Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.
Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.
Christopher G. Healey is the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics and a Professor in the Department of Computer Science at North Carolina State University. His research spans visualization, data analytics, text analytics, sentiment analysis, machine learning, cognitive psychology, computer graphics, and social media analytics. He has graduated 15 Ph.D. and 26 master's students and secured over $6 million in research funding from agencies including the National Science Foundation, Department of Defense, National Security Agency, Army Research Office, and various industry partners. He has published over 100 peer-reviewed articles and is a senior member of both IEEE and ACM, as well as a member of the NC State Academy of Outstanding Teachers. His research focuses on developing visualization techniques that leverage visual perception to support rapid, accurate, and effective analysis of large, complex datasets. More recently, he has been investigating machine learning for natural language processing and text analytics. His work includes projects on visualizing election results, sentiment estimation for social media, and wildfire narratives using large-scale social media data. His publications demonstrate a strong trend toward integrating machine learning with visualization, particularly for text analytics and social media analysis. He has made significant contributions to visualizing deep neural networks, cyber situation awareness, and pandemic response analytics, showing how visualization can enhance understanding of complex systems and large datasets across multiple domains. IBM Faculty Award (2007, 2008, 2010, 2011, 2012) Senior member, Association of Computing Machinery (ACM) (2007) Senior member, Institute of Electrical and Electronics Engineers (IEEE) (2007) NC State Academy of Outstanding Teachers inductee (2003) National Science Foundation Faculty Early CAREER Award (2001) He has successfully mentored numerous graduate students and secured significant research funding across multiple projects. His work with the Laboratory for Analytic Sciences, National Science Foundation, and Department of Defense demonstrates strong industry and government partnerships. His recent projects focus on visualizing social media narratives, deep neural networks for text understanding, and predictive analytics for large document collections. He leads research groups focused on visualization and analytics, working with teams to develop innovative approaches for data exploration and analysis. His current work continues to push the boundaries of how visualization can be used to enhance understanding of complex data across domains including public health, cybersecurity, and social media analysis.
Duncan Wilson is a Professor of Connected Environments at the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London. His work bridges academia and industry, focusing on IoT, AI, and spatial analysis to enhance understanding of built and natural environments. Current role: Professor of Connected Environments at UCL Education: PhD in Artificial Intelligence and Machine Vision (UCL, 1997), BEng (Hons) in Electrical Engineering (Loughborough University, 1993) Research interests include: Cognitive computing at the network edge Extraordinary sensory systems for data capture Spatial reasoning and digital twins IoT for healthcare and biodiversity Edge AI and TinyML Recent articles span digital twin development , IoT for biodiversity monitoring , and smart healthcare infrastructure . He has received recognition for collaborative R&D approaches during his directorship at Intel's Sustainable Connected Cities institute. Teaching: Leads MSc Connected Environments and modules on IoT ethics, AI on microcontrollers, and sensor network deployment Projects: IoT Living Lab at UCL, Project Hercules for eye clinic analytics, and Shazam for Bats environmental monitoring Professional activities: Former Director of Intel Collaborative Research Institute (2012-2018), ex-member of Smart London Board (2017-2022)
Mahnoosh Alizadeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), affiliated with the Institute for Energy Efficiency and the Center for Control, Dynamical Systems and Computation (CCDC). She directs the Smart Infrastructure Systems laboratory and focuses on scalable control frameworks, data analytics, and market mechanisms for sustainable cyber-physical systems in smart grids and electric transportation. PhD in Electrical and Computer Engineering from UC Davis (2014) Recipient of the National Science Foundation CAREER award (2019) Associate Editor for IEEE Transactions on Control of Network Systems and IEEE Open Journal of Control Systems Her research spans theoretical work in networks, optimization, and AI, with applications in smart grids , electric transportation , and resilient infrastructure . She has contributed to safe optimization algorithms, decentralized learning, and game-theoretic approaches in resource allocation. Recent publications highlight advancements in safe optimization (safe linear bandits, conservative linear bandits), decentralized learning (robust federated learning), game theory (General Lotto games, resource allocation), and smart charging (mobility-aware EV scheduling). These works emphasize real-time decision-making under constraints, security, and robustness in cyber-physical systems. NSF Early CAREER Award Northrop Grumman Excellence in Teaching Award Her research group includes PhD students Spencer Hutchinson, Arghavan Zibaei, Nanfei Jiang, and Sajjad Ghiasvand, with alumni placed at institutions like Apple, Toyota, and the University of Colorado.
Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au
Leid Zejnilovic is an Assistant Professor at Nova School of Business and Economics (Nova SBE), where he co-founded the Data Science Knowledge Center and serves as Academic Director, and co-founded the Open and User Innovation Knowledge Center as Scientific Deputy Director. He also co-founded the Patient Innovation platform, enabling patients and caregivers to share self-made healthcare solutions. With a double PhD from Carnegie Mellon University and Católica-Lisbon School of Business and Economics, his career spans over 20 years of international consulting, academic entrepreneurship, and teaching at institutions like Imperial College Business School and Ludwig Boltzmann Institute. PhD in Strategy, Entrepreneurship and Technological Change (Carnegie Mellon University / Catholic University of Portugal, 2014) Master in Engineering and Public Policy (Carnegie Mellon University, 2012) Master in Information Technology (Dzemal Bijedic University, 2007) Bachelor in Telecommunications (University of Sarajevo, 2002) His research focuses on Technology and Innovation Management, Human-Computer Interaction, and data-driven solutions across healthcare, tourism, and education. He has published extensively in journals like California Management Review , PLoS ONE , and Marine Policy , with recent work analyzing big data in tourism, machine learning for oral health, and pandemic impacts on fisheries. As an Associate Editor for Data & Policy Journal , his contributions bridge academic research and real-world applications. Co-founding the Data Science for Social Good Foundation and leading over 100 talks in industry and academia, Zejnilovic's career emphasizes translating innovation into social and economic impact through platforms, policy, and education.
Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.
Ada Gavrilovska is a Professor at Georgia Tech's School of Computer Science under the College of Computing. Her work focuses on systems software for emerging technologies, including hybrid memory systems, edge computing, and cloud infrastructure. She leads projects in the PRISM Center and ADA Center , with funding from NSF, DoE, SRC, and industry leaders like Cisco and VMware. Education: PhD in Computer Science, Georgia Tech (2004) Research Interests: Designing systems for new hardware and applications, including edge computing, heterogeneous memory management, and LEO satellite platforms. Her work bridges low-level OS mechanisms with high-level distributed systems challenges. Recent Publications highlight trends in LEO satellite resource scheduling Edge-based ML preprocessing Hybrid memory OS abstractions Disaggregated graph analytics Compiler-assisted performance optimization Scientific Awards: Best paper, NFV World Congress (2016) Spotlight paper, IEEE Transactions on Cloud Computing (2014) ISCA-50 25-year retrospective (2023) Advising & Grants: Ada has mentored over 15 PhD students and 10 MS students, with research supported by NSF, DoE, SRC, and industry grants. She serves as PI in the SRC/DARPA PRISM Center.
Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for effective and responsible analytics, leveraging techniques from causal inference, data management, theoretical computer science, machine learning, and human-computer interaction to address challenges in trustworthy system design including robustness, explainability, and fairness. Education: Postdoc: University of Chicago PhD: University of Massachusetts Amherst (supervised by Barna Saha) BTech: Indian Institute of Technology Delhi (IIT Delhi) (supervised by Prof. Amitabha Bagchi) Research Interests: Dr. Galhotra's research spans several interconnected areas in data science and artificial intelligence. His work primarily focuses on Responsible Data Science , where he develops methods to ensure that data-driven systems operate fairly and transparently. Within this broad area, his specific interests include: Causal Inference techniques for understanding cause-effect relationships in complex data Algorithmic Fairness approaches to mitigate bias in machine learning systems Explainable AI methods that make black-box models more interpretable Data Management systems for efficient and reliable data processing Entity Resolution techniques for integrating data from multiple sources Trustworthy System Design that addresses robustness, explainability, and fairness His recent publications demonstrate a clear trend toward developing frameworks that combine causal reasoning with practical data management systems, particularly focusing on how to make data-driven decisions more transparent and equitable. The intersection of database systems with fairness considerations appears to be a particularly active area of his research. Scientific Awards: Rising Star in Data Science at the Data Science Institute, UChicago (Oct 2021) Computing Innovation Fellowship Award Recipient (by CRA, CCC and NSF) (Apr 2021) DAAD AInet Fellow (Feb 2021) ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist (May 2020) Most reproducible paper award in SIGMOD 2018 and 2019 (Jun 2019) First recipient of Krithi Ramamritham Computer Science Scholarship (Jun 2019) Best paper award in SIGSOFT FSE 2017 (May 2017) Dr. Galhotra is actively seeking students to collaborate with on his research projects. His work has been supported by various fellowships and awards, including the prestigious Computing Innovation Fellowship. He has mentored several students through his research projects, with a focus on developing the next generation of data scientists who can build responsible and trustworthy systems. His research group appears to focus on the intersection of database systems and responsible AI, developing tools like HypeR for causal reasoning, Ver for view discovery, and Nexus for correlation discovery in spatio-temporal data. This work suggests a cohesive research agenda centered around making data systems more transparent, fair, and user-friendly.
Mehmet Koyutürk serves as the Andrew R. Jennings Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering, with additional affiliation as a Member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. His computational research bridges algorithm development with biological applications, focusing on network-structured data analysis to address complex biomedical challenges. Dr. Koyutürk earned his Ph.D. in Computer Science from Purdue University following B.S. and M.S. degrees in Electrical Engineering and Computer Engineering from Bilkent University. His primary research domains include high-throughput biological data analysis, systems/network biology methodologies, data mining algorithms, and scientific computing optimization, with particular emphasis on phosphorylation networks, genomic interactions, and multi-omics integration. Recent publication trends reveal expanding applications of his network science expertise into Alzheimer's disease phosphoproteomics, bipolar disorder biomarker discovery, and intimate partner violence analysis, while maintaining core contributions to graph neural networks and biological link prediction. His group actively develops open-source analytical tools like RokaiXplorer for phospho-proteomic data accessibility. Scientific Recognition Andrew R. Jennings Professorship Dr. Koyutürk leads multiple NIH-funded initiatives including R01-LM012980 for phosphoproteomics analysis, U01-CA198941 (BD2K program) for big network integration, and R01-LM011247 for GWAS enhancement, complemented by NSF CAREER Award CCF-0953195. He serves on the steering committee for CWRU's Systems Biology and Bioinformatics graduate programs and as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), with extensive collaboration through Mark Chance's Center for Proteomics and Bioinformatics. His laboratory specializes in developing scalable algorithms for biological network analysis, currently advancing projects on kinase-substrate association prediction, co-phosphorylation network characterization in cancer, and network-based approaches to intimate partner violence data mining, with strong emphasis on translating computational methods into biomedical insights through open-source software dissemination.
Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. Previously, he was a Postdoctoral Member at the Institute for Advanced Study and earned his PhD from Stanford University under the supervision of Aaron Sidford. He completed his undergraduate studies at MIT, graduating in May 2018. His educational background includes: PhD in Computer Science, Stanford University (Advisor: Aaron Sidford) Bachelor's degree, Massachusetts Institute of Technology (graduated May 2018) Dr. Liu's research spans the intersection of mathematics and computer science, with particular focus on graph algorithms , optimization , high-dimensional geometry , and additive combinatorics . His work often develops novel algorithmic techniques that bridge theoretical insights with practical applications. He has made significant contributions to areas such as convex optimization, linear programming, and combinatorial problems. His teaching includes courses like "A Principled Approach to Optimization" (CS 15-759), which covers rigorous treatments of convex optimization topics including gradient descent, interior point methods, linear regression, linear programming, and sparsification. His extensive publication record in top-tier conferences (FOCS, STOC, SODA) demonstrates a consistent focus on developing almost-linear time algorithms for fundamental graph problems, optimization techniques, and combinatorial theorems. Recent work shows increasing emphasis on combinatorial lines, corners theorem, and k-CSP approximability, while maintaining strong connections to optimization theory and graph algorithms. Dr. Liu has received notable recognition for his work: National Defense Science and Engineering Graduate (NDSEG) Fellowship (2018-2021) Google PhD Fellowship (2022-2023) Best Paper award at FOCS 2022 for "Maximum Flow and Minimum-Cost Flow in Almost Linear Time" Best Student Paper at STOC 2021 for "Discrepancy Minimization via a Self-Balancing Walk" His research has been supported by prestigious fellowships including the NDSEG Fellowship and Google PhD Fellowship. His work on graph algorithms, optimization, and combinatorics involves collaborations with researchers across theoretical computer science and mathematics. His publications often involve co-authors from multiple institutions, suggesting active research collaborations across the field. Dr. Liu maintains an active research program with a focus on developing efficient algorithms for fundamental computational problems. His recent work continues to push the boundaries of what's computationally feasible in graph algorithms, optimization, and combinatorial mathematics, with particular emphasis on achieving almost-linear time complexity for challenging problems.
Murat Kantarcioglu is a Professor of Computer Science at Virginia Tech, affiliated with the College of Engineering. He is also a Faculty Fellow at the Commonwealth Cyber Initiative (CCI) and directs the Data Security and Privacy Lab. Previously, he held the Ashbel Smith Professorship at the University of Texas at Dallas. His research focuses on data and AI security, privacy, blockchain, and cybersecurity. He has received notable awards, including the NSF CAREER Award and IEEE Technical Achievement Award, and is a Fellow of AAAS and IEEE. Education: Ph.D. in Computer Science (Purdue University), B.S. in Computer Engineering (Middle East Technical University). Research Interests: Privacy-preserving machine learning and data analytics Adversarial machine learning and cybersecurity Blockchain technology and applications Healthcare data security and genomics privacy Risk and incentive models for assured data sharing Awards and Recognition: NSF CAREER Award AMIA Homer R. Warner Award IEEE ISI Technical Achievement Award Fellow of AAAS and IEEE Distinguished Member of ACM Advising and Labs: Directed over 20 PhD/Master’s students, many in cybersecurity and privacy domains. Founder and director of Virginia Tech’s Data Security and Privacy Lab. Associate at Harvard’s University Data Privacy Lab. Service and Leadership: Extensive program committee roles in top conferences (KDD, AAAI, IEEE ICDE). Former CCI co-chair for IEEE TrustCom. Co-authored influential textbooks on adversarial machine learning.
Sarah E. Stein is an Associate Professor and Deloitte Foundation Faculty Fellow in the Department of Accounting and Information Systems at Virginia Polytechnic Institute and State University (Virginia Tech). She serves as Director of the Ph.D. Program within the Pamplin College of Business. Her academic journey includes a Ph.D. from the University of Missouri, a B.S. and M.S. in Accounting from Truman State University, and CPA licensure in Colorado. Prior to academia, she worked as an audit manager at Deloitte in Denver. Education: Ph.D., University of Missouri; B.S. and M.S., Truman State University; CPA (Colorado) Roles: Deloitte Foundation Faculty Fellow, Director of Ph.D. Program Dr. Stein’s research focuses on auditor expertise, corporate governance, auditing networks, and financial reporting challenges. Her work has been published in top journals like The Accounting Review and Auditing: A Journal of Practice & Theory . She received the 2019 Best Paper Award in Issues in Accounting Education and the 2020 Innovation in Auditing Education Award for her data visualization audit case. Her recent articles (2023–2025) highlight themes such as audit committee responsibilities, CFO-audit partner dynamics, and small accounting firm strategies. Her teaching includes graduate auditing courses and PhD seminars. Research trends in her articles emphasize audit quality, governance evaluation, and emerging technologies in auditing. She actively contributes to professional standards through commentaries on PCAOB releases. Awards: 2019 Best Paper Award, 2020 Innovation Award, Deloitte Doctoral Fellowship Dr. Stein advises the Ph.D. program and collaborates with industry on audit innovation. Her lab focuses on auditing education and case development, reflecting her commitment to bridging academic research and practical application.
Professor Line Roald is a faculty member in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on power system optimization, renewable energy integration, grid resilience, and wildfire risk mitigation using stochastic optimization and data-driven methods. Education : PhD (2016), MS (2012), BS (2009) from ETH Zurich Key Research Areas : Power Systems Optimization, Renewable Energy Integration, Wildfire Risk Mitigation, Stochastic Programming, Grid Decarbonization Her work addresses critical challenges in sustainable energy systems, including balancing grid efficiency and risk, optimizing electrolyzer scheduling for flexibility, and predicting cascading blackout severity using graph neural networks. She has developed frameworks for carbon intensity comparison and wildfire risk assessment in power systems. Scientific Awards : 2024 Inclusion, Equity and Diversity in Engineering Award 2024 Vilas Faculty Early Career Investigator Award 2023 IEEE Power Tech Best Student Paper Award 2021 NSF CAREER Award 2019 MTLE Fellow Professor Roald mentors graduate students and teaches courses including Introduction to Optimization and On-Line Control of Power Systems . Her publications highlight innovative approaches to grid security, carbon-efficient energy markets, and climate resilience in infrastructure systems.
