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Igor Aizenberg is a Professor and Chair of the Department of Computer Science at Manhattan College (Riverdale, NY) since 2016. He holds a Ph.D. in Computer Science (1986) and M.S. in Mathematics (1982) from Dorodnicyn Computing Center (Moscow) and Uzhhorod National University (Ukraine), respectively. Ph.D. in Computer Science, Dorodnicyn Computing Center, USSR (1986) M.S. in Mathematics, Uzhhorod National University (1982) His research focuses on complex-valued neural networks (CVNNs) with multi-valued neurons (MVN), emphasizing classification , pattern recognition , intelligent image processing , and spectral techniques . He pioneered the MLMVN (Multilayer Neural Network with Multi-Valued Neurons), which excels in learning speed and generalization for nonlinear separability problems like XOR. His recent publications (2025-2016) highlight advancements in CVNNs for frequency domain image filtering , power system fault diagnosis , DC-DC converter monitoring , and CNNMVN (Convolutional Neural Networks with Multi-Valued Neurons). Key trends include edge detection , impulse noise filtering , and financial time series prediction . $300,000 NSF grant (2009-2012) Fulbright Specialist Awards (2014, 2015) Senior Member, IEEE Aizenberg has developed MATLAB simulators and executable tools for MLMVN and CNNMVN, freely available for non-commercial research. These tools address classification , regression , and multi-class problems . He has also contributed to books and tutorials on CVNNs, including the 2011 Springer monograph Complex-Valued Neural Networks with Multi-Valued Neurons .

• complex-valued neural networks
• classification
• pattern recognition
• +2 more

Jonathan Chisum is an Associate Professor in the Department of Electrical Engineering at the University of Notre Dame's College of Engineering. His research focuses on developing advanced millimeter-wave (MMW) circuit and antenna systems for wireless communications and sensing, leveraging cutting-edge materials and technologies to achieve unprecedented performance and functionality. MMW artificial materials/metamaterials for GRIN lens antennas in satcom and 5G networks Phase-change materials for programmable antennas and circuits Nonlinear microwave/magnetic transceivers and sensors for low-power applications He has contributed to hybrid optimization workflows for lens design, 3D-printed GRIN lenses, and energy-efficient wireless solutions. His work has been recognized through awards such as the ONR Senior Research Fellow designation. ONR Senior Research Fellow

• Millimeter-Wave Technology
• Antenna Design
• Wireless Communications
• +7 more

Juan G. Restrepo is a Professor in the Department of Applied Mathematics at the University of Colorado Boulder, where he maintains an active research program in complex systems and network science. His work bridges theoretical mathematics with applications in physical, biological, and social systems. Professor Restrepo's research focuses on complex network dynamics, particularly synchronization phenomena, nonlinear dynamics and chaos, spread of information and disease in social networks, and criticality in brain networks. His work spans theoretical foundations of network science while addressing real-world applications from cardiac dynamics to social contagion processes. He has made significant contributions to understanding higher-order interactions in networks through hypergraphs and simplicial complexes, revealing new synchronization phenomena and dynamical behaviors. Analysis of his recent publications reveals a strong trajectory toward higher-order network structures, with increasing focus on hypergraphs, simplicial complexes, and topological approaches to network dynamics. His work connects mathematical theory with applications in neuroscience, quantum systems, epidemiology, and social dynamics, demonstrating the unifying power of network science across disciplines. Recent papers show growing integration of machine learning techniques with traditional dynamical systems approaches. Professor Restrepo maintains an active research group investigating fundamental questions in network dynamics while exploring applications to real-world systems. His work on criticality in neural networks and resource transport mechanisms has implications for understanding brain function and developing more efficient networked systems.

• Complex Networks
• Synchronization
• Nonlinear Dynamics
• +5 more

Andreas Weichselbaum is a Staff Scientist at Brookhaven National Laboratory's Condensed Matter Physics and Materials Science Department since 2018. His research focuses on tensor network methods, quantum many-body systems, and non-Abelian symmetries in quantum materials. 2004 Ph.D. in theoretical condensed matter physics, Ohio University 1997 M.Sc. experimental semiconductor physics, Johannes Kepler University His research interests include: Tensor Network Simulations for strongly correlated quantum systems Quantum Impurity Models with applications to dynamical mean field theory Quantum Information and topological aspects of quantum states Non-Abelian Symmetries in lattice models Notable scientific contributions include the development of the QSpace tensor library for non-Abelian symmetries and groundbreaking work on exponential thermal tensor network approaches. His publications reveal: Quantum coherence in spin chains Advances in thermal state simulations Entanglement detection methods Non-equilibrium Kondo physics Topological phase transitions Gauge theory mass gaps Scientific recognition includes: Heisenberg fellow, German Research Foundation (2015) Notable Alumni Award, Ohio University (2019) Collaborators include Alexei Tsvelik (BNL), Jan von Delft (LMU Munich), and Wei Li (CAS Beijing). He maintains open-access resources through his QSpace library and arXiv publication portal.

• Condensed Matter Physics
• Quantum Many-Body Systems
• Quantum Information Science
• +3 more

Andreas H. Hielscher is Professor and Chair of the Department of Biomedical Engineering at the New York University Tandon School of Engineering. He leads the recently established Department of Biomedical Engineering and directs research in his Clinical Biophotonics Laboratory (CBL). Prior to joining NYU Tandon in summer 2021, he spent 20 years at Columbia University where he chaired the Provost's Tenure Track Advisory Committee and built a distinguished career in biomedical optics research. Dr. Hielscher received his education from the following institutions: Postdoctoral Fellow, Division Biosciences and Biotechnology, Los Alamos National Laboratories (1995-1998) Ph.D., Department of Electrical and Computer Engineering, Rice University (1991-1995) "Diplom" (~Master of Science), Department of Quantum Optics, University of Hannover, Germany (1989-1991) Dr. Hielscher's research focuses on developing optical tomography as a viable biomedical imaging modality for clinical practice. His work centers on creating patient-centered approaches that address modern precision medicine through cutting-edge imaging hardware and software. His team develops technology that provides 3-dimensional distributions of physiologically relevant parameters such as oxygen saturation and total hemoglobin concentrations, including wearable devices for continuous patient monitoring. The Clinical Biophotonics Laboratory applies this technology in clinical and preclinical studies focusing on breast cancer, arthritis, peripheral artery disease (PAD), diabetic foot syndrome (DFS), and real-time brain activity monitoring. Analysis of Dr. Hielscher's recent publications reveals a strong translational research trajectory, moving from fundamental algorithm development to clinical validation across multiple medical specialties. His work demonstrates particular innovation in developing practical, wearable, and non-contact imaging systems that can be readily implemented in clinical settings, with significant contributions to breast cancer monitoring during chemotherapy, vascular medicine (PAD and DFS), and rheumatology (arthritis and lupus). Dr. Hielscher has received research support from multiple prestigious funding sources: National Heart, Lung, and Blood Institute (Grant No. NHLBI-1R01-HL115336) Wallace H. Coulter Foundation Society of Vascular Surgery Columbia University Fu Foundation School of Engineering and Applied Science New York University Tandon School of Engineering Dr. Hielscher's laboratory includes researchers such as Nisha Maheshwari and Alessandro Marone, and maintains strong collaborations with clinicians to develop state-of-the-art biomedical optical imaging technology. The laboratory's recent work has demonstrated significant clinical impact, particularly in the 2022 study that achieved 93% accuracy in predicting wound healing outcomes for peripheral artery disease patients within one month after surgical intervention.

• Biomedical Imaging
• Medical Instrumentation
• Biophotonics
• +12 more

Song Han is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). His research focuses on efficient deep learning computing, bridging algorithm and hardware design to enable scalable AI systems. PhD in Electrical Engineering from Stanford University Research Interests Efficient Deep Learning Neural Network Compression Hardware-Aware Transformers Sparse Attention Mechanisms Quantization Techniques Edge and IoT Computing Recent Publication Trends highlight advances in LLM optimization, diffusion model quantization, and quantum-classical co-design. His work emphasizes reducing computational costs while maintaining model fidelity. Scientific Awards Best Paper, ICLR and FPGA Symposium NSF CAREER Award MIT Technology Review 35 Innovators Under 35 Collaborations include the MIT-IBM Watson AI Lab, focusing on AI hardware and system co-design. Many of his techniques are integrated into commercial AI chips.

• Efficient Deep Learning
• Neural Network Compression
• Hardware-Aware AI
• +7 more

Sangchul Oh serves as an Assistant Professor in the School of Physics and Applied Physics at Southern Illinois University, Carbondale, where he joined the faculty in Fall 2023 after extensive postdoctoral experience. He earned his Ph.D. in theoretical condensed matter physics from Pohang University of Science and Technology, South Korea. His research spans quantum computing , quantum information , and quantum machine learning , with specific expertise in quantum teleportation, entanglement of many-body systems, superconducting qubits, quantum algorithms, random circuit sampling, geometric phases, Aharonov-Casher effects, single-electron transistors, and solar cell simulations. His work bridges theoretical physics with practical quantum computing applications. Dr. Oh directs the Quantum Computer Lab at SIU, which focuses on advancing quantum information science through computational and theoretical approaches. No scientific awards are documented in the available institutional records. Details regarding graduate student advising and external research grants are not specified in the current faculty profile, though he teaches advanced courses including Phys 530A/B Quantum Mechanics.

• Quantum Computing
• Quantum Information
• Quantum Machine Learning
• +2 more

Zhaojun Bai is a Distinguished Professor in the Department of Computer Science and Department of Mathematics at the University of California, Davis, and a Faculty Scientist at Lawrence Berkeley National Laboratory's Scalable Solver Group. He obtained his PhD from Fudan University, China, and completed postdoctoral training at the Courant Institute of New York University. Research Interests Professor Bai's research spans several key areas of computational mathematics: Numerical Linear Algebra and Matrix Computations : Developing algorithms for eigenvalue problems and matrix functions Mathematical Software Engineering : Creating high-performance libraries like LAPACK Scientific Computing : Applications in circuit simulation, MEMS, and quantum systems Information-based Computing : Methods for data clustering and image segmentation Quantum Simulations : Development of QUEST software for quantum Monte Carlo methods Publication Trends His recent publications (2015-2020) primarily focus on advanced numerical methods for eigenvalue problems, quantum Monte Carlo simulations, and high-performance computing. Research themes include nonlinear eigenvalue solvers, model reduction techniques, quantum system simulations, and applications in materials science and data science. Awards and Honors SIAM Fellow Research Teams and Labs Professor Bai leads significant collaborations including the PETAMAT project for next-generation quantum simulation software and contributes to the LBL Scalable Solver Group. He has supervised multiple software development projects including LAPACK, QUEST quantum simulation toolbox, and various numerical algorithm implementations.

• Numerical Linear Algebra
• Matrix Computations
• Scientific Computing
• +4 more

Luca Caucci is an Assistant Research Professor in Medical Imaging at the University of Arizona, affiliated with the Arizona Health Sciences Center (AHSC). His research integrates optical sciences and computational imaging to advance medical imaging technologies, particularly in nuclear medicine and 3D imaging . He has pioneered methods for photon-processing detectors and stochastic modeling in oncology and virology. Research Trends: His publications focus on positron emission tomography (PET) , machine learning for image reconstruction , and radiance sensor development . Key themes include GPU acceleration , list-mode data processing , and statistical decision theory in biomedical imaging. His work addresses challenges in 3D imaging , noise reduction , and task performance optimization . Collaborative Efforts: While the text does not explicitly mention lab teams or grants, his contributions to FastSPECT III calibration and alpha/beta emission tomography suggest active involvement in interdisciplinary projects bridging optical engineering and medical diagnostics .

• Medical Imaging
• Optical Sciences
• Biomedical Imaging
• +6 more

Yan Li is an Assistant Professor in Electrical Engineering with extensive research activity spanning 2013–2025 and 76 research outputs. Their work intersects power systems engineering , quantum computing , and computational symmetry with applications in microgrid stability , neuroimage analysis , and human pose dynamics . Active in UN Sustainable Development Goals related to sustainable energy systems Key research areas: Quantum Computing Training , Transient Dynamics Analysis , Software-defined Networking , and Data-driven Modeling Recent publications focus on adaptive control algorithms for microgrids, quantum computing applications in power systems, and geometric machine learning for differential equations on manifolds. Their work combines theoretical innovation with practical implementations in energy and biomedical domains. Grant funding includes: National Science Foundation (CyberTraining: Quantum Computing Training for Power Engineers, 2024–2026) U.S. Navy (Quantum Analysis of Renewable Energy Transients, 2022–; SCADA Network Security via SDN, 2021–) National Science Foundation (Data-driven Modeling for Renewable Systems, 2022–2025)

• Power Engineering
• Quantum Computing
• Microgrid Stability
• +5 more

Mahima Agumbe Suresh serves as an Associate Professor in the Department of Computer Engineering at San Jose State University's Charles W. Davidson College of Engineering, a promotion effective August 2025 following tenure approval. Previously, she was an Assistant Professor at SJSU after serving as a Visiting Assistant Professor at Texas A&M University and a postdoctoral researcher at Xerox Research Center India. Her educational foundation includes a Ph.D. in Computer Science and Engineering from Texas A&M University (2015) and a B.Tech in Computer Engineering from India's National Institute of Technology Karnataka. Research interests span Cyber-Physical Systems, Internet of Things, Smart City Data Analytics, and Augmented Reality applications for safety and education, with emphasis on practical implementations in urban infrastructure and educational technology. Her publication trajectory (2020-2025) reveals three dominant research thrusts: (1) Cyber-physical security systems for critical infrastructure like water networks and distributed controls, (2) AI-driven educational innovations including specifications grading and active learning methodologies across algorithms, networks, and data science courses, and (3) Applied computer vision and NLP solutions for human trafficking detection, e-bike safety, and product design optimization. This interdisciplinary work consistently bridges theoretical advances with real-world societal impact. Recognition includes: Quantum Faculty Fellow Award (2025) Quantum Faculty Fellow Grant (2024) VPRI Teaming Award (2024) Faculty Excellence in Teaching Service Award (2023) She directs an active research program funded by an NSF grant for mixed reality/edge computing in human-robot interaction (2024) and mentors MS thesis students in computer engineering. Service roles include Assessment Coordinator and ABET Report author for SJSU's Computer/Software Engineering programs, Chair of the Student Fairness Committee (2022-2024), and College of Engineering Mace Bearer at 2023 commencement. Her collaborative research ecosystem integrates academic-industry partnerships with Xerox Research and government entities, focusing on edge computing architectures, cyber-physical security frameworks, and educational technology innovations that address urban infrastructure challenges and pedagogical transformation in STEM education.

• Cyber-Physical Systems
• Internet of Things
• Data Analytics
• +5 more

C.R. Ramakrishnan is a Professor in the Department of Computer Science at Stony Brook University, specializing in logic programming, programming languages, and formal verification. His research spans theoretical computer science with significant contributions to model checking, concurrent systems analysis, and more recently quantum computing applications. His educational background includes a Ph.D. in Computer Science from Stony Brook University (1995) and dual master's degrees in Computer Science (M.Sc.(Tech)) and Physics (M.Sc.(Hons)) from Birla Institute of Technology and Science, Pilani, India (1987). Ramakrishnan's research interests have evolved from traditional logic programming toward cutting-edge quantum computing applications while maintaining his foundational work in verification systems. His current work focuses on quantum circuit distribution, entanglement management, and statistical-logical knowledge systems. His research methodology often involves encoding system semantics as logical inference rules and formulating analysis problems as inference tasks. His recent publication trend shows a strategic shift toward quantum computing with numerous high-impact papers on quantum network communication, entanglement distribution, and quantum circuit optimization. These works represent an evolution from his earlier focus on logic programming and model checking toward applying similar formal methods to quantum systems. National Science Foundation Faculty Early Career Award (1999-2003) National Science Foundation Postdoctoral Research Associateship (1995-1997) Catacosinos Fellowship for Excellence in Computer Science (1993-1994) His research has been consistently funded by grants from the National Science Foundation (NSF) and the Office of Naval Research (ONR), supporting his work across multiple domains from traditional programming languages to quantum computing. While specific students aren't listed in the provided materials, his teaching includes numerous graduate and undergraduate courses in computer science.

• Logic Programming
• Programming Languages
• Formal Verification
• +6 more

Dr. Yongsun Kim is a Professor at the Department of Physics and Astronomy , Sejong University. Holding a Ph.D. from MIT (2013) and a B.S. from the University of Illinois Urbana-Champaign (2007), he conducted postdoctoral research at Korea University (2013-2017) and UIUC (2017-2018). His work bridges high-energy physics and nuclear structure , focusing on heavy-ion collisions , quark-gluon plasma , and Higgs boson phenomenology . Research Interests Heavy Ion Collisions at LHC/RHIC Nuclear Symmetry Energy in Rare Isotopes High-Density Nuclear Physics Detector Development for sPHENIX and ECCE Scientific Contributions With over 1073 research outputs , his recent publications include measurements of W+W- production, studies of top quark entanglement , and searches for exotic Higgs decays . His work spans CMS detector upgrades, charm hadronization, and QCD investigations . Scientific Awards No specific awards mentioned in the provided data.

• Heavy Ion Collision
• Nuclear Physics at High Density
• Nuclear Symmetry Energy
• +3 more

Chandrakalavathi Thota is a researcher at the University of Michigan specializing in advanced sensor technologies and nanomaterials engineering. Her work focuses on developing microfluidic systems, gas sensors, and optoelectronic devices with applications in environmental monitoring, biomedical diagnostics, and chemical analysis. She has published extensively on topics such as monolithic gas chromatography, 2D material-based sensors, and wearable analytical platforms. Keywords: Nanotechnology, Sensor Technology, Materials Science, Environmental Monitoring, Biomedical Engineering, Analytical Chemistry Recent publications highlight her expertise in: Microfluidic integration of sensors for compact analytical systems Nanorod-based gas detection mechanisms Wearable and portable health monitoring devices Deep learning applications in chromatography data analysis Self-powered UV photodetectors using hybrid nanomaterials

• Nanotechnology
• Sensor Technology
• Materials Science
• +3 more

Zhengping Jay Luo serves as Assistant Professor II in Rider University's Department of Computer Science and Physics, specializing in cybersecurity and computer science with research spanning machine learning security, network defenses, and quantum computing applications. Education: Ph.D. Systems and Security, University of South Florida, Tampa, Florida, United States M.S. Computer Application Technology, Jinan University, Guangzhou, China B.S. Computer Science and Technology, Hebei University of Science and Technology, Shijiazhuang, China His research centers on three interconnected pillars: securing machine learning systems within IoT and communication infrastructures, developing advanced network security protocols, and exploring quantum computing's security implications—particularly in quantum machine learning frameworks. This work addresses critical vulnerabilities in AI-driven systems while pioneering defense mechanisms against next-generation threats. Analysis of his 15 most recent publications (2025-2017) reveals a consistent trajectory toward securing emerging technologies, with 2025-2023 works dominating publications on AI security (SpeechGPT jailbreaks), quantum machine learning, and UAV network defenses. His research bridges theoretical innovation with practical implementations across wireless communications, cryptography, and adversarial machine learning. Scientific Awards: None documented in provided materials. Professor Luo actively mentors Rider students through CYB/CSC 490 Independent Research projects and advises both the ACM Club and Asian American Students Association. His professional service includes NSF Panelist roles (2024-2025), CCSC Eastern conference committee membership, Mercer County Science Fair judging, and NJCCIC affiliation, though specific research grants remain unlisted. He maintains active engagement with the cybersecurity community through invited talks at international conferences and summer tournaments, focusing on quantum computing mathematics and RSA algorithm fundamentals for diverse audiences.

• Machine Learning Security
• Network Security
• Quantum Computing
• +3 more