جستجوی استاد راهنمای دانشگاهی

Sangbae Kimمشاهده پروفایل

استاد

• Bio-inspired Robotics
• Bipedal Locomotion
• Manipulation
• +۷ مورد دیگر

Professor Sangbae Kim is the Jerry McAfee (1940) Professor in Engineering at the Massachusetts Institute of Technology (MIT), School of Engineering, Department of Mechanical Engineering. His research focuses on bio-inspired robotics, extracting principles from animal biomechanics to develop high-performance robotic systems. Education: B.S. from Yonsei University (2001), M.S. (2004) and Ph.D. (2008) from Stanford University. Research Interests: Bio-inspired Robotics, Robotic Actuators, Locomotion Dynamics, Composite Sensor Fabrication, and Minimally Invasive Surgical Robotics. His notable achievements include the MIT Cheetah robot capable of 13mph outdoor running and autonomous obstacle jumping, and Stickybot, a climbing robot featured in TIME's Best Inventions (2006). Recent publications emphasize soft robotics, energy-efficient legged locomotion, and bio-inspired actuator design. Kim has received prestigious awards including the NSF CAREER Award (2014), DARPA Young Faculty Award (2013), and Ruth and Joel Spira Award for Distinguished Teaching (2015). Scientific Awards: NSF CAREER (2014), DARPA YFA (2013), TIME Best Invention (2006), multiple best paper awards. Professional Service: Associate Editor roles, NSF review panels, and leadership in IEEE and ASME organizations.

Clyde Kruskalمشاهده پروفایل

دانشیار

• Parallel Computing
• Algorithms
• Computer Architecture
• +۳ مورد دیگر

Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.

Amro Awadمشاهده پروفایل

دانشیار

• Computer Architecture
• Secure Hardware Architectures
• Memory Systems
• +۳ مورد دیگر

Amro Awad is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at North Carolina State University's College of Engineering. He previously served as an Assistant Professor at the University of Central Florida and as a Senior Member of Technical Staff at Sandia National Laboratories. Dr. Awad earned his Ph.D. and Master's in Computer Engineering from NC State and a Bachelor's from Jordan University of Science and Technology. Research Focus His research spans computer architecture and security, emphasizing secure hardware systems , memory security , and integration of emerging technologies . Key contributions include novel approaches to secure and efficient GPU memory management, FPGA resource scheduling, and DRAM simulation. Publications & Awards Dr. Awad's work appears in top-tier venues like ISCA, MICRO, ASPLOS, and HPCA. He holds six U.S. patents and received the prestigious R. Ray Bennett Faculty Fellow Award and recognition as a Goodnight Early Career Innovator . Funding & Collaborations His research group has been supported by DARPA , Sandia National Laboratories , NSF , Naval Surface Warfare Center , and Air Force Research Lab . Collaborations include AMD Research, Los Alamos National Lab, HP Labs, and Air Force Research Laboratory.

Ahmed El Alaouiمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Probability Theory
• Algorithms on Random Structures
• +۴ مورد دیگر

Ahmed El Alaoui is an Assistant Professor in the Department of Statistics and Data Science at Cornell University, with a secondary affiliation in the Department of Computer Science. He joined Cornell in 2021 after completing a postdoctoral fellowship at Stanford University under Andrea Montanari. His research focuses on high-dimensional statistics, probability theory, algorithms on random structures, and statistical physics, with particular emphasis on spin glasses and algorithmic thresholds. El Alaoui holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2018), advised by Michael I. Jordan, and a Master's from Ecole Normale Supérieure/Ecole des Ponts Paristech. His work bridges theoretical and applied domains, addressing challenges in high-dimensional inference, computational trade-offs, and probabilistic models. He has contributed to foundational results in random matrix theory, detection limits in spiked models, and algorithmic approaches to complex systems. His teaching includes courses on high-dimensional statistics, probability models, and theoretical computer science. He has been recognized for his innovative approaches to sampling and optimization in disordered systems, with publications in top journals like Annals of Probability and venues such as NeurIPS and FOCS. El Alaoui's research explores the interplay between statistical physics principles and algorithm design, aiming to uncover computational barriers in high-dimensional problems. His lab develops methodologies for analyzing complex systems and improving the efficiency of statistical estimation in challenging scenarios.

Michael Gastparمشاهده پروفایل

استاد

• Information Theory
• Signal Processing
• Communications
• +۳ مورد دیگر

Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.

David Whitneyمشاهده پروفایل

استاد

• Visual Perception
• Cognitive Neuroscience
• Ensemble Perception
• +۶ مورد دیگر

David Whitney is a Professor of Psychology at the University of California, Berkeley , with affiliations in Cognitive Science and Neuroscience . His research focuses on visual perception, particularly how humans process information in cluttered environments through mechanisms like ensemble perception , serial dependence , and perceptual crowding . He employs techniques such as psychophysics, fMRI, and TMS to study these phenomena. Whitney's recent work examines serial dependence in schizophrenia, emotion perception in crowds , and medical image analysis for dermatology and radiology. His studies reveal how the brain uses dynamic predictive templates and motion cues to stabilize perception despite neural processing delays. Publications span Current Biology , Nature Reviews Psychology , and PLoS ONE , with a strong emphasis on interdisciplinary applications of perceptual science. Scientific contributions include foundational studies on visuomotor control , blind spot filling-in , and holistic face processing . His lab investigates perceptual stability across eye movements, spatial localization, and cross-modal interactions. Whitney has received grants such as NIH EY018216 to support his research on motion-dependent visual coding. Detailed information about his work is available on the Whitney Lab website .

Lucas Jansonمشاهده پروفایل

دانشیار

• High-Dimensional Inference
• Statistical Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Lucas Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His research focuses on high-dimensional inference, statistical machine learning, and applications in genetics, political science, and climatology. He teaches courses such as Statistical Inference I, Reinforcement Learning, and Statistical Machine Learning. His work bridges theoretical advancements with practical applications, including contributions to robotics motion planning and microbiome data analysis. Key research areas include variable importance inference, safe reinforcement learning, compositional data analysis, and robust paleoclimate reconstructions. His methodologies are implemented in software packages like Floodgate, EigenPrism, and Fast Marching Tree (FMT*). He advises a dynamic group of PhD students and has mentored alumni now in academia and industry roles. Notable contributions include the development of model-X knockoffs for controlled variable selection, conditional randomization tests, and optimization algorithms for adaptive control systems. His work emphasizes statistical rigor while addressing real-world challenges in healthcare, environmental science, and robotics.

Aurora Maccaroneمشاهده پروفایل

پژوهشگر ارشد

• Single-Photon Imaging
• Underwater LiDAR
• Quantum Sensing
• +۴ مورد دیگر

Aurora Maccarone serves as an RAEng Research Fellow within the Institute of Photonics and Quantum Sciences at Heriot-Watt University's School of Engineering & Physical Sciences. Her work focuses on advanced photonics applications for challenging environments, particularly underwater and obscured conditions. Her research expertise spans: Single-photon LiDAR systems for underwater 3D imaging Photon-counting detector arrays for depth profiling Real-time reconstruction algorithms for obscurant-penetrating imaging Quantum sensing applications in marine environments Recent publications demonstrate consistent innovation in single-photon imaging techniques, with emphasis on underwater applications (2023-2024) and obscurant penetration (2022). Her work shows strong interdisciplinary connections between optical engineering, computational imaging, and environmental sensing. Key publications reveal growing impact in underwater LiDAR technology, with multiple high-citation papers on photon-efficient imaging systems. Dr. Maccarone actively supervises PhD students and has created significant research datasets. Her collaborations span international institutions, with notable contributions to sensor hardware development and computational imaging algorithms. Recent work shows increasing integration of machine learning techniques with traditional photon-counting approaches.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Rayan Saabمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Applied and Computational Harmonic Analysis
• +۵ مورد دیگر

Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.

Fariya Sharmeenمشاهده پروفایل

دانشیار

• Urban Planning
• Travel Behavior Modeling
• Social Network Analysis
• +۵ مورد دیگر

Fariya Sharmeen is an Associate Professor of Mobility and Urban Planning at KTH Royal Institute of Technology's School of Architecture and the Built Environment (ABE), affiliated with the Digital Futures Faculty. She holds a PhD from Eindhoven University of Technology and has previously served as Assistant Professor at Radboud University, Lecturer at Bangladesh University of Engineering and Technology (BUET), and research fellow at institutions including TU Delft and Imperial College London. Her research focuses on sustainable mobility transitions, social network dynamics in travel behavior, and policy responses to emerging transport technologies like MaaS and cycling innovations. Notable honors include the 2017 Piet Rietveld Award for transport research and a 2013 Royal Geographic Society award for transport geography. Sharmeen advises doctoral and master’s students on topics such as urban transformation and mobility governance. She coordinates courses like Sustainable Mobility (FAG3187) and leads projects like Bicification and ENCom. Her work integrates quantitative methods with policy analysis, addressing challenges in both global north and south contexts.

Helen Xuمشاهده پروفایل

استادیار

• Parallel Computing
• Cache-Efficient Algorithms
• High-Performance Computing
• +۶ مورد دیگر

Helen Xu is an Assistant Professor at Georgia Tech's School of Computational Science and Engineering (College of Computing). She holds a Ph.D. from MIT (2022) under Charles E. Leiserson and was a Grace Hopper Postdoctoral Fellow at Lawrence Berkeley National Lab (2022). Her research focuses on parallel algorithms, cache-efficient data structures, and high-performance computing. Xu has interned at Microsoft Research, NVIDIA Research, and Sandia National Laboratories, and her work has been supported by prestigious fellowships including the National Physical Sciences Consortium and Chateaubriand awards. **Education**: Ph.D., Computer Science, MIT, 2022 Postdoctoral Research, Lawrence Berkeley National Lab (2022) **Research Interests**: Parallel and cache-friendly algorithms Dynamic graph and data structure optimization Algorithm performance engineering Sparse matrix/tensor operations **Awards**: Grace Hopper Postdoctoral Scholar (2022), Best Artifact Award (PPoPP 2024), National Physical Sciences Consortium Fellowship, Chateaubriand Fellowship. **Advising & Teaching**: Advises PhD/M.S. students in parallel computing and high-performance systems. Teaches courses like CSE 6220 (Introduction to HPC) and CSE 6230 (HPC Tools). Supervised MIT M.Eng. projects on BP-Trees and parallel prefix sums. **Labs/Teams**: Active in Georgia Tech's High-Performance Computing community, collaborating with researchers like Aydın Buluç and Prashant Pandey on graph containers and dynamic data structures.

Behnaam Aazhangمشاهده پروفایل

استاد

• Signal and Data Processing
• Information Theory
• Neuroengineering
• +۴ مورد دیگر

Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.

Mohammed Aledhariمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .

• Nonlinear Control
• Robust Control
• Adaptive Control
• +۷ مورد دیگر

Mike Grimble is a Research Professor in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. His work is centered on advanced control systems with applications across automotive, aerospace, marine, and industrial domains. He is actively involved in theoretical and applied research, particularly in nonlinear and robust control methodologies. Research Interests: Theory and application of nonlinear and robust control for multivariable systems Adaptive control and estimation methods Benchmarking and performance assessment of control systems Condition monitoring and industrial applications Real-time control and embedded systems His recent publications highlight a strong trend in applying predictive and adaptive control techniques to electric vehicles, battery systems, underwater robotics, and industrial machinery. These works emphasize real-time implementation, energy optimization, and robustness—critical for modern sustainable and autonomous systems. Scientific Recognition and Activities: Invited speaker on the benefits and challenges of advanced control in industrial applications (2013) Contributor to UN Sustainable Development Goals, particularly in sustainable industry and innovation Active research output with over 158 publications, including journals, conferences, and book chapters Research Leadership and Funding: Principal Investigator on multiple EPSRC and RSE-funded projects Co-investigator in interdisciplinary initiatives such as the Medical Devices Doctoral Training Centre Organizer of international workshops on hybrid and predictive control Labs and Research Teams: He is associated with the Industrial Control Centre at the University of Strathclyde, a leading hub for control engineering research. His collaborations span departments and institutions, involving real-time LabVIEW implementations, hardware demonstrations, and partnerships with industry players like National Instruments and Quanser Inc.