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Talitha WashingtonView profile

Professor

• Applied Mathematics
• Dynamical Systems
• Nonstandard Finite Difference Schemes
• +7 more

Talitha Washington, Ph.D., is a Professor of Mathematics and Executive Director of the Center for Applied Data Science and Analytics (CADSA) at Howard University. She also holds the Sean McCleese Endowed Chair in Computer Science, Race, and Social Justice and serves as co-Chair of the President's AI Advisory Council. Her work bridges mathematical research, data science, and social equity, with leadership roles extending to the NSF National Data Science Alliance and the Association for Women in Mathematics. Dr. Washington earned her B.S. in Mathematics from Spelman College, followed by M.S. and Ph.D. degrees from the University of Connecticut. She also received an honorary Doctor of Science from UConn. Her academic journey includes faculty positions at The College of New Rochelle, University of Evansville, and Clark Atlanta University, along with significant leadership roles at the National Science Foundation. Her research spans applied mathematics, dynamical systems, nonstandard finite difference schemes, and the ethical development of artificial intelligence. She is deeply committed to equity in STEM, focusing on inclusive data science education and the responsible use of AI in addressing societal challenges. Her recent publications explore AI ethics, discrete heat equations, image encryption, and curriculum design. Recent publications reflect a strong interdisciplinary focus, combining mathematical rigor with real-world applications in AI ethics, health equity, and engineering systems. Her work emphasizes dynamic consistency in numerical modeling and promotes fairness and diversity in technology development. 2019 BEYA STEM Innovator Award 2019 Outstanding Faculty Award from Howard University 2020 NSF Director’s Award for Superior Accomplishment Fellow of the American Mathematical Society (AMS) Fellow of the Association for Women in Mathematics (AWM) Fellow of the American Association for the Advancement of Science (AAAS) Fellow of the African Scientific Institute (ASI) Elected to Phi Beta Kappa and Sigma Xi Dr. Washington has advised major national initiatives including the NSF’s first Hispanic-Serving Institutions Program, which awarded $85 million in grants, and served as Program Director at NSF in the Division of Undergraduate Education and the Convergence Accelerator. She was the inaugural Director of the Atlanta University Center Data Science Initiative and leads the NSF National Data Science Alliance. Her grants and leadership focus on expanding access to data science for underrepresented communities and fostering interdisciplinary collaboration. She leads the Center for Applied Data Science and Analytics at Howard University, a hub for research and education that leverages data to address societal inequities. She also contributes to national advisory bodies such as the U.S. Census Bureau’s Scientific Advisory Committee and the AI Advisory Council at Howard. Her work fosters collaboration across institutions, particularly among HBCUs, to build a diverse talent pipeline in AI and data science.

Papaloukas KonstantinosView profile

Professor

• Bioinformatics
• Computational Biology
• Biomedical Signal Processing
• +7 more

Professor Papaloukas Konstantinos is affiliated with the University of Ioannina, where he leads research in bioinformatics and computational biology. His work focuses on integrating machine learning with biomedical data to address challenges in healthcare, such as disease prediction, biomarker discovery, and personalized medicine. He teaches courses including Bioinformatics and Introduction to Computer Science, and directs the Bioinformatics Laboratory. Research interests span computational analysis of proteins, automated tissue classification, and biomedical signal processing. Key areas of exploration include cardiovascular disease mechanisms, diabetes management, autoinflammatory disorders, and cancer biomarkers. His most recent studies leverage ensemble learning for hypoglycemia prediction and deep learning for autoinflammatory biomarker discovery. Publications highlight contributions to multimodal patient data integration, synthetic data generation for chronic diseases, and multimodal glucose prediction systems. Collaborative projects span cardiology, oncology, and infectious disease research. Despite no explicitly listed awards, his work demonstrates significant impact in computational medicine and bioinformatics. As part of his academic role, he advises students in bioinformatics and computational biology, though specific advisee names are not documented in available materials. His laboratory focuses on translating genomic and clinical data into actionable insights for clinical decision support.

Michael GaldeView profile

Assistant Professor

• Cybersecurity
• Malware Analysis
• Industrial Control Systems (ICS) Security
• +10 more

Michael Galde is an Assistant Professor at the University of Arizona's College of Engineering, Department of Electrical and Computer Engineering, where he specializes in cybersecurity education and research. He holds a Master's in Cybersecurity and a Bachelor's in Political Science from the University of Nebraska and brings over a decade of experience from defense intelligence, industrial cybersecurity, and academic instruction. MS in Cybersecurity, University of Nebraska BA in Political Science, University of Nebraska His research and teaching focus on malware analysis , Industrial Control Systems (ICS) security , reverse engineering , and hands-on cyber operations training . He develops advanced courses and practical tools to strengthen cybersecurity resilience in critical infrastructure. His work bridges technical depth with educational accessibility, empowering the next generation of cyber professionals. His recent technical projects reflect a strong trend in applied cybersecurity research , especially in OT/ICS monitoring , network visualization , and the integration of AI and NLP into security operations . Projects like GRID-LM, IAES-SOC, and PCAPMap demonstrate innovation in real-time threat detection, large-scale data analysis, and user-friendly tooling for cyber defense. Notable scientific credentials include: Global Industrial Cyber Security Professional (GICSP) GIAC Response and Industrial Defense (GRID) Michael Galde actively contributes to cybersecurity through teaching (80% responsibility), university service (20%), research (2024–present), and external consulting (15%). He mentors students through project-based learning and leads initiatives in developing robust educational and technical frameworks. His research team works on projects including SPINE, DaRIA, and IAES-SOC, focusing on scalable NLP ecosystems and intelligent network monitoring. He leads multiple research and development efforts, including the IAES-SOC for OT network monitoring, PCAPMap for traffic visualization, and SPINE for NLP infrastructure. These projects are supported by hands-on development and integration with tools like Wazuh, ELK stack, Scapy, and Bokeh.

Sangmi Lee PallickaraView profile

Professor

• Big Data
• Data Science
• Data Engineering
• +13 more

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

Stefan ĆirkovićView profile

Lecturer

• Cybersecurity
• Artificial Intelligence
• Computer Vision
• +3 more

Stefan Ćirković is a Teaching Associate in the Department of Information Technologies at the Faculty of Technical Sciences in Čačak, University of Kragujevac. His office is located at Saint Sava 65, 32102 Čačak, Serbia. He is currently pursuing doctoral studies in Information Technology (2023–present) at the same institution. Education: Doctoral Academic Studies – Information Technology, University of Kragujevac (2023–present) Master of Academic Studies – Information Technology, University of Kragujevac (2022–2023) Basic Academic Studies – Information Technology, University of Kragujevac (2018–2022) Specialized Program – Cybersecurity, University of Kragujevac (2024) Research Focus: Ćirković specializes in applied artificial intelligence with significant work in cybersecurity, medical imaging, and network systems. His research demonstrates strong emphasis on practical AI implementations including real-time object detection (YOLO algorithm), anomaly detection in networks, federated learning for healthcare applications, and cybersecurity techniques using large language models. His work frequently intersects with medical informatics, particularly in diagnostic applications. Publication Trends: Recent publications (2024–2025) show predominant focus on AI/ML applications across multiple domains. Cybersecurity remains a core theme with novel approaches to web application security and network anomaly detection. Medical applications feature prominently, especially in diagnostic imaging (kidney stones, skin cancer) and hemodialysis optimization. Methodologically, there's consistent exploration of deep learning architectures and real-time systems. Laboratory Affiliations: Associated with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak. Utilizes institutional resources including the Moodle e-learning platform and Microsoft 365/Teams infrastructure for academic activities.

• Cybersecurity
• Information Security Governance
• Attack Graph Analysis
• +5 more

Alessandro Palma is a researcher affiliated with Sapienza University of Rome , Italy, focusing on Cybersecurity and Information Security Governance . His work involves attack graph analysis, incident management compliance, and network vulnerability evaluation. Palma collaborates extensively with researchers like Marco Angelini, Silvia Bonomi, and Fabian J. Theis. Recent publications highlight his contributions to: Attack Graph Scalability (2025): Developing frameworks for evaluating network security. Incident Management Compliance (2024): Creating systems for quantitative process assessment. Multi-Modal Single-Cell Generation (2025): Applying machine learning to computational biology. His research integrates graph theory , visual analytics , and machine learning to address cybersecurity challenges in IoT and federated data systems. No scientific awards or student advisement information is publicly available in the provided data.

• Medical Imaging
• Radiomics
• Machine Learning
• +6 more

Renato Cuocolo is an Associate Professor at the Department of Medicine, Surgery, and Dentistry "Scuola Medica Salernitana" of the University of Salerno. He actively participates in multiple European radiology societies, including chairing the ESR eHealth and Informatics Subcommittee and contributing to EuSoMII and ESUR committees. MD from University of Campania "Luigi Vanvitelli" Residency in Radiology at University of Naples "Federico II" PhD in Biomorphological and Surgical Sciences at University of Naples "Federico II" His research focuses on radiomics and machine learning applications in medical imaging, with specific emphasis on: Prostate cancer MRI analysis Musculoskeletal and genitourinary radiology Neuroradiology Systematic reviews and meta-analyses Key contributions include: Quality-checking the PROSTATEx public prostate cancer MRI database Developing classification models for cartilaginous tumors (CT/MRI) Creating radiomics pipelines for endometrial cancer risk prediction He maintains active coding skills in Python and R for medical image analysis.

Keshab K. ParhiView profile

Professor

• VLSI Signal Processing
• Machine Learning
• Biomedical Signal/Image Processing
• +6 more

Keshab Parhi is the Distinguished McKnight University Professor and inaugural Erwin A. Kelen Chair in Electrical Engineering at the University of Minnesota's College of Engineering. He has been a permanent faculty member since 1988 and serves as Editor-in-Chief of the IEEE Circuits and Systems Magazine. B.Tech., IIT Kharagpur (1982) M.S., University of Pennsylvania (1984) Ph.D., UC Berkeley (1988) His research spans VLSI architecture design for signal processing, machine learning , biomedical systems , homomorphic encryption , and DNA/molecular computing . Key projects include high-speed transceivers, low-power bio-signal classification, and novel cryptographic architectures. Recent publications focus on homomorphic encryption for privacy-preserving AI, hyperdimensional computing for energy-efficient classification, and neural network accelerators with pipelined architectures. Scientific Awards IEEE Fellow (1996) IEEE Kiyo Tomiyasu Award (2003) IEEE W.R.G. Baker Prize (2001) ACM Fellow (2020) AIMBE Fellow (2022) National Academy of Inventors (2020) Dr. Parhi has advised 52 PhD students and 65+ MS/MEng theses, while leading the Parhi Research Group. His work contributes to UN SDGs through advancements in low-power computing and medical diagnostics .

Carla E. BrodleyView profile

Professor

• Computer Science Education
• Machine Learning
• Diversity in Computing
• +5 more

Carla E. Brodley is a Professor and Dean of Inclusive Computing at Northeastern University, where she also serves as Founding Executive Director of the Center for Inclusive Computing (CIC) – a $50M initiative focused on systemic, sustainable change to remove institutional barriers in computing. She has led Khoury College of Computer Sciences (2014-2021), served on the Presidential Cabinet, and previously held faculty roles at Tufts University and Purdue University. Research interests include: Computer Science Education Machine Learning Diversity in Computing Data Science Intersectional Analysis Entropy-based Diversity Metrics Curriculum Reform Belonging in Computing Recent publications focus on intersectional data analysis, entropy-based diversity metrics, and institutional barriers in computing education. She examines how cohort-based analysis provides more accurate longitudinal insights than traditional percentage-based approaches, particularly for gender, race, and ethnicity representation. Scientific honors include Fellowships with: Association for Computing Machinery (ACM) Association for the Advancement of Artificial Intelligence (AAAI) American Association for the Advancement of Science (AAAS) She has advised numerous PhD students (now at institutions like NASA Jet Propulsion Laboratory, Oregon State University, and Northeastern) and leads the CIC’s efforts to help over 60 U.S. universities implement inclusive computing practices through intersectional data analysis and institutional consulting.

• Infectious Diseases
• Clinical Microbiology
• Antibiotic Resistance
• +5 more

Jan-Erik Berdal is an Associate Professor at the Faculty of Medicine, University of Oslo, affiliated with the Department of Microbiology and Infectious Diseases. His research focuses on infectious disease epidemiology, antibiotic resistance, and critical care microbiology, with extensive contributions to understanding hospital-acquired infections, sepsis, and SARS-CoV-2 dynamics. He collaborates with Akershus University Hospital and participates in multinational studies like ISARIC-COVID-19. Research interests include: Microbial pathogenesis in critical care settings Antibiotic therapy and resistance patterns Viral infections and immune responses (notably SARS-CoV-2) Development of diagnostic tools for infections Recent publications highlight his work on fecal microbiota transplantation for C. difficile , longitudinal transcriptomics in COVID-19, and biomarker utility in predicting outcomes. Studies span clinical trials, cohort analyses, and computational biology tools. Key collaborations include institutions across Norway and international networks (e.g., ISARIC). His work bridges clinical microbiology, public health, and data-driven diagnostics, emphasizing real-world applications in hospital settings.

Yanda TaoView profile

Researcher

• Machine Learning Systems
• Distributed Deep Learning
• Automatic Parallelism
• +2 more

Yanda Tao is a Researcher at the Department of Computing, Imperial College London, funded by Samsung since March 2024. He graduated with distinction from the MSc Advanced Computing program at Imperial College London (2022-2023) and holds an Ingénieur degree in AI from CentraleSupélec, Université Paris-Saclay (2019-2023). During his master's, he developed HetML , a system for heterogeneity-aware automatic parallel training of deep learning models under the supervision of Professor Peter Pietzuch at LSDS (Large-Scale Data and Systems Group). His research focuses on machine learning systems design for heterogeneous GPU clusters, optimizing cloud infrastructure for deep learning workflows, and improving distributed training efficiency. Technical expertise includes system architecture, parallel computing, and ML pipeline deployment gained through internships at Alstom (Data Scientist) and SAP France (Software Engineer). His work bridges theoretical system design with practical implementation challenges in large-scale ML environments. The HetML project (2023, in preparation) demonstrates his specialization in automatic parallelism techniques and heterogeneity-aware optimization for distributed deep learning. This aligns with his broader interest in scalable machine learning infrastructure and cost-efficient system design for democratizing large-scale AI applications.

• Scientific Computing
• Numerical Analysis
• Numerical Methods for PDE
• +4 more

Professor Talal Rahman is a faculty member at the Western Norway University of Applied Sciences, where he works in the Department of Computer Science, Electrical Engineering and Mathematical Sciences. His office is located at Bergen KRONSTAD D305, and he can be reached at phone number +47 55 58 72 46. Professor Rahman's research spans several key areas in computational mathematics and scientific computing. His primary research interests include: Scientific Computing Numerical Analysis Numerical Methods for Partial Differential Equations Preconditioning Finite Element with Domain Decomposition Methods Variational Image Processing Artificial Intelligence and Machine Learning applications Professor Rahman's extensive publication record demonstrates a strong focus on domain decomposition methods, particularly Schwarz methods and their applications to multiscale problems. His recent work shows an increasing integration of machine learning techniques with traditional numerical methods, as evidenced by publications on neural network applications for environmental modeling and capelin migration patterns. His research also extends to biomedical applications, including computational analysis of biodegradable materials and bone tissue engineering scaffolds. He has made significant contributions to the development of adaptive preconditioners and parallel algorithms for solving complex numerical problems, with his work on the TV-Stokes model for image processing representing an important contribution to the field of variational image processing. Professor Rahman has supervised numerous research projects and students, though specific student names are not provided in the available information. His research appears to be supported by grants related to computational science and engineering, though specific grant details are not mentioned in the provided text. Based on his research areas, Professor Rahman likely collaborates with various research groups focused on computational science, with potential connections to biomedical engineering labs and environmental research teams studying the Barents Sea ecosystem.

• Healthcare Data Analysis
• Machine Learning
• Deep Learning
• +5 more

Awais Ashfaq is a Postdoctoral Researcher at Halmstad University's School of Information Technology, focusing on healthcare data analytics and AI applications in clinical decision-making. His work bridges the gap between advanced machine learning techniques and practical healthcare solutions, with particular emphasis on emergency department diagnostics and cardiovascular disease analysis. Dr. Ashfaq's research interests center around healthcare data analysis, applying statistics, traditional machine learning, deep learning, and language models to extract insights for informed clinical and management decisions. His work demonstrates strong expertise in interpretable AI models for medical diagnostics, particularly in emergency settings for conditions like dyspnea. He has made significant contributions to the development of evidence-based diagnostic tools that integrate seamlessly with clinical workflows. His publication record shows a consistent focus on leveraging complete healthcare system data to address real clinical challenges. The research trends indicate increasing sophistication in model interpretability while maintaining high diagnostic accuracy. His work spans multiple healthcare domains including emergency medicine, cardiology, and pediatric care, demonstrating the versatility of his methodological approaches. Dr. Ashfaq collaborates extensively with physicians across all care levels to ensure his research translates effectively to clinical practice and improves patient outcomes. His work on regional healthcare information platforms demonstrates commitment to building infrastructure that supports ongoing medical research and quality improvement initiatives.

Younes SmaniView profile

Professor

• Microbiology
• Antimicrobial Resistance
• Bacterial Pathogenesis
• +3 more

Dr. Younes Smani is a Professor at Pablo de Olavide University , leading the Expresión Génica en Bacterias de Interés Medioambiental research group at the Andalusian Center of Developmental Biology (CABD) . His work focuses on Microbiology , particularly antimicrobial resistance mechanisms in Gram-negative bacilli like Acinetobacter baumannii , Pseudomonas aeruginosa , and Enterobacterales . Key Research Areas : Host-pathogen interactions, outer membrane protein targets (e.g., OmpA), and development of novel antibiotic classes targeting bacterial enzymes like enolase. Methodology : In vitro studies, animal models, proteomics, and computational approaches for drug discovery. Recent Publications highlight advancements in antimicrobial strategies , including AI-driven antibiotic discovery, enolase-targeting compounds, and repurposing drugs like tamoxifen metabolites. His team emphasizes combination therapies with existing antibiotics and anti-virulence approaches to combat resistance.

Naohiro OkadaView profile

Associate Professor

• Neuroimaging
• Psychiatric Disorders
• Adolescent Development
• +7 more

Naohiro Okada is an Associate Professor at the Graduate School of Medicine, Department of Psychiatry, The University of Tokyo, and concurrently serves as an Associate Professor at the International Research Center for Neurointelligence (IRCN). He is also the Core Manager of the Human fMRI Core facility. Dr. Okada has extensive experience in neuroimaging research, particularly focusing on psychiatric disorders and adolescent brain development. Dr. Okada obtained his M.D. from the University of Tokyo Faculty of Medicine in 2004. After completing his residency, he began psychiatry training at the University of Tokyo Hospital in 2006 and worked as a psychiatrist at Tokyo Metropolitan Matsuzawa Hospital from 2007 to 2012. He earned his Ph.D. from the Graduate School of Medicine, University of Tokyo in 2017, and joined IRCN in 2019 where he manages the human fMRI scanner facility. His research primarily focuses on using functional magnetic resonance imaging (fMRI) to understand brain structure and function in healthy adolescents and patients with psychiatric disorders. Dr. Okada's work aims to elucidate the neurobiological substrates of psychiatric disorders and develop biomarkers for clinical applications. He has published extensively on topics including schizophrenia, autism spectrum disorder, depression, and adolescent brain development, with a particular emphasis on multi-site collaborative studies and data-driven approaches to psychiatric classification. Dr. Okada's recent publications demonstrate a strong focus on developing novel diagnostic classifications based on neuroimaging data, investigating resting-state neural signatures across psychiatric disorders, and establishing robust infrastructure for multi-center MRI research. His work bridges human and animal research while incorporating machine learning and AI techniques to advance the field of psychiatric neuroimaging. Dr. Okada is actively involved in several research projects funded by the Japan Society for the Promotion of Science, including the Advanced Bioimaging Support Platform (2022-2028), MRS analysis of basal ganglia GABA dysfunction in schizophrenia (2021-2025), and studies on dose reduction and brain function changes in schizophrenia patients (2018-2022). He is a member of the Society for Neuroscience and the Japanese Society of Biological Psychiatry, reflecting his commitment to advancing neuroscience research and its applications in understanding and treating psychiatric disorders.