جست‌وجوی استاد و پژوهشگر

Richard Scherlمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Automated Reasoning
• +۸ مورد دیگر

Richard Scherl is an Associate Professor in the Department of Computer Science and Software Engineering at Monmouth University, where he has been teaching since 2002. His office is located in James and Marlene Howard Hall 222. Education: Ph.D. in Computer Science, University of Illinois M.A. & Ph.D. in Anthropology, University of Chicago Diploma in Tamil Research, Madurai University (India) Research Interests: Professor Scherl's primary research focuses on logic-based knowledge representation and automated reasoning using formal frameworks like situation calculus. His work bridges Artificial Intelligence with Natural Language Processing , Cognitive Science , and Computational Social Sciences to model common sense knowledge, linguistic context, conversational actions, and socio-cultural phenomena. He investigates how reasoning systems integrate database structures with natural language semantics while addressing philosophical and anthropological dimensions of knowledge construction. Publications: His scholarly output demonstrates a consistent trajectory in formalizing context-aware reasoning systems. Recent works (2022) develop situation calculus models for knowledge, belief, and conversation, while earlier research (2015-2010) explores socio-cultural inference from text, semantic classifiers for big data, and epistemic planning. The publications reveal an interdisciplinary methodology connecting AI with linguistics and social sciences, yielding applications in homeland security, social computing, and narrative analysis. Awards: No scientific awards were mentioned in the provided information. Advising and Grants: The available documentation does not specify details regarding student advising or research grant funding. Labs and Teams: No information about dedicated research laboratories or collaborative teams was provided in the source materials.

Amit V. Deokarمشاهده پروفایل

استاد

• Business Analytics
• Business Process Management
• Machine Learning
• +۵ مورد دیگر

Amit V. Deokar serves as Associate Dean of Undergraduate Programs & Accreditation and Professor in the Department of Operations and Information Systems at UMass Lowell's Manning School of Business. He holds a Ph.D. in Management Information Systems from the University of Arizona and brings expertise from industrial and mechanical engineering backgrounds. His educational credentials include: Ph.D. in Management Information Systems, University of Arizona (2006) M.S. in Industrial Engineering, University of Arizona (2002) B.S. in Mechanical Engineering, V.J. Technological Institute, University of Mumbai (2000) Dr. Deokar's research centers on business analytics, machine learning applications, and business process management, with significant contributions to data mining, text mining, and artificial intelligence. His work bridges theoretical frameworks with practical implementations across healthcare, e-commerce, and transportation sectors, emphasizing real-world impact through industry collaborations and consulting engagements. His publication portfolio reveals a strategic focus on extracting actionable insights from complex data sources, particularly through process mining and text analytics. Recent work demonstrates increasing integration of generative AI techniques while maintaining strong methodological rigor in empirical validation. Key recognitions include: AIS Distinguished (cum laude) Member (2020) Association for Information Systems Teaching Excellence Award (2020) UMass Lowell IBM Faculty Award (2014) IBM Merill D. Hunter Award for Excellence in Research (2010) Dakota State University recognition As Associate Vice President of Technology for the Association for Information Systems, Editor-in-Chief of e-Service Journal, and leader of major conferences like AMCIS 2024, Dr. Deokar shapes academic discourse while securing research funding such as the 2017 Internal Seed Grant for urban transportation analytics. His industry consulting and keynote presentations on generative AI adoption further demonstrate applied scholarship.

Hamed Haddadiمشاهده پروفایل

استاد

• User-Centered Systems
• IoT
• Applied Machine Learning
• +۴ مورد دیگر

Professor Hamed Haddadi holds the position of Professor of Human-Centred Systems at the Department of Computing, Faculty of Engineering, Imperial College London. He is also an Imperial College Research Fellow and serves as Security Science Fellow at the Institute for Security Science and Technology. In industry, he acts as Chief Scientist at Brave. His research focuses on User-Centred Systems, IoT, Applied Machine Learning, Privacy, and Human-Data Interaction. He leads the Imperial-X initiative and holds an EPSRC Open Plus Fellowship (2022-2027). His work bridges academia and industry, addressing challenges in privacy-preserving technologies, edge computing, and secure AI systems. Affiliations include the Artificial Intelligence Network, Centre for Cryptocurrency Research and Engineering, Data Science Institute, Energy Futures Lab, and Institute for Security Science and Technology. He actively supervises PhD students through programs like the President’s PhD Scholarships and Islamic Development Bank scholarships. Recent research trends emphasize privacy-preserving AI, secure edge computing, federated learning, and IoT security. His publications span topics like differential privacy, adversarial ML defense, and energy-efficient neural networks. Notable awards include the EPSRC fellowship supporting his work on Human-Centred Systems. Grants include Imperial College Research Fellowships and collaborations with industry partners. He directs the NetSys group and contributes to interdisciplinary initiatives like the Energy Futures Lab. His work often involves collaborative projects with industrial partners such as Brave Software to advance real-world applications of secure technologies.

Roman Kernمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Data Science
• +۷ مورد دیگر

Assoc. Prof. Roman Kern is affiliated with the Institute of Machine Learning and Neural Computation at Graz University of Technology and serves as Chief Scientific Officer at Know-Center Research GmbH , a competence center for Big Data analytics. His research bridges natural language processing, data science, and machine learning with a focus on causal inference and discovery, applied to scientific publication mining, intelligent transportation systems, and smart production. Research Interests : Causal inference, NLP, data science, trustworthy AI, knowledge discovery, and interdisciplinary applications. Projects : VanillaFlow (sustainable electrolytes for batteries), REWAI (causal discovery in textile manufacturing), and EPN-2024-RI (planetary science infrastructure). Teaching : Courses include Knowledge Discovery, Natural Language Processing, and Introduction to Scientific Working at TU Graz. His recent publications analyze causal relationships in semiconductor manufacturing, differential privacy, and LLM applications in healthcare and environmental monitoring. Roman Kern actively mentors students through thesis topics on causality, LLMs, and data privacy frameworks, while leading applied research projects with industry partners.

Navid Ghaffarzadeganمشاهده پروفایل

دانشیار

• System Dynamics
• Health Policy
• Science Policy
• +۵ مورد دیگر

Navid Ghaffarzadegan is an Associate Professor in Industrial and Systems Engineering at Virginia Tech, College of Engineering. He holds a PhD in Public Policy (System Dynamics) from SUNY Albany, an MBA from Sharif University of Technology, and a B.S. in Mechanical Engineering from the same institution. His research focuses on applying system dynamics modeling to address complex policy challenges in health, science, and socio-technical systems. He has led grants from NIH and industry partners like Dell, Inc., and co-edits the *System Dynamics Review*. Notable awards include the College of Engineering’s excellence award for outstanding new Assistant Professors (2016). Research interests include epidemic modeling, science policy, and behavioral dynamics. His lab, SoDA, explores complex systems using system dynamics, agent-based models, and data analytics. Recent work addresses post-traumatic stress disorder (PTSD) dynamics, behavioral responses to epidemics, and data-intensive service systems. Collaborations with Dell and NIH highlight his applied focus on real-world policy solutions. Students under his guidance have contributed to impactful studies in healthcare, workforce policy, and infrastructure systems. Grants include NIH-funded projects on science workforce modeling and Dell-sponsored research on service adoption dynamics. His articles span policy analysis, health systems, and complex systems modeling. He actively mentors PhD and master’s students, fostering interdisciplinary approaches to systemic challenges.

• NMR Spectroscopy
• Nanobiotechnology
• Microfluidics
• +۳ مورد دیگر

Sander Baas is an Education/Research Officer in the BioNanoTechnology department at Wageningen University. His role involves academic research and educational initiatives, focusing on developing innovative hardware solutions for NMR spectroscopy and microscopy. He holds a PhD from Wageningen University, awarded in 2024, for his thesis on DIY NMR systems, magnets, and hyperpolarization hardware. His academic career has been centered around interdisciplinary research at the intersection of engineering and life sciences. Education: Sander Baas completed his educational background at Wageningen University, where he earned his PhD in 2024. His academic journey includes: PhD in BioNanoTechnology (2024) Research Interests: Sander Baas’ research interests span NMR Spectroscopy, Nanobiotechnology, and Microfluidics. He is deeply involved in designing advanced transceiver ASICs for multinuclear NMR applications and optimizing magnet systems for enhanced spectral resolution. Additionally, his work explores the integration of CRISPR technology with microscopy frameworks for in vivo tracking and the repurposing of 3D printers into biomedical tools like programmable syringe pumps. His studies often bridge electronics engineering with molecular biology, aiming to create accessible and high-performance instrumentation for scientific research. Articles Overview: His recent publications (2021–2023) emphasize the development of affordable and versatile NMR instrumentation, including a 2 T magnet with high sensitivity and a syringe pump derived from a 3D printer kit. He also focuses on microfluidic applications, such as hyperpolarization techniques using CIDNP, and advancing ASIC-based transceiver systems for multinuclear NMR. Earlier work (2018–2019) highlights contributions to single-molecule localization microscopy and CRISPR/dCas9 tracking in live bacteria, demonstrating a consistent thread of innovation in both hardware and biological imaging technologies. Scientific Awards: No scientific awards mentioned. Advising & Grants: In his academic journey, Sander Baas was a PhD candidate in the NanoSpin project (2018–2024), supervised by Prof. A. Velders and co-supervised by Dr. V. Saggiomo. This project addressed challenges in NMR spectroscopy signal intensity and polarization transfer within nanobiotechnology. No formal grants or advising roles are explicitly listed in the provided texts. Labs & Teams: He contributed to the NanoSpin research initiative at Wageningen University, focusing on NMR spectroscopy and nanobiotechnology advancements. Collaborations include work with the Universidad de Castilla-La Mancha on microfluidic datasets and open-source hardware projects. His research also involves partnerships with institutions in Biomedical Engineering and Electronics Design.

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +۲ مورد دیگر

Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).

• Cybersecurity
• Quantum Computing
• Energy Efficiency in Computing
• +۴ مورد دیگر

Haider Ali is a Lecturer in Computer Science at the College of Science and Engineering. His research focuses on cybersecurity, quantum computing, energy-efficient computing, and optimization algorithms, particularly in IoT and real-time systems. Research interests include quantum encryption protocols, zero-day exploit detection in web platforms like WordPress, energy-aware scheduling in embedded systems, and metaheuristic algorithms for wireless sensor networks. His work bridges theoretical advancements with practical applications in edge computing, cyber-physical systems, and healthcare IoT. Notable contributions include developing novel scheduling techniques for MPSoCs, energy-optimized data aggregation in IoT, and frameworks for detecting cyber threats. Articles span reputable journals like IEEE Access and Future Internet, highlighting interdisciplinary approaches to modern computing challenges. No awards or grants are explicitly documented. Advising activity details are not provided in the text.

Bimal Viswanathمشاهده پروفایل

دانشیار

• Machine Learning Security
• Privacy
• Adversarial Attacks
• +۲ مورد دیگر

Dr. Bimal Viswanath is an Associate Professor in the Department of Computer Science at Virginia Tech. His research focuses on security and privacy aspects of large online services, particularly at the intersection of machine learning and security. His work covers three main directions: attacks using ML, attacks on ML systems, and ML for improved security/privacy. He has received multiple awards including the Distinguished Paper Award at SOUPS 2014 and Best Paper Award at COSN 2015. Dr. Viswanath completed his Ph.D. in Computer Science at the Max Planck Institute for Software Systems and was a postdoctoral researcher at UCSB's SAND lab. His research group currently includes several PhD students working on security and machine learning projects. His work has been supported by grants from CCI, NSF and 4-VA. Recent research has focused on security challenges in generative AI systems, including deepfake detection, chatbot vulnerabilities, and adversarial attacks on ML systems. His team develops practical defenses against emerging threats in AI-powered systems. Teaching activities include courses on Foundation Models and Security, Secure Computing Capstone, and Security Analytics. Dr. Viswanath serves on program committees for top security conferences including CCS, USENIX Security, and IEEE S&P.

• Machine Learning
• Computer Vision
• Data Analytics
• +۱۲ مورد دیگر

Michael Dittenbach is a researcher at TU Wien's Institute of Software Technology and Interactive Systems (E188). He contributes to interdisciplinary work combining machine learning, computer vision, and tourism informatics. His career spans over 15 years of publications in semantic web technologies, data analytics, and 3D virtual environments. Current research focuses on computer vision applications for cultural heritage, tourism recommender systems, and self-organizing map visualization Key projects include Media Square, SOIRE, and Web 3D environments Collaborates extensively with Helmut Berger, Andreas Rauber, and Robert Sablatnig His recent work (2022) explores computer vision techniques for digital library preservation, while earlier studies (2005-2015) focused on tourism informatics, data analytics frameworks, and semantic web applications. Publications demonstrate a consistent trajectory toward integrating machine learning with user-centric digital systems. As part of TU Wien's research infrastructure, he contributes to technical reports, conference proceedings, and multimedia system development without explicit mentions of scientific awards in available records.

• Machine Learning
• Intelligent Data Analysis
• Biomedical Signal Processing
• +۷ مورد دیگر

Jose Rafael Magdalena Benedicto is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is a core member of the Intelligent Data Analysis Laboratory (IDAL), where he leads research in machine learning, biomedical signal processing, and data science applications in health and industry. Doctorate: Universitat de València (2000) His research interests span machine learning , intelligent data analysis , biomedical engineering , neurocognitive disorders in metabolic and psychiatric diseases , algebraic geometry , and quantum machine learning . His work bridges theoretical advances in symbolic computation and applied AI with real-world problems in medicine and industrial systems. Recent publications show a strong trend in applying machine learning to anomaly detection in enterprise systems, asymptotic analysis of algebraic curves and surfaces, and identifying inflammatory and metabolic biomarkers for cognitive impairment in diabetes and psychiatric disorders. He also explores multimodal video analysis for public safety and quantum reinforcement learning. While no scientific awards are listed in the provided text, his extensive publication record and leadership in research projects indicate significant scholarly contributions. He has supervised doctoral students, including Dr. Juan Francisco Guerrero Martínez and Dr. Javier Calpe Maravilla. His research is supported by ongoing projects in intelligent data analysis, though specific grants are not detailed. He contributes to educational innovation, having developed tools like BiomedChallenge to enhance data science learning. He is actively involved in the Intelligent Data Analysis Laboratory (IDAL) , which focuses on developing and applying advanced data analysis techniques to real-life applications in biomedicine, education, and industry.

Saulius Preidysمشاهده پروفایل

استادیار

• Big Data Processing
• E-learning Methodology
• Business Intelligence
• +۳ مورد دیگر

Roles & Affiliations: Assistant Professor at the Department of Organizational Information and Communication Research, Faculty of Communication, Vilnius University. Active in research projects, academic conferences, and editorial roles. Education: PhD in Mathematics and Informatics (2012), Vilnius University, Thesis: 'The applications of datamining methods to personalized learning environments.' MA in Informatics (1995), Vilnius Pedagogical University. Research Interests: Focus on e-learning methodologies, big data processing, educational technology, and business intelligence. Explores applications of machine learning in finance and personalized learning systems. Publications & Projects: Over 15 articles in peer-reviewed journals and conferences (e.g., Applied Sciences, Mykolas Romeris University Proceedings). Led the EU-funded project 'Development and Introduction of Multilingual Teacher Education Programmes in Georgia and Ukraine (DIMTEGU)' (2012–2016). Active in conference organizing and editorial work. Grants & Memberships: Participated in EU-funded projects. Member of the National Association of Distance Education (NADE), Lithuanian Association of Distance and e-Learning (LieDM), and Lithuanian Computer Society (LIKS). Labs & Teams: Involved in research groups focused on educational technology, data analytics, and digital transformation initiatives within the Faculty of Communication.

• E-Learning
• Distance Education
• MOOCs (Massive Open Online Courses)
• +۱۲ مورد دیگر

Dr. Andrew Alexander Adams is a researcher with extensive contributions in the domains of e-learning, distance education, MOOCs, open educational resources, information ethics, and privacy in digital environments. His work often explores the societal implications of technology, particularly in educational and cross-cultural contexts. He has collaborated with scholars across institutions, with frequent co-authorship from researchers like Tharindu R. Liyanagunawardena, S. A. Williams, and N. Rassool. Educational focus on technology-mediated learning Strong emphasis on equity and access in global education Interdisciplinary research bridging computer science, education, and ethics His research interests center around the ethical and practical challenges of integrating technology into education and society. Key areas include the design and effectiveness of e-learning systems, the cultural dimensions of online privacy, and the policy frameworks needed to support open and equitable access to knowledge. He has investigated how learners in developing countries engage with MOOCs and OER, highlighting infrastructural and socioeconomic barriers. The body of his recent work shows a consistent trend toward understanding the human side of digital transformation—how users interact with systems, how cultural norms shape online behavior, and how institutions can responsibly support technological adoption. His publications span journals in education, information ethics, and computer science, reflecting a truly interdisciplinary approach. Notable scientific contributions include co-authoring a comprehensive study on MOOCs and publishing on privacy norms in the UK and Japan. While no formal awards are listed in the provided text, his publications in high-impact journals and conferences indicate scholarly recognition. Co-authored Pandora’s Box – Social and Professional Issues of the Information Age (2007) Published in British Journal of Educational Technology , International Review of Research in Open and Distance Learning , and SCRIPTed Active in international conferences such as Ethicomp and European e-Learning conferences Dr. Adams has contributed to research on customer-driven development of learning objects and the use of pair programming in educational software. While no formal lab or team is mentioned, his collaborative work suggests involvement in interdisciplinary research groups focused on educational technology and information ethics.

• Machine Learning
• Data Science
• Algorithms
• +۳ مورد دیگر

Krishna Bathula is a Clinical Assistant Professor at the Seidenberg School of CSIS within Pace University , specializing in Computer Science . He teaches courses including Introduction to Computing, Algorithms, Database Management Systems, Machine Learning, and Data Science. Office Locations: New York City, Room 1107, 15 Beekman (Tue 2:30pm-5:30pm) Westchester, Room 313, Goldstein Academic Center (Thu 12:15pm-2:15pm) Research Interests: Machine learning and data science applications in computing Database theory and practical analytics Natural language processing for software team analysis Recent Publication Trends: Focuses on applying NLP techniques to software development methodologies like Scrum, with emphasis on team dynamics and problem-solving in agile environments.

Manlu Liuمشاهده پروفایل

استاد

• AI and Business Intelligence
• Big Data
• Accounting and Finance Analytics
• +۳ مورد دیگر

Manlu Liu is a Professor in the Department of MIS, Marketing, and Analytics at the Rochester Institute of Technology (RIT) 's Saunders College of Business , where she serves as Program Director for the MS in Business Analytics. She holds a Ph.D. in Management Information Systems from the University of Arizona and an MBA in Accounting and Finance from the Hong Kong University of Science & Technology. Specializes in AI/Business Intelligence, Blockchain Technology, and Accounting Analytics Co-initiator of RIT's Advanced Certificate in Accounting and Financial Analytics Represents RIT on the university-wide AI Task Force Her research spans community source software development, sustainability reporting, and health informatics. Publications appear in Accounting, Organizations and Society , Journal of Strategic Information Systems , and Information Systems Journal . She has served on editorial boards for Electronic Commerce Research and International Journal of Electronic Finance . Recent work focuses on blockchain applications in accounting, AI-generated NFT markets, and sustainability dynamics in auditing. She has held leadership roles in international conferences including ICIS and SIGBPS , and co-chaired the Workshop on E-Business (WeB) 2024 .