Gregory Erhardt is an Associate Professor at the University of Kentucky's Stanley and Karen Pigman College of Engineering. His research focuses on advancing transportation forecasting through evidence-based policy decisions, integrating big data and activity-based models to address challenges in transit investment and emerging mobility technologies. Research Interests: Activity-Based Travel Models, Big Data Applications, Forecast Accuracy Assessment Email: greg.erhardt@uky.edu Erhardt's work spans the intersection of transportation infrastructure, data science, and policy analysis. He specializes in developing modeling tools to predict the effects of transit projects, evaluating ride-hailing impacts, and improving forecasting methodologies. Recent publications highlight trends in transportation network companies (TNCs), activity-based modeling frameworks, and transit ridership dynamics. His research explores ride-hailing driver participation, multi-modal optimization, and the relationship between microtransit and congestion patterns. Key themes include the integration of longitudinal datasets, bias mitigation in TNC data analysis, and evaluating the accuracy of traffic forecasting systems. His work provides actionable insights for policymakers in a rapidly evolving transportation landscape.
Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.
Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.
Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.
Benjamin Moll is the Sir John Hicks Professor of Economics at the London School of Economics and Political Science (LSE). He is also a Research Associate at the National Bureau of Economic Research (NBER), a Research Fellow at the Centre for Economic Policy Research (CEPR), and an ECONtribute Research Fellow since 2020. His work focuses on how inequality impacts the macroeconomy and macroeconomic policy. Dr. Moll received his PhD from the University of Chicago in 2010 after studying in London. His academic journey has included positions at Princeton University before joining LSE. Moll specializes in macroeconomics with distribution , particularly studying how the enormous heterogeneity observed at the micro level—especially large disparities in income and wealth—impact the macro economy and macroeconomic policy. His research employs heterogeneous-agent models to analyze monetary and fiscal policy transmission mechanisms. He has made significant contributions to the development of numerical methods for solving complex macroeconomic models with multiple endogenous state variables. His recent publications demonstrate a strong focus on the distributional effects of macroeconomic policies and energy economics , particularly examining the macroeconomic and distributional consequences of energy import disruptions. His work bridges theoretical macroeconomic modeling with practical policy analysis, especially regarding the German and European economies. Among his professional honors and affiliations: Research Associate, National Bureau of Economic Research (NBER) Research Fellow, Centre for Economic Policy Research (CEPR) ECONtribute Research Fellow since 2020 Co-editor, American Economic Review Professor Moll actively contributes to policy discussions, particularly regarding European economic policy and responses to the Russian invasion of Ukraine. He has collaborated with economists across Europe on analyses of energy policy and macroeconomic stabilization. His work on numerical methods for heterogeneous-agent models has created valuable resources for the macroeconomics community, with code implementations available on his website. Moll maintains an active research group focused on macroeconomics with distributional considerations, collaborating with economists at LSE and internationally. His research program continues to expand the understanding of how micro-level heterogeneity shapes macroeconomic outcomes and policy effectiveness.
Dr. Ninghao Liu is an Assistant Professor of Computer Science in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences. He holds a Ph.D. in Computer Science from Texas A&M University (2021) and an M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2015). His research focuses on Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection, with notable contributions to foundational AI techniques and their applications in education, healthcare, and environmental sciences. Dr. Liu has secured significant funding, including a three-year NSF grant (2022–2025) for 'Graph-Oriented Usable Interpretation' and a five-year $10 million grant from the U.S. Department of Education (2024–2029) for the GenAI Empowered National Initiative for STEM+C Education. He has also been honored with the Outstanding Paper Award at ICML 2022, Best Paper Award Shortlist at WWW 2019, and other distinctions. His work emphasizes interpretable machine learning, graph neural networks, and addressing algorithmic bias. He collaborates across disciplines, contributing to radiology AI, climate-smart forestry, and pandemic prediction through knowledge-enhanced deep learning. His lab is based at the Boyd Research and Education Center, where he advances research in trustworthy AI systems and data-centric solutions.
Andrea Appolloni is an Associate Professor at the Department of Management and Law, University of Rome Tor Vergata. His academic career focuses on Management with emphasis on Sustainable Supply Chain Management , Digital Transformation , and Circular Economy . His research explores the intersection of technological innovation and sustainability, particularly through topics like AI in Logistics , Green Procurement , and Policy Optimization . Publications span both theoretical frameworks and empirical studies in China, Italy, and Malaysia, with a strong focus on environmental impact and organizational performance. Recent work includes digital twin applications for human-AI collaboration, blockchain integration in sustainable supply chains, and analyzing barriers to circular economy adoption. His 15 most recent articles (2025-2022) demonstrate a trend toward combining Artificial Intelligence , Operations Management , and Environmental Governance .
Alice Guerrini is a Research Fellow at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) at the University of Trento, specializing in infant cognitive development and neural mechanisms of social cognition. Her research focuses on infant social cognition , particularly examining how infants process communicative signals, attribute mental states, and develop theory of mind. Key methodologies include electrophysiological recordings (ERPs, theta oscillations) to investigate neural correlates of belief attribution and agent recognition in infants as young as 4 months. Her work consistently explores false belief correction , non-human agency perception , and communicative information transfer during early development. Analysis of her 2025 publications reveals a cohesive research trajectory centered on neural anticipation mechanisms in infant social cognition. All seven papers investigate how infants process communicative cues through electrophysiological markers, with particular emphasis on theta oscillations for mental state attribution and N170 responses for agent recognition. The research spans multiple international conferences including Dubrovnik Cognitive Science, CogEvo, and Lancaster Infant Development conferences. Guerrini collaborates extensively with Eugenio Parise and Giulia Mazzi , forming a core research team at CIMEC. Her work demonstrates strong integration between developmental psychology and cognitive neuroscience methodologies to uncover foundational mechanisms of human social cognition.
Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.
Jane Wang is a Professor in the Department of Food Science at the University of Arkansas , where she has served since 1999, progressing from Assistant to Full Professor. She also holds the title of Director of the Experiment Station in the Department of Food Science. Her research focuses on starch structure-functionality relationships , rice quality , and biomaterial utilization , with over 120 refereed publications and 5 patents. Education: B.S. in Agricultural Chemistry (1986) from National Taiwan University , M.S. in Food Science (1989) from the University of Minnesota , and Ph.D. in Food Science (1992) from Iowa State University . Postdoctoral research in starch chemistry at Iowa State University (1993-1994). Research Interests: Jane Wang's work explores starch chemistry, rice processing optimization, and value-added applications of agricultural byproducts. She investigates how starch modifications affect food and pharmaceutical properties, with a particular focus on parboiling, germination, and enzymatic treatments. Her research also examines the impact of environmental factors on rice starch development and quality. Scientific Awards: Outstanding Departmental Research Award (2008) Outstanding Volunteer, IFT Carbohydrate Division (2007) Outstanding Mentor, University of Arkansas (2005) Grants & Professional Service: She has secured over $3M in research funding, including USDA-NIFA grants and industry contracts with more than 50 food companies. Jane has served on numerous academic committees (Patent, Promotion & Tenure, Curriculum) and held leadership roles in professional organizations like IFT and AACC. She has also acted as associate editor for Cereal Chemistry and Carbohydrate Polymers , and reviewed for multiple journals and agencies. Labs & Teams: Dr. Wang leads the Carbohydrate Research Program at the University of Arkansas, focusing on starch structure-functionality, rice fortification, and biomaterial development. Her lab collaborates with industry partners and academic institutions to advance food science applications.
Cuo Zhang is a Lecturer of Power Engineering and ARC DECRA Fellow at the University of Sydney's School of Electrical & Computer Engineering. He holds a B.E. (Hons.) from the University of Sydney (2014) and a Ph.D. in Electrical Engineering from UNSW (2018). His research focuses on smart grids, renewable energy integration, voltage control, and optimization of power systems. Key interests include distributed generation planning, energy storage systems, and demand response mechanisms. Dr. Zhang leads research on enhancing distribution network resilience through advanced control strategies and participates in the Net Zero Institute. He has secured grants such as the 2024 ARC DECRA project on renewables hosting capacity. Supervised students include Yunqing Zhang, working on robust renewables integration. His recent publications emphasize data-driven approaches, decentralized energy trading, and adaptive control for unbalanced networks. He explores machine learning applications in energy management and stochastic optimization under uncertainty. Awards include the University Medal for academic excellence. Awards: ARC DECRA Fellow Grants: 2024 Robust Renewables Hosting Capacity Enhancement Labs/Teams: Member of the Net Zero Institute
Jonas Beskow is a Professor and Head of Division at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. His research focuses on multimodal interaction, speech synthesis, robotics, and human-robot interaction. He leads the Learning style variation in nonverbal behaviour for social robots and agents project as part of Digital Futures, a cross-disciplinary research center. His work involves developing social robots like the Furhat head and advancing technologies for gesture synthesis, audio-driven motion, and adaptive intelligent systems. He holds roles as Co-PI for the Advanced Adaptive Intelligent Systems (AAIS) and Adaptive Intelligent Homes (AIH) projects. His research spans robotics, computer graphics, and clinical applications such as dementia detection through multimodal patient behavior analysis. Beskow also contributes to educational initiatives, supervising courses in computer science and engineering, including degree projects in machine learning and systems engineering. Publications highlight innovations in gesture generation, speech-driven animation, and socially-aware robotics. Collaborations with institutions like Stockholm University and RISE Research Institutes drive interdisciplinary solutions. His work bridges artificial intelligence, human-computer interaction, and assistive technologies, emphasizing ethical and societal impacts of emerging digital systems.
Eva Jaspers is Professor of Empirical and Theoretical Sociology at Utrecht University's Faculty of Social and Behavioural Sciences, where she also serves as Bachelor Director for Sociology. She is a member of the Interuniversity Center for Social Science Theory and Methodology (ICS) and leads the ICS Research Line on Migration and Social Stratification. Her educational background includes a PhD in Sociology (2008) and an MA in Sociology cum laude (2003), both from Radboud University Nijmegen, where she also obtained her University Basic Teaching Qualification in 2007. Professor Jaspers' research focuses at the intersection of networks, diversity and inequality, examining how people's contexts, interactions and relations with (dis)similar others shape their behavior and outcomes across various domains. Her work spans social networks, ethnic relations, gender and sexuality, discrimination, and gender equality, utilizing social network analysis as a key methodology. She has published in top journals including American Sociological Review , European Sociological Review , and Social Networks . Analysis of her recent publications reveals a strong focus on intergroup relations, social networks, and inequality, with particular emphasis on interethnic contact, gender dynamics in workplaces and families, and the impact of social norms on polarization. Her work increasingly employs computational methods and longitudinal data to examine complex social dynamics. Fellow of the Gravitation Program SCOOP (research and training center for sustainable cooperation) Professor Jaspers has managed multiple large-scale data collections and secured significant funding from NWO and Volkswagen Stiftung. She leads international projects including INCLUSIVITY (network interventions to counter polarization) and GENPARENT (research on transition to parenthood for same-sex couples). She is also a co-applicant in the prestigious NWO Summit Grant SOCION for research on social cohesion. Her academic leadership includes chairing the Dutch Sociological Association and organizing major conferences like the INSNA Sunbelt Conference. She is affiliated with several research initiatives including the Institutions for Open Societies (IOS) program, focusing on Future of Work, Gender Diversity and Global Justice, and Migration and Societal Change. Her work with the CILS NL data and SOCON panels demonstrates her commitment to longitudinal research on social networks and societal change.
Dr. Clara Colombatto is an Assistant Professor in the Department of Psychology at the University of Waterloo, where she directs the Vision and Cognition Lab. She holds an honorary lecturer position at University College London and a PhD from Yale University. Her research explores how visual perception extracts social and cognitive states from others, focusing on attentiveness, confidence, and metacognition. She investigates non-biological agents like AI and the perceptual roots of social cognition, including group dynamics and moral judgment. Education: BS in Neuroscience and Philosophy, Duke University PhD in Psychology, Yale University Postdoctoral Research Fellow, University College London Research Interests: Perception of attentiveness and metacognition, social group perception, AI ethics, moral judgment, and visual cognition. Her work bridges cognitive psychology, vision science, and social psychology to understand how humans perceive other minds and interact with non-human agents. Recent Trends in Articles: Recent work emphasizes human-AI collaboration, trust in technology, and perceptual foundations of social interaction. Key themes include gaze dynamics, confidence attribution in AI, and optimal group size perception. Grants & Collaborations: Collaborates with institutions like Princeton University, University of Oxford, and Microsoft Research. Her team includes students working on topics like robot teleoperation and moral narratives. Labs & Teams: Leads the Vision and Cognition Lab at Waterloo, focusing on experimental psychology and computational modeling. Current projects explore metacognition in advice-taking and perceptual grouping in social interactions.
Lin Ma is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, since August 2023. His research focuses on advancing database systems through machine learning integration, with a particular emphasis on self-driving DBMS, query optimization, and GPU acceleration. He holds a PhD from Carnegie Mellon University, where he also served as a postdoctoral researcher, and previously worked as a Software Engineer at Databricks. Research Interests: Lin’s work bridges database management systems and machine learning, aiming to create autonomous systems capable of self-optimization. Key areas include workload forecasting, behavior modeling for self-driving DBMS, and leveraging GPU capabilities for large-scale analytics. His contributions have been recognized through publications in top venues like VLDB, SIGMOD, and CIDR. Service: He actively serves on program committees for major conferences including SIGMOD, VLDB, and CIDR, and has held roles such as Web/Information Chair for SIGMOD (2023). His contributions extend to academic service, including admissions and faculty search committees at CMU and UMich. Labs & Projects: Lin leads research initiatives in database systems, including the QueryBot5000 framework for workload forecasting, and collaborates on projects like Vortex and Database Gyms to advance GPU-accelerated analytics and self-driving system design.
