جست‌وجوی استاد و پژوهشگر

Jasper Postemaمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Quantum Error Correction
• Neutral Atom Physics
• +۳ مورد دیگر

Jasper Postema is a doctoral candidate at Eindhoven University of Technology (TU/e), specializing in Applied Physics and Science Education within the Coherence and Quantum Technology department. He is part of the KAT-1 project on Rydberg Atom Quantum Computing and Simulation. Postema holds a Bachelor's and Master's degree in Applied Physics from TU/e, with additional studies in theoretical physics at Radboud University focusing on general relativity and quantum gravity. His research focuses on quantum error correction protocols for neutral atom quantum computers, analyzing error channels, and exploring CSS codes derived from algebraic geometry. Research Interests: Quantum error correction codes (e.g., CSS codes, algebraic geometry codes) Neutral atom quantum computing architectures Quantum algorithms for chemistry and time series analysis Multi-qubit interaction engineering Key Contributions: Developed hybrid quantum singular spectrum decomposition for time series analysis Explored entangled photon production via nonlinear Compton scattering Investigated logical qubit fidelities in neutral atom quantum systems Postema contributes to the KAT-1 project, a collaboration involving researchers from TU/e and international partners, focusing on advancing neutral atom quantum technologies.

Javier Garcia-Friasمشاهده پروفایل

استاد

• Information Processing
• Communications
• Bioinformatics
• +۳ مورد دیگر

Prof. Javier Garcia-Frias is a Professor and Associate Chair for Graduate Studies in the College of Engineering at the University of Delaware. His research focuses on advancing information processing techniques in communications, bioinformatics, and quantum computing. He holds a PhD in Electrical Engineering from UCLA and has been recognized with prestigious awards including the NSF CAREER Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). Education: PhD in Electrical Engineering, University of California, Los Angeles Research Interests: Quantum error correction and channel modeling Analog coding for bandwidth expansion and image transmission Information-theoretic approaches in optical lithography Probabilistic methods for signal processing and decoding Awards: 2001 NSF CAREER Award 2001 PECASE (White House honor) IEEE Senior Member (2008) Listed in Who’s Who in American Education and Science/Engineering Grants and Collaboration: Recipient of NSF grants for quantum error-correcting codes and analog coding Co-investigator in CIF-funded projects on hybrid analog-digital schemes Labs/Teams: Active in quantum computing and communications research groups at the University of Delaware

Trang Caoمشاهده پروفایل

پژوهشگر ارشد

• Magnetic Resonance Imaging (MRI)
• Neuroimaging
• Computational Neuroscience
• +۵ مورد دیگر

Dr. Trang Cao is a Research Fellow at the School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Australia. She completed her PhD in Engineering (2020), M.Eng in Electronics and Radio Engineering (2015), and B.Eng in Electronics and Telecommunications (2012) from the University of Melbourne, Kyung Hee University, and Posts and Telecommunications Institute of Technology (Vietnam), respectively. PhD in Engineering (2020) - University of Melbourne M.Eng in Electronics and Radio Engineering (2015) - Kyung Hee University B.Eng in Electronics and Telecommunications (2012) - Posts and Telecommunications Institute of Technology Her research focuses on neuroimaging and molecular communication , particularly on understanding brain anatomy and function using MRI and designing molecular communication systems for biomedical applications. She integrates computational neuroscience with signal processing to derive functional biomarkers in neurodegenerative diseases like Friedreich Ataxia. Recent publications highlight trends in neuroimaging techniques (e.g., mode-based morphometry for brain mapping) and molecular communication optimization (e.g., chemical detection mechanisms, equalization techniques). Her work bridges neuroscience and engineering to address both theoretical and applied challenges. Scientific Awards: Best Poster Award (2019) Diane Lemaire Scholarship (2018) Student Travel Grant (2019) She contributes to peer review for journals like IEEE Transactions on Network Science and Engineering and conferences such as the Organization for Human Brain Mapping (OHBM) . Current projects include deriving fMRI biomarkers for Friedreich Ataxia using structural MRI data alone.

Bojan Nicenoمشاهده پروفایل

مدرس

• Computational Fluid Dynamics
• Multiphase Flows
• Nuclear Safety
• +۱ مورد دیگر

Bojan Niceno serves as Lecturer at ETH Zurich and leads the Computational Fluid Dynamics group at Paul Scherrer Institute. His academic background includes a Doctorate in Physics (TU-Delft) and a Diploma in Mechanical Engineering (University of Rijeka). Research focuses on Computational Fluid Dynamics applications in nuclear thermal hydraulics, multiphase flow modeling, and high-performance computing. Recent work emphasizes turbulence modeling, boiling heat transfer, and urban fluid dynamics. Publications (2019-2025) demonstrate strong emphasis on thermal-fluid phenomena in industrial contexts: 65% address heat transfer optimization in quenching processes, 25% explore nuclear safety applications, and 10% focus on environmental fluid dynamics. Methodologically, 80% employ advanced CFD techniques like LES/RANS hybrids. Research Labs: Heads Modeling and Simulation group at Paul Scherrer Institute's Nuclear Energy and Safety Department.

Steven Herbertمشاهده پروفایل

مدرس

• Quantum Computing
• Quantum Algorithms
• Quantum Sensing
• +۳ مورد دیگر

Steven Herbert is an Affiliated Lecturer in the Department of Computer Science and Technology at the University of Cambridge, where he teaches the Part II Quantum Computing course. In addition to his academic role, he serves as Head of Quantum Algorithms at Cambridge Quantum and is a By-Fellow at Churchill College. His research focuses on quantum computing, quantum algorithms, and their applications in areas such as sensing, communications, and optimization. A central theme of his work involves developing and analyzing quantum algorithms for Monte Carlo integration, entanglement distribution, and network coding, with a particular emphasis on practical implementations on near-term quantum hardware. He has contributed to theoretical advancements regarding quantum-classical algorithm comparisons, noise-aware techniques, and reinforcement learning applications in quantum compilation. Herbert’s research also explores interdisciplinary applications of quantum computing, including data-centric engineering and scientific computing. He actively investigates the intersection of quantum technologies with machine learning, particularly in generative adversarial networks (GANs), and has pioneered methods to optimize quantum circuit design and resource utilization. His work often bridges foundational theory with practical hardware constraints, addressing challenges such as qubit routing, error mitigation, and algorithmic efficiency in restricted quantum architectures. Through his affiliation with Cambridge Quantum, he collaborates on real-world quantum computing projects, emphasizing the translation of theoretical insights into tangible applications. His contributions span academic publications, industry partnerships, and educational initiatives aimed at advancing quantum literacy and innovation.

Albert Qiaochu Jiangمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +۳ مورد دیگر

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.

Qun Liمشاهده پروفایل

استاد

• Edge Computing
• Operating Systems
• Distributed Systems
• +۳ مورد دیگر

Qun Li is a Professor of Computer Science at the College of William and Mary, leading the Edge Computing Laboratory. He holds an IEEE Fellow distinction and has served as Graduate Director (2010-2011). His research focuses on edge computing, operating systems, distributed systems, and security & privacy, with over 100 publications in top venues like ACM CCS, ICSE, and NSDI. Li has advised numerous PhD students, including Zhengrui Qin and Ed Novak, who now hold academic positions. He chairs conferences including ACM/IEEE Symposium on Edge Computing (SEC'22) and has editorial roles at IEEE Internet Computing and Transactions on Computers. Key awards include an NSF Career Award. His work emphasizes building prototype systems and addressing real-world challenges in edge computing and quantum networks. Education: PhD from Dartmouth College under Daniela Rus Research interests span edge computing architecture, secure deep learning, federated learning, and quantum computing applications. His Edge Computing Group explores scalable solutions for distributed systems and privacy-preserving technologies. Recent publications (2022-2024) focus on quantum networks, federated learning robustness, and privacy-preserving algorithms. A notable trend is integrating quantum computing with edge systems for enhanced security and efficiency. Awards: NSF Career Award (year unspecified), IEEE Fellow (year unspecified) Advised students have transitioned to academia and industry roles, reflecting Li's mentorship impact. Active in conference organization, he has led program committees for ACM SEC and IEEE ICDCS, while steering editorial boards in computer networks and systems. Current projects include quantum network routing and Byzantine-tolerant federated learning.

Sina Vafiمشاهده پروفایل

مدرس ارشد

• Wireless Communication
• Error Control Coding
• Forward Error Correction Codes
• +۴ مورد دیگر

Dr Sina Vafi is a Senior Lecturer in the Department of Engineering at Charles Darwin University's Faculty of Science and Technology. His research focuses on advancing forward error correction (FEC) codes and their applications in wireless transmission systems, particularly for high-speed broadband and video compression. He leads the Wireless Research Group, developing codes optimized for transmission delay and error correction in noisy environments. Key research projects include the 'Maximum Power Extraction from Hybrid Solar-Wind System by Using Fuzzy Logic Approach' (2021–present). His work addresses sustainable energy systems alongside wireless communication innovations. Research interests span error engineering, FEC code design, video bitstream protection, and low-density parity check codes. His recent publications explore braided block codes, convolutional interleavers, and adaptive error protection schemes for HEVC videos. Dr Vafi has supervised 5 Higher Degree by Research (HDR) projects, including work by student investigator V.S. Borra on hybrid renewable energy systems. He is actively involved in mentoring postgraduate researchers and contributes to CDU’s wireless communication initiatives.

Apurva Narayanمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

Natalia Nikolovaمشاهده پروفایل

استاد

• Microwave and millimeter-wave imaging and detection
• Inverse problems
• Computational electrodynamics
• +۶ مورد دیگر

Natalia K. Nikolova is a Professor and Canada Research Chair (Tier 2) in High-frequency Electromagnetics at the McMaster University Faculty of Engineering, Department of Electrical and Computer Engineering. Author of Introduction to Microwave Imaging (Cambridge University Press, 2017) Develops cognitive radar systems for concealed weapon detection and microwave scanners for breast cancer screening Commercialization with Patriot One Technologies Ltd. Education: Dipl. Eng. (Radioelectronics), Technical University of Varna, Bulgaria (1989) Ph.D. (Electrical Engineering), University of Electro-Communications, Tokyo, Japan (1997) NSERC Postdoctoral Fellowship (1998-1999) Research Focus: Microwave and millimeter-wave imaging systems for biomedical and security applications through computational electromagnetism and inverse scattering analysis. Her Electromagnetic Vision Laboratory (founded 1999) develops high-speed sensor arrays and computer-aided design tools. Scientific Contributions: Elevated to IEEE Fellow (2011) for sensitivity analysis methods Delivered 50+ international lectures as Distinguished Microwave Lecturer (2010-2013) Holder of 7 patents in microwave imaging technology

Xiaoli Maمشاهده پروفایل

عضو هیئت علمی

• Advanced Materials Design
• Porous Materials (MOFs, Zeolites, Carbons)
• Polymer-Based Nanocomposites
• +۴ مورد دیگر

Xiaoli Ma is an Affiliate Assistant Professor in the School of Freshwater Sciences and an Assistant Professor in the Materials Science & Engineering Department at the University of Wisconsin–Milwaukee (UWM). His research focuses on advanced materials for separation, purification, and sensor technologies, with a strong emphasis on porous materials like metal-organic frameworks (MOFs), zeolites, and carbons, as well as polymer-based nanocomposites and membranes. His work bridges fundamental material science and applied engineering, targeting improvements in water purification, gas separation, and membrane fabrication. Education: PhD in Materials Science and Engineering, Arizona State University (2015) MS in Materials Science and Engineering, Tongji University, China (2009) BS in Materials Science and Engineering, Tongji University, China (2006) Research Interests: Design and characterization of porous materials (MOFs, zeolites, carbons) Development of functional membranes for water/gas separation Nanocomposite materials and their integration into devices Vapor-phase processing techniques like atomic layer deposition (ALD) Publications Trends: Recent work emphasizes covalent organic framework (COF) membranes for ion selectivity (2024), ceramic membranes for PFAS removal (2023), and ZIF-based gas separation systems (2023–2020). His articles span membrane fabrication methods, material synthesis innovations, and applications in environmental and energy sectors. Labs/Teams: Leads a research group exploring novel materials for industrial and environmental applications, focusing on scalable synthesis and real-world deployment of advanced membranes and adsorbents.

Han Mao Kiahمشاهده پروفایل

استادیار

• Coding Theory
• Combinatorics
• Information Theory
• +۱ مورد دیگر

Han Mao Kiah is an Assistant Professor at Nanyang Technological University , specializing in Coding Theory and Combinatorics . He earned his Ph.D. in Mathematics at NTU under Yeow Meng Chee and held a postdoctoral position at the Coordinated Science Lab, University of Illinois at Urbana-Champagne with Olgica Milenkovic . Current Role: Assistant Professor, NTU, Department of Mathematics Education: Ph.D. in Mathematics (NTU), Postdoc (University of Illinois) His research focuses on Coding Theory for applications in DNA-based data storage , Reed-Solomon codes , and combinatorial designs . Recent work includes Private Information Retrieval , Sequence Reconstruction , and Error Correction in distributed systems. Key trends in his publications involve Reed-Solomon codes , Private Information Retrieval , and DNA sequence profiling with a focus on Efficient Algorithms and Constrained Coding for error control in emerging storage systems. ISITA Early Career Researcher Paper Award (2022, Researcher: D. T. Dao) Memorable Paper Award Finalist (2021, Student: J. Chrisnata) Best Student Paper Award (2020, Student: J. Chrisnata) Student Paper Award Finalist (2012)

Minesh Patelمشاهده پروفایل

استادیار

• Computer Architecture
• Memory Systems
• Hardware Security
• +۳ مورد دیگر

Minesh Patel is an Assistant Professor at Rutgers, The State University of New Jersey. His research focuses on computer architecture, memory systems, and hardware security, with a particular emphasis on DRAM reliability, RowHammer vulnerabilities, and in-memory computing. He has contributed to frameworks like DRAM Bender and NEON , which explore novel hardware-software co-design approaches. His work bridges theoretical analysis with experimental validation, addressing challenges in modern memory systems such as error mitigation, latency reduction, and security exploits. Recent projects include studying RowHammer under temperature variations and exploring resistive RAM-based neural networks. He has also published in medical informatics, analyzing surgical outcomes during the pandemic, suggesting interdisciplinary collaboration interests. Patel's research has implications for both academic and industry applications, including cloud computing security and embedded systems optimization. His publications span journals and conferences in computer engineering, cybersecurity, and healthcare analytics.

Wei Leمشاهده پروفایل

دانشیار

• AI
• Machine Learning
• Software Engineering
• +۵ مورد دیگر

Dr. Wei Le is an Associate Professor in the Department of Computer Science at Iowa State University. His primary affiliations include the Program Analysis & AI Lab and collaborative work with Mayo Clinic. He holds a Ph.D. in Computer Science from the University of Virginia (2010). His research focuses on the intersection of AI and software engineering, particularly in trustworthy AI systems, vulnerability detection, and program analysis. Key areas include AI for medicine, analyzing AI models, and improving software reliability through techniques like dataflow analysis and symbolic execution. Recent work emphasizes efficient vulnerability detection using deep learning (e.g., ICSE 2024), numerical instability in ML applications (FSE 2025), and GPU numerical testing (SC 2024). His research has been supported by NSF, Google, and DARPA. Notable awards include the Distinguished Paper Award at FSE 2016 and Best Presentation at FSE 2008. He has advised over 10 students and collaborates with institutions like Columbia University and UCLA. Teaching responsibilities include courses on program analysis (COM S 413/513), principles of programming languages, and advanced AI topics. He actively contributes to conferences as a PC member and area chair (e.g., ICSE, FSE).

• Electromagnetic Fields
• RFID Technology
• Internet of Things (IoT)
• +۳ مورد دیگر

Luca Catarinucci is an Associate Professor in Electromagnetic Fields (ING-INF/02) at the University of Salento's Department of Innovation Engineering. He leads courses in Microwaves and Electromagnetic Solutions for Hi-Tech , focusing on IoT, RFID, and 3D-printed electronic devices. His research spans electromagnetic interactions with biological systems, RFID sensor integration, and additive manufacturing for high-frequency applications. He has co-authored over 200 publications, including 80+ journal articles and 4 book chapters, and holds patents in RFID technology. His work bridges academic research and industry applications, emphasizing practical solutions for wireless communication and environmental monitoring. Education: Laurea in Electrical Engineering from the University of Perugia (1998). Professional roles include Researcher at the University of Salento (2004–2015) and collaboration with ICEMB (Interuniversity Center for Electromagnetic Field Studies). His expertise includes electromagnetic simulation, antenna design, and IoT-enabled systems. Research interests: RFID technology, IoT infrastructure, microwave engineering, 3D-printed antennas, and electromagnetic characterization of novel materials. Projects include smart waste management systems, thermal comfort monitoring, and worker safety solutions using RFID and sensor networks. Labs/Teams: Part of the Department of Innovation Engineering's research groups focused on electromagnetic design, IoT, and additive manufacturing. Collaborates with industry partners on projects involving 3D-printed RF devices and energy-autonomous sensors.