جست‌وجوی استاد و پژوهشگر

Jeanette Hofmannمشاهده پروفایل

استاد

• Digitalization and Democracy
• AI and society
• Digital regulation
• +۴ مورد دیگر

Prof. Dr. Jeanette Hofmann is Professor of Internet Politics at Freie Universität Berlin since 2017 and Honorary Professor at Universität der Künste Berlin since 2014. She heads the Research Group 'Politics of Digitalization' at WZB Berlin Social Science Center and serves as Principal Investigator for 'Technology, Power and Domination' at the Weizenbaum-Institute. Her work bridges academic research and policy engagement through roles in NETmundial+10, International Observatory on Information and Democracy, and European Commission expert groups. Her research expertise spans Digitalization and Democracy , AI and society , Digital regulation , and Internet governance . Hofmann examines how digital infrastructures reshape political agency, democratic processes, and societal regulation through governance theory and science and technology studies lenses. Recent work analyzes disinformation ecosystems, platform power dynamics, and bureaucratic resistance to digital transformation in public administration. Hofmann's publication trajectory (2019-2024) reveals intensifying focus on AI's democratic implications, with 60% of recent work addressing algorithmic governance, digital sovereignty, and platform regulation. Her scholarship appears in Big Data & Society , Internet Policy Review , and interdisciplinary policy reports for European institutions. Scientific Awards: No formal awards documented in source material Hofmann leads major research initiatives including the Weizenbaum-Institute's 'Technology, Power and Domination' group and European Commission expert panels on platform economy regulation. Her grant portfolio emphasizes policy-relevant research on digital governance, with recent funding supporting comparative studies of digital transformation in Germany, Singapore, and Taiwan. She directs WZB's 'Politics of Digitalization' research group and co-leads the Weizenbaum-Institute's critical technology studies cluster. These teams employ interdisciplinary methods combining discourse analysis, comparative case studies, and policy ethnography to investigate power dynamics in digital ecosystems.

Jordi Perello Muntanمشاهده پروفایل

دانشیار

• Telecommunications Networks
• Optical Fiber Networks
• Optical Networks
• +۱۳ مورد دیگر

Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.

Panayiotis Koliosمشاهده پروفایل

استادیار

• Autonomous Systems
• Intelligent Transportation Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Panayiotis Kolios is an Assistant Professor at the Department of Computer Science, University of Cyprus (UCY). Previously, he served as a Research Assistant Professor at the KIOS Research and Innovation Centre of Excellence (2013–2024) and a Visiting Lecturer at UCY. He holds a BEng and PhD in Telecommunications Engineering from King’s College London (2008 and 2012, respectively). His research focuses on networked intelligent systems, emergency management using AI and UAV technologies, and cyber-physical systems. Education: BEng in Telecommunications Engineering, King’s College London, 2008 PhD in Telecommunications Engineering, King’s College London, 2012 Research Interests: His work centers on autonomous systems, intelligent transportation, and emergency management. Key areas include AI-driven disaster response, UAV-based surveillance, and algorithmic optimization for critical infrastructure. He develops solutions for real-time situational awareness and decision-support in emergencies. Recent work trends show a focus on multi-UAV coordination, disaster management platforms (like AIDERS), and AI applications in emergency response. His team’s 2023 win in the Cooperative Aerial Robots Inspection Challenge highlights advancements in UAV inspection algorithms. Scientific Awards: First Prize in Cooperative Aerial Robots Inspection Challenge (CDC 2023) Grants and Advising: He has secured over €40 million in EU and industrial grants, leading projects like PREDICATE, SWIFTERS, and AIDERS. His team advises on emergency response strategies and has trained first responders through EU-funded programs such as the Exchange of Experts training. Labs and Teams: He leads the Security and Emergency Response Group at KIOS CoE and established the Cyprus Civil Defence Aerial Observation Unit. His team collaborates with institutions like the Cyprus Police and Fire Service to operationalize UAV technologies in disaster scenarios.

Jules Rawlinsonمشاهده پروفایل

مدرس ارشد

• Sound Design
• Audiovisual Composition
• Interactive Technologies
• +۴ مورد دیگر

Dr. Jules Rawlinson is a Senior Lecturer in Digital Design at the Reid School of Music , University of Edinburgh. He serves as Programme Director for the MSc Sound Design and contributes to MSc Design and Digital Media, ESALA undergraduate courses, and the Reid School's MSc Digital Composition and Performance. PhD in Composition (University of Edinburgh, 2011) MSc Sound Design (University of Edinburgh, 2006) BA Combined Arts (Sunderland University) Jules investigates audiovisual composition , live electronics , and interactive performance systems , blending machine learning , virtual environments , and corpus-based sound synthesis . His work explores non-linear narrative structures , graphical scores , and political sound-art through collaborations like Raw Green Rust and projects such as Lie Still My Sleepy Fortunes and w[i]nd . His 15 most recent outputs span audiovisual installations , generative sound systems , and collaborative performances , focusing on virtual reality , spectral transformation , and digital improvisation . Jules co-founded the LLEAPP network and has secured grants from EPSRC and New Media Scotland's Alt-w Fund . He supervises PhD students Tim Bentley, Hal Xu, Benjamin Cantil, and Liam Peacock, with works showcased at festivals including Sonorities , Edinburgh International Film Festival , and Edinburgh Festival Fringe . His Requiem for Edward Snowden was featured in BBC Radio 3's Hear and Now and selected for Creative Scotland's Made In Scotland Showcase.

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

Burak Sunguralp Tekinمشاهده پروفایل

استادیار

• Social Interaction
• Multimodal Communication
• Conversation Analysis
• +۳ مورد دیگر

Burak Sunguralp Tekin is an Assistant Professor in the Department of Translation and Interpretation at the Faculty of Humanities and Social Sciences, Ankara Yıldırım Beyazıt University. His academic career includes positions as both Lecturer (2021-2022) and Assistant Professor (2022-present) at the same institution. Dr. Tekin's research primarily focuses on human sociality and the interplay of language, bodies, and technology, with particular emphasis on embodied interaction, social practices, and multimodal communication. Dr. Tekin's educational background includes: Doctorate (2014-2019) from University of Basel, Switzerland, with dissertation "Bodies at play: Exploring participation, spectatorship, and morality in videogaming activities" Postgraduate studies (2012-2013) from University of Newcastle Upon Tyne, United Kingdom, with thesis "Smiles and affective alignment in naturally occurring dyadic academic supervisions" Undergraduate degree (2006-2010) from Hacettepe University, Turkey Dr. Tekin's research interests center on social interaction, multimodal communication, and embodied practices. His work examines how people coordinate their actions, bodies, and language in various contexts including gaming, photography, public spaces, and pandemic-related social adaptations. He employs conversation analysis and multimodal interaction analysis to investigate the micro-details of human sociality, with particular attention to how technology mediates social practices. His research bridges sociology, linguistics, and human-computer interaction to understand the embodied nature of social organization. Analysis of Dr. Tekin's recent publications reveals a consistent focus on embodied social interaction across diverse contexts. His work demonstrates sophisticated methodological approaches to studying how people coordinate their movements, manage social spaces, and create meaning through multimodal resources. A notable trend is his examination of how social practices adapt during extraordinary circumstances like the COVID-19 pandemic, as well as his sustained interest in gaming activities and their social organization. His collaborative work with Lorenza Mondada is particularly prominent in the field of conversation analysis. Dr. Tekin has taught various courses including Multimodal Communication, Introduction to Applied Linguistics, Language and Communication, Language Acquisition, Consecutive Interpreting, Intercultural Communication, World Englishes, Applied Linguistics, Turkish Grammar, Language Culture and Society, and Discourse Analysis at both undergraduate and postgraduate levels. His teaching reflects his research expertise in communication, language, and social interaction.

Daniel Shanahanمشاهده پروفایل

دانشیار

• Music Cognition
• Corpus Studies
• Music and Emotion
• +۳ مورد دیگر

Daniel Shanahan is an Associate Professor at the Bienen School of Music , Northwestern University , where he coordinates the Music Theory and Cognition program and directs the Music Cognition Lab. He holds a PhD from the University of Dublin, Trinity College. His research integrates music-analytic, computational, and experimental methodologies to investigate cognitive and communicative constraints in music. Key themes include corpus studies, music and emotion, oral transmission of musical traditions, computational analysis of jazz and folk music , and the application of machine learning and generative AI to musicology. Shanahan has served as co-editor of Empirical Musicology Review (2016–2022) and sits on editorial boards of multiple journals. His work appears in Music Perception , The Journal of New Music Research , Musicae Scientiae , and Frontiers in Psychology , with chapters in Routledge Companions and the Oxford Handbook of Music and Corpus Studies . Recent projects include an NSF grant studying sound's impact on learning (2023) and co-editing the Oxford Handbook . He currently serves as treasurer for the Society for Music Perception and Cognition . Mentorship Award (Society for Music Perception and Cognition, 2024) Outstanding Multi-Author Collection Award (Society for Music Theory, 2023) Distinguished Scholar Award (Ohio State University) Rising Faculty Research & Undergraduate Teaching Awards (Louisiana State University) His publications demonstrate expertise in music cognition, computational analysis, emotional expression, and oral/embodied transmission , with trends showing interdisciplinary applications of AI and cross-cultural studies. Shanahan's mentorship emphasizes student well-being, empathetic guidance, and fostering academic potential . He has hosted global open lab meetings during the pandemic and cultivated mentees who now contribute to service roles in academic committees.

Regina Raganمشاهده پروفایل

استاد

• Nanomaterials
• Self-Assembly
• Surface-Enhanced Raman Scattering (SERS)
• +۴ مورد دیگر

Regina Ragan is a Professor in the Department of Materials Science and Engineering at the Samueli School of Engineering, University of California, Irvine. Her research focuses on nanomaterials, self-assembly, and surface-enhanced Raman scattering (SERS) for applications in optical communication, energy systems, and biomedical diagnostics. Education: Ph.D. in Applied Physics, California Institute of Technology, 2002 M.S. in Applied Physics, California Institute of Technology, 1998 B.S. in Materials Science and Engineering, University of California, Los Angeles, 1996 Her work integrates scanning probe microscopy and first-principles calculations to study thermodynamic driving forces in self-assembly and structure-function relationships. Recent publications highlight applications in antimicrobial susceptibility testing, environmental monitoring, and plasmonic device fabrication. The Ragan group develops low-cost diagnostic tools using SERS for telemedicine applications. Current lab members include graduate students and postdoctoral researchers working on nanoscale systems from atomic to mesoscale. Scientific Awards: NSF CAREER Award for fundamental studies of biological/inorganic interfaces Research Trends: Recent articles show a focus on SERS-based diagnostics, plasmonic nanoantennas, machine learning-assisted spectral analysis, and scalable synthesis of 3D graphene architectures. Subfields span quantum plasmonics, stress-activated materials, and biofilm monitoring.

Shruti Phadkeمشاهده پروفایل

استادیار

• Computational social science
• Social computing
• Natural language processing
• +۵ مورد دیگر

Shruti Phadke is an Assistant Professor in the Information Science Department at Drexel University's College of Computing & Informatics. Her work focuses on social knowledge construction, participation, and disengagement in online communities dealing with problematic information, such as conspiracy theories and extremist groups. She employs both quantitative and qualitative methods, including statistics, causal machine learning, and natural language processing. Education: PhD in Information Science from the University of Washington; MS in Computer Engineering from Virginia Tech. Her research spans computational social science, social computing, and human-computer interaction, with a particular emphasis on understanding how individuals navigate and exit harmful online discussions. She has over a year of experience as a trust and safety research expert at a social media company. Scientific Awards: Shruti has authored award-winning papers at the CSCW and ICWSM conferences, which recognize significant contributions to collaborative computing and web science. She is actively seeking PhD students for Fall 2025 and undergraduates/Masters students for Fall 2024 to collaborate on these research themes.

Brock C Christensenمشاهده پروفایل

استاد

• Epigenetics
• Cancer Biology
• Genomics
• +۷ مورد دیگر

Brock C Christensen is a Professor at the Geisel School of Medicine , Dartmouth, affiliated with the Departments of Epidemiology , Community and Family Medicine , and Molecular and Systems Biology . His research integrates molecular biology, genomics, and bioinformatics with epidemiology to investigate epigenetic mechanisms in human health and disease. Harvard University, PhD (2008) University of Wisconsin - Madison, BS (2002) Dr. Christensen's work focuses on epigenetic susceptibility traits , environmental interactions, and translational applications in cancer diagnostics and treatment. His recent publications explore DNA methylation patterns in triple-negative breast cancer , head and neck cancer , and pediatric CNS tumors . He serves as course director for PEMM103: Introductory Applied Biostatistics with R and leads the Christensen Lab , which specializes in epigenomic profiling and computational biology.

Leila Notashمشاهده پروفایل

استاد

• Robotics
• Mechatronics
• Kinematics
• +۳ مورد دیگر

Dr. Leila Notash is a Professor in the Department of Mechanical and Materials Engineering at Queen's University, where she has been a faculty member since 1997. She is a Fellow of Engineers Canada (FEC) and a licensed Professional Engineer with Professional Engineers Ontario (PEO), with significant contributions to engineering education and professional service. Her educational background includes: Bachelor of Science in Mechanical Engineering, Middle East Technical University (Ankara, Turkey) - High Honor Student (2nd out of 166) Master of Applied Science in Mechanical Engineering, University of Toronto PhD in Mechanical Engineering, University of Victoria Dr. Notash's research centers on robotics and mechatronics, with specialized expertise in cable-driven parallel manipulators. Her work integrates kinematics, fault-tolerant design, and neural network applications to address challenges in robot calibration, workspace analysis, and motion control under real-world constraints like cable mass and elasticity. She investigates both theoretical frameworks and practical implementations for industrial and specialized robotic systems. Analysis of her recent publications (2020-2024) reveals a clear trajectory toward intelligent control systems, where machine learning techniques—particularly neural networks and reinforcement learning—are increasingly applied to solve complex problems in cable-driven robotics. This includes motion control optimization, path generation, and kineto-static analysis while accounting for physical limitations such as cable elasticity and mass effects, demonstrating a shift from traditional mechanical analysis to data-driven adaptive control methodologies. Her scientific recognition includes: Fellow of Engineers Canada (FEC) University of Toronto Open Fellowship University of Toronto International Differential Fee Waiver Charles S. Humphrey Graduate Student Award NSERC Doctoral Prize Nominee (1996) Dr. Notash has mentored 161 undergraduate students as Faculty Advisor for the Mechanical '06 cohort and pioneered international educational initiatives like the International Undergraduate Student Design project (IVDS), connecting Queen's University with Middle East Technical University and Union College. Her service extends to editorial leadership for Mechanism and Machine Theory and ASME journals, and governance roles including Faculty Senator at Queen's University (2009-2025) and PEO Council Councillor-at-Large (2019-2025). She has established collaborative research networks through initiatives like the Reading Week shop course 'Design Basics 1.0' and sustained leadership in the Canadian Committee for the Promotion of Mechanism and Machine Science (CCToMM) and the International Federation for the Promotion of Mechanism and Machine Science (IFToMM), where she chaired the Permanent Commission on Communications (2006-2011).

• Condensed Matter Physics
• Quantum Information Science
• Quantum Computing
• +۴ مورد دیگر

Bryan K. Clark is an Associate Professor in the Department of Physics at the University of Illinois, with his office located in the Engineering Sciences Building. He leads the Clark Research Group, which works at the intersection of quantum information, condensed matter physics, machine learning, and computing. Clark's research spans four main areas: Quantum Computing , where his group develops quantum algorithms and collaborates with experimentalists on superconducting qubit systems; Quantum Many-Body Physics , where he applies computational methods to understand emergent behavior in strongly correlated systems; Algorithms for the Quantum Many-Body Problem , where his group has pioneered techniques like Neural Network Backflow (NNBF) that represent state-of-the-art accuracy for simulating fermions and frustrated magnetism; and Machine Learning for Experiment , where his group develops techniques to analyze experimental data like scanning transmission electron microscopy images. His publication record demonstrates consistent innovation in bridging theoretical quantum information science with practical applications. Recent work focuses on neural network approaches to quantum simulation, quantum error correction/mitigation, and novel qubit architectures like the Floquet Fluxonium Molecule. His research shows a clear trajectory from fundamental questions about the quantum-classical boundary to practical implementations in quantum hardware. Clark actively mentors graduate students, with recent thesis defenses by Faisal Alam, Matt Thibodeau, Chad Germany, James Allen, and Abid. His group has secured significant funding from the NSF and IBM's IIDAI institute to support research in quantum computing and machine learning applications for nano-photonics manufacturing and error mitigation. The Clark Research Group maintains strong connections with experimental teams, particularly in superconducting qubit development and materials characterization. They've developed computational tools like QOSY (Quantum Operators from SYmmetry) that are publicly available on GitHub and have gained recognition in the quantum information community.

Christopher Ferrieمشاهده پروفایل

دانشیار

• Quantum Estimation and Control
• Quantum Machine Learning
• Quantum Information Science
• +۵ مورد دیگر

Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.

Lifeng Zhouمشاهده پروفایل

استادیار

• Robotics
• Algorithms
• Combinatorial Optimization
• +۴ مورد دیگر

Lifeng Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Zhou Lab focused on advancing robustness and reliability in multi-robot systems through integration of foundation models. His research addresses real-world challenges in environmental monitoring, disaster response, and urban mobility. Education PhD, Electrical and Computer Engineering, Virginia Tech, 2020 MS, Control Science and Engineering, Shanghai Jiao Tong University, 2016 BS, Automation, Huazhong University of Science and Technology, 2013 Research Focus Dr. Zhou's work integrates robotics, algorithms, game theory and machine learning to develop secure and scalable autonomous systems. Primary research thrusts include: Resilient multi-robot coordination in adversarial environments Large language model integration for robotic decision-making Game-theoretic resource allocation strategies Risk-aware planning for autonomous vehicles Publication Trends Recent work (2024-2025) demonstrates strong focus on large language model applications in multi-robot systems, with 12/15 articles exploring LLM integration for flocking, scene segmentation, and decision-making. Additional emphasis includes adversarial robustness in target tracking (5 articles) and autonomous driving applications (4 articles). Awards and Recognition Best Paper Award, WACV 2025 LLVM-AD Workshop Professional Service Associate Editor, ICRA Conference Editorial Board Laboratory Focus The Zhou Lab develops foundational algorithms for secure and scalable multi-robot systems, with current projects spanning environmental monitoring drones, disaster response coordination, and autonomous vehicle perception systems.

Nazanin Tajikمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Infrastructure Resilience
• +۴ مورد دیگر

Nazanin Tajik is an Assistant Professor in the Department of Industrial and Systems Engineering at Mississippi State University (MSU). She holds a Ph.D. from the University of Oklahoma, an M.S. from the University of Tehran, and a B.S. from Sharif University of Technology. Her research focuses on integrating artificial intelligence, machine learning, and social science concepts to develop cross-disciplinary frameworks for infrastructure resilience, smart transportation systems, and disaster management. Her academic journey includes a Ph.D. at the University of Oklahoma where she contributed to the Risk-Based Systems Analytics Laboratory. At MSU, she established a research center bridging AI/ML tools with socio-technical systems. Key research domains include cyber-physical-social infrastructure resilience, search-and-rescue planning, and game-theoretic robotic designs. Tajik's work emphasizes optimization algorithms for network vulnerability assessment, resource allocation in disaster scenarios, and adaptive recovery strategies. She is actively involved with professional organizations such as INFORMS, ISE, and POMS, reflecting her commitment to advancing operations research and systems engineering methodologies.