating how activation density metrics and noise sensitivity can drive optimization in neural network deployment. Scientific recognition includes: 2019 Amazon Research Award for innovative contributions to energy-efficient machine learning As director of Yale's Intelligent Computing Lab, she leads a research ecosystem focused on sustainable AI development. Her team's work addresses critical challenges in edge computing and large-scale data centers where energy constraints fundamentally shape system design. Current efforts emphasize creating deployable AI solutions that maintain robustness without compromising efficiency.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Han Gao is an academic affiliated with Technical University of Denmark (DTU), specifically within the Department of Informatics and Mathematical Modelling at DTU Informatics. Their research focuses on interdisciplinary areas spanning artificial intelligence, machine learning, robotics, and control systems. They have also collaborated with institutions like Zhejiang University, Huawei Technologies, and others, indicating a broad network of academic and industrial partnerships. Key research interests include generative AI models, adversarial machine learning, swarm robotics, and energy systems optimization. Their work often bridges theoretical advancements and practical applications, such as improving UAV localization accuracy under GNSS denial conditions and enhancing cybersecurity in industrial IoT. Publications highlight contributions to 3D reconstruction (e.g., DIG3D), robust backdoor attack mitigation, and unified vision models like DINO-X. Collaborations span domains like healthcare (tongue image segmentation) and environmental sensing (remote sensing analysis). While no explicit grants or awards are listed, their prolific publication record (over 150 papers since 2004) underscores active involvement in cutting-edge research. Ongoing projects include risk-aware robotics, edge computing for logistics, and explainable AI frameworks.