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Max EhrlichView profile

Adjunct Assistant Professor

• Machine Learning
• Computational Imaging
• Video Compression
• +8 more

Max Ehrlich is an Adjunct Assistant Professor at the University of Maryland in the Department of Computer Science and a research scientist at NVIDIA. His work spans machine learning, computational imaging, and compression technologies. Adjunct Assistant Professor, University of Maryland Research Scientist, NVIDIA Research Interests: Combines machine learning with computational imaging to solve real-world problems. Key areas include video/image compression, land cover segmentation, and explainable AI. Research focuses on first-principles understanding rather than black-box models. Recent Publication Trends: 2025 work on implicit neural representations for video compression, 2024 studies on metadata-driven video enhancement, 2023 contributions to adaptive networks, and earlier work on JPEG artifacts, multi-task learning, and remote sensing. Scientific Awards: 3rd place in 2018 CVPR DeepGlobe challenge Teaching: Instructed CMSC421 Intro to Artificial Intelligence (Spring 2024) and CMSC422 Intro to Machine Learning (Spring 2022). Also served as a mentor for high school students.

Christopher K. MayView profile

Academic

• Computer Vision
• Generative Adversarial Networks
• Image Synthesis
• +3 more

Christopher K. May is an Assistant Teaching Professor in the Department of Computer Science at Purdue University, joining in Fall 2024. He holds a B.S., M.S., and Ph.D. in Computer Science from Purdue University, all completed between 2013 and 2024. His research focuses on computer vision, generative adversarial networks (GANs), and 3D rendering techniques, particularly in omnidirectional image synthesis and satellite imagery analysis. His educational background includes a decade of study at Purdue University, culminating in his doctorate in 2024. His work spans applications such as video frame interpolation, facade synthesis from satellite data, and interactive 3D rendering optimization. His research often intersects machine learning and computer graphics, addressing challenges in synthetic image generation and geospatial data processing. Dr. May's publications reflect a strong emphasis on generative models, with recent work exploring explicit camera control in omnidirectional synthesis (EpipolarGAN, 2024) and cube-based GAN architectures (CubeGAN, 2023). Earlier contributions include video folding techniques for framerate enhancement (2021) and satellite-based 3D building regularization (2020). No scientific awards or grants are explicitly listed in the provided information. His current position emphasizes teaching responsibilities alongside research.

• Molecular and Cellular Engineering
• Quantitative Phase Imaging
• Neurological Disorders
• +4 more

Catherine Alicia Best-Popescu is a Research Assistant Professor in the Department of Bioengineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in the Beckman Institute and Neuroscience Program. She specializes in quantitative phase imaging (QPI) and cellular neuroscience, focusing on neurological disorders such as Glioblastoma, Multiple Sclerosis, and Traumatic Brain Injury (TBI). Her work bridges bioengineering, neuroscience, and medicine, utilizing advanced optical microscopy techniques like Spatial Light Interference Microscopy (SLIM). Education: B.S. in Biology and Psychology (University of Massachusetts), M.S. and Ph.D. in Biomedical Sciences (Northeastern University). Postdoctoral training at Brigham and Women's Hospital and Harvard University. Research interests include developing label-free imaging tools for disease etiology, particularly in neurodegenerative and traumatic brain conditions. Her lab, the Cellular Neuroscience Imaging Lab (CNI), employs quantitative phase imaging to study cellular processes and disease mechanisms. She also led the SHIELD Saliva COVID-19 Molecular Diagnostic Laboratory, processing 10,000–20,000 daily samples during the pandemic. Publications highlight contributions to SARS-CoV-2 detection, neuroimaging advancements, and optical techniques in neuroscience. Awards include the Golden Apple Award for teaching and recognition for pandemic-related efforts. She holds leadership roles in committees for curricular development, diversity initiatives, and medical education. Her lab hosts 4 graduate and 10 undergraduate students, emphasizing undergraduate education and mentorship. Current projects include neural network applications for imaging analysis and engineered tumor models for therapeutic evaluation.

Goce TrajcevskiView profile

Adjunct Associate Professor

• Mobile Data Management
• Moving Objects Databases
• Sensor Networks
• +5 more

Goce Trajcevski is an Adjunct Associate Professor at Northwestern University's McCormick School of Engineering, Department of Electrical and Computer Engineering, and holds the Kingland Associate Professor title at Iowa State University's Department of Electrical and Computer Engineering. His research focuses on mobile data management, sensor networks, and reactive behavior in distributed systems. He earned his Ph.D. in Computer Science from the University of Illinois at Chicago. Education: Ph.D. Computer Science (UIC, 2002), M.S. Computer Science (UIC, 1995), B.S. Informatics and Automatics (University of Sts. Kiril and Metodij, 1989). Research interests include software systems, spatial-temporal data analysis, and machine learning applications. His work spans trajectory analysis, human mobility modeling, and cybersecurity. Recent publications explore graph neural networks for geolocation, adversarial learning in mobility, and heterogeneous data integration for vehicular networks. Key contributions include trajectory-based social circle inference, deep learning for user identity linkage, and privacy-preserving environmental sensing. He collaborates on projects involving autonomous systems, smart transportation, and academic impact analysis.

Chenyang LuView profile

Professor

• AI for Health
• Cyber-Physical Systems
• Internet of Things (IoT)
• +4 more

Chenyang Lu is the Fullgraf Professor of Computer Science & Engineering at Washington University in St. Louis, with joint appointments in Anesthesiology and Medicine. As the founding director of the AI for Health Institute, he leads interdisciplinary efforts to address healthcare challenges through AI and data-driven approaches. His research focuses on machine learning for health outcomes prediction, cyber-physical systems, and real-time embedded systems. Education: PhD (University of Virginia, 2001), MS (Chinese Academy of Sciences, 1997), BS (University of Science and Technology of China, 1995). Research interests include AI-driven healthcare, wearable sensor systems, and cyber-physical co-design. Notable projects include predictive models for postoperative risks, HIV treatment adherence, and phenotyping neurological disorders like Chiari malformation. His work has produced foundational AI tools for perioperative care, physician burnout prediction, and mental health monitoring. Awards include ACM Fellow (2020), IEEE Fellow (2016), and the 2022 IEEE TCRTS Outstanding Technical Achievement Award. He has published over 200 papers and serves as Editor-in-Chief of ACM Transactions on Cyber-Physical Systems . Led teams securing grants from NIH, NSF, and industry. Advises over 30 PhD students who now hold roles at top universities and tech companies like Google, Meta, and Amazon. Directs the Cyber-Physical Systems Laboratory and oversees the AI for Health Institute’s translational research initiatives.

• Signal Processing
• Acoustics
• Audio Engineering
• +3 more

Amy Bastine is a Researcher at the School of Engineering within the ANU College of Systems & Society at the Australian National University. Her work focuses on advanced audio signal processing, acoustics, and machine learning applications in spatial audio environments. She is affiliated with the Information & Signal Processing cluster, emphasizing interdisciplinary research. Her research interests include room acoustic modeling, spatial audio capture using spherical microphone arrays, and developing neural network-based solutions for sound field analysis. She explores topics like active noise control, source localization in reverberant environments, and immersive audio technologies. Recent projects involve creating datasets for acoustic analysis and optimizing algorithms for real-world applications. Her publications from 2022-2025 highlight trends in physics-informed neural networks for sound field estimation, sparse representation techniques, and multi-channel ANC systems. These contributions aim to bridge theoretical acoustics with practical implementations in wearable devices and recording studios. No scientific awards or grants are explicitly listed in the provided texts. Her research has been applied to datasets like room impulse responses and spherical microphone array measurements. She collaborates on projects involving acoustic imaging, HRTF interpolation, and environmental noise mitigation strategies.

• Underwater Acoustic Communications
• Signal Processing
• Communication Systems
• +3 more

Dr. Jaden Chen is a Research Fellow at Curtin University's School of Electrical Engineering, Computer and Mathematical Sciences (EECMS), within the Faculty of Science and Engineering. His research focuses on underwater acoustic communication systems, particularly in the areas of OFDM technology, channel estimation, and impulsive noise mitigation. He holds a portfolio role in the Office of the Provost, indicating administrative involvement alongside his research activities. Chen's work emphasizes practical implementation, as seen in his LabVIEW-based real-time underwater acoustic OFDM system and NI CompactDAQ hardware integration. His research spans theoretical algorithm development (e.g., compressive sensing-based iterative algorithms) to applied engineering solutions in underwater communication environments. His publications (2015–2020) consistently address challenges in underwater acoustic channels, including noise estimation, channel tracking, and system design. While no awards are listed, his prolific output in IEEE journals and conferences highlights technical expertise in signal processing and communication systems. No advising or grant information is explicitly provided. His contributions likely extend to laboratory environments focused on underwater communication prototyping and real-time system development.

Jonathan MuckellView profile

Teaching Professor

• Geospatial Compression Algorithms
• Assistive Living Devices
• Performance Benchmarking
• +1 more

Dr. Jonathan Muckell serves as Professor of Practice in the Department of Electrical & Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He mentors the IEEE Student Branch and World of Engineering Living Learning Community. He holds a PhD in Informatics from University at Albany (2013), MS in Computer & Systems Engineering from Rensselaer Polytechnic Institute (2008), and BS in Computer Science & Mathematics from St. Lawrence University (2006). His research explores geospatial compression algorithms, assistive living devices, and performance benchmarks. His publications predominantly focus on trajectory data compression techniques and healthcare technology innovations. Recent work examines wearable systems for injury prevention, while earlier research developed frameworks for GPS trajectory processing and terrain modeling. Awards include the University's Outstanding Achievement in Experiential Education Award (2019) and Distinguished Dissertation Award (2013). His work has resulted in patented systems for emissions reduction and backhaul opportunity detection.

• Remote Sensing
• Radar Signal Processing
• Microwave Imaging
• +4 more

Dr. Saibun Tjuatja is an Associate Professor in the Department of Electrical Engineering at The University of Texas at Arlington (UTA), where he also serves as the Undergraduate Program Advisor. He holds a BSEE (1987), MSEE (1988), and Ph.D. (1992) from UTA and Purdue University. His research focuses on wave propagation in random media, remote sensing, radar imaging, and subsurface sensing. He has served in leadership roles such as Associate Chairman of the Electrical Engineering Department (1999–2002) and as General Co-Chair of IEEE IGARSS 2017. Currently, he chairs the Progress in Electromagnetics Research Symposia Publications Committee and serves as an Associate Editor for IEEE Transactions on Geoscience and Remote Sensing. Education highlights include a Ph.D. in Microwave Remote Sensing from UTA, with a dissertation on snow and sea ice scattering models. His professional memberships include Fellow of the Electromagnetics Academy and Senior Member of IEEE. He has advised over 15 students in Ph.D. and Master's programs, focusing on radar imaging, microwave sensors, and subsurface detection. His work spans grants totaling millions from NSF and federal agencies, addressing structural health monitoring, soil characterization, and radar network development. Research contributions include pioneering models for microwave emission from layered media, radar target isolation using compressive sensing, and subsurface scattering analysis. He has authored/co-authored over 100 peer-reviewed articles and book chapters, with key works presented at IEEE IGARSS conferences. Awards include the Outstanding Teacher Award (1997) and multiple international fellowships.

Mohammed Al-KhalidiView profile

Associate Professor

• AI Security
• IoT Security
• Mobile Computing
• +3 more

Mohammed Al-Khalidi is a Senior Lecturer (Associate Professor) in Cyber Security at Manchester Metropolitan University and serves as the MSc Cyber Security Course Leader. He has secured over £500,000 in research funding from UKRI’s Global Challenges Research Fund and other bodies for projects in AI Security and Network Security. His research spans AI security, IoT security, Mobile Computing, Cloud Computing, Ad-Hoc Networks, and Information Centric Networks. He has contributed to EU projects including POINT (iP Over IcN - the betTer IP). He currently supervises PhD students working on Intelligent Mobility Management in 6G, Zero Trust Security for Internet of Drones, UAV Security using AI/Blockchain, and Cybersecurity Frameworks for Intelligent Transport Systems.

Ramtin ZandView profile

Assistant Professor

• Hardware Design for Machine Learning Systems
• Neuromorphic Computing
• Emerging Nanoscale Electronics
• +7 more

Ramtin Zand is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research focuses on hardware design for machine learning systems, neuromorphic computing, emerging nanoscale electronics (e.g., spintronic devices), and energy-efficient VLSI circuits. He leads the ICAS Lab, which explores reconfigurable architectures and low-power computing solutions. Education: Ph.D., Computer Engineering, University of Central Florida (2019) M.S., Electrical Engineering, Sharif University of Technology (2012) Research Interests: Dr. Zand’s work spans hardware-software co-design for AI, neuromorphic systems, and novel devices like MRAM and memristors. He emphasizes practical applications such as manufacturing anomaly detection, edge computing, and energy-efficient neural network deployment. Recent Trends in Articles: His publications emphasize hybrid PIM architectures, LLM-driven hardware design, and neuromorphic edge systems. Themes include optimizing communication efficiency, leveraging memristive crossbars, and bridging vision transformers with embedded platforms. Grants/Funding: Funded by AFRL for smart manufacturing projects ONR grants for perception systems research NSF support for in-memory computing Labs/Teams: The ICAS Lab collaborates on projects such as neuromorphic accelerators and low-power sensor systems. Recent milestones include DAC 2025 awards and CVPR 2025 demonstrations.

Juan CaicedoView profile

Professor

• Structural Dynamics
• Model Updating
• Structural Health Monitoring
• +2 more

Juan Caicedo serves as Professor and Chair of the Civil and Environmental Engineering Department at the University of South Carolina's Molinaroli College of Engineering and Computing. His research in structural dynamics focuses on vibration-based monitoring for infrastructure and health applications. Education: D.Sc., Washington University in St. Louis M.S., Washington University in St. Louis B.S., Universidad del Valle Research integrates numerical modeling and experimental validation for structural health monitoring, earthquake resilience, and human activity detection. Recent innovations include probabilistic methods for gait analysis using floor vibrations and impact localization algorithms. Publications demonstrate strong industry-academia collaboration, particularly in sustainable structural design and educational initiatives.

Panagiotis TsakalidesView profile

Professor

• Statistical Signal Processing
• Compressed Sensing
• Machine Learning
• +3 more

Panagiotis Tsakalides is Professor of Computer Science at the University of Crete and Head of the Signal Processing Laboratory at FORTH-ICS. His research develops statistical signal processing and machine learning methods with applications in computational imaging, sensor networks, and remote sensing. He has secured over €15M in research funding through 22 projects, including: Horizon Europe TITAN Project (€2.5M): Frugal AI for astrophysics applications H2020 PHYSIS Project (€1M): Sparse processing for hyperspectral systems FP7 CS-ORION Project (€1.3M): Compressed sensing for aerial surveillance Research specialties include non-Gaussian signal modeling, compressed sensing architectures, and distributed processing algorithms. Applications span medical imaging, environmental monitoring, and space systems.

Ajai SinghView profile

Professor

• Finance
• Investment Banking
• Corporate Finance
• +2 more

Ajai Singh is a Professor and Chair of the Department of Finance at the University of Central Florida's College of Business, where he also serves as Director of the Dr. P. Phillips School of Real Estate. Previously, he held the Bolton-Perella Endowed Professorship at Lehigh University and taught at Case Western Reserve University, where his undergraduate finance program was ranked #1 by Bloomberg BusinessWeek. His research examines investment banking and corporate finance, with focus on rational and behavioral market responses to capital acquisition events. Research interests span finance, investment banking, corporate finance, banking, and real estate, with publications in top-tier journals including the Journal of Finance and Journal of Financial Economics. His work integrates practical insights from his experience at State Bank of India's investment banking division. Recent research trends show publications across finance, agricultural science, materials engineering, geophysics, and statistics, demonstrating interdisciplinary applications of quantitative methods and market analysis. Awards and Honors: Undergraduate Teaching Excellence Award, Case Western Reserve University (2010-2011) As department chair and program director, he oversees academic initiatives and curriculum development while maintaining active research collaborations.

Annalisa AppiceView profile

Assistant Professor

• Knowledge Discovery
• Data Mining
• Big Data Analytics
• +7 more

Annalisa Appice is an Assistant Professor at the Department of Computer Science, University of Bari, Italy. She holds a Ph.D. in Computer Science from the same institution, with her thesis focusing on 'Learning Relational Model Trees'. Her international research experience includes positions at the University of Bristol (UK) and Jozef Stefan Institute (Slovenia). Her research spans: Knowledge discovery and data mining Big data analytics and stream data mining Spatio-temporal data mining and remote sensing analysis Network data analysis and process mining Artificial Intelligence applications in cybersecurity and environmental monitoring She develops innovative machine learning approaches including deep neural networks, vision transformers, and ensemble methods for applications ranging from forest health monitoring to cybersecurity threat detection. Appice has significant leadership experience in the academic community, having served as Program co-Chair for major conferences including DS 2020, ECML-PKDD 2015, and ISMIS 2017. She regularly contributes to program committees of premier conferences such as AAAI, IEEE ICDM, KDD, ICML, and IJCAI. As an editorial board member for the Journal of Intelligent Information Systems, she oversees publications in intelligent information systems. She has also organized six international workshops and guest-edited three special journal issues.