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Alina VereshchakaView profile

Assistant Professor

• Optimal control in complex systems
• Social behavior modeling
• Deep reinforcement learning
• +5 more

Alina Vereshchaka is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo. She holds a PhD in Computer Science from the same institution (2021). Her research focuses on optimal control in complex systems, including social behavior modeling, deep reinforcement learning, multi-agent systems, adversarial machine learning, transportation dynamics, and large-scale social system dynamics. She leads projects involving epidemic modeling, smart city infrastructure optimization, and disinformation detection. Education: PhD in Computer Science, University at Buffalo, 2021 Her work bridges theoretical machine learning advancements with real-world applications in public health, transportation, and crisis management. Key research themes include: Optimizing epidemic intervention strategies using reinforcement learning Urban traffic prediction through vehicle trajectory analysis Automated disinformation detection systems Multi-agent resource allocation during disasters Dr. Vereshchaka's recent publications (2018-2024) emphasize data-driven decision-making frameworks across transportation networks, healthcare systems, and social media platforms. Though no specific grants or awards are listed, her work reflects interdisciplinary collaboration with engineering and public health domains. She currently advises no listed students and maintains an active research website and dblp profile for further details.

Douglas H. WedellView profile

Professor

• Affective Neuroscience
• Context Effects in Judgment
• Spatial Memory
• +2 more

Douglas H. Wedell is a Professor in the Department of Psychology at the University of South Carolina and Director of the Institute for Mind and Brain. He earned his Ph.D. in Cognitive Psychology from UCLA (1984). His research examines how context shapes judgment, memory, and decision-making through behavioral experiments and neuroimaging. Current projects investigate affective representations across sensory modalities, contextual influences on preference formation, spatial memory biases, and music cognition. He employs methods including fMRI, psychophysiology, and computational modeling. Wedell's publications demonstrate integration of behavioral and neuroscientific approaches to contextual cognition. Recent work focuses on cross-modal affective processing, reinforcement learning in changing contexts, and physiological correlates of decision outcomes. He directs the NSF-funded Summer Research Experience in Brain and Cognitive Sciences and teaches courses in cognitive psychology, judgment/decision-making, and quantitative methods.

• Biomedical Engineering
• Health Informatics
• Machine Learning
• +5 more

Mirjam Jonkman serves as an Adjunct Senior Lecturer in Engineering within the Faculty of Science and Technology, specializing in biomedical engineering and health informatics. Her research applies machine learning and signal processing to develop practical solutions for biomedical challenges including cancer detection, chronic kidney disease modeling, and cyber security applications. Her research program focuses on: Prediction and detection of abnormalities in biosystems using EEG and medical imaging Development of efficient deep learning models for clinical diagnostics Explainable AI implementations ensuring clinical trustworthiness Real-world translation of AI techniques to medical and security domains Analysis of her 88 publications reveals dominant trends in medical AI applications since 2023, with emphasis on transformer models, attention mechanisms, and computational efficiency for cancer diagnostics, organ segmentation, and disease classification. Her work consistently bridges technical innovation with clinical relevance across oncology, radiology, and gastroenterology. Jonkman has supervised 8 students and led significant research projects including the Rainmaker Start-up for kidney disease modeling (2021-2022) and a Bronchiectasis diagnostics system (2022-2023). Her collaborative approach involves interdisciplinary teams with medical professionals and computer scientists, evidenced by multi-PI projects addressing real healthcare challenges through AI innovation.

• Machine Learning
• Computer Vision
• Brain-Computer Interfaces
• +2 more

Huseyin Ozkan is an Associate Professor of Electronics Engineering at Sabancı University's Faculty of Engineering and Natural Sciences. His research spans machine learning, computer vision, signal processing, brain-computer interfaces, and visual neuroscience. He received his Ph.D. from Bilkent University (2015) and completed postdoctoral research at MIT (2017). Ozkan's scholarly output includes significant contributions to neural engineering and signal processing, with publications receiving recognition including the Alper Atalay Best Student Paper Award at IEEE SIU 2021. His research group currently advises Ph.D. candidate Soner Ozgun Pelvan and M.Sc. students Can Aksoy, Zobia Batool, and Berke Bakar Aksoy. Recent work demonstrates strong focus on medical AI applications and neural engineering innovations, particularly in Alzheimer's detection (2025), adaptive BCI systems (2023-2024), and video anomaly detection frameworks (2023). His laboratory utilizes computational neuroscience approaches to solve problems in healthcare technology and network systems.

Fahad SaeedView profile

Associate Professor

• Machine Learning
• Computational Proteomics
• Neuroscience
• +3 more

Dr. Fahad Saeed is a tenured Associate Professor and Graduate Program Director in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). His research focuses on the intersection of machine learning, high-performance computing, and computational biology with applications in healthcare and proteomics. He directs the Saeed Lab, which develops scalable algorithms for big biomedical data analysis. Education : - Ph.D. in Electrical and Computer Engineering, University of Illinois at Chicago (2010) - B.Sc. in Electrical Engineering, University of Engineering and Technology, Lahore (2006) Research Interests : Machine learning for neurodegenerative diseases (e.g., Alzheimer’s), computational proteomics, big data analytics, and high-performance computing frameworks for omics data. His work emphasizes scalable algorithms for healthcare IoT and edge computing. Key Contributions : Over 90 peer-reviewed publications, 4 conference proceedings, and $3.9M in external grants (NSF, NIH). Notable awards include the NSF CAREER Award (2017) and NIH Postdoctoral Fellowship (2010). His lab pioneered tools like SpeCollate for mass spectrometry data and HiCOPS for proteomics database searches. Awards : NSF CRII, WMU Distinguished Research Award, FIU Top Scholar (2022) Grants : NSF CAREER Award ($600K), NIH grants for Alzheimer’s research Labs & Teams : Director of the Saeed Lab at FIU, collaborating on NIH-funded projects in computational biology and neuroimaging analysis.

Angela LombardiView profile

Assistant Professor

• eXplainable Artificial Intelligence
• Complex Networks
• Big Data Processing
• +4 more

Angela Lombardi is an Assistant Professor (tenure-track) at Polytechnic University of Bari, Italy, with expertise in eXplainable Artificial Intelligence (XAI), Complex Networks, and e-Health. She holds a Telecommunications Engineering degree (2014) and a PhD in Signal Processing & Computational Neuroscience (2018, cum laude) from Polytechnic University of Bari. Previously, she worked as a post-doctoral researcher at Istituto Nazionale di Fisica Nucleare (2018–2020) and as an Assistant Professor at the University of Bari (2020–2022). Her research focuses on developing novel algorithms for neurodegenerative disease diagnosis using AI in distributed computing environments. She is affiliated with SisinfLab and part of the CLAIRE Research Network. Education: BEng (Telecommunications Engineering), Polytechnic University of Bari, 2014 PhD (Signal Processing & Computational Neuroscience), Polytechnic University of Bari, 2018 Research Interests: XAI for healthcare decision support Complex network modeling of biological systems Big data analytics for personalized medicine Neuroimaging and EEG-based emotion recognition Publications Trends: Her recent work emphasizes XAI applications in neurological diagnostics, brain age prediction using CNN models, emotion recognition via EEG, and wearable BCI systems. Key themes include interpretability of AI models, clinical validation of algorithms, and interdisciplinary healthcare solutions. Labs & Affiliations: Active in SisinfLab (Polytechnic University of Bari), contributing to projects like CyberChallenge2025@PoliBa and the 22nd International Conference on Web Engineering (ICWE 2022).

Malek AdjouadiView profile

Professor

• Pattern Recognition
• Medical Imaging
• Biometrics
• +2 more

Malek Adjouadi serves as Ware Professor and Director of the Center for Advanced Technology and Education (CATE) in the Department of Electrical and Computer Engineering at Florida International University, where he has been a Full Professor since 2007 and previously served as Acting Department Chair from 1997-2001. His academic foundation includes: Ph.D. in Electrical Engineering, University of Florida (1985) M.S. in Electrical Engineering, University of Florida (1981) B.S. in Electrical Engineering, Oklahoma State University (1978) Adjouadi's research bridges Pattern Recognition with critical medical and security applications. His primary focus involves Medical Imaging analysis for pediatric epilepsy using fMRI and EEG data, where he develops advanced classification algorithms for language network mapping and seizure detection. This work extends into Biometrics through thermal imaging authentication systems and iris recognition, while also encompassing Signal Processing techniques for neurological data and contributions to Cryptography through biometric security frameworks. His interdisciplinary approach consistently applies computational methods to solve complex problems in neuroscience and security domains. Analysis of his 14 publications from 2006-2013 reveals a dominant medical neuroscience trajectory focused on pediatric epilepsy diagnostics, with significant contributions to biometric security emerging around 2011-2012. The medical works predominantly employ machine learning for brain imaging analysis, while his security publications introduce novel approaches to facial authentication and geospatial data management. As Director of CATE, Adjouadi leads technology education initiatives while teaching advanced courses including Digital Image Processing, Computer Vision, and Neural Systems. His professional service includes representation at State University System of Florida meetings and Board of Regents presentations, demonstrating ongoing institutional leadership.

• Computer Vision
• Data Science
• Data Visualization
• +5 more

Michael D. Grossberg is an Assistant Professor in the Department of Computer Science at the City College of New York (CUNY). He is affiliated with the NOAA-CREST Institute and the Computational Science of Data Intensive Remote Sensing (CS:dir$) group. His research spans interdisciplinary fields including computer vision, data science, remote sensing, climate science, and medical imaging. Grossberg holds a BA in Mathematics from the University of Pennsylvania, a PhD in Mathematics from MIT, and has held academic positions at Columbia University before joining CCNY. Education: Bachelor of Arts in Mathematics, University of Pennsylvania Doctor of Philosophy in Mathematics, Massachusetts Institute of Technology Postdoctoral Fellow at Hebrew University (Jerusalem) Assistant Professor of Mathematics, Columbia University Instructor and Research Scientist in Computer Science, Columbia University Research Interests: Grossberg's work focuses on applying computational methods to solve challenges in science and engineering. Key areas include: Quantitative image restoration for satellite data (e.g., MODIS, VIIRS) Machine learning applications in climate modeling and sea ice classification Semi-automatic medical image segmentation for radiation treatment planning Data visualization tools for climate and remote sensing data Lossless compression algorithms for Earth science imagers His research is supported by grants from NIH, NOAA, NASA, and the Office of Naval Research. Grants & Collaborations: NIH P20 grant for medical image segmentation NOAA-funded projects on satellite data restoration and compression Climate research collaborations with Columbia University and Memorial Sloan Kettering Cancer Center Labs & Teams: Grossberg leads the CCNY research lab focusing on interdisciplinary projects. Collaborators include Irina Gladkova (NOAA-CREST), Gikas Mageras (MSKCC), and climate scientist Niko Krakauer. His lab supports student projects in web development, data science, and medical imaging through programs like the NOAA-CREST HIRES initiative.

Bulent YenerView profile

Professor

• Applied Machine Learning
• Bioinformatics
• Medical Informatics
• +4 more

Bulent Yener is a Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) with a courtesy appointment in Electrical, Computer, and Systems Engineering. He serves as the founding Director of the Data Science Research Center and Associate Director of the Institute for Data Exploration and Applications (IDEA). He holds a Ph.D. (1994) and M.S. (1987) in Computer Science from Columbia University and previously worked at Bell Laboratories. Research Focus: Dr. Yener's work spans interdisciplinary applications of machine learning and data science, with core domains including: Bioinformatics/medical informatics: Tuberculosis pathogenesis modeling, computational pathology, and biomarker discovery Cyber security: Adversarial machine learning, causal inference attacks, and malware analysis Clinical applications: EEG-based seizure prediction and neurological disorder diagnostics Theoretical foundations: Graph neural networks, trustworthy AI, and combinatorial optimization Research Leadership: He directs multidisciplinary initiatives through: Data Science Research Center (petabyte-scale analytics combining cloud/HPC) IDEA Institute (data-driven scientific exploration) Affiliations with Cognitive and Immersive Systems Lab (CISL) and Center for Computational Innovations (CCI) Honors: IEEE Fellow for contributions to applied machine learning ACM Senior Member recognizing computing advancements

• Machine Learning
• Communication Theory
• Data Storage
• +3 more

Elnaz Banan Sadeghian is an Assistant Professor at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science and the Department of Electrical and Computer Engineering. She holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology (2017), alongside MS degrees from Georgia Tech (2015) and AmirKabir University (2008), and a BS from Shahid Beheshti University (2005). Her research focuses on Communication Theory, Machine Learning, Data Storage (particularly Magnetic Recording), Assistive Technology, and Brain-Computer Interfaces (BCI). Recent work includes neural network-based approaches for multitrack detection in magnetic recording and low-latency BCI systems leveraging time-series prediction and self-attention mechanisms. She also explores subject-independent BCI frameworks using supervised autoencoders. Dr. Sadeghian has received notable grants including an NSF CAREER Award and NSF CRII funding for machine learning applications in magnetic storage. Her contributions span over 20 peer-reviewed publications since 2007, addressing topics like asynchronous track detection algorithms, timing error correction in read channels, and fractal analysis of EEG signals for BCI. She is an ASTC Fellow and active member of IEEE and EMBS societies. Currently, she advises PhD candidates with expertise in machine learning and communication theory.

Nelly ElsayedView profile

Assistant Professor

• Applied AI
• Machine Learning
• Healthcare Informatics
• +7 more

Dr. Nelly Elsayed is an Assistant Professor at the School of Information Technology, University of Cincinnati. She leads the Applied Machine Learning and Intelligence (AMLI) Lab, focusing on AI, cybersecurity, healthcare informatics, and smart technologies. Her research emphasizes practical applications in IoT security, neural networks, and predictive modeling. Her work includes grants from the National Science Foundation, Procter & Gamble, and federal agencies, totaling over $600k. Notable projects include IoT security frameworks, mental health monitoring systems, and cyberbullying detection. Dr. Elsayed has received multiple recognitions, including the Golden Apple Award for Teaching and UCAADA Advising Excellence. She actively serves on technical committees for IEEE and ACM conferences, contributing to academic service and outreach. Research Interests: Applied AI & Machine Learning Cybersecurity for IoT/Healthcare Deep Learning Models (LSTM, CNN) Data Science & Pattern Recognition Smart Technologies for Accessibility Recent Research Trends: Her publications emphasize hybrid models (e.g., LSTM-CNN) for intrusion detection, cybersecurity frameworks, and AI-driven solutions for mental health and elderly care.

Nedret BillorView profile

Professor

• Robust Multivariate Data Analysis
• Robust Functional Data Analysis
• Outlier Detection
• +3 more

Nedret Billor is a Professor of Statistics in the Department of Mathematics and Statistics at Auburn University, within the College of Sciences and Mathematics. She holds a Ph.D. in Statistics from the University of Sheffield, UK. Her research focuses on robust multivariate data analysis, outlier detection, and functional data analysis, with applications in healthcare, environmental science, and astrophysics. She has published extensively and received notable awards, including an Elected Membership in the International Statistical Institute (ISI) since 2012 and a Fullbright Specialist Scholar designation in 2016. Dr. Billor has advised numerous graduate students and served on interdisciplinary committees. Her work emphasizes practical solutions to real-world problems, leveraging statistical methodologies to address challenges in STEM fields. She has contributed to medical data analysis, machine learning, and environmental modeling, while also advocating for women in statistics through her role in the ISI Committee on Women in Statistics (CWIS). Her teaching excellence has been recognized through awards for Outstanding Teacher of Graduate Students and Excellence in Teaching. Her research spans diverse domains, including robust statistical modeling for biomedical data, functional regression techniques, and outlier detection in high-dimensional datasets. Recent articles highlight advancements in Gaussian process modeling, image classification in astronomy, and post-surgical cardiac function analysis using machine learning. She also explores the impact of public health policies on disease transmission through data-driven epidemiological models. Dr. Billor’s career reflects a commitment to bridging statistical theory with practical applications, contributing to advancements in science, healthcare, and environmental sustainability.

Elisa KallioniemiView profile

Assistant Professor

• Neural Engineering
• Brain Stimulation
• Neurological Disorders
• +2 more

Elisa Kallioniemi is an Assistant Professor in the Department of Biomedical Engineering at NJIT and Director of the Brain Stimulation Lab. Her research uses brain stimulation to study neural mechanisms and develop therapies for neurological and psychiatric disorders. She holds a Ph.D. in Applied Physics from the University of Eastern Finland and M.S./B.S. in Electronics and Electrical Engineering from Aalto University. Dr. Kallioniemi's work integrates neural engineering, neuroimaging (TMS-EEG/EMG), and computational approaches to map cortical excitability, understand neuroplasticity, and develop diagnostic biomarkers. She leads collaborative initiatives through the ENIGMA-Neuromodulation working group and investigates the effects of interventions like hypnosis combined with neurostimulation. Awards include IEEE Senior Membership, ACNP Travel Award, and Cornell Rising Star recognition. Her lab focuses on translating neuroengineering innovations to clinical applications.

Sarah PowerView profile

Associate Professor

• Biomedical Engineering
• Physiological Signal Processing
• Brain-Computer Interfacing
• +1 more

Sarah Power is Associate Professor jointly appointed in the Faculty of Engineering and Applied Science (Electrical and Computer Engineering) and Faculty of Medicine at Memorial University. She holds a B.Eng. from Memorial (2006), M.A.Sc. from Toronto, and PhD in Biomedical Engineering from Toronto. Her research applies signal processing and pattern recognition to medical/rehabilitation challenges, focusing on mental state classification using EEG and physiological signals. This includes workload assessment, stress detection, and brain-computer interfaces. Power's publications demonstrate expertise in EEG signal classification, fNIRS-BCI development, and real-world neurotechnology applications. Recent work advances hierarchical classification systems and addresses validation challenges in passive BCI designs.

Xin-She YangView profile

Associate Professor

• Nature-inspired computing
• Mathematical modelling
• Engineering simulation
• +3 more

Xin-She Yang is an Associate Professor in Simulation & Modelling at Middlesex University, London. He holds a DPhil in Applied Mathematics from the University of Oxford and has over 25 years of experience in research and teaching. His academic roles include serving as an Editor-in-Chief, editorial board member for multiple journals, and chair of IEEE CIS Task Force on BIKM (2015-2020). He is a Fellow of the Institute of Mathematics and its Applications (2021), Asian Computational Intelligence Society (2023), and Soft Computing Research Society (2025). Research interests span nature-inspired computing, mathematical modelling, engineering simulation, AI, algorithms, and optimization. Notable achievements include being listed as a top 0.1% scientist in Web of Science since 2016 and receiving multiple Leader Awards in Computer Science (2023–2025). He supervises PhD students in areas like parameter tuning, nature-inspired computing, and optimization. Teaching includes courses on engineering simulation, mathematical optimization, and partial differential equations. He has authored books like Optimization Techniques and Applications with Examples and contributed to Springer's Nature-Inspired Computing and Optimization series. Education: DPhil in Applied Mathematics, University of Oxford (1998) Affiliations: IEEE, IMA, Asian CIS Grants: UKRI Doctoral Training Centres, National Research Councils (Iceland, Ireland, etc.)