جست‌وجوی استاد و پژوهشگر

Aleksander Kubicaمشاهده پروفایل

استادیار

• Quantum Information Science
• Quantum Error Correction
• Fault Tolerance
• +۳ مورد دیگر

Aleksander Kubica is an Assistant Professor of Applied Physics at Yale University, specializing in quantum information science with a focus on quantum error correction and fault tolerance. His research bridges quantum many-body physics and topological codes, particularly exploring applications in superconducting circuits and quantum architectures. He holds a Ph.D. from the California Institute of Technology and a B.S. from the University of Warsaw. Dr. Kubica's work addresses foundational challenges in scalable quantum computing, including optimizing error correction protocols, developing fault-tolerant architectures, and analyzing the intersection of quantum metrology with error mitigation. Recent contributions include advancements in erasure qubits, correlated noise decoding, and topological code adaptations. His research often involves interdisciplinary approaches, combining theoretical physics with algorithm design and hardware-efficient solutions. Key themes in his publications include improving error correction thresholds, designing low-overhead quantum architectures, and exploring novel decoding strategies for topological codes. While no specific awards are listed, his active research trajectory and contributions to quantum computing indicate significant scholarly engagement in the field.

Eugene Tangمشاهده پروفایل

استادیار

• Quantum Information Theory
• Quantum Computing
• Quantum Error Correction
• +۳ مورد دیگر

Eugene Tang is an Assistant Professor in the Department of Mathematics and Physics at Northeastern University. His research focuses on quantum information theory and the theoretical limitations of quantum computing, particularly quantum error correction and efficient protocols using high-rate codes. He received his PhD from the California Institute of Technology in 2021. Dr. Tang's research interests include quantum error correction, the development of efficient quantum protocols surpassing conventional schemes, and the study of quantum algorithms such as QAOA. He explores the theoretical boundaries of quantum computing, with a focus on optimizing error detection and decoding methods for quantum LDPC codes and subsystem codes. His work also intersects with quantum gravity, particularly in the context of black hole interiors and bulk geometry construction through tensor methods. His recent publications highlight advancements in quantum error correction, including optimal locality in subsystem codes and efficient decoding strategies for quantum LDPC codes. His work on variational quantum optimization addresses challenges in scalability, such as QAOA's performance at large qubit scales and symmetry-related obstacles. Earlier contributions include research on superoscillations and hybrid quantum-classical algorithms for graph coloring. No scientific awards or grants are explicitly mentioned in the provided information. No specific labs or teams are associated with his work in the given data.

Eunjung Leeمشاهده پروفایل

استادیار

• Educational Technology
• Gifted Education
• Teacher Education
• +۳ مورد دیگر

Eunjung Lee serves as Assistant Professor of Business Analytics in the College of Business at Lewis University, bringing extensive industry experience from Samsung and LG alongside prior academic roles at Indiana State University. Her expertise bridges business analytics and educational technology with a pronounced focus on equity-driven research. Her educational foundation includes: Ph.D. in Information Technology Management, University of Wisconsin-Milwaukee M.B.A., Korea University B.S., Kwangwoon University Dr. Lee's research trajectory evolved from business analytics toward educational equity, with recent work emphasizing computational thinking in mathematics education, teacher preparation models, and machine learning applications for gifted identification. She investigates how digital tools transform pedagogy while addressing systemic barriers for underrepresented students. Analysis of her 2022-2025 publications reveals dominant themes in equitable gifted identification through cross-cultural validation of the HOPE rating scale, computational thinking integration in teacher training, and longitudinal studies of enrichment program impacts. Her methodology consistently combines quantitative analysis with equity-centered frameworks. No scientific awards were documented in available sources. While specific advising details remain unreported, her courses in Business Intelligence and Forecasting demonstrate applied analytics instruction. Research grants weren't specified in source materials. Collaborative structures like the Lowell Stahl Center for Entrepreneurship provide institutional context, though dedicated labs or research teams weren't explicitly referenced.

Nilanjan Banerjeeمشاهده پروفایل

استاد

• Mobile Computing
• Pervasive Computing
• Sensor Systems
• +۶ مورد دیگر

Dr. Nilanjan Banerjee is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He leads the Mobile, Pervasive, and Sensor System Lab, focusing on embedded and distributed systems for mobile, pervasive, and sustainability-based computing. His research spans renewable energy-driven systems, health diagnostics, mobile usability, and experimental testbed design. He holds a Ph.D. in Computer Science from the University of Massachusetts (2009), an M.S. from the same institution (2007), and a B.Tech. (Hons) from the Indian Institute of Technology (2004). Dr. Banerjee's work emphasizes interdisciplinary innovation, including low-power wearable devices for health monitoring (e.g., RestEaZe), cybersecurity frameworks for embedded systems (e.g., CARE), and sensor-based solutions for environmental sustainability. His contributions address challenges in mobility, energy efficiency, and accessibility, such as the Presight sidewalk localization system for visually impaired riders and the Inviz gesture-recognition textile sensors. His recent publications (2018–2021) reflect a focus on health technology, cybersecurity, and sustainable systems. Notable trends include: Integration of machine learning with sensor data for medical applications (e.g., sleep analysis, infection detection) Development of lightweight security protocols for embedded devices Exploration of renewable energy solutions for mobile and sensor networks No scientific awards are explicitly listed in the provided text. His academic advising and grant activities are not detailed here, but his lab's active research suggests significant collaborative projects. The lab also pioneers educational strategies in mobile app development and inclusive faculty recruitment through peer education programs like STRIDE.

Marko Cetinaمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Optics
• Atomic Physics
• +۳ مورد دیگر

Marko Cetina is an Assistant Professor of Physics and the Department of Electrical and Computer Engineering at Duke University, affiliated with the Duke Quantum Center. He holds a B.S. from the California Institute of Technology (2004) and a Ph.D. from the Massachusetts Institute of Technology (2011). His research focuses on quantum computing, quantum optics, and atomic physics, with particular emphasis on trapped-ion systems, quantum error correction, and quantum simulation. He teaches courses including Atomic Physics and Quantum Optics, Advanced Topics in Physics, and introductory mechanics. His work explores foundational aspects of quantum mechanics and applied technologies for scalable quantum computers. Notable contributions include demonstrating multi-body interactions in trapped ions and advancing measurement-induced quantum phases. His team actively develops fault-tolerant qubit control and novel quantum architectures using cavity-mediated systems. Publications highlight advancements in quantum error correction protocols, lattice gauge theories, and quantum verification protocols. While no explicit awards are listed, his involvement in high-impact studies like the Duke Quantum Center underscores his contributions to the field. Current projects include optimizing stabilizer codes for logical qubit memory and simulating NMR experiments via digital quantum methods. His lab collaborates across disciplines to bridge theoretical quantum mechanics with experimental implementations in trapped-ion platforms.

David Hongمشاهده پروفایل

استادیار

• Big Data Analysis
• Low-Rank Matrix and Tensor Methods
• Unsupervised Learning
• +۵ مورد دیگر

David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.

• Mobile and cloud computing
• Energy-efficient computing
• Image and video processing
• +۲ مورد دیگر

Yung-Hsiang Lu is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on mobile/cloud computing, energy-efficient computing, and image/video processing. He holds a BSEE from National Taiwan University (1992), an MSEE (1996), and a PhD (2002) from Stanford University. Dr. Lu's academic background includes significant contributions to VLSI and circuit design, with primary emphasis on computer engineering. His work spans theoretical and applied domains, including optimizing neural networks for edge devices, securing deep learning models, and leveraging large language models for software development. Recent research trends in his articles emphasize energy efficiency in AI systems, interdisciplinary applications of transformers (e.g., music analysis), and challenges in model interoperability and security. His publications also highlight innovations in global camera networks and real-time visual data analysis. While no specific grants or awards are explicitly mentioned, his extensive list of publications reflects sustained academic engagement. His educational contributions include developing C programming resources and teaching large-scale image processing using global camera networks. Dr. Lu's professional address is at Purdue's Materials and Electrical Engineering Building in West Lafayette, Indiana, where he maintains an active research lab focused on embedded systems and low-power computing innovations.

Naoki Saitoمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Graph Signal Processing
• Multiscale Transforms
• +۶ مورد دیگر

Naoki Saito is a Professor in the Department of Mathematics at the University of California, Davis, and the Director of the UC Davis TETRAPODS Institute of Data Science (UCD4IDS). His research lies at the intersection of applied mathematics, signal processing, and data science, with a focus on multiscale analysis and harmonic analysis on graphs and networks. His research interests include Applied and Computational Harmonic Analysis , Graph Signal Processing , Multiscale Transforms , Wavelets , Spectral Graph Theory , and Mathematical Data Representation . He develops theoretical frameworks and practical algorithms for analyzing complex datasets, particularly through the use of Laplacian eigenfunctions and multiscale basis dictionaries. The recent publications reflect a strong trend toward graph-based signal processing , scattering transforms , and topological data analysis . His work emphasizes the construction of natural, adaptive bases for signals on graphs and simplicial complexes, enabling efficient and interpretable data analysis. The integration of harmonic analysis with machine learning techniques is a recurring theme. Although no specific scientific awards are listed in the provided texts, his sustained scholarly output and leadership in the field are evident. Dr. Saito advises a number of students and postdoctoral researchers, including J. Irion, Y. Shao, H. Li, and others. His research has been supported by various grants, though specific funding sources are not detailed in the provided materials. He leads the UCD4IDS, a research institute focused on data science, indicating active involvement in collaborative, interdisciplinary research and academic leadership.

Chris Quattroمشاهده پروفایل

استادیار

• Urban Planning
• Environmental Policy
• Land Use Planning
• +۳ مورد دیگر

Chris Quattro is an Assistant Professor in the Department of Geography and Planning, currently based in Boone. Previously, they held roles as a fellow at the University of Pennsylvania and an Associate Adjunct Professor at Temple University. Their work emphasizes urban planning, environmental policy, and social justice, focusing on zoning practices, climate resilience, and equitable urban development. Research interests include climate change adaptation in mountain cities, zoning laws' impact on low-income populations and communities of color, and innovative land use strategies like infill development. They have also explored the structure of urban planning frameworks and fostering diversity in planning governance. Chris has contributed to studies on zoning's spatial equity, housing disparities, and the role of setback regulations in urban design. While no awards are listed, their publications reflect a commitment to advancing inclusive urban policies and environmental sustainability. Prior professional experience includes directing family and children’s programs, including a Parent/Child mobility and gymnastics initiative and STEM recreational activities in Upstate NY. They are actively engaged in building faculty networks through the EFPN and promoting teaching excellence.

• Artificial Intelligence
• Neural Networks
• Quantization
• +۳ مورد دیگر

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

Paul Bealeمشاهده پروفایل

استاد

• Theoretical Physics
• Statistical Mechanics
• Thermodynamics
• +۹ مورد دیگر

Paul Beale is a Professor in the Department of Physics at the University of Colorado Boulder, where he has been a faculty member since 1984 and currently serves as Chair of the Department. He holds a Ph.D. in Physics from Cornell University (1982) and a B.S. from the University of North Carolina at Chapel Hill (1977). His academic career has been centered at CU Boulder, where he advanced from Assistant to Associate and then to full Professor. Ph.D., Physics, Cornell University, 1982 B.S., Physics, University of North Carolina at Chapel Hill, 1977 Paul Beale's research is in theoretical condensed matter physics, with a focus on statistical mechanics and thermodynamics. His work spans a broad range of topics including phase transitions, critical phenomena, ferroelectrics, hysteresis, grain boundary kinetics, and Monte Carlo methods. He has made significant contributions to the exact calculation of energy distributions in the two-dimensional Ising model and developed a novel class of scalable parallel pseudorandom number generators based on Pohlig-Hellman exponentiation ciphers. His research integrates analytical theory, computational modeling, and applications in materials science and complex systems. His recent publications reflect a strong trend in computational and theoretical physics, particularly in developing robust algorithms for simulation and advancing fundamental understanding of phase behavior in soft and condensed matter systems. His work on pseudorandom number generation has implications for high-performance computing and statistical sampling. He has also contributed to physics education research, focusing on curriculum transformation and quantum mechanics instruction. Notable scientific contributions include: Co-author of the textbook Statistical Mechanics (with R.K. Pathria), a standard graduate-level reference. Development of exact methods for the 2D Ising model partition function. Innovation in scalable pseudorandom number generation for parallel computing. Paul Beale has advised numerous students and collaborated widely, though specific advisees are not listed. He has held significant administrative roles, including Director of the Honors Program and Associate Dean for Natural Sciences. He maintains active research through publications, code development, and academic leadership, with no indication of retirement. He is associated with the following labs and research groups: Condensed Matter Laboratory (former Director, 1999–2001) Statistical Physics and Computational Modeling Group (implied through research topics and code repositories)

Yuzhu Liمشاهده پروفایل

استاد

• Digital Transformation
• Blockchain
• No-code/Low-code Development
• +۷ مورد دیگر

Dr. Yuzhu Li is a Professor at the Department of Decision & Information Sciences , Charlton College of Business , University of Massachusetts Dartmouth . Her expertise spans Digital Transformation , Blockchain , Information Systems Development , and Project Management . She teaches courses in Customer Analytics , Business Analytics , and Agile Development , emphasizing CRM strategies , Data Mining , and Information Visualization . PhD in Business Administration, University of Central Florida (2008) Master of Technology Management , Washington State University (2002) BA in Information Systems , Nankai University (1999) Her research focuses on Emerging Technologies like blockchain and AI, Behavioral Project Management , and E-commerce frameworks. Key themes include Team Resilience , Control Systems in Interorganizational Teams , and Digital System Security , with recent articles analyzing Bitcoin vulnerabilities and collaborative project governance. Dr. Li serves as Treasurer of the New England Association of Information Systems Chapter, an Editorial Review Board Member of the Project Management Journal, and Associate Editor of the Nankai Business Review International. She actively contributes to the academic community through roles in international conferences and editorial committees.

• Digital Design Education
• Low-Power Electronics
• Hardware-Software Co-Design
• +۳ مورد دیگر

Dr. Mark Johnson is a Senior Lecturer at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette, Indiana. His role focuses on advancing educational methodologies and curriculum development in electrical and computer engineering. He is affiliated with Purdue’s Brown Family Hall and maintains an active research agenda centered on digital design education, low-power electronics, and hardware-software co-design. Research interests include integrating industry standards (e.g., ABET) into teaching, FPGA applications, and fostering student success through innovative pedagogical frameworks. His work emphasizes practical tools like SOC prototyping and embedded systems design, alongside active learning strategies and technology integration in classrooms. Publications highlight a consistent focus on educational innovations, such as tablet-based documentation, dual-core prototyping challenges, and FPGA performance analysis. Despite no listed formal awards, his contributions to curriculum design and student project resources have significantly impacted undergraduate education in electrical engineering. No formal advisees or grants are listed, but his teaching portfolio includes senior project courses on x86 motherboards and RTL code structures. His work reflects a commitment to bridging academic theory with hands-on engineering practice.

Joshua I Glaserمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Control of Movement
• Interpretable Machine Learning
• +۷ مورد دیگر

Joshua I Glaser is an Assistant Professor at Northwestern University, holding dual appointments in the Department of Neurology (Feinberg School of Medicine) and the McCormick School of Engineering. His research focuses on computational neuroscience, particularly the neural control of movement and interpretable machine learning. Glaser earned a BS in Physics and Math from the University of Illinois Urbana-Champaign (2011), a PhD in Neuroscience from Northwestern University (2018), and completed postdoctoral training at the University of Pennsylvania and Columbia University. Research Interests: Glaser’s lab develops interpretable machine learning tools to understand neural computations underlying movement and behavior. Key areas include neural population interactions, flexible behavior control, and disease-related neural dynamics. Projects include interpretable dimensionality reduction, decoding models linking neural activity to behavior, and multi-population neural interaction analysis. Lab & Collaborations: The Glaser Lab collaborates with experimental neuroscientists and statisticians. Current projects involve developing novel methods for analyzing neural data while maintaining interpretability. They host a team of PhD, master’s, and undergraduate students. Grants & Awards: While no specific awards are listed, his work reflects sustained funding in computational neuroscience and engineering. Code & Resources: Lab-developed tools include Neural_Decoding (machine learning for neural decoding), SSM (state-space models), and Sparse Component Analysis (interpretable latent factor analysis). These are publicly available on GitHub.

• Housing Law
• Community Development
• Litigation Strategy
• +۳ مورد دیگر

Stefan H. Krieger is The Richard J. Cardali Distinguished Professor in Trial Advocacy and Professor of Law at Hofstra University, where he has taught since 1992 after 13 years of clinical teaching at the University of Chicago Law School and Southern Methodist University School of Law. As Director Emeritus of Clinical Programs, he specializes in clinical legal education and Evidence, directing the Law Reform Advocacy Clinics that serve Nassau County communities. His educational background includes a JD from the University of Illinois Urbana-Champaign and a BA from the University of Chicago. Following law school, he clerked for Judge Hubert L. Will of the U.S. District Court for the Northern District of Illinois and worked as a staff attorney at Chicago's West Side Office of Legal Assistance Foundation from 1977-1979. Professor Krieger's research centers on housing and community development law, where his clinic has secured landmark victories including a 2004 New York Court of Appeals decision protecting eviction notice rights and a 2008 Supreme Court victory invalidating rent guidelines for 12,000 tenants. His scholarly work explores litigation strategy, legal reasoning development, and innovative pedagogical methods like film storyboarding for trial preparation, with significant publications on experiential education assessment and conceptual competence. His publication trends reveal deep integration of clinical practice with legal education theory, consistently examining how experiential learning shapes reasoning skills while maintaining active engagement in housing justice advocacy. Earlier work focused on public utility regulation, demonstrating scholarly range across legal domains. Scientific Awards: No awards, fellowships, or medals were documented in the source material. Through the Law Reform Advocacy Clinics, Professor Krieger has supervised generations of students in high-impact litigation, including the Farmingdale discrimination case settlement that compelled housing restoration for displaced immigrant families. His clinic model uniquely bridges student training with community advocacy, though specific grant funding details remain undisclosed in available records. He directs the Law Reform Advocacy Clinics' housing justice initiatives and collaborates with external partners like NBC/Universal consultants to develop innovative trial preparation methodologies, maintaining the clinic's role as both educational laboratory and community resource in Nassau County.