جست‌وجوی استاد و پژوهشگر

• large scale learning
• extreme multi-label classification
• deep learning
• +۲ مورد دیگر

Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness. His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems. Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation. Scientific recognition includes: Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies. The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.

James Fowlerمشاهده پروفایل

استاد مدعو

• Criminal Justice
• National Security
• Law Enforcement
• +۳ مورد دیگر

James Fowler is an Adjunct Professor in the Department of Criminal Justice at Mercy University's School of Social and Behavioral Sciences since January 2025. He integrates his extensive law enforcement experience into classroom instruction, emphasizing practical applications of criminal justice theory. Professional Background: Retired FBI intelligence division member, specializing in criminal and national security investigations using classified technology. Former NYPD Sergeant with 20 years of service, including roles in patrol precincts, recruit instruction, and defensive tactics training. Contributed to high-profile events like the Boston bombing response through FBI Tactical Operations Center assignments. Research Focus: Fowler's expertise centers on law enforcement methodologies, tactical operations, and national security systems. His work bridges operational practices with academic analysis in criminal justice education.

• Machine Learning
• Robotics
• Natural Language Processing
• +۱ مورد دیگر

Mahmoud Rahat is a Senior Lecturer at the Department of Information Technology , Halmstad University , Sweden. He is affiliated with the Center for Applied Intelligent Systems Research (CAISR) since 2019, focusing on data-driven solutions for industrial applications. Research Expertise: Machine Learning, Predictive Maintenance, Robotics, Natural Language Processing Industrial Collaboration: Volvo Group Truck Technologies, Northern Illinois University (2015-2016) His work bridges academia and industry through projects in intelligent mobility and automated idea detection . He has developed innovative approaches in survival analysis and XAI (Explainable AI) for predictive maintenance systems. Current trends in his publications include: Explainable AI methods for industrial data (Shapley values, Integrated Gradients) Survival analysis in neural networks Domain adaptation for automotive sensor data Federated learning frameworks Virtual sensors via tensor completion Mahmoud supervises academic projects including: Lecturer: Machine Learning for Predictive Maintenance, Big Data Parallel Programming Course Responsible: Applied Data Mining Examiner: Degree Project in Computer Science and Engineering

• Computer Science
• Artificial Intelligence
• Bayesian Networks
• +۳ مورد دیگر

Dr. ir. Janneke Bolt is a Researcher at the Department of Information and Computing Sciences , Faculty of Science , Utrecht University . Her work focuses on Bayesian networks , probabilistic graphical models , and independence relations in Artificial Intelligence and Data Science . She has published extensively on topics such as probabilistic independence , loopy propagation , and sensitivity functions . Her recent research includes self-adhesivity in lattices of abstract conditional independence models and Bayesian network applications in medicine . Collaborators include L.C. van der Gaag and S. Renooij . Research Areas: Bayesian Networks Probabilistic Inference Independence Relations Machine Learning Uncertainty Quantification Medical AI Recent Publications (2025-2014): Self-adhesivity in Lattices (2025) Bayesian Networks in Medicine (2024) Semi-Graphoid Rule Generalizations (2023) Lattice-Based Independence Representations (2020) Multi-Dimensional Bayesian Classifier Tuning (2016) Collaborations: L.C. van der Gaag S. Renooij J. de Bock A. Hommersom

William Turkettمشاهده پروفایل

دانشیار

• Computational Biosciences
• Bioinformatics
• Network Security
• +۵ مورد دیگر

Dr. William Turkett serves as Associate Professor and Department Chair of the Department of Computer Science at Wake Forest University, a position he has held since 2020. He concurrently acts as Graduate Program Director and serves on Wake Forest's Information Technology Advisory Board, with prior experience as an ABET/CSAB Peer Evaluator. His educational background includes: Ph.D. in Computer Science and Engineering from University of South Carolina (2004), specializing in probabilistic reasoning B.S. in Computer Science from College of Charleston (1998) Dr. Turkett's research program bridges computational biosciences and cybersecurity, with core expertise in DNA/amino acid sequence analysis for prediction/classification tasks. His work consistently addresses systems-scale sequential and temporal datasets, while maintaining active interests in computer science education methodologies and bioinformaticians' software development practices. Recent investigations include evolutionary cyber defense strategies and community-focused mobile application development. Publication analysis reveals three dominant research thrusts: bioinformatics (sequence-based protein classification, gene expression networks), cybersecurity (resilient systems, moving target defense), and education (non-traditional student engagement, STEM incubators). His interdisciplinary approach connects biological systems with computational modeling while addressing practical security challenges. Dr. Turkett oversees graduate education as Program Director and contributes to curriculum development through courses like CSC 201 (Data Structures) and CSC 231 (Programming Languages). His ABET accreditation experience supports program quality assessment, while community outreach initiatives demonstrate commitment to expanding computer science participation. Collaborative research efforts span computational biology labs and cybersecurity teams, though specific laboratory affiliations aren't documented in available sources. Current work continues to evolve at the intersection of biological sequence analysis and adaptive security frameworks.

Sarah Parsonsمشاهده پروفایل

استادیار

• Computer Science Education
• Machine Learning
• Data Science
• +۳ مورد دیگر

Sarah Parsons is an Assistant Professor in the Department of Computer Science at Wake Forest University . She holds dual undergraduate degrees in Physics and Mathematics from Wake Forest University, alongside a Master’s degree in Computer Science from the same institution. B.S. in Physics , Wake Forest University B.A. in Mathematics , Wake Forest University M.S. in Computer Science , Wake Forest University Her research spans interdisciplinary domains including: Computational approaches to computer science education pedagogy Machine learning and data science applications in biology and ethics Game theory for privacy-preserving data acquisition Algorithm visualization tools for parallel programming Publication trends reveal expertise in Python programming , dynamic programming , and topic modeling for educational research. Her work combines technical rigor with ethical frameworks across computational disciplines. Contact: parssy18@wfu.edu

Hamed Mozaffari Maarefمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Hamed Mozaffari Maaref is a researcher at National Research Council Canada 's Institute for Research in Construction (IRC) , with a Ph.D. in Electrical and Computer Engineering from the University of Ottawa . His work bridges Artificial Intelligence and Fire Safety , leveraging Deep Learning and Signal Processing to solve complex problems in emergency response systems. Education: Ph.D. in Electrical and Computer Engineering (2020), University of Ottawa Current Role: Assistant Research Officer (PDF), Institute for Research in Construction, NRC Canada Research spans Raman Spectroscopy , Computer Vision for fire prediction, and Ultrasound Imaging for speech analysis. Publications focus on 1D CNNs for spectroscopy, Transformers for flashover prediction, and ICA Techniques for biomedical imaging. Key contributions include the Penalized Aggregate Dynamic Classifier (Pad-Classifier) and BowNet for tongue contour tracking. Scientific achievements include the Ontario Trillium Scholarship . Collaborations with Won-Sook Lee's Lab and institutions like Hiroshima University demonstrate his interdisciplinary approach to advancing Machine Learning applications in safety and healthcare domains.

Srijan Kumarمشاهده پروفایل

استادیار

• AI for Security
• Security for AI
• Graph Learning
• +۷ مورد دیگر

Srijan Kumar is an Assistant Professor in the Computer Science and Engineering department at the College of Computing, Georgia Institute of Technology. His research focuses on developing AI and data mining methods to enhance web safety and security, with particular expertise in graph and multimodal learning for detecting malicious actors and harmful content. Dr. Kumar earned his Bachelor's degree from the Indian Institute of Technology at Kharagpur, followed by his Master's and Ph.D. from the University of Maryland, College Park. Prior to joining Georgia Tech, he was a visiting researcher at Google AI and a postdoctoral researcher at Stanford University. His research spans four main areas: (1) AI for Security, developing methods to detect misinformation, malicious actors, and online hate; (2) Secure, Robust, and Responsible AI, investigating the trustworthiness of AI systems; (3) Graphs and Networks, modeling large-scale interconnected data; and (4) Recommender Systems and Behavior Modeling. His work has been deployed at Flipkart and has influenced Twitter's Birdwatch platform. Dr. Kumar leads The CLAWS (Computational Data Science Lab for the Web and Social Media), which has received funding from NSF, DARPA, CDC, Adobe, Google, Facebook, and Microsoft. His lab mentors numerous PhD, MS, and undergraduate students working on cutting-edge web safety and AI security challenges. NSF CAREER Award recipient Kavli Fellow by the National Academy of Sciences Forbes 30 Under 30 (2022) Rising Star in Data Science by Frontiers in Big Data ACM SIGKDD Doctoral Dissertation Award Runner-Up WWW Best Paper Award Honorable Mention

Debra Aaronsمشاهده پروفایل

مدرس ارشد

• Linguistic Analysis of Humor
• Pragmatics
• Signed Language Linguistics
• +۵ مورد دیگر

Dr. Debra Aarons is a Senior Lecturer in Linguistics within the School of Humanities & Languages at the University of New South Wales (UNSW), specializing in the linguistic analysis of humor and signed language linguistics. With a distinguished academic career spanning decades, she has established herself as a leading researcher at the intersection of language structure, cultural expression, and comedic performance. Her educational background includes: BA (Majors English and Philosophy) from Witwatersrand University (1976) BA Honours (English Literature) from Witwatersrand University (1977) Higher Dip. Ed PG from Witwatersrand University (1979) BA Honours (Applied Linguistics) from Witwatersrand University (1986) PhD (Applied Linguistics) from Boston University (1994) Dr. Aarons' research centers on the linguistic structures of humor , examining both formal grammatical properties and pragmatic dimensions of jokes and comedic performance. She investigates how linguistic play reveals tacit knowledge of language systems and how cultural contexts shape humorous expression. Her work spans multiple domains including standup comedy analysis, Jewish comedy traditions, and language play in both spoken and signed modalities. Earlier in her career, she made significant contributions to South African Sign Language linguistics , examining syntactic structures, sociolinguistic variation, and educational inclusion for deaf communities. Analysis of her publication record reveals a clear evolution from foundational work in sign language linguistics to her current specialization in humor studies, while maintaining connections between these fields through her interest in language structure. Recent publications demonstrate particular focus on socially critical standup comedy, with detailed analyses of performers like Sarah Silverman and examinations of Jewish comedy traditions across historical contexts. Her work consistently bridges theoretical linguistics with cultural analysis, revealing how language structures underpin comedic expression across diverse media and social settings. Her scholarly recognition includes: British Council Scholarship (Lancaster UK, 1986) Fulbright Scholarship (Boston University, 1987) University of Bologna, Senior Visiting Fellowship (2017) Dr. Aarons maintains an active research program with ongoing collaborative projects, particularly with Marc Mierowsky. She is currently co-authoring a monograph titled 'Playing with Food and Language: Eating, Joking and Disgust' with Delia Chiaro for Taylor and Francis. Her research methodology combines rigorous linguistic analysis with cultural context, examining how language structures facilitate humor across different communicative settings. Though specific laboratory structures aren't detailed, her collaborative publication pattern suggests active research partnerships across disciplines. Her work bridges linguistics with cultural studies, performance studies, and cognitive science, creating a multidisciplinary approach to understanding the complex relationship between language structure and humorous expression.

Zahraa S Abdallahمشاهده پروفایل

مدرس ارشد

• Data Science
• Machine Learning
• Stream Mining
• +۶ مورد دیگر

Dr Zahraa S Abdallah is a Senior Lecturer at the University of Bristol's School of Engineering Mathematics and Technology, specializing in data science and machine learning applications for healthcare, climate change, and smart metering systems. Her research bridges theoretical algorithm development with real-world implementation challenges in evolving data environments. Education: PhD BSc Research Focus: Dr Abdallah's work centers on time series and streaming data analysis using adaptive machine learning techniques. She develops novel frameworks for concept drift handling and real-time pattern recognition, with particular emphasis on healthcare monitoring systems and environmental data processing. Her fingerprint analysis reveals strong activity in Activity Recognition (100%), Data Streams (59%), and Time Series Data (37%) within computer science. Publication Trends: Her recent work demonstrates consistent innovation in streaming data applications, evolving from mobile activity recognition systems to sophisticated time series classifiers. The research trajectory shows increasing focus on healthcare integration, particularly through diabetes management applications using physiological data streams. Scientific Recognition: Australian Postgraduate Award (2010) for doctoral studies Mollie Holman Award (2016) for outstanding thesis contribution Research Leadership: As Co-Investigator on the UK T1D closed-loop systems project (2023-2024), she led AI components for physiological data integration. Her dataset contributions include the BrisT1D-Restricted Dataset (2025) for diabetes research and ML surrogates for genome design (2024). Collaborative Networks: Actively engaged with the Intelligent Systems Laboratory and Infection and Immunity research theme, maintaining international collaborations evidenced by multi-country research networks and 106+ Mendeley readers across publications.

• Tensor Methods
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Kim Batselier is an Associate Professor at Delft University of Technology, affiliated with the Delft Center for Systems and Control. He earned his Master’s and Ph.D. in Electrical Engineering from KU Leuven, Belgium, under the supervision of Prof. dr. ir. Bart De Moor. Prior to his current role, he worked at BioRICS (2005–2009) on real-time monitoring algorithms for AC Milan football players and was a Post-doctoral Research Fellow at the University of Hong Kong (2013–2018). Education: Master’s and Ph.D. in Electrical Engineering, KU Leuven Previous Affiliations: BioRICS, University of Hong Kong His research focuses on innovative tensor methods for high-dimensional data problems in signal processing, machine learning, system identification, and control. Notable contributions include a tensor network-based MIMO Volterra model estimator (achieving 10 million coefficient estimation in 1 second with 5% relative error), Tensor Network Kalman filters, polynomial classifiers for image processing, and tensor decomposition algorithms for nonlinear system identification. All implementations are open-source. Kim is also passionate about education and science popularization , emphasizing accessible dissemination of engineering concepts.

Steven Lupienمشاهده پروفایل

مدرس

• Blockchain
• Digital Assets
• Cryptocurrency
• +۱ مورد دیگر

Steven Lupien is the Ada Lovelace Director of the University of Wyoming Center for Blockchain & Digital Innovation and a Lecturer in blockchain, digital assets, and cryptocurrency at the College of Business. He is affiliated with the University of Wyoming, a Carnegie R1-classified institution. Research Focus: Blockchain technology, digital innovation in agriculture, and cryptocurrency applications. Key Contributions: Co-authored "Blockchain Fundamentals for Web 3.0" (2023), founding member of BeefChain (USDA-approved blockchain agricultural initiative). Leadership: Executive Director of the Digital Asset Trade Association (DATA), Board Member of Cyber Wyoming and the Digital Ledger Governance Association (DLGA), and advisor to blockchain organizations.

Patrick F Walshمشاهده پروفایل

استاد

• Intelligence Studies
• Biosecurity
• Health Security
• +۳ مورد دیگر

Professor Patrick F. Walsh is a leading academic and practitioner in Intelligence and Security Studies at Charles Sturt University’s Australian Graduate School of Policing and Security. With a PhD, MSocSc, and BA from the University of Queensland, he bridges academic rigor with practical experience as a former intelligence analyst in Australian national security and law enforcement agencies. Current roles: Senior researcher at AGSPS, editorial board member of Intelligence and National Security Journal , and Honorary Visiting Fellow at University of Leicester (UK) Key research areas: Intelligence governance, biosecurity/health security intelligence, leadership in security agencies, and intelligence ethics His work analyzes the intersection of cybersecurity, biotechnology, and global security threats. Notably, he has received the AIPIO Certificate of Merit (2012) and been recognized as an ERA active researcher . Walsh has secured significant grants including a $277,551 ARC Discovery Grant (with Seamus Miller) and portions of the $140M CRC Cyber Security project. Walsh’s scholarship addresses: Intelligence frameworks for emerging threats Leadership challenges in Five Eyes alliance Post-COVID-19 health security intelligence reforms Ethical implications of surveillance technologies A sought-after consultant, he works with federal and state agencies on strategic intelligence matters. His international engagement includes: Vice President (International) at the International Association for Intelligence Educators (2021–2022) Contributions to global intelligence education curricula Collaborations with NATO and UN stakeholders

• Neurosurgery
• Spinal Disorders
• Orthopedics
• +۵ مورد دیگر

Robert Gamburd is a Professor in the Department of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine. His academic career integrates neurosurgery, orthopedics, and radiology, with a focus on spinal pathology and peripheral nerve compression syndromes. Education and Training: MD from University of Michigan Inteflex Program (1980) Internship in Medicine at UCSF (1981) Residency at Stanford University (1983) Fellowship in Sports Medicine at PM&R SOAR (1984) Research Interests: Gamburd's work emphasizes spinal diseases, nerve compression syndromes, and diagnostic imaging techniques like MRI. His conceptual networks include topics such as synovial cysts, spinal nerve roots, laminectomy, and neural conduction studies. Publication Trends: His research output (1988–1994) centers on spinal imaging, differential diagnosis between radiculopathy and peripheral nerve entrapment, and surgical interventions for spinal stenosis. All three publications are classified under Orthopedics or General Medicine by NLM. Professional Networks: He collaborates with UCSF's Neurological Surgery department colleagues and peers like Christopher Ames (UCSF) and Praveen Mummaneni (UCSF), focusing on shared spinal and nerve compression research themes.

• Data Science
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Piotr Lech Artiemjew is a Professor at the Chair of Mathematical Methods of Informatics within the Institute of Computer Science , affiliated with the Faculty of Mathematics and Computer Science at the University of Warmia and Mazury in Olsztyn . His research focuses on Data Science , Machine Learning , and Artificial Intelligence , with applications in database systems, image analysis, and decision systems. His work emphasizes improving data classification quality, accuracy, and modeling techniques. Scopus H-index: 9 Web of Science H-index: 7 Total ministerial score: 2,883