Miloš N. Mladenović is an Associate Professor at the Spatial Planning and Transportation Engineering Group , affiliated with the Department of Built Environment under the School of Engineering at Aalto University. He also serves as a Vice Dean and leads the MSc program Sustainable Urban Mobility Transitions . His work bridges academic research and practical policy, with a focus on governance and ethical dimensions of emerging mobility technologies. Education: BSc in Transport Engineering, University of Belgrade MSc and PhD in Civil Engineering, Virginia Tech His research centers on: Societal Impact of Emerging Mobility Technologies , such as Mobility as a Service and automated vehicles Decision-Support Systems for urban transport planning Sustainable Transportation and accessibility frameworks Ethics in Transport Policy , including projects like EVA-HEL (electric scooters) and BATS (mobility solutions) Analysis of his recent publications shows: Development of open-source models for urban transport equity Focus on cultural governance in Finland Interdisciplinary approaches combining social sciences and engineering Scientific Recognition: 2024 Moshe Givoni Prize from Transport Reviews 2024 Linkki Pin Permit award Academic Leadership: Editorial Board Member, European Transport Research Review (2019-2024) Organizing Chair, Aalto University Summer School on Transportation (ASTRA) His work connects directly with the United Nations Sustainable Development Goals for sustainable cities and climate action.
Marianne Baxter is Professor of Economics at Boston University, specializing in macroeconomics, international finance, and business cycle analysis. Her research examines global economic interactions including risk-sharing mechanisms, consumption-investment dynamics, and pricing strategies in international markets. Major research streams investigate household consumption patterns across different wealth groups, exchange rate pass-through in retail pricing (notably IKEA case studies), and determinants of bilateral trade flows. Her work integrates theoretical modeling with empirical analysis using firm-level and household datasets. Professor Baxter teaches international macroeconomics and finance courses. She has contributed methodological frameworks for analyzing saving-investment correlations and terms of trade fluctuations.
Joseph Devietti is an Associate Professor in the Department of Computer & Information Science at the University of Pennsylvania. His research focuses on improving programmability and performance of multiprocessor systems through architectural and programming model innovations. He actively advises PhD students and has supervised numerous graduates now employed at leading tech companies and academic institutions. Education: PhD (2012), MS (2009) in Computer Science and Engineering from University of Washington; BSE (2006) in Computer Science and BA (2006) in English from University of Pennsylvania. Employment: Associate Professor (2020–present), Assistant Professor (2013–2020) at University of Pennsylvania; Principal Scientist & Co-founder at Cloudseal, Inc. (2018–2020). Devietti’s research spans computer architecture, parallel programming, and deterministic execution. Key areas include cache/memory optimization (prefetching, false sharing repair), GPU programming models (race detection, block-size independence), and hardware-software co-design for concurrency safety. His recent work addresses dynamic runtime prefetch tuning (RPG 2 ), online code layout optimization (OCOLOS), and intelligent BTB prefetching (Twig) for data center applications. His publications from 2024–2017 reveal trends in instruction/cache optimization (2024–2020), GPU determinism (2018–2017), and race detection (2018–2016). Awards include the 2024 Penn Engineering Ford Motor Company Award, Radhia Cousot Best Paper (2018), and IEEE Micro Top Picks recognition (2023, 2009, 2008). Scientific Awards : 2024 Penn Engineering Ford Motor Company Award Radhia Cousot Young Researcher Best Paper Award (SAS 2018) IEEE Micro Top Picks (2023, 2009, 2008) Intel Early Career Faculty Honor Program (2013) Intel Ph.D. Fellowship (2011) Advising : Supervised 15+ PhD/Master’s students with placements at Google, Microsoft, Amazon, NYU, and the United States Naval Academy. Collaborations : Works with industry leaders (NVIDIA, Facebook) and academic institutions (University of Washington, Penn).
Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.
Dr. Yanjun Zhang is an Honorary Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on privacy-preserving technologies, federated learning, cybersecurity in IoT systems, and machine learning security. He holds a PhD in Privacy-Preserving Sharing for Genome-Wide Analysis from The University of Queensland (2021). Education: PhD in Information Technology, School of Information Technology and Electrical Engineering, The University of Queensland (2021) Research Interests: Designing secure collaborative machine learning frameworks Defending against adversarial attacks in cyber-physical systems Privacy preservation in distributed genomic and medical data analysis Compliance and ethics in virtual personal assistant applications Key Contributions: Developed privacy-preserving federated learning frameworks (AgrAmplifier, PrivColl) Conducted foundational studies on evasion attacks in IoT systems Created datasets for analyzing malicious browser extensions and Alexa skills Labs/Teams: Active contributor to UQ Cyber initiatives, including the 2021-2022 Seed Funding project on federated deep learning for medical imaging.
Dr. Robert Verity is an Assistant Professor in the School of Public Health at Imperial College London, affiliated with the Faculty of Medicine. His research focuses on malaria parasite genetics, particularly using molecular surveillance to understand transmission dynamics. He leads projects like the SIMPLEGEN pipeline funded by the Bill and Melinda Gates Foundation, aiming to optimize resource use in malaria research. His work integrates spatial data analysis, Bayesian methods, and population genetics to address global health challenges. During the pandemic, he contributed to early estimates of COVID-19 fatality rates as part of the Imperial College response team. Key affiliations: Imperial College Network of Excellence in Malaria, MRC Centre for Global Infectious Disease Analysis Current projects: Molecular inversion probes analysis in DRC, malaria drug resistance quantification Research interests include Plasmodium falciparum population structure, antimalarial resistance mechanisms, and spatial epidemiology. His work bridges field data collection with advanced computational methods to inform public health strategies.
Dr. Pavel Naumov is a Lecturer in Computer Science at the University of Southampton, affiliated with the Agents, Interaction, and Complexity research group. His work focuses on Responsible Mechanism Design , integrating scientific, philosophical, and legal principles to study shared responsibility in collaborative decision-making systems. He holds a PhD from Cornell University and a summa cum laude diploma from Lomonosov Moscow State University, specializing in mathematical logic and automated theorem proving. Naumov actively supervises PhD students including Qi Shi and Benjamin James Plummer, and collaborates internationally on projects spanning ethics, logic, and multi-agent systems. His research examines how social norms, trust, and knowledge influence accountability in both human and machine decision-making. Key areas include responsibility diffusion in multi-step decisions, ethical dilemmas in strategic games, and anonymization frameworks for data privacy. Naumov’s recent publications explore topics like three-valued logic, trust-based belief systems, and dynamic logic in clandestine operations. His work bridges formal logic with real-world applications, aiming to ensure transparency and fairness in complex systems.
Dr. Eman El-Sheikh is a Professor and Associate Vice President at the University of West Florida (UWF), affiliated with the Hal Marcus College of Science and Engineering, the Department of Computer Science, and the Center for Cybersecurity. She is a globally recognized leader in artificial intelligence, machine learning, and cybersecurity, with over 30 years of experience and more than $26 million in competitive research funding. Ph.D. in Computer Science and AI, Michigan State University (2002) M.Sc. in Computer Science, Michigan State University (1995) B.Sc. in Computer Science, American University in Cairo (1992) Her research centers on the integration of Artificial Intelligence and Machine Learning in cybersecurity, with a strong emphasis on education, workforce development, and ethical AI. She leads the development of AI-Cyber curricula for national adoption and is a pioneer in experiential learning models such as the Cybersecurity for All® and CyberSkills2Work® programs. The trends in her recent publications reflect a consistent focus on AI-driven cybersecurity solutions, secure AI systems, national workforce development, experiential education, and inclusive cyber training. Her work bridges technical innovation with policy, leadership, and pedagogy, demonstrating a holistic approach to strengthening the cybersecurity ecosystem. Dr. El-Sheikh has received numerous prestigious honors, including: 2024 GISEC Global Educator of the Year 2024 and 2025 Cybersecurity Woman of the Year (World Finalist) UWF Million Dollar Research Hall of Fame ($25M and $1M levels) 2020 Women Leaders in Cybersecurity, Security Magazine Achievement Award for Advancing Cybersecurity Education (SAM 2016) NSF/NSA AI-Cyber Task Force Member (2024–2025) Founder, Women in Cybersecurity (WiCyS) Florida Affiliate She has led major grant-funded initiatives such as the National Cybersecurity Workforce Development Program and CyberSkills2Work®, securing over $26 million to advance cybersecurity education and training. She mentors students and emerging professionals, particularly through diversity and inclusion efforts. Dr. El-Sheikh also serves as Chief Strategic Alliance Officer and USA Ambassador for the Global Council for Responsible AI, influencing national and global AI policy and education standards. She founded and leads the Artificial Intelligence Research Group at UWF and is actively involved in national task forces and advisory boards, including the Florida Cybersecurity Task Force and the WiCyS Florida Affiliate Advisory Board. Her leadership extends to shaping the future of AI and cybersecurity through collaborative research, innovation in higher education, and strategic partnerships.
Thomas Wachtler-Kulla is a Professor in the Department of Biology II at Ludwig-Maximilians-University Munich, where he leads the Computational Neuroscience research group. He is a GSN full member and serves as the GSN Ombudsperson, offering neutral and confidential counseling for students. He is also the Group Leader and Scientific Director at the German Neuroinformatics Node (G-Node), contributing significantly to neuroscience data infrastructure. His research focuses on how the brain processes sensory signals, particularly in the visual system, aiming to understand neural coding, perceptual stability, and color vision under natural conditions. He employs neurophysiology, psychophysics, and computational modeling to investigate sensory processing, eye movement compensation, and efficient coding mechanisms. At G-Node, he develops software and hardware tools for organizing, storing, analyzing, and sharing neurophysiological data, promoting reproducible research. His recent work spans Bayesian models of hue perception, data management frameworks like DataLad and odML, and studies on honeybee neuroethology. He actively supervises graduate students and contributes to major initiatives such as NFDI-Neuro and the International Neuroinformatics Coordinating Facility, advancing standards for open and FAIR neuroscience. Scientific Contributions and Leadership: Scientific Director, German Neuroinformatics Node (G-Node) GSN Ombudsperson for student conflict resolution Key contributor to NFDI-Neuro and INCF Developer of tools for metadata management and data sharing He advises several current and former graduate students and is deeply involved in shaping data policies and infrastructure for the neuroscience community, ensuring scientific rigor and accessibility.
Lisa Larrimore Ouellette is the Deane F. Johnson Professor of Law at Stanford Law School, where she has established herself as a leading scholar in intellectual property law and innovation policy. Her work spans patent law, trademark law, pharmaceutical policy, and the intersection of artificial intelligence with legal frameworks. Professor Ouellette's research focuses on the intersection of law, economics, and innovation. Her scholarship examines how intellectual property systems influence technological development, with particular attention to pharmaceutical innovation, biomedical research, and emerging technologies. She has made significant contributions to understanding patent systems, trademark law, and the policy frameworks governing innovation. Her work often employs empirical methods to analyze real-world impacts of legal rules on innovation incentives and outcomes. Analysis of her recent publications reveals a strong focus on contemporary challenges in intellectual property law, including the impact of artificial intelligence on patent systems, equity in patent inventorship, pharmaceutical pricing mechanisms, and innovation policy responses to public health emergencies like the COVID-19 pandemic. Her work demonstrates a consistent pattern of addressing timely policy questions with rigorous empirical analysis and thoughtful legal reasoning. Professor Ouellette has collaborated extensively with leading scholars in law and economics, including Daniel J. Hemel, Jonathan Masur, Mark Lemley, and others. Her research has been supported by prestigious institutions including the National Bureau of Economic Research (NBER), and she has contributed to numerous policy discussions through amicus briefs and responses to government requests for comments. While specific advising relationships aren't detailed in the available information, her extensive publication record with co-authors suggests active mentorship of junior scholars and students.
Kwaku Ohene-Asare is a Lecturer in Business Analytics at De Montfort University, UK, within the School of Leadership, Management and Marketing. He holds a PhD in Operational Research and Management Science from the University of Warwick, an MSc in Economics and Finance (with distinction) from Loughborough University, and a BSc in Economics (first-class honors) from the University of Ghana-Legon. He also completed a certificate in Decision Science and Machine Learning at MIT, USA. He has held visiting professorships at Warwick University and Stellenbosch University and plays a senior lecturer role at the University of Ghana. His educational background includes: PhD in Operational Research and Management Science, University of Warwick, UK (2012) MA in Decision Science and Machine Learning, MIT, USA MSc in Economics and Finance, Loughborough University, UK (Distinction) BSc in Economics, University of Ghana-Legon (First Class) PGCAP (Part 1), University of Warwick, UK (2009) Certificate in Nonparametric & Bootstrap Methods, Sapienza University of Rome, Italy (2012) Kwaku's research interests span business analytics, management science, artificial intelligence, data science, machine learning, economic efficiency, productivity analysis, data envelopment analysis (DEA), stochastic frontier econometrics, and their applications in energy, finance, insurance, and credit unions. He has developed a research-based DEA course at the University of Ghana and pioneered the advanced quantitative research methods course for PhD students since 2015. His work integrates cutting-edge computational techniques and econometric modeling to address real-world economic and business challenges. The recent trend in his publications shows a strong focus on efficiency and productivity analysis across sectors—particularly in energy, banking, and insurance—using advanced non-parametric and parametric methods. He frequently applies DEA, Malmquist indices, and stochastic frontier models to assess performance in African and ECOWAS economies, with a growing emphasis on sustainability, undesirable outputs, and dynamic efficiency. His work bridges theoretical rigor with practical policy implications. His scientific awards include: Global Leadership Award (2021) DFID Shared Scholarship Scheme Award (2004) Doctoral Research Scholarship, Warwick Business School (2007) He has received multiple research grants, primarily from the University of Ghana Business School (UGBS), as Principal Investigator, including projects on data science and machine learning, energy productivity, banking efficiency, and multinational operations. He has supervised PhD students through course development and research mentorship. His consultancy work includes efficiency analysis for the National Petroleum Authority, Ghana, and market entry feasibility studies for international firms. He is affiliated with the Centre for Enterprise and Innovation (CEI), the Institute for Sustainable Economics, and the Institute of Energy and Sustainable Development (IESD) at DMU, where he contributes to interdisciplinary research on sustainable economic development. He is an active member of professional societies including the Operational Research Society (UK), INFORMS, Association of European Operational Research Societies, British Academy of Management, Productivity Analysis Research Network (USA), and the Economic Society of Ghana.
Ahmed Bin Zaman serves as an Assistant Professor in the Department of Computer Science at George Mason University, where his research bridges computational methods with biological discovery. His academic profile emphasizes innovative approaches to protein structure prediction and optimization challenges. His educational foundation includes: PhD in Computer Science, George Mason University (2021) Master of Science in Computer Science, George Mason University (2020) Zaman's research program centers on computational biology, with specialized expertise in evolutionary computation and artificial intelligence applied to protein conformation analysis. He develops stochastic optimization frameworks to enhance protein structure prediction accuracy, focusing on conformational space mapping and decoy ensemble generation. His methodology integrates evolutionary algorithms with multi-objective optimization to navigate complex molecular landscapes, contributing significantly to template-free protein structure determination. His publication trajectory from 2017-2022 reveals distinct research phases: initial work in cybersecurity threat detection evolved into a concentrated focus on computational structural biology. Thirteen protein-related publications demonstrate consistent innovation in conformational sampling techniques, while maintaining methodological rigor through evolutionary computation and machine learning integration. Key contributions include conformation space mapping frameworks and adaptive stochastic optimization systems that address decoy diversity challenges. Professional development shows progression from industry experience at Technext Limited (as team leader/researcher) and lecturing at Metropolitan University to his current academic role. His teaching philosophy emphasizes cultivating independent problem-solving capabilities in students through cognitive tool development.
Jonny Holmström serves as Professor at Umeå University's Department of Informatics and directs the Swedish Center for Digital Innovation (SCDI), which he co-founded. He holds an additional affiliation as Professor at the Centre for Transdisciplinary AI, focusing on bridging theoretical research with practical AI applications across sectors including forestry, banking, and public services. His work appears in premier journals such as MIS Quarterly, Information Systems Journal, and Journal of Information Technology. His research centers on digital innovation, transformation, and entrepreneurship, examining how organizations navigate digital change through platform governance, AI integration, and entrepreneurial storytelling. Recent work investigates generative AI's impact on business model design, data work practices, and organizational transformation, emphasizing practical frameworks for managing digital transitions while addressing resistance and ethical considerations. Analysis of his 15 most recent publications (2024-2026) reveals a dominant focus on generative AI's organizational implications, particularly its role in reshaping platform governance, facilitating innovation through prompting, and transforming business models. Concurrent themes include digital platform evolution, data flow management in innovation networks, and citizen-centric digital government design, reflecting a consistent emphasis on practical implementation challenges in real-world contexts. Holmström leads significant research initiatives including a 28 MSEK program at Umeå University and the Kempe Foundation-funded SCDI AI Business Lab. His current project 'Using No-Code AI to Teach Machine Learning in Higher Education' (2024) aims to democratize AI education. He serves on editorial boards for CAIS, EJIS, Information and Organization, and JAIS, and heads the Swedish Center for Digital Innovation research group while participating in 'AI and society' collaborations. He founded and directs the Swedish Center for Digital Innovation (SCDI), which operates the SCDI AI Business Lab exploring practical AI applications for businesses. His work integrates with the Centre for Transdisciplinary AI to advance cross-sector AI implementation, particularly in public services and sustainable business models within the circular economy framework.
Ulf Hedestig is a Senior Lecturer at the Department of Informatics, Umeå University. Based in MIT building, Umeå, Sweden, he focuses on technology-mediated education and digital government research. Contact: ulf.hedestig@umu.se , +46 90 786 61 32. Research Themes : IoT in public spaces, user-centered design, digitalization in education and government, mobile learning environments, knowledge transfer challenges Recent Trends : 2020 work on IoT urban applications, 2018 publications on China's digital strategies and O2O business models Key Collaborations : Mikael Söderström, Daniel Forest, Victor Kaptelinin Scientific Recognition : Excellent Teacher pedagogical qualification Distinguished University Teacher educational qualification
Yannic Maus is a University Professor at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology (TU Graz), Austria, where he heads the newly founded Institute of Algorithms and Theory (established in 2025). He also serves as co-leader of one of the five fields of expertise at TU Graz (FoE Information, Communication & Computation). His academic journey includes: PhD in Computer Science from University of Freiburg, Germany (2014-2018) MSc in Mathematics from RWTH Aachen, Germany BSc in Mathematics and Computer Science from RWTH Aachen, Germany (with a year at National University of Singapore) Professor Maus specializes in theoretical computer science and algorithm design, with a particular focus on distributed computing. His research spans distributed graph algorithms, efficient algorithms, data structures, complexity theory, and geometric algorithms. He approaches problems with both theoretical rigor and practical applications in mind, seeking clean mathematical solutions to questions motivated by real-world systems. His recent publications show a strong focus on distributed and parallel algorithms, particularly in graph theory. The research trends include distributed graph coloring, symmetry breaking, vertex cover problems, and massively parallel computing models. His work often bridges theoretical computer science with practical distributed systems considerations, with applications to large-scale networks and highly parallel systems. Professor Maus has received numerous accolades for his research: 2020 Principles of Distributed Computing Doctoral Dissertation Award Wolfgang-Gentner-Nachwuchsförderpreis 2019 GI Dissertationspreis 2018 Best Paper Awards at SIROCCO 2016, DISC 2016, and DISC 2017 Professor Maus actively mentors PhD students and has secured significant research funding, including FWF grants P36280-N (2023-2027), DOC 183 (2024-2028), I6915 (2024-2028), and FFG grant No. 59263962. His research group maintains strong international collaborations with institutions across Germany, Finland, Iceland, Israel, and beyond, providing students with opportunities for international research visits. He leads the Algorithms & Complexity research group at TU Graz, which includes PhD students Manuel Jakob, Florian Schager, Malte Baumecker, and Kritika Kashyap, as well as postdoc Tijn de Vos. The group is actively involved in theoretical computer science research with a focus on distributed and parallel algorithms, particularly for large-scale networks and highly parallel systems.
