Javier Osorio is an Assistant Professor at the University of Arizona's School of Government and Public Policy. His research focuses on the micro-foundations and dynamics of political and criminal violence in Latin America, employing quantitative methods such as natural language processing, GIS, and big data analytics. He has received awards from the UNODC and MPSA, and his work appears in leading journals like the Journal of Peace Research . Dr. Osorio holds a Ph.D. in Political Science from the University of Notre Dame (2013) and previously taught at John Jay College of Criminal Justice. He leads the Academy for Security Analysis, funded by USAID, and collaborates with the NSF and DoD’s Minerva Initiative. His research projects include supervised event coding for conflict analysis and randomized controlled trials in Central America. His key contributions span conflict language models (e.g., ConfliBERT), criminal violence classification, and multilingual event coding frameworks. He has advised interventions in El Salvador and Nicaragua, emphasizing data-driven approaches to security challenges.
Dr. Arash Habibi Lashkari is an Associate Professor and Canada Research Chair in Cybersecurity at York University's School of Information Technology. He holds a PhD from the University of Technology Malaysia and completed a Post-Doc at the University of New Brunswick. With over 25 years of teaching experience, he specializes in cybersecurity risk management, malware analysis, and threat hunting. He pioneered Canada’s first cybersecurity Capture the Flag (CTF) competition for post-secondary students and has authored 10 books and over 110 academic articles. Research Interests: Cybersecurity, Network Security, Threat Hunting, Malware Analysis, Cybersecurity Risk Management, and Blockchain Security. He leads the Behaciour-Centric Cybersecurity Center (BCCC), focusing on vulnerability detection technologies and cybersecurity dataset generation. Awards: Mitacs 150 Top Researchers (2017), University of New Brunswick Teaching Innovation Award (2020), Gold Medal at 2020 Canadian Online Publishing Awards, and multiple international security competition awards. His work has been featured in CBC, CIC, and global cybersecurity conferences. Key Projects: Development of intrusion detection datasets (e.g., CIRA-CIC-DoHBrw-2020), malware analysis frameworks, and AI-driven cybersecurity tools. He actively collaborates with organizations like Aviva and NICT on cybersecurity resilience strategies. Teaching: Offers courses in Digital Forensics, Network Security, and Cybersecurity Management. His 'Think-Que-Cussion' teaching methodology emphasizes interactive learning.
Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University and a Senior Performance Engineer. She holds a PhD in Computer Science from Simula Research Lab and Universitetet i Oslo (2017), focusing on robustness in multipath transport protocols like MPTCP. Her research spans network performance, security, and congestion control in mobile/5G networks and the Internet. She collaborates actively with academia and industry, co-supervising students in areas such as edge computing, container orchestration, and distributed systems. Affiliations: Department of Informatics, Karlstad University; Red Hat Research; Ericsson R&D. Education: PhD (2017), Simula/UiO; Master’s and Undergraduate studies emphasized networking and electronics. Her work includes projects like AIDA (AI-driven edge networking) and DRIVE (latency-sensitive mobile services). She has published over 50 papers on topics like QUIC, eBPF, and containerized microservices. Awards include the Best Paper at IEEE ICIN 2021 and ANRP 2025 Prize. Teaching responsibilities include Future Internet Design and Service Quality . Advising spans 15+ students across institutions like TU Berlin, KTH, and Unifesp. She chairs conferences (e.g., ACM SIGCOMM 2025) and serves on editorial boards (IEEE Communications Magazine). Key interests: network observability, low-latency protocols, and sustainability in networking.
Terence Williamson is an Associate Professor at the School of Architecture, Landscape Architecture and Urban Design, University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His research focuses on thermal performance, sustainability, and urban microclimate design, with a particular emphasis on the built environment's impact on older populations. Educated in engineering and architecture in Australia, he has authored/co-authored over 100 publications including books and articles on sustainable architecture and energy efficiency. Research interests include adaptive thermal comfort models, building energy codes (e.g., NatHERS), and urban microclimate modeling (e.g., the CAT model for street canyon temperatures). His work bridges engineering and social science, addressing ethical and cultural dimensions of sustainable design. He currently supervises Masters and PhD candidates, emphasizing sustainability in building design and climate-resilient housing solutions for vulnerable groups. Collaborations include projects with Dr. Evyatar Erell (Ben-Gurion University) on urban microclimate models and investigations into occupants' thermal behaviors in aged care facilities. His research contributes to policy frameworks for energy efficiency and health-focused housing standards in Australia.
Sándor Ádány is a Professor at the Department of Structural Mechanics , Budapest University of Technology and Economics. His work focuses on advanced structural analysis of thin-walled members and systems-based design methodologies. Research Interests: Specializes in buckling behavior of thin-walled structures, finite element modeling, cold-formed steel stability, and modal decomposition techniques. Key areas include Lateral-torsional buckling Displacement mapping in constrained FEM Stiffener optimization in plate structures Combined loading stability of tubular members Prebuckling deformation effects Fourier-based numerical methods Article Trends (2023-2025): Recent publications emphasize elastic stability analysis of thin-walled beams and tubular structures under complex loading conditions, with particular attention to prebuckling deflections, torsional rigidity effects, and numerical validation of analytical models. Innovations include Fourier-series displacement approximations and constrained finite element methodologies.
Dr. Nabil J. Sarhan is an Associate Professor of Electrical and Computer Engineering at Wayne State University , where he directs the Computer Systems and Deep Learning Research Laboratory and the interdisciplinary M.S. Program in Artificial Intelligence (Systems and Hardware Track) . He previously served as Graduate Program Director and College of Engineering Faculty Assembly Chair. Education : Ph.D. and M.S. in Computer Science and Engineering from Pennsylvania State University (GPA 4.0), B.S. in Electrical Engineering from Jordan University of Science and Technology Internationally recognized for expertise in computer systems , deep learning , video streaming , and AI hardware accelerators , Dr. Sarhan's research bridges theoretical and applied domains in automated video surveillance and medical AI systems . His work includes novel approaches to epileptic seizure prediction , energy-efficient neural network accelerators , and mixed-signal circuit design for edge computing. Recent grant highlights include a $418,907 National Science Foundation award for reconfigurable AI accelerator development. His patent (US Patent 9,313,463) on automated video surveillance systems demonstrates practical technology impact. Scientific Awards IEEE SEM Outstanding Professional of the Year Wayne State President's Excellence in Teaching Award College of Engineering Excellence in Teaching Award WSU Academy of Teachers (2022) IEEE Senior Member (2016) As an educational reform leader , he has chaired over 20 ABET accreditation panels in Saudi Arabia and Palestine, evaluating programs in electrical engineering, computer science, and software engineering. His intellectual property expertise spans significant patent infringement cases including ACQIS v. ASUSTek and VideoShare v. Google .
Gulen Ozkula is an Assistant Professor of Civil Engineering at the University of the District of Columbia's School of Engineering and Applied Sciences. She specializes in seismic design, evaluation, and rehabilitation of steel structures, with a focus on steel columns and their behavior under extreme loading conditions. Her research integrates experimental methods, numerical modeling, and performance-based design principles to enhance structural resilience against earthquakes and seismic hazards. Education: Executive MBA, Istanbul University Post-Doctorate, Tokyo Institute of Technology Post-Doctorate, University of California, San Diego Ph.D., University of California, San Diego M.S., University of Illinois at Urbana-Champaign B.S., Celal Bayar University Research Interests: Ozkula’s work addresses critical challenges in seismic engineering, including cyclic stability of steel columns, high-performance steel materials, and seismic risk assessment. She explores innovative solutions for retrofitting existing structures and improving design codes through advanced testing methodologies and data analysis. Key Research Trends: Her publications emphasize experimental testing of steel beam-column subassemblages, field reconnaissance of recent earthquakes (e.g., Turkey 2023), and classification of buckling modes in steel columns. Findings often inform practical design guidelines and safety protocols for earthquake-prone regions. Grants & Advising: While specific grants are not listed, her active research program suggests involvement in funded projects related to seismic engineering. No current advisees are noted in the provided information. Labs & Teams: While not explicitly mentioned, her experimental work implies affiliation with structural testing facilities and interdisciplinary teams focused on earthquake engineering and materials science.
Anjali Sandip is a Teaching Assistant Professor in the Mechanical Engineering Department at the University of North Dakota's College of Engineering and Mines. She holds a Ph.D. in Mechanical Engineering from the University of Kansas and maintains an active research program in computational mechanics, high-performance computing, and machine learning. Her educational background includes a Doctor of Philosophy and Master of Science in Mechanical Engineering from the University of Kansas, and a Bachelor of Engineering in Mechanical Engineering from Osmania University in Hyderabad, India. She previously served as a post-doctoral researcher at the University of Nebraska, where she developed patient-specific computational models for peripheral artery disease treatment. Dr. Sandip's research spans computational mechanics, high-performance computing, uncertainty quantification, physics-informed machine learning, and multi-physics modeling. Her work has significant applications in ice sheet dynamics, medical device modeling, and multi-phase flow simulations. She has developed open-source software frameworks that integrate finite element and finite volume methods with uncertainty quantification tools. Her recent publications demonstrate a strong focus on developing computational frameworks for multi-physics problems, with particular emphasis on GPU acceleration, uncertainty quantification, and machine learning integration. The research shows consistent application of these methods to challenging problems in earth sciences, biomedical engineering, and traditional mechanical engineering domains. Scientific Awards: NSF EPSCoR Research Fellow (2024-25) Dr. Sandip actively mentors both undergraduate and graduate researchers, and she is currently seeking Master's and Ph.D. students interested in computational mechanics, applied mathematics, scientific machine learning, and earth sciences. She serves as an active member of the Association of Computational Mechanics (USACM & IACM) and has delivered numerous presentations at professional conferences. Her research has received support from prestigious organizations including the National Science Foundation (NSF), Department of Energy (DOE), and NVIDIA. She teaches courses including Introduction to Mechanical Engineering, Thermodynamics, Machine Component Design Laboratory, Advanced Finite Element Methods, Modeling Glaciers and Ice Sheets, Statics, and Engineering Ethics, demonstrating her broad expertise across mechanical engineering disciplines.
Lerina Aversano is an Associate Professor in the Department of Engineering (DING) at the University of Sannio, specializing in Information Processing Systems (ING-INF/05). Her research focuses on the intersection of business processes and information technology, with particular expertise in business/IT alignment, process mining, and machine learning applications. Her research interests include: Business/IT alignment and strategic integration Machine learning and deep learning applications in healthcare diagnostics Process mining and predictive analytics for business processes Software engineering and service-oriented computing Semantic integration of heterogeneous data sources Over her extensive career, Aversano has published 145 research items with a clear evolution from foundational work in business/IT alignment to cutting-edge applications of AI in healthcare and business process management. Her most recent publications demonstrate expertise in explainable AI for process prediction, AI applications in medical diagnosis (including Parkinson's and thyroid diseases), and security for IoT systems, showing her ability to adapt to emerging technologies while maintaining focus on core alignment issues between business needs and technological solutions. She maintains an active research group with frequent collaborations with Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, and Maria Tortorella, producing significant contributions to information systems literature. Her work appears in reputable venues including IEEE conferences, Springer publications, and journals like Information and Software Technology.
Marcos Caballero serves as Associate Professor at both the University of Oslo's Center for Computing in Science Education and Michigan State University. His work bridges physics education research with computational science instruction across educational levels from high school to graduate programs. His educational background includes: B.S. in Physics from University of Texas at Austin (2004) M.S. in Physics from Georgia Institute of Technology (opto-microfluidics research) Ph.D. in Physics Education Research from Georgia Tech (2011, first PER-focused doctorate there) Postdoctoral research at University of Colorado Boulder Caballero's research investigates how computational tools and science practices shape physics learning, employing both cognitive and sociocultural theoretical frameworks. Key projects examine measurement uncertainty assessment, computational literacy development, and equity in graduate admissions. His work spans micro-level analyses of student coding comprehension to macro-level studies of computing's impact across degree programs, with significant contributions to transforming upper-division physics curricula toward active learning environments. His recent publications (2021-2024) reveal concentrated focus on measurement uncertainty assessment instruments , computational thinking frameworks , and holistic graduate admissions reform . These works demonstrate increasing methodological sophistication through NLP applications and large-scale educational data analysis, while maintaining practical relevance for physics classroom transformation. Caballero co-founded Georgia Tech's Physics Education Research group and currently leads UiO's Center for Computing in Science Education and Michigan State's Physics Education Research Lab . His international partnership for computing in science education drives cross-institutional curriculum development, focusing particularly on integrating computational practices into core physics instruction while addressing equity challenges in STEM education.
Noortje Venhuizen is an Assistant Professor in the Department of Cognitive Science and Artificial Intelligence at the Tilburg School of Humanities and Digital Sciences, Tilburg University. She serves as Academic Director for the BSc Cognitive Science and Artificial Intelligence program since 2024, and has held academic positions at Saarland University (2015-2022) including roles as Scientific Staff member and Principal Investigator in SFB 1102 projects. PhD in Computational Semantics (University of Groningen, 2015) MSc in Logic (ILLC, University of Amsterdam, 2011) BSc in Artificial Intelligence (Utrecht University, 2009) Her research focuses on expectation-based language comprehension, neurocomputational modeling of semantic processing, distributional formal semantics, and pragmatic reasoning in discourse. Key contributions include PDRT-SANDBOX (Haskell NLP library) and DFS Tools (Prolog implementation of distributional formal semantics). Recent publications (2023-2025) explore multimodal word meaning, informativity in reference production, and neurocognitive models of surprisal processing. Her work combines formal semantics with cognitive neuroscience and computational modeling. LOT Grotevragenprijs essay contest - Second Prize She has presented research at major conferences including CogSci, AMLaP, and Sinn und Bedeutung. Current teaching includes courses on artificial intelligence, statistics, and semantic theory.
Burak Berk Üstündağ is a Professor in the Department of Computer Engineering at the Faculty of Computer and Informatics, Istanbul Technical University (ITU). He has been a key academic figure at ITU since the 1990s, progressing through the ranks from Research Assistant to full Professor, a position he attained in 2019. He has also held significant administrative roles, including Director of the Application and Research Center and membership in the ITU Informatics Institute Management Board. PhD, Control and Computer Engineering, Istanbul Technical University (2000) MSc, Control and Computer Engineering, Istanbul Technical University (1994) BSc, Electrical Engineering, Istanbul Technical University (1991) His research is deeply rooted in Artificial Intelligence , with a focus on Neural Networks , Wavelet-based models , and machine learning applications in environmental, agricultural, and maritime domains. He has developed frameworks like PECNET for multivariate time series forecasting and has pioneered work in cognitive communication systems, particularly for underwater and agricultural monitoring. His work bridges theoretical AI models with real-world applications in precision agriculture, water quality monitoring, and ionospheric forecasting. The most recent articles show a strong trend in applying deep learning (LSTM, DNNs) and hybrid models (Wavelet-NN) to complex, real-time systems. His research spans environmental data science , smart agriculture , underwater acoustics , and cognitive risk management . He emphasizes performance, real-time operation, and intelligence quantification in AI systems. His scientific awards include: Outstanding Young Scientist of the Year (2004) Junior Chamber International - Year's Professional Award (2003) Yılın Meslek Ödülü (2002) Service Award from Air Force Academy (2000) Gelişimine Katkı Ödülü from ITU (1996) Prof. Üstündağ has actively supervised research and led multiple projects as Principal Investigator, including national and institutional grants in AI-driven software systems, social media robots, real-time cognitive risk management, and elderly support devices. He has advised students in AI, neural networks, and intelligent systems, though specific names are not listed. His lab activities are centered around the Software Development Laboratory and cognitive systems research under various funded projects.
Dr. Reena Sarkar is a Research Fellow in the Department of Forensic Medicine at Monash University and the Victorian Institute of Forensic Medicine’s Academic Division. Her research focuses on one-punch death prevention, medicolegal death investigation systems, and forensic-epidemiologic approaches to family violence homicide. She holds a PhD from Monash University (2021) and a Master of Dental Surgery from the University of Mumbai (2004). Previously, she served as a Senior Oral and Maxillofacial Pathologist and academic leader in Indian dental institutions, including National Dental College Dera Bassi Mohali. Research Interests: Global capacity analysis of medicolegal systems UN Right to Life charter applications Data bias correction in family violence homicide studies Forensic-epidemiologic injury trends analysis Her work bridges clinical dentistry, forensic medicine, and public health, with a focus on injury prevention and policy development. Recent projects include analyzing single-punch assault fatalities in Australia and developing standardized death certification protocols. Recent Projects: Ethical forensic methodologies for extrajudicial killings investigations ICD coding standardization for family violence injuries Motorcycle crash characteristic data registries Awards: FMNHS ECR Travel Grant (2024) Monash University Postgraduate Research Scholarship (2017) Expertise: Mixed methods research, large dataset triaging, forensic dentistry, legal database analysis.
Professor Ran Levi is a Chair in Mathematical Sciences at the University of Aberdeen, affiliated with the School of Natural and Computing Sciences and the Department of Mathematics. His research bridges pure mathematics and neuroscience, focusing on algebraic topology and its applications to neural systems. BSc in Mathematics, Hebrew University, Jerusalem (1988) PhD in Mathematics, University of Rochester (1993) His research interests include Algebraic Topology , Homotopy Theory of classifying spaces , Combinatorial Topology , and Neuro-Topology —the application of topological methods to neuroscience. He leads the Aberdeen Neuro-Topology Research Group and collaborates with the Blue Brain Project at EPFL. His work explores how topological and geometric frameworks can model and predict neural structures, potentially inspiring new mathematical concepts. Recent publications reveal a strong trend in topological data analysis of neural circuits , p-local groups , and combinatorial constructions in topology. His interdisciplinary work appears in journals such as iScience , eLife , PLOS ONE , and Algebraic & Geometric Topology . His research has been supported by grants including: EPSRC: Topological Analysis of Neural Systems (EP/P025072/1) Collaboration with École Polytechnique Fédérale de Lausanne (Blue Brain Project) Professor Levi has advised several researchers, including Dejan Govc, Janis Lazovskis, Henri Riihimaki, and Jason Smith. He is actively involved in international collaborations with mathematicians and neuroscientists across Europe and the US. He is a key member of the Institute of Pure and Applied Mathematics (IPAM) at Aberdeen and contributes to the AberdeenML and Cybersecurity and Privacy research groups. His work exemplifies the growing synergy between mathematics and computational neuroscience.
Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.
