Find Academic Supervisors

Danijel SkočajView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

Jamal AtifView profile

Professor

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +4 more

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Sameer SinghView profile

Professor

• Machine Learning
• Natural Language Processing
• Model Interpretability
• +3 more

Sameer Singh is a Professor of Computer Science at the University of California, Irvine's Donald Bren School of Information and Computer Sciences. He also holds affiliations with Linguistics and EECS departments. His research primarily focuses on the robustness and interpretability of machine learning algorithms, along with models that reason with text and structure for natural language processing. Dr. Singh received his PhD from the University of Massachusetts, Amherst in 2014, an MS in Computer Science from Vanderbilt University in 2007, and a BEng in Electrical Engineering from the University of Delhi in 2004. His research interests span machine learning robustness, natural language processing, model interpretability, and knowledge representation. Singh investigates how to make AI systems more reliable and understandable, particularly focusing on testing methodologies for NLP models and developing techniques to improve model behavior. His work bridges theoretical understanding with practical applications in AI safety and reliability. Analysis of Singh's recent publications reveals a strong focus on language model interpretability, bias detection, and model robustness. His work explores how language models process information, where they fail, and how to make them more reliable. A significant portion of his recent research examines the limitations of multimodal models, language model alignment techniques, and addressing social biases in AI systems. Dr. Singh has received numerous prestigious awards including the Kavli Fellowship from the National Academy of Sciences, the NSF CAREER award, UCI Distinguished Early Career Faculty award, and the Hellman Faculty Fellowship. His papers have won multiple awards including at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020. His research group has secured substantial funding from major organizations including the Allen Institute for AI, Amazon, NSF, DARPA, Adobe Research, Hasso Plattner Institute, NEC, Base 11, and FICO. Singh previously served as an Allen Fellow at the Allen Institute for AI (2021-2023) and is currently a co-founder and CTO of Spiffy AI in Seattle. He completed postdoctoral research at the University of Washington after earning his PhD. Dr. Singh maintains an active presence in the AI community through his work on projects like AutoPrompt and Checklist, which have become influential tools for testing and interpreting NLP models. His research continues to shape how the field approaches model evaluation and interpretability.

Jonathan BlazekView profile

Assistant Professor

• Cosmology
• Astrophysics
• Galaxy Clustering
• +5 more

Jonathan Blazek is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational and theoretical cosmology. His research focuses on large-scale astronomical surveys to understand cosmic structure and dark energy, particularly through galaxy clustering and weak gravitational lensing. He is a key member of the Dark Energy Survey and Vera C. Rubin Observatory collaborations, leading efforts to combine multi-wavelength datasets for cosmological insights. Blazek earned his Ph.D. from UC Berkeley and completed postdoctoral fellowships at EPFL (Switzerland) and Ohio State University. Education: Ph.D. in Physics, University of California, Berkeley Postdoctoral Fellowships: EPFL (Switzerland), Ohio State University Research Interests: His work centers on cosmological modeling using galaxy surveys, particularly refining analytic and numerical methods to connect observations with theoretical frameworks. Key areas include: Weak gravitational lensing and galaxy clustering Combined-probe cosmology (integrating datasets across wavelengths) Dark matter and dark energy dynamics Large-scale structure formation Publications & Grants: Blazek has authored over 50 peer-reviewed articles, including foundational work on intrinsic alignment modeling and cosmic shear analysis. He leads the NSF CAREER grant project exploring dark sector physics with galaxy surveys. His recent publications address baryonic feedback effects, CMB lensing cross-correlations, and next-generation survey methodologies. Labs & Collaborations: He contributes to the Northeastern Cosmology Group and the Dark Energy Science Collaboration, advancing projects like the Legacy Survey of Space and Time (LSST) at Vera Rubin Observatory.

Sorawoot SrisumaView profile

Associate Professor

• Econometrics
• Empirical Industrial Organizations
• Dynamic Discrete Choice Models
• +3 more

Sorawoot Srisuma is an Associate Professor in the Department of Economics at the National University of Singapore (NUS). His research focuses on econometrics and empirical industrial organization, with particular emphasis on structural estimation, dynamic discrete choice models, and market structure analysis. He holds a PhD from the London School of Economics. Key research areas include the analysis of subjective well-being data, nonparametric identification in economic models, and the welfare effects of market frictions. His work often combines advanced econometric techniques with applications in industrial organization and development economics. Recent publications explore topics such as search models with heterogeneous agents, spatial autoregressions with varying coefficients, and the impacts of mental health policies. He is affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His teaching areas include advanced econometrics, and he has contributed to methodological advancements in structural estimation, particularly in dynamic games and Markov decision processes.

Nicolas LebbeView profile

Researcher

• Shape and Topology Optimization
• Homogenization
• Nanophotonics
• +3 more

Nicolas Lebbe is a CNRS researcher at the Laplace laboratory in Toulouse, France, specializing in applied mathematics and computational physics for photonic and electromagnetic systems. Current CNRS Researcher (2023–) Postdoctoral Researcher at Langevin Institute (2022) Postdoctoral Researcher at INRIA Sophia-Antipolis (2020–2022) PhD in Applied Mathematics from CEA & LJK (2016–2019) His research focuses on shape and topology optimization , homogenization , and scientific computing applied to nanophotonics and metasurfaces . He developed methods to optimize photonic components using the level-set approach and General Sheet Transmission Conditions (GSTC) for metasurface modeling. His work bridges mathematical rigor with practical electromagnetic and photonic device design. Nicolas has published extensively on Maxwell equations , plasmonics , and finite element simulations , with key contributions to robust optimization and interface homogenization . His recent publications (2023–2025) emphasize homogenization techniques for curved and quasi-periodic metasurfaces, while earlier works (2017–2019) span robust optimization theory and mid-IR photonics. He collaborates with institutions like the Langevin Institute, INRIA, and École polytechnique, and has presented at international workshops including OWTNM and GDR MecaWave. His PhD thesis, Contribution in topological optimization and application to nanophotonics (2019), laid foundational work for nanophotonic device optimization.

János KertészView profile

Professor

• Statistical Physics
• Complex Networks
• Financial Analysis
• +9 more

János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.

Hashmat U. KhanView profile

Professor

• Macroeconomics
• Business Cycles
• Monetary Policy
• +7 more

Hashmat U. Khan is a Professor in the Department of Economics at Carleton University, Ottawa, Canada. He also serves as Associate Vice-President (Academic Programs and Strategic Initiatives) and is Co-Director of the Centre for Monetary and Financial Economics (CMFE). His research focuses on macroeconomics, business cycles, monetary and fiscal policy, and DSGE modeling. Education: B.A. from Shri Ram College of Commerce (SRCC) M.A. from Delhi School of Economics Ph.D. from the University of British Columbia (UBC) His research interests center on macroeconomic dynamics , particularly the role of monetary policy, inflation environments, and structural frictions in driving business cycles. He specializes in Dynamic Stochastic General Equilibrium (DSGE) models , nominal and real frictions , and open-economy macroeconomics . His recent work assesses the impact of U.S. tax reforms on Canada, the role of collateral shocks, and the effectiveness of unconventional monetary policy. The 15 most recent publications demonstrate a strong focus on empirical and theoretical macroeconomics , with recurring themes in monetary policy transmission , fiscal multipliers , household and firm behavior , and pandemic-related economic responses . His work frequently appears in top journals such as Journal of Money, Credit and Banking , Journal of Monetary Economics , and Canadian Journal of Economics . Scientific Awards and Recognition: 2023 SSHRC Insight Development Grant 2020 Research Achievement Award 2019 SSHRC Insight Development Grant Professor Khan has supervised Ph.D. students and collaborated extensively with scholars across North America. He has secured competitive research funding, including from SSHRC, to support his work on macroeconomic stability and policy evaluation. His research has practical implications for central banks and fiscal authorities, especially in open economies like Canada. He is actively involved in the CMFE, where he contributes to research initiatives and mentorship. His team engages in quantitative modeling of macroeconomic phenomena, often using large datasets and structural models to inform policy.

Dan WarrenView profile

Academic

• Population Biology
• Ecology
• Evolution
• +9 more

Dan Warren is a Senior Research Fellow at the Gulbali Research Institute, Charles Sturt University, where he conducts interdisciplinary research in population biology, ecology, evolution, and conservation. His work centers on developing and refining species distribution models and environmental niche models to understand biodiversity and support conservation under global change. PhD in Population Biology, University of California, Davis Dan Warren's research interests lie in the development and application of quantitative tools for ecology and evolution. He focuses on species distribution models (SDMs), environmental niche models (ENMs), and their use in understanding biodiversity patterns, evolutionary processes, and conservation planning. His methodological innovations, such as those in the ENMTools R package, are widely adopted. His work spans animal behavior, climate change impacts, and conservation management, with strong relevance to UN Sustainable Development Goals on climate action and life on land. His recent publications reflect a consistent focus on improving the accuracy, interpretation, and application of species distribution models. Trends include addressing bias in model outputs, enhancing methodological standards, and developing robust tools for conservation under climate uncertainty. He frequently publishes in top ecological journals such as Ecography and Methods in Ecology and Evolution , and his work integrates software development with theoretical ecology. Dan Warren has served in key scientific roles, including as Associate Editor for Ecography and Systematic Biology , and as a reviewer for the IPBES global assessment. These contributions highlight his leadership in advancing scientific rigor and policy relevance in biodiversity science. He is actively involved in mentoring and collaborative research, contributing to datasets and methodological frameworks used by the broader ecological community. His work supports both academic inquiry and practical conservation, emphasizing robust, data-driven decision-making.

• Quantum Computing
• Quantum Information Science
• Quantum Networking
• +3 more

Norbert Linke is an Adjunct Assistant Professor in the Department of Physics at the University of Maryland , affiliated with the Joint Quantum Institute . His research focuses on experimental quantum computing with trapped atomic ions, emphasizing quantum information processing, quantum algorithms enhanced by machine learning, quantum simulation, and quantum networking using entangled photons. He holds a Dipl. Phys. from the University of Ulm (2007) and a D.Phil. in Atomic & Laser Physics from the University of Oxford (2013), where he worked under David Lucas. Before joining UMD, he conducted postdoctoral research at Oxford. Education: Bachelor's (Dipl. Phys.): University of Ulm, Germany (2007) Doctorate (D.Phil.): University of Oxford, U.K. (2013) Research Interests: Quantum algorithms with machine learning integration Quantum simulation of complex systems (e.g., quantum chromodynamics) Quantum networking via Sr+ ion-photon entanglement Development of robust quantum hardware (e.g., 3D monolithic traps) His recent work explores quantum advantage in finance, blind calibration of quantum computers, and optical control of qubits. The lab emphasizes scalable architectures and hybrid analog-digital simulations. While no scientific awards are explicitly listed, his contributions to trapped-ion systems and quantum networking are widely recognized in the field. Norbert advises researchers at the Joint Quantum Institute and collaborates on grants related to quantum computing hardware and algorithms. His team’s lab focuses on advancing ion-trap technology and explores future applications in quantum communication and computation.

Michael PreussView profile

Professor

• Metallurgy
• Materials Science
• Materials Engineering
• +23 more

Professor Michael Preuss is a leading academic in the Department of Materials Science & Engineering at Monash University, Faculty of Engineering, where he joined in August 2020. He also holds a 20% continuing position at the University of Manchester, UK, where he previously served in multiple leadership roles. His research focuses on the relationship between manufacturing, processing, and performance of structural materials, particularly titanium and zirconium alloys, nickel-base superalloys, and steels for high-temperature and nuclear applications. First Degree: Technical University Berlin, Germany PhD: Technical University Hamburg-Harburg, Germany Michael Preuss’s research interests lie at the intersection of materials processing and performance prediction. He investigates how microstructural evolution during manufacturing affects mechanical behavior, with a focus on reducing safety margins in safety-critical components such as aeroengine parts and nuclear fuel claddings. His work emphasizes in-situ characterisation using advanced tools like synchrotron X-ray , neutron diffraction , digital image correlation , and 3D X-ray tomography . The research is highly interdisciplinary, combining experimental data with modelling to understand degradation mechanisms under stress, temperature, and irradiation. The recent publications highlight a strong focus on irradiation damage in zirconium alloys , plasticity in Ni-base superalloys , and advanced alloy development . These works employ cutting-edge diffraction and imaging techniques to probe dislocation structures, phase evolution, and mechanical onset at micro scales, reflecting a trend toward physically based lifetime prediction models. His work is closely tied to large-scale facilities and national initiatives like the European Spallation Source and the Sir Henry Royce Institute. Scientific awards include: Grunfeld Memorial Medal (IOM3, 2013) ASTM Kroll Medal (lifetime achievement in zirconium research) EPSRC Leadership Fellowship (2011) Fellow of Materials, Minerals and Mining (2016) MWA Research Activation Fund (2024) Michael Preuss actively supervises PhD students and leads major research projects, including those funded by EPSRC and focused on fuel cladding (MIDAS) and advanced manufacturing. He collaborates extensively with researchers across institutions and industries. He chairs the Scientific Advisory Committee of the European Spallation Source and serves on panels for neutron facilities like ILL and ISIS. His labs and research teams are equipped for solid-state additive manufacturing, in-operando micromechanical testing, and advanced microstructural analysis, forming a robust ecosystem for materials innovation.

Stuart ShieberView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Theoretical Linguistics
• +5 more

Stuart Shieber is the James O. Welch, Jr. and Virginia B. Welch Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He is a prominent researcher in computational linguistics and natural language processing, with significant contributions across multiple related fields including theoretical linguistics, computer-human interaction, automated graphic design, and the philosophy of artificial intelligence. Professor Shieber's research interests focus primarily on computational linguistics, examining natural language from the perspective of computer science. His work spans scientific and engineering goals, utilizing foundational formal and mathematical tools. He has made significant contributions to grammar formalisms, psycholinguistics, semantics, and synchronous grammars with applications in machine translation and sentence compression. Beyond computational linguistics, his research extends to automatic layout of charts and maps, novel interaction techniques for document reading and diagram layout, online auction mechanisms, library book access prediction, biological evolution tree reconstruction, and the philosophical basis for Turing's test for machine intelligence. His recent publications demonstrate a continued focus on neural language models, syntactic agreement mechanisms, readability assessment, conversational understanding, and bias detection in language models. His research has evolved from traditional grammar formalisms to incorporate modern neural network approaches while maintaining a strong theoretical foundation. The trend shows increasing attention to ethical considerations in NLP, particularly around bias detection and mitigation, alongside continued theoretical work on language structure. Presidential Young Investigator award (1991) Presidential Faculty Fellow (1993) John L. Loeb Associate Professorship in Natural Sciences (1993) Harvard College Professorship (2001) Fellow of the American Association for Artificial Intelligence (2004) Fellow of the Association for Computing Machinery (2014) Fellow of the Association for Computational Linguistics (2017) Professor Shieber has advised numerous PhD students who have gone on to successful careers at institutions including UCSD, Cornell University, Microsoft Research, Google, and various academic institutions. His work on open access and scholarly communication policy, particularly his development of Harvard's open-access policies, led to his appointment as the first director of the university's Office for Scholarly Communication. He is also the founding director of the Center for Research on Computation and Society and a faculty co-director of the Berkman Center for Internet and Society. His laboratory work has focused on advancing computational linguistics through both theoretical and applied research, with numerous patents and co-founding of Cartesian Products, Inc., a high-technology research and development company. His future work appears to be focusing on the intersection of neural network approaches with traditional linguistic theory, particularly in understanding and mitigating bias in language models, while continuing his long-standing interest in the theoretical foundations of language processing.

Mingyi HongView profile

Associate Professor

• Optimization
• Machine Learning
• Signal Processing
• +7 more

Mingyi Hong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota , where he leads the OptimAI-Lab . His work bridges optimization theory , machine learning , and signal processing , with a focus on foundation models like LLMs and diffusion models. Education : Not explicitly mentioned in the text Current Projects : NSF grants on bilevel optimization, LLM unlearning, and inverse reinforcement learning Research Themes : Bilevel Optimization : Applications in LLM alignment, unlearning, and wireless systems LLM Safety : Unlearning, alignment with human feedback, robustness Diffusion Models : Inference-time alignment, adversarial training Distributed Optimization : Privacy-preserving algorithms, federated learning Recent Publications highlight trends in LLM unlearning (BLUR, LUME), optimization theory (Barrier Functions, νSAM), and diffusion models (Direct Noise Optimization). His group has secured NSF , AWS , Cisco , and Open Philanthropy grants. Scientific Recognition : IEEE Fellow (2025) SPS Best Paper Award (2022, 2021, 2018) Doctoral Dissertation Fellowship (2024) IBM Pat Goldberg Memorial Award (2022) He mentors PhD students like Siliang Zeng and Xinwei Zhang , and collaborates with institutions including Michigan State University , Amazon , and NIH on projects spanning UHF MRI technology to climate-smart agriculture .

James CookView profile

Professor

• Ecology
• Evolution
• Symbiosis
• +8 more

Professor James Cook is a Theme Leader for Plants, Animals and Interactions at the Hawkesbury Institute for the Environment (HIE) at Western Sydney University. He holds the academic rank of Professor and leads research focused on ecology, evolution, and symbiosis, with particular emphasis on pollination systems and insect ecology. Professor Cook received his BA in Zoology from Oxford University and his PhD in Evolutionary Biology from Imperial College London. His previous academic appointments include positions at La Trobe University, Imperial College London, and the University of Reading. Professor Cook's research interests lie at the intersection of ecology, evolution, and symbiosis , with particular emphasis on pollination systems and insect-microbe interactions . His work spans multiple scales from molecular to ecosystem levels, investigating how species interactions shape biological communities. He is particularly renowned for his expertise in fig-pollinator mutualisms and stingless bee biology . His research integrates field studies, laboratory experiments, and molecular approaches to address fundamental questions in evolutionary ecology while also addressing applied challenges in agricultural pollination and conservation biology . Analysis of Professor Cook's recent publications reveals a strong focus on pollination ecology and insect-microbe symbioses . His work spans both fundamental research on evolutionary processes and applied studies addressing agricultural challenges. A significant portion of his recent work examines stingless bees as potential managed pollinators for Australian horticulture, alongside investigations into alternative pollinators beyond honey bees. His research increasingly incorporates molecular methods to understand microbial communities and their impacts on host biology. There's also a growing emphasis on climate change impacts on pollination systems and the development of resilient pollination strategies for future agricultural systems. Natural Environment Research Council (NERC, UK) College Member (2007-2010) Royal Entomological Society Symbiosis Special Interest Group Convenor (2005-2012) Editorial Board Member, Journal of Evolutionary Biology (2005-2008) NERC Advanced Research Fellow (1998-2003) SERC Research Fellow (1993-1995) Royal 1851 Commission Research Fellow (1991-1993) Australian Entomological Society Conservation Committee Member (2017-present) Invited collaboration with Professor Wan-Jin Liao's group at Beijing Normal University Professor Cook currently leads multiple significant research projects focused on pollination services for Australian horticulture. His grant portfolio includes projects on stingless bee husbandry , alternative pollinators including flies, and pollinator health . He collaborates extensively with researchers across Australia and internationally, with projects spanning from basic research on fig-wasp mutualisms to applied studies on crop pollination. Professor Cook has authored over 70 research articles and six book chapters, demonstrating sustained research productivity throughout his career. Professor Cook's research involves extensive field, laboratory, and glasshouse work, with a strong emphasis on molecular methods. His work connects with collaborators across Australia, India, China, France, the UK, and the USA, creating a robust international research network focused on pollination ecology and insect symbioses. His leadership in the Plants, Animals and Interactions research theme at HIE provides a framework for integrating fundamental ecological research with practical applications for sustainable agriculture and conservation.

Katya DrozdovaView profile

Professor

• Cyberpower
• International Relations
• Cybersecurity
• +10 more

Katya Drozdova is a Professor at Seattle Pacific University’s School of Business, Government, and Economics , with a joint appointment in Business, Information Systems, and Political Science. She holds a PhD and MPhil in Information Systems from NYU Stern School of Business and MA/BA in International Relations from Stanford University. Expertise in cyberpower, geopolitical strategy, counterterrorism, and asymmetric threat analysis Principal Investigator for the Mining Afghan Lessons from Soviet Era (MALSE) program funded by the U.S. Department of Defense Visiting Fellow at Stanford’s Hoover Institution and affiliate with the Empirical Studies of Conflict Project (ESOC) Her research combines information theory with security studies , focusing on: Strategic decision-making under uncertainty Human and technological network optimization in hostile environments U.S.-Russia relations and their geopolitical implications Early-warning systems for terrorist network detection Cybersecurity policy balancing civil liberties Recent publications analyze cyberpower (SAGE, 2017), asymmetric warfare (Journal of Cold War Studies, 2019), and geo-religious literacy in Russia (Routledge Handbook, 2022). Her work has been cited in Computational and Mathematical Organization Theory and Hoover Digest . Key affiliations include: NYU’s Alexander Hamilton Center for Political Economy Stanford’s Center for International Security and Cooperation (CISAC) Consortium for Research on Information Security and Policy (CRISP) funded by NSA Community engagements: Hosted Qatar’s Prime Minister Sheikh Abdullah Al-Thani (2016) Public dialogue with General Keith Alexander (2015) Interview with Secretary Madeleine Albright (2014)