جست‌وجوی استاد و پژوهشگر

• National Security
• Intelligence Studies
• Information Security
• +۳ مورد دیگر

Miodrag Lemajic is an Assistant Professor at Singidunum University since September 2024. Previously, he held roles at the Academy for National Security since 2013, serving as a lecturer and assistant professor. His career includes over a decade in Serbia's security sector, working in the Security Information Agency until end-2023. He has contributed to education programs for security personnel and held leadership roles in ethics and admissions committees at the Academy for National Security. Lemajic holds a Bachelor’s from the Faculty of Security Studies (1988–1992), a Master’s from the Faculty of Political Science (2005–2008), and a PhD in Political Science (2010–2011). His research focuses on national security training, intelligence system governance, and risk management in transitional political environments. He has authored works on classified information handling, EU security policy, and paramilitary influence analysis. Lemajic actively participates in academic governance, including editorial roles for the journal National Security . No scientific awards are listed in the provided texts. He has no recorded advisees, though his teaching spans academic security studies. His professional activities merge practical intelligence experience with academic research, emphasizing institutional reforms and oversight mechanisms in security systems.

• Swarm Intelligence
• Optimization Algorithms
• Machine Learning
• +۳ مورد دیگر

Eva Tuba is a researcher at Singidunum University , Serbia. Her work focuses on swarm intelligence , metaheuristic optimization , and machine learning applications across domains like medical imaging, wireless sensor networks, and digital forensics. Research Interests: Swarm optimization algorithms (bat, firefly, fireworks), machine learning, image/signal processing, cloud computing, and explainable AI. Publications: 15+ recent papers in journals such as Journal of King Saud University , IEEE Transactions , and Springer volumes, often in collaboration with researchers like Milena Tuba and Nebojsa Bacanin. Her academic contributions include hybridized algorithms for convolutional neural network tuning , medical diagnostics , and environmental monitoring . While she has no listed awards, her work spans both theoretical and applied computational intelligence.

Ihab F. Ilyasمشاهده پروفایل

استاد

• Data Quality
• Machine Learning
• Database Systems
• +۴ مورد دیگر

Ihab F. Ilyas is a Professor and holds the Thomson Reuters–NSERC Industrial Research Chair in Data Quality at the University of Waterloo's Department of Computer Science. His research focuses on data quality, machine learning for data enrichment, probabilistic data management, and knowledge graph systems. He leads projects like Holoclean (a probabilistic data repair system) and Data Civilizer (a data unification platform). His work bridges database systems and machine learning to address challenges in data integration, error detection, and large-scale knowledge representation. Education: PhD in Computer Science from Purdue University (2004), MSc and BSc from Alexandria University, Egypt (1999 and 1995). His publications span vector search, knowledge graph construction, and data curation techniques, with recent emphasis on adaptive indexing and scalable systems. He has contributed to open-source tools and frameworks for data cleaning and machine learning integration. Research interests include probabilistic data management, machine learning applications for data quality, and scalable systems for big data. His work often addresses real-world challenges in data integration and semantic representation, with a focus on practical, deployable solutions.

Adrian Lupascuمشاهده پروفایل

دانشیار

• Quantum Computing
• Superconductivity
• Quantum Information Processing
• +۵ مورد دیگر

Dr. Adrian Lupascu is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC) and the Department of Physics and Astronomy. He holds a cross-appointment in the Department of Electrical and Computer Engineering. His research focuses on superconducting quantum devices, including quantum annealing, quantum control, and strong light-matter interactions. Lupascu leads the Superconducting Quantum Devices Group, which explores novel architectures for quantum computing and sensing. Education: BSc in Physics (University of Bucharest, 2000); PhD in Physics (Delft University of Technology, 2005). Postdoctoral training included work at Delft University of Technology and École Normale Supérieure Paris under Professors Haroche and Raimond, supported by a Marie Curie Fellowship. Research Interests: Development of superconducting flux qubits for quantum information processing, quantum sensing, and ultrastrong light-matter interactions. Key projects include quantum annealing error suppression, qubit-qutrit gate optimization, and advanced flux qubit fabrication techniques. Awards: Alfred P. Sloan Fellowship (2011), Early Researcher Award (2011), Waterloo Nanotechnology Institute Research Leader Award (2018, 2022). His work has been funded by NSERC, MRI, and industry partnerships. Teaching: Courses include Quantum Nanophysics (PHYS 461), Nanoelectronics for Quantum Computing (QIC 880/ECE 770), and Quantum Optics Theory (QIC 895). Lab Activities: Active in developing scalable superconducting qubit systems, cryogenic microwave engineering, and quantum device fabrication. Collaborates with industry and academic partners on quantum computing hardware advancements.

Sourabh Boseمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Reinforcement Learning
• Neural Networks
• +۳ مورد دیگر

Sourabh Bose is an Assistant Teaching Professor and MS Computer Science Graduate Coordinator at Wichita State University's College of Engineering, School of Computing. He advises students in the MS Computer Science program and holds office hours via Zoom on Wednesdays (12:30-2:00 PM without appointment) and Fridays (10:00-11:00 AM with email预约). His research focuses on machine learning and artificial intelligence, with particular emphasis on reinforcement learning, neural networks, and policy optimization techniques. His research interests include developing reinforcement learning frameworks for representation learning, policy gradients in neural network training, and semi-unsupervised clustering methods. He has contributed to advancements in incremental learning systems and adaptive classifier architectures using reinforcement learning principles. While no specific awards or grants are listed in the provided text, his work demonstrates a strong focus on bridging theoretical AI concepts with practical neural network implementations. His academic role includes both teaching responsibilities and graduate program coordination within the Department of Computer Science.

• ASL Linguistics
• Sign Language Syntax
• ASL Classifiers
• +۳ مورد دیگر

Candice Hsu is an Adjunct Professor in the Department of Linguistics at Montclair State University, College of Humanities and Social Sciences. She teaches introductory ASL courses and focuses on research in American Sign Language (ASL) syntax, classifiers, and linguistics. Her work emphasizes staying current with ASL linguistic trends through workshops and training. Education: B.S. in Business Administration (with minor in ASL) from Northeastern University Deaf Interpreting Training Series [Certified Deaf Interpreter, CDI] at RDI/College of the Holy Cross Awards: 2012: Salutes Champions for Disabilities Award from Passaic County Advisory Council on Disability 2021: Spotlighted by NJ DHS for disability employment advocacy Advising/Grants: No advisees or grant information provided. Labs/Teams: None explicitly mentioned.

Reshma Karمشاهده پروفایل

استادیار

• Health Informatics
• Machine Learning
• Data Analysis
• +۳ مورد دیگر

Dr. Reshma Kar is an Assistant Professor at the Data Science Institute, Saint Peter’s University. She holds a Ph.D. in Engineering from Jadavpur University, India, where she previously served as a project fellow, senior project fellow, and guest faculty. Her research focuses on health informatics and machine learning, with applications in healthcare analytics and predictive modeling. Dr. Kar teaches graduate-level courses including Data Analysis and Decision Modeling, Python in Data Science, and Machine Learning. Her professional achievements include a Regional i-Corps Grant from the NSF for diabetes prediction research (2024) and multiple Faculty Research Associate Awards at Saint Peter’s University. Notable research contributions include developing interpretable machine learning algorithms for ICU length-of-stay prediction and analyzing brain signals for motor activity detection. She has also contributed to natural language processing studies, such as classifying social media data related to the pandemic. Dr. Kar has over 20 peer-reviewed publications and actively mentors students in research projects, emphasizing programming and data science methodologies. Her work bridges computational techniques with real-world healthcare challenges, reflecting her commitment to advancing both academic and applied data science.

• Prostate Cancer Research
• Cancer Metastasis
• Tumor Microenvironment
• +۴ مورد دیگر

Darryl T. Martin, PhD is an Assistant Professor in the Department of Urology at Yale School of Medicine. He holds affiliations with the Cancer Signaling Networks program and the Janeway Society. Dr. Martin completed his PhD in BioMedical Sciences at Memorial University of Newfoundland, followed by postdoctoral training at Yale under Dr. Robert M. Weiss. His research focuses on mechanisms driving cancer metastasis, tumor microenvironment modulation, and nanoparticle-based drug delivery systems. Education: PhD in BioMedical Sciences from Memorial University of Newfoundland (2010). Postdoctoral training in drug delivery and cancer progression at Yale University. Research Interests: Three core projects include investigating metastasis biology through biologic factors like testosterone and molecular determinants such as CTCs and MMPs; developing strategies to reprogram immunosuppressive tumor microenvironments using T-cell recruitment mechanisms; and advancing nanoparticle technologies for targeted drug delivery in urologic cancers. Publications highlight machine learning applications in prostate biopsy optimization, genomic classifier validation, and nanoparticle-based imaging/therapy. His work is supported by DoD and foundation grants. Dr. Martin collaborates with leading clinicians and researchers in urology and oncology. Lab: The Martin Lab focuses on translational research bridging molecular mechanisms and clinical applications in prostate and bladder cancers. Current projects explore phenotypic plasticity in metastasis, immune checkpoint modulation, and tumor-homing nanoparticle development.

• Interstitial Lung Disease
• Respiratory Medicine
• Pulmonary Fibrosis
• +۵ مورد دیگر

Dr. Lauren Troy is a Clinical Associate Professor at the Central Clinical School, University of Sydney's Sydney Medical School, within the Faculty of Medicine and Health. Her expertise lies in interstitial lung disease (ILD), particularly diagnostic techniques, geographical and environmental influences on ILD, and respiratory care advancements. She advises current PhD students Laura GLENN and Doris LAN. Dr. Troy's research focuses on novel diagnostic methods (e.g., cryobiopsy, genomic classifiers), the impact of air pollution on ILD outcomes, and telehealth innovations. She has contributed to clinical guidelines for hypersensitivity pneumonitis and home oxygen therapy. Her work includes studies on post-COVID-19 respiratory sequelae and palliative care integration in ILD services. Education and Training: While specific educational details are not explicitly listed, her academic role and publications suggest advanced training in respiratory medicine and clinical research. Research Interests: Her primary areas include ILD diagnosis and management, pulmonary fibrosis, autoimmune-related ILD, and the application of minimally invasive diagnostic tools. She explores socio-environmental factors influencing ILD prevalence and outcomes, with a focus on precision medicine and digital health solutions. Publications: Over 50 peer-reviewed articles since 2012, including seminal works on cryobiopsy accuracy, telehealth efficacy in ILD, and the role of antifibrotic therapies. Recent studies highlight her leadership in the Australasian ILD Registry (AILDR) and the REBUILD-SM trial for self-management tools. Grants and Awards: Lead or co-investigator on grants like the REBUILD-SM trial (2022) and the PFOX trial (2020), funded by NHMRC/MRFF. Her work emphasizes translational research bridging clinical practice and patient needs. Labs/Teams: Affiliated with Sydney's multidisciplinary ILD research groups and collaborates internationally on projects like the COLDICE study evaluating cryobiopsy efficacy. She is active in the Thoracic Society of Australia and New Zealand, contributing to clinical practice guidelines.

Zhiying Qianمشاهده پروفایل

استادیار

• Applied Linguistics
• Psycholinguistics
• Neurolinguistics
• +۳ مورد دیگر

Zhiying Qian is an Assistant Professor in the Department of Modern Languages and Linguistics at Florida State University (FSU). She holds a Ph.D. in East Asian Languages and Cultures with a certificate in Second Language Acquisition and Teacher Education from the University of Illinois at Urbana-Champaign (2015). Her research focuses on psycholinguistics, second language acquisition, and language program management, employing neuroscientific methods like EEG and eye-tracking to study cognitive processes in language comprehension. She leads the Chinese Language Teachers Association’s Research Special Interest Group, fostering collaboration among over 200 researchers in Chinese language education. **Education**: - Ph.D. in East Asian Languages and Cultures (UIUC, 2015) - Certificate in Second Language Acquisition and Teacher Education (UIUC, 2015) - Advanced Japanese studies at the Inter-University Center for Japanese Language Studies (Yokohama, Japan) **Research Interests**: - Applied linguistics and neurolinguistics - Sentence processing and classifier systems in Chinese/Japanese/English - Second language acquisition mechanisms - Technology-enhanced language pedagogy - Blended learning and teacher training **Grants and Funding**: - Seed Grant ($100k, FSU, 2024–2026) - Collaboration with Dr. Zilong Xie on neural tracking of linguistic units - Multiple institutional grants (e.g., $20k First-Year Assistant Professor Grant, 2019) **Lab & Outreach**: Directs the Language Processing and Eye-Tracking Lab, offering research opportunities in cognitive linguistics and second language acquisition. She serves as Review Editor for the Journal of Chinese as a Second Language (2024–2027) and actively promotes collaborative research through her SIG leadership.

Xiangyang Meiمشاهده پروفایل

استادیار

• Advanced Radiation Therapy Techniques
• Medical Physics Education
• Quality Assurance in Radiation Oncology
• +۳ مورد دیگر

Dr. Xiangyang Mei is an Assistant Professor specializing in Radiation Oncology and Medical Physics. His clinical interests include advanced radiation therapy techniques such as Image Guided Radiation Therapy (IGRT), Intensity Modulated Radiation Therapy (IMRT), Volume Modulated Arc Therapy (VMAT), Stereotactic Ablative Radiation Therapy (SABR), High Dose Rate (HDR) Brachytherapy, and Orthovoltage X-Ray Radiation Therapy. He focuses on improving quality assurance processes for safe and accurate radiation delivery. In education, Dr. Mei emphasizes training medical physics and radiation oncology residents, as well as teaching undergraduate and graduate students in medical physics. His research explores novel HDR Brachytherapy methods and the implementation of advanced external beam radiation therapy techniques. He also investigates new quality assurance protocols and their impact on clinical outcomes. Dr. Mei’s academic contributions span remote sensing and geoinformatics, with publications addressing climate change, urbanization, and environmental monitoring using satellite data. His methodologies often integrate machine learning, deep learning, and spatial analysis to solve complex problems in health, ecology, and urban planning. His work demonstrates interdisciplinary collaboration across medical physics, environmental science, and data science, with a focus on advancing both clinical practices and geospatial technologies.

Kristian Nybomمشاهده پروفایل

مدرس ارشد

• Edge Computing
• Edge Analytics
• Systematic Mapping Study
• +۷ مورد دیگر

Kristian Nybom is a Senior Lecturer in the Faculty of Natural Sciences and Engineering at Åbo Akademi University, Finland, affiliated with the Department of Information Technology. He is actively contributing to research in computer science, with a strong focus on edge computing, IoT systems, and software engineering practices. Research Interests: His research spans several key areas in modern computing, particularly Edge Computing and Edge Analytics , where he has conducted multiple systematic mapping studies to synthesize research trends and challenges. He is also deeply involved in API documentation generation , exploring tools and methodologies to improve developer experience and software maintainability. Additional interests include IoT applications in maritime environments , distributed sensor systems , and data engineering . The analysis of his recent publications reveals a consistent methodological focus on systematic mapping studies , which are used to survey and classify research in emerging domains. These works often sit at the intersection of software engineering and distributed systems, emphasizing practical tools and real-world applications, particularly in edge and maritime IoT contexts. Projects: He is currently active in multiple research initiatives, including the EU-funded AIDOart project and national projects like ADMO: 5G-Advanced for Digitalization of Maritime Operations and Cyber security training cooperation network . Earlier projects include N4S: Need for Speed and Enabling Next Generation Networks for broadcast Services . Collaboration and Funding: His work is supported by prominent funding bodies such as the European Commission and Business Finland. He frequently collaborates with researchers like Ivan Porres, Joakim Björkqvist, and Dragos Truscan. While no formal advisees are listed, his role as a senior lecturer suggests involvement in student supervision and curriculum development. Laboratories and Research Groups: Though not explicitly named, his affiliation with the Turku Center for Computer Science (TUCS) and participation in large-scale projects indicate integration within active research teams focused on software systems, edge computing, and digitalization.

Sebastian Heroldمشاهده پروفایل

دانشیار

• Software Architecture
• Software Evolution
• Technical Debt
• +۷ مورد دیگر

Sebastian Herold is an Associate Professor in Computer Science at Karlstad University, Sweden. His research focuses on software architecture, design, and quality, with an emphasis on mitigating architecture erosion and technical debt through empirical industry collaboration. He has held roles at RWTH Aachen University (2005 diploma/Master's equivalent), Clausthal University of Technology (PhD 2011), and Lero - The Irish Software Research Centre (2013–2015). Education: RWTH Aachen University of Technology, Germany: Diplom-Informatik (2005) Clausthal University of Technology, Germany: PhD in Computer Science (2011) Research interests span software architecture evolution, empirical studies in software engineering, privacy patterns (GDPR compliance), and usability evaluation in digital health. His work includes developing tools like InMap for automated code-to-architecture mapping and organizing workshops like SAEroCon on architectural consistency. Publications highlight trends in synthetic data for healthcare usability, machine learning for architecture mapping, and empirical validation of software practices. Recent work integrates AI methods for software engineering challenges and explores efficiency in digital health evaluations. Awards: None explicitly listed. Grants and advising: No specific grants or student advisees mentioned; collaborations with industry and Lero are emphasized. Labs/Teams: Active in Karlstad University's Department of Computer Science, with contributions to interdisciplinary projects like GDPR-focused MOOCs and healthcare software development studies.

Tudor Dumitrașمشاهده پروفایل

دانشیار

• System Security
• Machine Learning
• Big Data
• +۵ مورد دیگر

Tudor Dumitraș is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Maryland, with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and core faculty status in the Maryland Cybersecurity Center. His research focuses on data-driven security, empirical studies of adversary behavior, and the security of machine learning systems. Education Ph.D. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University Diplôme d’Ingénieur in Computer Science from Ecole Polytechnique, Paris, France B.S. in Computer Science from "Politechnica" University, Bucharest, Romania Dumitraș's work bridges system security, big data analysis, and machine learning. He developed the Worldwide Intelligence Network Environment (WINE), an innovative platform for sharing cybersecurity field data between academia and industry. His research includes malware detection using machine learning, analysis of zero-day attacks, and mitigation of adversarial threats against AI models. Recent publications highlight his contributions to adversarial machine learning, vulnerability exploit prediction, and neural network security. Scientific awards include the NSA Best Scientific Cybersecurity Paper Honorable Mention (2013), A.G. Jordan Award from Carnegie Mellon (2011), ACM SIGPLAN John Vlissides Award (2009), and multiple conference best paper awards. He has secured research funding from Amazon Web Services and received scholarships from the French government. Dumitraș advises graduate students and contributes to professional communities through program committee service, including on USENIX Security'24. He leads the development of security datasets and tools like Exploitability.app, HardwareFail.ml, and FeatureSmith. His outreach includes TED-style talks explaining cybersecurity concepts to broad audiences.

Ioanna Miliouمشاهده پروفایل

مدرس ارشد

• Data Science for Social Good
• Nowcasting
• Forecasting
• +۶ مورد دیگر

Ioanna Miliou is a Senior Lecturer in the Department of Computer and Systems Sciences (DSV) at Stockholm University, affiliated with the Data Science Research Group. She holds a Ph.D. in Computer Science from the University of Pisa and a diploma in Electrical and Computer Engineering from the National Technical University of Athens. Her research focuses on Data Science for Social Good, Nowcasting, and Forecasting, leveraging Big Data Analytics, Data Mining, and Machine Learning. Key applications include healthcare (e.g., sepsis prediction, glucose forecasting), public health (influenza nowcasting), and peace/sentiment analysis using global news data. She leads projects such as A3S (AI asthma app), OrganoFeed (organoid research), and MASICU (ICU mortality prediction). Teaching includes courses like Foundations of Data Science and Data Mining. She advises Ph.D. students Franco Rugolon and Lena Mondrejevski. Her work emphasizes explainable AI, federated learning for privacy, and multimodal data integration. She contributes to initiatives like the Data Science Research Group and collaborates on EU-funded projects.