جست‌وجوی استاد و پژوهشگر

Rajendra Acharyaمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare
• Machine Learning
• +۵ مورد دیگر

Rajendra Acharya is a Professor (Artificial Intelligence in Health) at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds qualifications including BEng, MTech, two PhDs, and a DSc. His research focuses on AI applications in healthcare, pattern recognition, and medical diagnostics, with notable contributions to EEG analysis, deep learning, and disease detection. Awards include multiple Research.com Leader Awards in Computer Science for Australia and Singapore (2022–2025). His work spans over 650 publications, with high-impact studies on automated disease diagnosis via AI, including COVID-19 detection using X-rays and EEG-based seizure detection. His research interests integrate machine learning, signal processing, and healthcare technologies. He collaborates internationally and advises on AI-driven health solutions. No student list provided; however, his extensive supervision is implied through his research output.

Ralph Evinsمشاهده پروفایل

دانشیار

• Building Energy Simulation
• Energy System Optimization
• Machine Learning Applications in Energy
• +۳ مورد دیگر

Dr. Ralph Evins is an Associate Professor and Director of the Graduate Program in the Department of Civil Engineering at the University of Victoria. He holds affiliations with the Urban Energy Systems laboratory at Empa and ETH Zurich in Switzerland. His expertise spans building energy simulation, energy system optimization, and machine intelligence applications in sustainable design. Evins holds an MEng from Imperial College London and an EngD from the University of Bristol. His research focuses on computational problem-solving in energy systems, including surrogate modeling, optimization algorithms, and machine learning. He develops tools like the Holistic Urban Energy Simulation (HUES) platform and BESOS software framework to bridge building, district, and city-scale energy analysis. His work emphasizes holistic systems thinking, integrating energy hubs, thermal modeling, and digital twin technologies. Recent articles explore surrogate model refinement, inverse modeling for building characterization, and decarbonization strategies. He collaborates with industry to translate academic innovations into practical solutions. Evins advises students in energy systems and leads projects on net-zero building design, retrofit prioritization, and smart grid integration. His research addresses challenges in climate adaptation, energy efficiency, and sustainable urban development through interdisciplinary approaches.

Suining Heمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Smart & Connected Communities
• Human-Centered Computing
• +۱۰ مورد دیگر

Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.

LING Chun Kaiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Decision Making & Planning
• Machine Learning
• +۱۳ مورد دیگر

LING Chun Kai is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on multiagent systems, computational game theory, and machine learning applications in adversarial real-world domains like cybersecurity and logistics. Educational background includes a PhD in Computer Science (2017-2023) from Carnegie Mellon University and a First Class BEng in Computer Engineering (2015) from NUS. Previously, he was a Postdoctoral Research Scientist at Columbia University. Current research interests span computational game theory, machine learning for multi-agent systems, equilibrium characterization in imperfect information settings, and applications in network security, logistics, and recreational games. Key methodological contributions include scalable algorithms for game solving, differentiable game solvers, and copula-based statistical modeling. Recent publications focus on attacker-defender graph games, language negotiation agents, and modeling games with incomplete information. Collaborations include researchers from Columbia University, Carnegie Mellon, and institutions working on GameSec, AAAI, Neurips, and ICML venues. Scientific Awards: IJCAI 2018 Distinguished Paper Award GameSec 2023 Best Paper Award GameSec 2024 Best Paper Award Singapore Teaching and Academic Research Talent Scheme (2024) Teaching includes courses on AI Planning and Decision Making (CS4246, CS5446) and Advanced Topics in Artificial Intelligence (CS6208).

• Trustworthy Artificial Intelligence
• Privacy-Preserving Systems
• Adversarial Robustness
• +۴ مورد دیگر

Mathias Lécuyer is an Assistant Professor in the Computer Science Department at the University of British Columbia (UBC) , part of the Faculty of Science . His research focuses on trustworthy AI systems, with emphasis on privacy (differential privacy), adversarial robustness, and causal machine learning. He leads the Systopia Lab and collaborates with groups such as UBC S&P, TrustML, and CAIDA. Education: PhD in Computer Science from Columbia University (2019), MSc from Columbia University (2013) and École Polytechnique (2011). Research Interests: Ensuring rigorous guarantees for AI systems via differential privacy, robustness against adversarial attacks, and causal inference. His work spans theoretical foundations and practical implementations in areas like privacy-preserving systems, federated learning, and transparent machine learning. Awards: SOSP Distinguished Artifact Honorable Mention (2024), Google Research Award (2022), Bourse Carnot Fellowship (2011), and UBC’s top teaching evaluations (2021). Advising & Service: Supervised over 20 students (PhD, MSc, undergrad) across privacy and machine learning topics. Serves on PCs for top conferences (OSDI, S&P, NeurIPS) and organizes workshops like TrustML @ UBC. Actively mentors high school students in ML research through outreach programs. Labs/Teams: Leads the Systopia Lab at UBC, focusing on AI safety and privacy. Collaborates with MSR, Google, and industry partners on practical system implementations.

Xiaolei Fangمشاهده پروفایل

دانشیار

• Statistical Learning
• Deep Learning
• Federated Learning
• +۳ مورد دیگر

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

Anshumali Shrivastavaمشاهده پروفایل

دانشیار

• Large Scale Machine Learning
• Randomized Algorithms for Big Data
• Graph Mining
• +۳ مورد دیگر

Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.

Stefanie Czischekمشاهده پروفایل

استادیار

• Quantum many-body systems
• Quantum condensed matter
• Quantum computation and simulation
• +۴ مورد دیگر

Dr. Stefanie Czischek is an Assistant Professor in the Department of Physics at the University of Ottawa, leading the APRIQuOt research group focused on artificial and physically realizable intelligence for quantum applications. She joined uOttawa in 2022 after postdoctoral work at the University of Waterloo. Her research bridges quantum technologies and neural networks, with expertise in quantum simulation, neuromorphic computing, and machine learning applications in quantum physics. Research Interests: Quantum computation/simulation using neural networks Neuromorphic hardware implementations Quantum many-body systems Machine learning for quantum control and tomography Her publications demonstrate strong interdisciplinary focus, combining quantum physics with cutting-edge ML techniques. Recent works explore transformer models for quantum simulation, neural network quantum states, and quantum sensing applications. The research shows consistent evolution toward hardware-algorithm co-design for quantum problems. Awards: Springer Thesis Award (2020) for doctoral research on neural-network simulation of quantum systems. Research Group & Advising: Leads the APRIQuOt lab with 1 postdoc, 6 graduate students, and 1 undergraduate. Current projects include large language models for quantum states, quantum optimal control via reinforcement learning, and neuromorphic quantum simulations. The group collaborates with experimental teams and maintains strong industry-academia partnerships.

Shirin Saeedi Bidokhtiمشاهده پروفایل

استادیار

• Information Theory
• Networking
• Data Compression
• +۲ مورد دیگر

Shirin Saeedi Bidokhti is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science with primary appointment in Electrical and Systems Engineering and secondary appointment in Computer and Information Science. She is affiliated with the Warren Center for Network and Data Sciences. She holds M.Sc. and Ph.D. degrees from EPFL and completed postdoctoral work at Stanford and Technical University of Munich. Her research focuses on information theory, networking, data compression, and machine learning. Her recent publications demonstrate strong emphasis on neural compression algorithms, network optimization during the COVID-19 pandemic, and age-of-information theory. Awards include: 2023 IEEE Communications Society & Information Theory Society Joint Paper Award 2021 NSF CAREER Award 2019 NSF-CRII Award Swiss National Science Foundation Fellowships She advises PhD students including Xingran Chen. Current research involves developing data compression algorithms for IoT applications and network strategies for pandemic response.

Nikolai Matniمشاهده پروفایل

استادیار

• Machine Learning
• Control Theory
• Robotics
• +۱۰ مورد دیگر

Nikolai Matni is an Assistant Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He holds a secondary appointment in the Department of Computer and Information Science and is a member of the Applied Mathematics and Computational Sciences (AMCS) graduate group. His research focuses on integrating learning, optimization, and control for safety-critical and data-driven cyber-physical systems, with applications in robotics, autonomy, and distributed control. Secondary Appointment: Computer and Information Science (University of Pennsylvania) Labs/Centers: GRASP Lab, PRECISE Center His research bridges machine learning, robust control, and autonomous systems, particularly in developing guaranteed-safe strategies for cyber-physical systems. He emphasizes the importance of reliability and robustness in learning-based control for applications like self-driving vehicles and agile robots, where failures could be catastrophic. Recent publications highlight advancements in vision-based robotic control, neural ODEs for motion planning, adversarial exploration strategies, and stability-constrained learning. These works span conferences such as ICRA, CoRL, WACV, IROS, and L4DC, with a focus on safety-critical applications. Notable scientific awards include the NSF CAREER Award, George S Axelby Award (for his work on System Level Synthesis), AFOSR YIP award, and Google Research Scholar Award. He was also elevated to IEEE Senior Member. Matni advises Ph.D. students in Computer and Information Science (CIS), Electrical and Systems Engineering (ESE), and Applied Mathematics and Computational Sciences (AMCS). His teaching includes courses like ESE 2030 (Linear Algebra with Engineering/AI applications) and others focused on control theory and robotics.

• Biomedical Engineering
• Control Systems
• Brain-Computer Interfaces
• +۷ مورد دیگر

Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.

Athina E Markakiمشاهده پروفایل

استاد

• Tissue Engineering
• Biomaterials
• Regenerative Medicine
• +۳ مورد دیگر

Professor Athina E Markaki serves as Professor of Materials & Biomedical Engineering in the Department of Engineering at the University of Cambridge, leading research in advanced biomaterials and tissue engineering solutions for regenerative medicine with emphasis on vascularization and tubular scaffold development for human conduit replacement. Her academic credentials include a Diploma in Metallurgical Engineering (8.6/10) from the National Technical University of Athens and a PhD in Materials Science from the University of Cambridge. Markaki's research program centers on vascularisation techniques for clinically relevant tissue dimensions and tubular scaffolds to replace diseased or damaged human conduits, integrating biomaterials science with regenerative medicine principles. Key applications span liver tissue engineering, neural crest-derived stem cell differentiation, and vascular graft development, with strong translational focus on orthopaedic and cardiovascular medical devices. Analysis of her recent publications reveals dominant trends in biomimetic scaffold design, particularly collagen-based tubular structures and hydrogel systems for vascularized tissue constructs. Her work demonstrates interdisciplinary convergence of AI-driven retinal assessment, glioblastoma modeling, and self-healing cementitious materials, with consistent emphasis on clinically applicable regenerative solutions for liver, bone, and neural tissues. Her distinguished scientific contributions are recognized by major awards: Rosetrees Trust 2017 Interdisciplinary Award European Research Council (ERC) Starting Grant (2010) Advanced EPSRC Fellowship (2005) De Montfort Award at SET for Britain National Event (2004) Young Scientist Prize 2003 (5th Euromech Solid Mechanics Conference) Multiple academic excellence awards from Greek foundations Markaki directs a well-funded research program including ERC and EPSRC grants, mentoring graduate students in tissue engineering while teaching core engineering curricula covering plastic deformation, fracture mechanics, and medical materials design. Her group maintains strong industry and clinical partnerships to advance regenerative technologies. Her laboratory, accessible via http://www-memti.eng.cam.ac.uk/, specializes in vascularized tissue constructs and tubular scaffolds using laser-based manufacturing, biomimetic design, and hydrogel engineering to address critical challenges in tissue replacement and disease modeling.

• Reconfigurable Computing
• Machine Learning Acceleration
• Hardware Acceleration
• +۵ مورد دیگر

Aman Arora is an Assistant Professor at Arizona State University's Ira A. Fulton Schools of Engineering, specializing in the School of Computing and Augmented Intelligence. His research focuses on reconfigurable computing, hardware acceleration of machine learning, and non-traditional computing paradigms like Processing-In-Memory. With over a decade of semiconductor industry experience, he bridges academic research and industrial applications. PhD in Computer Science from The University of Texas at Austin Research interests emphasize domain-specific acceleration through FPGA optimization , compute-in-memory architectures , and machine learning for CAD/EDA . His work addresses critical challenges in energy efficiency and throughput for AI workloads. Recent publications demonstrate trends toward compute-in-memory systems , FPGA-based deep learning acceleration , and sustainable hardware design . Key contributions include frameworks like SAF, CSR, and GAMA for dynamic hardware optimization. Laboratory Website: ADVENT Lab Teaching includes courses on digital hardware design (CSE 320) and advanced topics in machine learning acceleration (CEN 524/CSE 524). Industry experience informs his practical approach to research and education.

M. Tamer Özsuمشاهده پروفایل

استاد

• Data Science
• Database Systems
• Distributed Data Management
• +۳ مورد دیگر

M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.

Ravid Shwartz-Zivمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۷ مورد دیگر

Ravid Shwartz-Ziv is an Assistant Professor and Faculty Fellow at NYU's Center for Data Science, with a dual role as Senior Research Scientist at Wand AI. His research bridges theoretical foundations and practical applications in artificial intelligence, focusing on Large Language Models (LLMs), information theory, and neural network interpretability. Ph.D. in Computational Neuroscience, Hebrew University of Jerusalem (2021) B.Sc. in Computer Science and Computational Biology, Hebrew University of Jerusalem (2014) His research spans: Developing min-p sampling for LLM text generation Preventing representation collapse in Transformers Creating contamination-free LLM benchmarks like LiveBench Advancing information-theoretic frameworks for neural networks Exploring representation learning and model adaptation Recent publications demonstrate expertise in LLM efficiency, self-supervised learning, and multi-agent systems. Notable awards include the Google PhD Fellowship, Moore-Sloan Fellowship, and multiple best paper recognitions. He has led research initiatives at Intel and Google AI, focusing on neural network compression, XGBoost comparisons for tabular data, and innovative benchmarking frameworks.