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Rocío Melissa RiveraView profile

Professor

• Reproductive Physiology
• Epigenetics
• Assisted Reproductive Technologies
• +3 more

Rocío Melissa Rivera is a Professor of Reproductive Physiology and Epigenetics in the Division of Animal Sciences at the University of Missouri. She received her Ph.D. from the University of Florida and completed postdoctoral training at the University of Pennsylvania. Her research investigates epigenetic disruptions in gametes and embryos caused by assisted reproductive technologies (ART). Key projects characterize Large Offspring Syndrome in ruminants and Beckwith-Wiedemann Syndrome in humans—both loss-of-imprinting overgrowth conditions linked to ART. She explores how superovulation and maternal aging alter oocyte DNA methylation patterns and gene expression. Dr. Rivera employs bovine and murine models to identify molecular triggers of epigenetic syndromes, using transcriptomic and chromatin analysis. Current work examines dietary interventions to mitigate ART-associated epigenetic errors. She mentors doctoral students in projects ranging from IGF2R regulation in fetal overgrowth to retroelement control in oocytes. As a Fulbright Senior Scholar at Spain's University of Murcia, she contributed to reproductive biology education. Her laboratory combines developmental biology, epigenetics, and molecular analysis to improve ART safety.

Wazir MuhammadView profile

Associate Professor

• Medical Physics
• Radiation Therapy
• Monte Carlo Simulations
• +6 more

Wazir Muhammad is an Associate Professor of Medical Physics at Florida Atlantic University (FAU), Department of Physics, within the Charles E. Schmidt College of Science. He also served as an Associate Research Scientist at Yale School of Medicine (2017–2019). His research focuses on medical physics applications in cancer treatment and diagnosis, including Monte Carlo simulations, AI-driven radiation therapy optimization, and big data analytics. Education: Ph.D. in Physics (Kyungpook National University, 2013), M.Sc. in Medical Physics (PIEAS, 2004), B.Sc. and A.Sc. in Physics (University of Peshawar, 2001–2000). Research interests include radiation transport modeling, proton therapy improvements, AI in oncology, and radiation protection. Collaborations involve institutions like University of Chicago, South Florida Proton Therapy Institute, and GenesisCare Oncology. His lab, the Muhammad Lab | Medical Physics-Owls, develops tools for cancer prediction, treatment planning, and imaging. Key projects include GPU-based MC dose engines, AI-driven treatment strategies, and digital health applications. Over 15 students have graduated from his lab, many entering medical physics residencies. The lab also explores prompt gamma ray monitoring and small-field dosimetry for stereotactic radiosurgery. Publications span Monte Carlo simulations, ML in radiation oncology, and radiation safety. Future work emphasizes personalized cancer treatment through AI integration and expanding clinical partnerships.

Christian Walter Peter OmlinView profile

Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +7 more

Christian Walter Peter Omlin is a Professor at the Department of Information and Communication Technology, University of Agder. He holds an honorary position at the University of South Africa and has held academic roles across institutions in South Africa, Cyprus, and Fiji. His expertise spans artificial intelligence, machine learning, and ethical AI. Omlin’s research emphasizes explainable AI, reinforcement learning, and applications in Industry 4.0, healthcare, and particle physics monitoring. He leads projects like the Telkom/Cisco Center for IP Computing and has contributed to virtue ethics in AI agent design. Education: PhD from Rensselaer Polytechnic Institute (1995), M.Eng from ETH Zurich (1987). Research Highlights: Developed affinity-based reinforcement learning (ab-RL) for interpretable AI agents. Advanced data quality monitoring (DQM) for CERN’s CMS detector using time-aware deep learning. Explored virtue ethics in AI through role-playing agents and ethical dilemma modeling. Teaching: Courses include Principles of AI, Algorithms, Urban Computing, and Digital Health. Grants/Advising: Led the Telkom/Cisco Center and the South African Innovation Fund’s “HearSEE” consortium. Supervised theses at multiple universities. Labs/Teams: Involved in CERN’s HCAL detector anomaly detection and dental radiology AI applications.

Muhammad Faisal AftabView profile

Associate Professor

• Control Systems
• Industrial Automation
• Machine Learning
• +5 more

Muhammad Faisal Aftab is an Associate Professor at the University of Agder's Department of Engineering Sciences. His research focuses on control systems, industrial automation, and data-driven analysis with applications in robotics, process control, and predictive maintenance. He is affiliated with research groups such as Artificial Intelligence, Biomechatronics, and Collaborative Robotics, as well as the Intelligent Mechatronics (iTron) and Robotics and Automation groups. His research interests span performance monitoring of industrial processes, oscillation detection in control loops, and the integration of machine learning techniques into automation systems. Recent work emphasizes enhancing cybersecurity in robotics (e.g., intrusion detection in ROS systems) and advancing Industry 5.0 through predictive maintenance strategies. Publications highlight contributions to signal processing for industrial diagnostics, digital twin frameworks for mechatronics systems, and adaptive control methodologies. His work bridges theoretical advancements with practical applications in manufacturing, automotive systems, and process optimization. Aftab holds no listed awards but demonstrates active engagement in interdisciplinary collaborations, evident through co-authorships with researchers in fields like biomedical engineering and offshore engineering. His research portfolio reflects a commitment to addressing challenges in both traditional and emerging automation domains.

• Compressible Flows
• Aeroacoustics
• Thermo-Fluid Dynamics
• +3 more

Dr. David Bacci is a Senior Research Fellow in Thermofluids Dynamics and Acoustics at the Oxford Thermofluids Institute, University of Oxford, with a joint appointment as Visiting Research Fellow in Military Aircraft Design at Cranfield University. He holds a BSc and MSc in Aerospace/Aeronautical Engineering from the University of Pisa and a PhD from the Defence Academy of the United Kingdom. His research spans unsteady compressible flows, aeroacoustics, thermo-fluid-dynamics, and military aircraft design. His work integrates computational fluid dynamics with experimental approaches to solve challenges in turbine cooling, weapons bay acoustics, and aircraft stealth characteristics. Publication analysis reveals three primary research streams: weapons bay aeroacoustics (40% of recent publications), turbine cooling technologies (33%), and aircraft stealth-stability tradeoffs (20%). His work shows consistent application of high-fidelity computational methods to military aerospace challenges. Member of Computational Fluid Dynamics Group Co-investigator on turbine heat transfer projects He leads research on thermoacoustic systems and collaborates on turbine cooling projects with industry partners. His group develops computational models for acoustic liner optimization and turbine thermal management that are applied in next-generation aircraft engines.

• Econometrics
• Financial Economics
• Machine Learning in Finance
• +3 more

Dr. Maria Grith is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research focuses on econometric methodologies applied to financial markets, with particular emphasis on machine learning techniques in finance, risk management, and time series analysis. She actively contributes to academic supervision, as evidenced by her involvement in bachelor thesis projects in Econometrics and Economics. Her research interests span quantitative finance, including applications of neural networks to volatility forecasting, cryptocurrency risk analysis, and reinforcement learning in high-dimensional settings. She also explores traditional econometric challenges such as delta-hedged options pricing, sovereign default risk, and macroeconomic persistence heterogeneity. Her publication record includes over 20 peer-reviewed articles, with recent work addressing cutting-edge topics like neural tangent kernels in finance and graphical models for multivariate time series. While no scientific awards are explicitly mentioned, her prolific output reflects a strong research trajectory in financial econometrics and computational methods.

Amin ZehtabianView profile

Researcher

• Computer Vision
• Pattern Recognition
• Biomedical Imaging
• +5 more

Dr. Amin Zehtabian is a Researcher in the Ewers Group - Membrane Biochemistry at the Department of Biochemistry, Freie Universität Berlin. He holds a PhD in Electrical Engineering (2016) with a focus on Pattern Recognition, Computer Vision, and Multispectral Signal Analysis. His work integrates computational methods with biological research, including quantitative microscopy data analysis, image segmentation, and deep learning applications in neuroscience and cell biology. Education: PhD in Electrical Engineering (minor in Communication Systems), 2016. Research Interests: Dr. Zehtabian specializes in computational biology and bioimaging, with expertise in 2D/3D image segmentation, neural networks, noise reduction, and genetic algorithms. His current projects explore cargo trafficking in recycling endosomes, circadian rhythm dynamics, and neuronal morphology analysis using unsupervised learning techniques. Publications: His recent work spans advancements in membrane biochemistry (e.g., ARF1 compartment dynamics), circadian protein turnover, and deep learning applications in superresolution microscopy. He also contributes to hyperspectral imaging innovations for environmental and medical diagnostics. Advising/Grants: No explicit grants or advisees listed, but coordinates an Open Position announcement for a Scientist/Project Coordinator at FU Berlin (2025). Labs/Teams: Core member of the Ewers Group, focusing on membrane biochemistry and quantitative imaging.

Peter de WithView profile

Professor

• Video Compression
• Image Analysis
• Healthcare Applications
• +4 more

Peter de With is a Part-time Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), leading the Video Coding & Architectures Group. His expertise spans video compression, image analysis in healthcare, surveillance, and multimedia. He holds a PhD from Delft University of Technology (1992) and has held roles at Philips Research, the University of Mannheim, and Cyclomedia Technology. His research focuses on video/image signal processing, machine learning, and healthcare applications such as esophageal cancer detection. He is an IEEE Fellow and has authored over 400 papers, receiving multiple awards for his work. Research interests include video coding, medical imaging, and system innovations in health, surveillance, and automotive sectors. Key publications address challenges in medical instrument detection, 3D ultrasound, and multiview rendering. He collaborates with industry partners like Bosch Security and hospitals for oncology research. His work bridges academic innovation with real-world applications, emphasizing practical solutions in healthcare technology and multimedia systems. Awards include IEEE Fellow status and paper awards from CE Chester Sall, SPIE, and Elsevier. He chairs program committees for IEEE conferences and holds 30 patents. His contributions to video analysis and system design have driven advancements in safety, security, and healthcare technologies.

Azin JananiView profile

Lecturer

• Signal Processing
• Artifact Removal
• Feature Extraction
• +4 more

Dr. Azin Janani is a Lecturer at the School of Electrical Engineering and Computer Science, University of Queensland. She is a member of the Electromagnetic Research Group where she conducts research on signal processing and disease classification using electromagnetic technologies for medical applications. Her educational background includes: B.S. in Biomedical Engineering from Amirkabir University (Tehran Poly-Technique), Iran (2008) M.S. in Biomedical Engineering from Amirkabir University (Tehran Poly-Technique), Iran (2010) Ph.D. from Flinders University, Australia (2019) Dr. Janani specializes in artifact removal, feature extraction, and disease classification from various biological signals including Electrocardiogram (ECG), Phonocardiogram (PCG), and Electroencephalogram (EEG). Her expertise in electromagnetic imaging has led to innovations in non-invasive diagnostic tools, particularly for liver disease detection. She is proficient in Python, Matlab, Java, and C programming languages, supporting her development of computer-aided diagnosis systems. Her recent publications demonstrate a strong focus on portable electromagnetic devices for medical diagnostics, with particular emphasis on torso imaging and liver health monitoring. These works integrate antenna design, signal processing, and biomedical applications to create novel diagnostic approaches that could transform non-invasive medical testing. Her notable recognition includes: Australian Endeavour Postgraduate Scholarship (2016) Dr. Janani is actively involved in research supervision and grant-funded projects. She currently supervises PhD research on portable electromagnetic devices for dental vitality testing. Her current funding includes an NHMRC IDEAS Grant (2025-2027) for developing a Portable Electromagnetic Torso Scanner, demonstrating her leadership in translating research into practical medical applications. As a key member of the Electromagnetic Research Group at the University of Queensland, Dr. Janani collaborates with interdisciplinary teams including Professor Amin Abbosh, Sasan Ahdi Rezaeieh, and Amin Darvazehban to advance electromagnetic sensing technologies for healthcare applications. Her work bridges electrical engineering, computer science, and medical diagnostics to create innovative solutions for challenging healthcare problems.

Eric M. LuiView profile

Professor

• Steel Structures
• Structural Stability
• Earthquake Engineering
• +4 more

Eric M. Lui is the Laura J. and L. Douglas Meredith Professor for Teaching Excellence in the Department of Civil and Environmental Engineering at Syracuse University. He holds a Ph.D. from Purdue University and specializes in steel structures, structural stability, earthquake engineering, and damage identification. His research focuses on nonlinear behavior of steel structures, seismic design, and energy dissipation systems. He has authored/co-authored numerous publications and serves as editor-in-chief of two journals. Lui has received multiple awards, including the Crouse Hinds Award for Excellence in Education (1997) and the ECS Faculty Excellence Award (2009). Education: Ph.D., M.S.C.E. (Purdue University), B.S.C.E. (University of Wisconsin at Madison, High Honors). Research interests include steel structures, structural dynamics, and earthquake-resistant design. He collaborates internationally, particularly with scholars in China, on topics like UHPC-filled steel columns and seismic performance of bridges. His work bridges theoretical models, numerical simulations, and experimental validation to enhance structural resilience against natural hazards. Awards include the Nellie Munsion Award (1982), ASCE Faculty Adviser Recognition (2000–2002), and TACNY Educator of the Year (2007). His contributions extend to textbook authoring, including the CRC Handbook of Structural Engineering and books on steel design and earthquake engineering. Grants and advising are central to his career, though specific student names are not listed. His research group explores innovations like segmented energy-absorbing walls and magnetic dampers, aiming to advance structural engineering practices globally.

Rahul ParhiView profile

Assistant Professor

• Machine Learning
• Neural Networks
• Signal Processing
• +9 more

Rahul Parhi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), where he has been since 2024. Previously, he was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) from 2022 to 2024, and completed his Ph.D. in Electrical Engineering at the University of Wisconsin–Madison in 2022. His research focuses on the mathematics of data science, particularly the theoretical foundations of neural networks and deep learning, with a strong emphasis on functional/harmonic analysis, signal processing, and nonparametric statistics. Key research themes include the mathematics of computed tomography, sparsity, compressed sensing, and the geometry of Banach spaces. Education: B.S. in Mathematics, University of Minnesota, Twin Cities (2018) B.S. in Computer Science, University of Minnesota, Twin Cities (2018) Ph.D. in Electrical Engineering, University of Wisconsin–Madison (2022) His research interests span the interplay between functional and harmonic analysis with data science, including: Foundations of neural networks and deep learning Approximation properties of neural networks Nonparametric statistical estimation Inverse problems and compressed sensing Wavelet-based signal processing Banach space theory His recent publications explore topics such as neural network optimization, attention mechanisms, and the theoretical limits of learning in high-dimensional spaces. He has contributed to understanding the mathematical principles underlying deep learning's success in breaking the curse of dimensionality. His work bridges abstract mathematical concepts with practical applications in signal processing and machine learning. Parhi's research also extends to collaborative projects with experts in optimization and applied mathematics, aiming to develop novel algorithms with rigorous theoretical guarantees. His lab at UCSD focuses on advancing interdisciplinary research at the intersection of mathematics and data science.

• Digital Signal Processing
• Machine Learning
• Respiratory Sound Analysis
• +3 more

Udantha Abeyratne is an Associate Professor at the University of Queensland's School of Electrical Engineering and Computer Science. He holds a PhD in Biomedical Engineering from Drexel University, MEng and BScEE degrees from Tokushima University, and graduate certificates in Higher Education (University of Queensland) and Paediatric Sleep Science (University of Western Australia). His research centers on developing innovative diagnostic technologies using signal processing and machine learning. Key areas include respiratory sound analysis for pneumonia and asthma detection, snore-based sleep apnea diagnostics, wearable medical devices, and mHealth solutions for resource-limited regions. His work has been funded by the Bill & Melinda Gates Foundation, Australian Research Council, and A*STAR Singapore. His publications demonstrate a consistent focus on translating engineering innovations into clinical applications, particularly in cough/snore acoustics, sleep physiology, and point-of-care diagnostics. Research frequently involves multisite collaborations and validation against gold-standard clinical measures. Awards include the 1990 Best Paper Award at the ISBET Brain Topography Conference and recognition as a finalist in the 1991 Young Investigators' Competition at the IFMBE World Congress. He has supervised over 15 PhD students in areas spanning biomedical signal processing, machine learning applications, and medical device development. Major grants support technologies like smartphone-based disease diagnosis and real-time fatigue monitoring systems. Leads research teams developing the Magithescope™ stethoscope and diagnostic platforms like SnoreSounds. Collaborates globally with hospitals and research institutions to validate technologies in clinical and community settings.

Lexing YingView profile

Professor

• Applied Mathematics
• Computational Science
• Quantum Computing
• +7 more

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.

Charles ConnerView profile

Professor

• Digital Signal Processing
• Image Processing
• Linux Systems
• +3 more

Dr. Charles Conner is a Professor of Engineering at Capitol Technology University, where he has held a full-time faculty position since transitioning from part-time teaching in 1984. He specializes in digital signal and image processing, with expertise in DSP Assembly/C++, MATLAB/Octave, and Linux systems development. Dr. Conner has also been instrumental in securing ABET accreditations for the university and held adjunct roles earlier in his career. His career spans over four decades in academia and industry, including roles as a Staff Scientist at Morehead State University’s Space Science Center, contributing to small satellite control software and the Lunar IceCube mission. He has developed critical systems such as the core Flight System (cFS) and AMMOS Instrumentation Toolkit (AIT). His technical skills include embedded systems design, Verilog/VHDL, and web development using Java/XML/Perl/Python. Research interests focus on signal/image processing, Linux systems, and aerospace applications. Notable projects include thermographic defect detection algorithms and free-space optical communication synchronization using Haar wavelets. His work bridges theoretical research and practical engineering, with contributions to both academic conferences and industry projects. Awarded multiple Esteemed Professor titles from Alpha-Chi Honor Society and recognized in Who’s Who publications, Dr. Conner emphasizes service, including Outstanding Service accolades at Capitol College and the Space Operations Institute. He advises IEEE student chapters and fosters interdisciplinary collaboration between engineering disciplines. Dr. Conner holds a B.S.E.E. (magna cum laude) and M.S.E.E. from the University of Maryland, and a Ph.D. in Electrical Engineering from Catholic University of America. He remains active in technical communities, including the Laurel Linux Users’ Group and Alpha-Chi Honor Society.

• Remote Sensing
• Environmental Science
• Atmospheric Science
• +5 more

Prof. Yong Xue is a faculty member in the College of Science and Engineering , specializing in remote sensing and environmental science. His work focuses on developing advanced algorithms for atmospheric and environmental monitoring using satellite data. Research interests include aerosol optical depth retrieval, air pollution dynamics, machine learning applications in remote sensing, and geospatial data analysis. He has contributed to methodologies for satellite-based air quality estimation, dust storm detection, and pandemic decision support systems. Key contributions include: Development of aerosol retrieval algorithms for multiple satellite platforms (Himawari-8, MSG, GOES-16) Integration of deep learning for minimally supervised image classification High-performance computing frameworks for geospatial data processing Environmental modeling for air pollution transmission and public health impact analysis Notable achievements include the creation of global hourly aerosol datasets and diagnostic models for pandemic risk assessment. His work bridges computational methods with environmental science, emphasizing real-world applications in pollution monitoring and Earth observation.