Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.
Jordi Galí is a Professor at the Department of Economics and Business at Universitat Pompeu Fabra (UPF), a Senior Researcher at the Center for Research in International Economics (CREI), and a Research Professor at the Barcelona School of Economics (BSE). He holds a PhD from MIT and has played a central role in shaping modern macroeconomic theory, particularly the New Keynesian framework used by central banks worldwide. Education: PhD in Economics, Massachusetts Institute of Technology (MIT), 1989 Master in International Management, ESADE, 1985 Bachelor in Economics, Universitat Pompeu Fabra, 1994 His research focuses on macroeconomic theory, monetary economics, and macroeconometrics. He is best known for his work on the New Keynesian Phillips Curve, optimal monetary policy rules, and the role of technology and expectations in business cycles. His influential book, Monetary Policy, Inflation and the Business Cycle , is a standard reference in graduate programs globally. The most recent articles highlight a continued focus on critical issues in modern macroeconomics: the implications of a low natural rate of interest (r*), the effectiveness of monetary policy at the zero lower bound, the role of wage and price flexibility, and the interaction between fiscal and monetary policy. His work increasingly integrates heterogeneity, financial frictions, and experimental methods, reflecting the evolving frontiers of the field. Scientific Awards: BBVA Foundation Frontiers of Knowledge Award (2025) Yrjo Jahnsson Award (2005) Premi Rei Jaume I d'Economia (2004) Premio Nacional de Investigación “Pascual Madoz” (2022) Three ERC Advanced Grants Foreign Honorary Member, American Economic Association (2020) Galí has advised numerous central banks, including the ECB, Federal Reserve, and Banque de France. He has held leadership roles as President of the European Economic Association (2012), co-editor of the Journal of the European Economic Association , and co-director of the CEPR International Macroeconomics Programme. He is a Research Fellow at CEPR, a Research Associate at NBER, and a Fellow of the Econometric Society. He has also been actively involved in public policy debates in Spain and Europe, particularly on issues of productivity, labor market reform, and fiscal policy. His research program continues to explore the design of stabilization policies in open and currency union economies.
Albert Lau is an Associate Professor of Civil and Environmental Engineering at the Norwegian University of Science and Technology (NTNU), located in Trondheim, Norway. He specializes in railway engineering, structural dynamics, and transportation systems. Lau holds leadership roles as the Study Program Leader for the MSc in Road, Railway, and Transportation Engineering, overseeing curriculum development and program coordination. His research focuses on railway track design, dynamic modeling of train-track interactions, and infrastructure maintenance, with projects such as the MeTinT initiative (Measurement with Train in Regular Traffic). He has extensive experience supervising master’s and PhD students, and his work emphasizes innovation in rail infrastructure and sustainable transportation solutions. Education and Professional Background: Lau earned his PhD from NTNU in 2018, focusing on numerical simulations of railway turnouts. Prior roles include Postdoc (2018–2020) and Assistant Professor (2017–2018) at NTNU, and teaching at Oslo Metropolitan University (2020). His industry experience includes roles as a Design Engineer (2010–2012) and Project Engineer (2013–2014) in Malaysia, where he managed construction projects and structural design. Research Interests: Lau’s work spans railway track dynamics, infrastructure health monitoring, and machine learning applications in transportation. Key projects include developing digital twins for railway test sites and analyzing ground displacement impacts on track anomalies. His contributions to the Road, Railway and Transport Group at NTNU aim to advance rail safety and efficiency through interdisciplinary approaches. Teaching and Outreach: Lau coordinates courses such as TBA4225 (Railway Engineering) and BA6012 (Fundamental Railway Technology). His outreach includes expert commentary on railway incidents, such as an interview on NRK (2024) discussing potential causes of a train accident. Current initiatives focus on revitalizing regional rail services and optimizing train positioning systems.
Dr. Angela Siegel is an Assistant Professor and Assistant Dean, Academic Outreach in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. She is actively involved in both academic leadership and research. Education: Ph.D. in Mathematics (Combinatorial Game Theory), Dalhousie University, 2011 M.Sc. in Mathematics, Dalhousie University, 2005 B.Sc. in Mathematics & Marine Geophysics, 1997 Her research focuses on combinatorial game theory, graph theory, discrete mathematics, and number theory, with a strong emphasis on computer science education and inclusive teaching . She investigates the challenges students face when transitioning into computer science programs, aiming to improve pedagogical approaches and support systems. Her work bridges theoretical mathematics and practical educational innovation. The recent publications highlight a dual focus: theoretical contributions to combinatorial games (e.g., partizan games, placement games, geography variants) and applied research in computing education, particularly student transition and inclusive practices. Her interdisciplinary work spans mathematics, computer science, and educational theory. Scientific Awards: Dr. Siegel has supervised and collaborated with students and researchers on topics including student transition into higher education computing, LEGO-based pedagogy, and workplace readiness. While no specific grants are listed, her repeated presentations and publications suggest active research funding and scholarly engagement. She has contributed to major conference proceedings and book volumes such as Games of No Chance . She is associated with research teams focused on combinatorial games and computer science education innovation, often collaborating with scholars like Richard Nowakowski, Neil McKay, and Mark Zarb. Her work in inclusive teaching and student support reflects a commitment to building accessible and equitable learning environments in computing.
Veronika Eyring serves as Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. She holds dual appointments at these leading institutions, directing cutting-edge research at the intersection of climate science and artificial intelligence. Education: 2008: Habilitation in Environmental Physics at the University of Bremen 1999: PhD in Physics from the University of Bremen 1994: Diploma in Physics from the University of Erlangen Professor Eyring's research program focuses on improving climate models and projections through innovative integration of machine learning techniques and spaceborne Earth observations. Her work spans process-oriented modeling, development of observationally-based performance metrics, and understanding systematic biases in climate models. She has pioneered approaches to weighting model projections based on their performance using machine learning, significantly advancing the field of climate model evaluation. Her research has critical applications across multiple sectors including aeronautics, space research, transportation, and energy systems. Analysis of her recent publications reveals a clear trajectory toward deeper integration of machine learning with traditional climate modeling approaches. Her work has increasingly focused on developing community tools like the Earth System Model Evaluation Tool (ESMValTool) and leading major international initiatives such as the USMILE project (Understanding and Modelling the Earth System with Machine Learning). The publications span climate science, machine learning, Earth system modeling, and remote sensing, with specific emphasis on climate model evaluation, parameterization techniques, and improved climate projections. Scientific Awards: AGU Ambassador Award (2024) TUM Distinguished Affiliated Professor (2024) Gottfried Wilhelm Leibniz Prize (2021) ERC Synergy Grant (2019) Thomson Reuters Highly Cited Researcher (2016-2021) Top female researchers award, Helmholtz-Society (2015) Professor Eyring actively supervises a large research group comprising PhD students working on ML-based sea ice parameterizations, causal model evaluation for air-sea interactions, and machine learning-based detection of droughts in climate projections. She leads the prestigious ERC Synergy Grant USMILE and secured significant funding through the DFG Gottfried Wilhelm Leibniz Prize. Her research group at DLR includes multiple postdocs, research scientists, and software engineers working collaboratively on climate informatics projects. Professor Eyring leads the Earth System Model Evaluation and Analysis Department at DLR, which encompasses research groups focused on CMIP model evaluation, ESMValTool development, and machine learning applications in climate science. She founded and supervises the 'Climate Informatics' Group at the DLR Institute for Data Science in Jena. Her department maintains strong international collaborations, particularly with the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, where she serves as an Affiliate Scientist.
Stefan Kipfer is a Professor at the Faculty of Environmental and Urban Change , York University. He coordinates the Cities, Regions, Planning program and holds a PhD in Political Science from York University. His research focuses on the intersections of spatial organization, social order, and political rule, with particular emphasis on urban politics, racial capitalism, and anti-colonial theory. Education: PhD in Political Science, York University MES in Urban Political Economy and Ecological Politics, York University BA in Political Science and French, York University Kipfer's research spans transnational urban studies, critical theory, and radical political ecology. He explores: Urban social movements in Zurich, Toronto, and Paris State interventions in public housing, transit, and environmental policy The capitalist and racialized dimensions of urbanization Right-wing populism and neo-fascism Connections between Henri Lefebvre, Frantz Fanon, and Antonio Gramsci Recent work analyzes: Urbanization as neocolonial process Anti-racist and decolonial spatial strategies Transit justice and free public services Comparative urban governance Resistances to far-right regimes Articulation theory and political strategy He supervises graduate students in Environmental Studies, Geography, and Political Science, and has contributed to critical urban theory through publications linking Marxist, anti-colonial, and feminist frameworks.
Ali Vakilian is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), with a strong academic background in theoretical computer science and algorithms. He will join the Department of Computer Science at Virginia Tech as an Assistant Professor in Fall 2025. His research bridges algorithmic theory and machine learning, focusing on scalable, fair, and efficient algorithms for massive data. Education: Ph.D. in EECS, Massachusetts Institute of Technology (MIT), advisors: Erik Demaine and Piotr Indyk M.S. in Computer Science, University of Illinois at Urbana-Champaign (UIUC), advisor: Chandra Chekuri B.S. in Computer Engineering, Sharif University of Technology Research Interests: Ali Vakilian's work centers on the algorithmic foundations of machine learning and data science. He develops streaming, sketching, and sublinear-time algorithms for massive datasets, and pioneers learning-augmented algorithms that use machine learning predictions to improve performance while maintaining worst-case guarantees. His research in trustworthy ML includes algorithmic fairness, fair clustering, and learning with strategic agents. He also contributes to combinatorial optimization and approximation algorithms for network design, set cover, and low-rank approximation. His recent publications (2023–2025) show a consistent focus on fair clustering (individual and group fairness), streaming graph algorithms , learning-augmented methods , and frequency estimation . These works appear in top venues such as NeurIPS, ICML, SODA, and ICALP, often with recognitions like oral or spotlight presentations. Scientific Awards: Outstanding Student Paper Highlight Award, AISTATS 2024 Notable-top-25% paper, ICLR 2023 Oral presentation, AISTATS 2024 Spotlight presentation, NeurIPS 2023 Advising and Grants: Ali Vakilian mentors several students and interns, including summer interns at TTIC and Fatima Fellows. His research is supported by the National Science Foundation (TRIPODS program), as noted in the press coverage of his work on LearnedSketch. He actively contributes to the academic community through advising, organizing workshops (e.g., Algorithms with Predictions, Learning-Augmented Algorithms), and serving on program committees (e.g., NeurIPS, ICML, AISTATS). Labs and Teams: He is affiliated with the theory and algorithms group at TTIC and collaborates with researchers at MIT, UIUC, and other institutions. His work on learning-augmented algorithms has led to influential workshops and collaborations with leading figures such as Piotr Indyk and Erik Demaine.
Dr. Sara Caputo is a British Academy Postdoctoral Fellow at the Department of History and Philosophy of Science and an Affiliated Lecturer at the Faculty of History, University of Cambridge. She serves as Director of Studies in History, History and Politics, and History and Modern Languages at Magdalene College. Her research spans transnational maritime history, the history of medicine, cartography, and imperial history. Education: PhD in History from the University of Cambridge (Robinson College), MSc in History from the University of Edinburgh, BA (Hons) in History from Cardiff University. Career Highlights: Lumley Junior Research Fellow (2019–2022), Senior Research Fellow (2022–present) at Magdalene College, Scouloudi Fellow (2018–2019), Lewis-AHRC Scholarship (2015–2018), Honorary Vice-Chancellor’s Scholar (2015–2018). Research Focus: Dr. Caputo explores maritime mobilities, knowledge exchange, and the instability of national boundaries in the British and European navies. Her work integrates transnational perspectives into the study of a quintessentially national institution, challenging notions of Britishness and foreignness. She combines qualitative and quantitative methodologies to analyze legal, social, and cultural contexts of naval service and cartographic practices. Notable Articles: Recent publications include studies on British naval medicine, the evolution of ship tracks into surveillance tools, and the transnational dimensions of naval exploration. Her articles span journals like Past & Present , English Historical Review , and Social History of Medicine , with chapters in edited volumes on transport history and postwar naval recruitment. Scientific Awards: Royal Historical Society Whitfield Prize (2024) British Commission for Maritime History Boydell & Brewer Prize (2020) Fachverband Medizingeschichte wissenschaftlichen Förderpreis (2022) International Committee for History of Technology Maurice Daumas Prize (2021) Scottish History Society Rosebery Prize (2020) Teaching and Outreach: Dr. Caputo teaches courses on the Global Eighteenth Century, Union and Disunion in Britain, and Mediterranean history. She actively participates in outreach programs with the Cambridge University Admissions Office and The Brilliant Club, focusing on widening participation in history education.
Christoph H. Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Machine Learning and Computer Vision (MLCV) Group. His research spans machine learning, computer vision, and trustworthy AI with emphasis on robustness and fairness. He serves as ELLIS Fellow and Unit Director for ISTA's ELLIS unit. His research program focuses on foundational challenges in machine learning including robustness against distribution shifts, fairness in algorithmic decision-making, and verification of neural networks. Key contributions include work on 1-Lipschitz networks for robust classification, multi-source learning frameworks, and federated learning architectures. The group maintains strong output in top-tier venues through theoretical and empirical approaches. Recent publications (2023-2025) demonstrate consistent focus on verification, robustness, and multi-source learning, with notable recognition including the DARPA Disruptive Ideas award for logic gate neural network verification. Work frequently bridges computer vision and machine learning theory, with applications in safety-critical systems. Scientific Awards: ELLIS Fellow DARPA Disruptive Ideas award at NeuS (2025) for "Logic Gate Neural Networks are Good for Verification" Professor Lampert has supervised 12+ PhD students including recent graduates Alex Peste (2023), Nikola Konstantinov (2022), and Mary Phuong (2021), with current advisees including Max Cairney-Leeming and Egor Zverev. His group secures consistent publication placements at NeurIPS, ICML, and ICLR while editing major volumes like "Advanced Structured Prediction" (MIT Press 2015). The MLCV group comprises 10+ members including postdocs and PhD students, operating within ISTA's ELLIS unit (approved 2019). The team maintains active collaborations across Europe through the ELLIS network and regularly hosts visiting researchers.
Frank L. Hammond III serves as Assistant Professor at Georgia Tech's Woodruff School of Mechanical Engineering since April 2015, directing the Adaptation Robotic Manipulation (ARM) Laboratory. A Carnegie Mellon PhD graduate, he previously held postdoctoral positions at MIT and Harvard as a Ford Fellow. His interdisciplinary work bridges mechanical engineering, biomedical applications, and computational design. Education Ph.D. in Mechanical Engineering, Carnegie Mellon University M.S. in Mechanical Engineering, University of Pennsylvania M.S. in Electrical Engineering, University of Pennsylvania B.S. in Electrical Engineering & Biomedical Engineering, Drexel University Hammond's research pioneers adaptive robotic manipulation (ARM) systems that operate in unstructured human environments through bioinspired computational design. His lab develops xenomorphic (non-biomorphic) robots using soft pneumatic actuation, flexible electronics, and machine learning to achieve biological-level versatility. Key application domains include wearable human augmentation devices , haptic-enabled surgical teleoperation , and autonomous soft platforms for medical and industrial use. The ARM methodology integrates empirical biomechanics characterization with simulation-driven optimization and rapid prototyping. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) Medical rehabilitation breakthroughs through intention-driven exoskeletons with soft bioelectronics, (2) Novel locomotion strategies for soft robots in complex environments (sand, water, cluttered spaces), and (3) Advanced haptic feedback systems leveraging multimodal sensory substitution for proprioceptive restoration. These works consistently bridge biomechanics, control theory, and human factors. Awards Ford Postdoctoral Research Fellowship at Harvard School of Engineering Hammond actively mentors graduate researchers including PhD candidates Lucas Tiziani (soft actuators) and Bangyuan Liu (earthworm robotics), and Master's student Alex Hart (pediatric haptics). His lab secures research funding for projects like tunable mechanical interfaces for neuropathy treatment and cognition-focused wearable devices, with strong industry and clinical partnerships evident in co-authored medical device publications. The ARM Lab maintains robust collaborations across Georgia Tech's robotics, neuroscience, and biomedical engineering communities. The Adaptation Robotic Manipulation Laboratory operates from Whitaker Building Room 4102, housing specialized facilities for soft robot fabrication (3D printing, shape deposition manufacturing) and biomechanics testing. Current projects include pediatric haptic feedback displays, biomimetic swimming robots, and kirigami-skinned earthworm robots for subsurface locomotion. The lab emphasizes translational research with multiple pending medical device patents and active participation in K-12 STEM outreach programs.
Tobias Pettersson is a Senior Lecturer at the Department of Pedagogy: Music, Dance and Drama , University of Gothenburg . His roles include Undergraduate Teaching Coordinator and Education Coordinator for both first/second cycle education and teacher training programs. He works at the intersection of musicology , gender studies , and pedagogical traditions . Current position since 2015 Active in teacher education since the 1990s Doctoral degree in musicology (2004) Research Focus: Analyzes the origins and persistence of musical traditions , gender equality in music life , and education for sustainable development . Led projects at the Centre for Environment and Sustainability (2012-2014) and developed web-based educational toolboxes. Key Contributions: Publications on Beethoven's canonization in Swedish education (2004), gender dynamics in performing arts (2006), and critical analyses of musicological epistemology (2005). His work bridges historical musicology , pedagogical theory , and sustainability education . Contact: Email: tobias.pettersson@gu.se Office: Eklandagatan 86, 41259 Göteborg
Glenn C. Walberg is an Associate Professor at the Grossman School of Business, University of Vermont. He holds a JD from the College of William and Mary School of Law and an LL.M. in Taxation from Georgetown University Law Center. Prior to his academic role, he worked as a senior manager in the national tax department of a Big Four accounting firm and taught federal tax accounting at the University of North Carolina at Wilmington. He currently serves on the Tax Methods & Periods Technical Resource Panel of the AICPA and teaches in continuing education programs for practicing accountants. Education: JD (William & Mary), LL.M. (Georgetown) His research focuses on accounting method and capitalization issues, with publications in journals such as Virginia Tax Review , Florida Tax Review , Tax Notes , Tax Adviser , and The Tax Lawyer . Office hours are by appointment.
Hamed Zamani is an Associate Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he also serves as Associate Director of the Center for Intelligent Information Retrieval (CIIR). He joined UMass Amherst in 2020 after working as a researcher at Microsoft. His research focuses on designing and evaluating statistical and machine learning models for information access systems, including search engines, recommender systems, and question answering. Education: PhD in Computer Science, University of Massachusetts Amherst MS in Computer Engineering, University of Tehran BS in Computer Engineering, University of Tehran Zamani's current research explores neural information retrieval, conversational search, and retrieval-enhanced machine learning. He develops efficient neural models for core IR tasks and emerging areas like conversational information seeking. His work bridges information retrieval with large language models to enhance capabilities in understanding complex queries and generating relevant responses. His recent publications demonstrate a strong focus on retrieval-augmented generation, personalized information access, and efficient neural ranking models. There's a clear trend toward integrating large language models with information retrieval systems, optimizing multi-agent frameworks, and developing evaluation metrics for generative AI applications in search contexts. Scientific Awards: NSF CAREER Award ACM SIGIR Early Career Excellence in Research & Community Engagement Awards (2023) UMass CICS Outstanding Dissertation Award Paper awards at SIGIR (2022, 2023, 2024), CIKM (2020), ICTIR (2019) Microsoft Research Award (AI and New Future of Work program) Amazon Research Award (Optimization of Retrieval-Enhanced ML Models) Zamani actively advises PhD students and postdoctoral researchers, with his students receiving prestigious awards including NSF Graduate Research Fellowships and SIGIR Best Paper awards. He leads the CIIR Talk Series, hosting IR researchers to share recent findings. His Alexa Prize TaskBot Challenge team was selected for two consecutive years, advancing task-oriented dialogue systems. He directs research at the Center for Intelligent Information Retrieval (CIIR), where he oversees projects in neural retrieval models, conversational AI, and retrieval-augmented generation. The center serves as a hub for developing next-generation information access systems with industry and academic collaborators.
Jennifer Lau, MD, is a Clinical Associate Professor of Anesthesiology at the Keck School of Medicine of the University of Southern California and serves as the Medical Director of ACCM Medical Education at Children's Hospital Los Angeles. Her work focuses on pediatric anesthesiology, simulation training, and advancing diversity, equity, and inclusion in medical education. Education: Medical School: David Geffen School of Medicine, UCLA Internship: Newton-Wellesley Hospital Residency: Massachusetts General Hospital Fellowship: Children's Hospital Los Angeles Research Interests: Dr. Lau’s research spans medical education, simulation-based training, and initiatives to promote diversity, equity, and inclusion. She specializes in pediatric anesthesiology, contributing to clinical care and educational frameworks in this field. Scientific Awards: Pasadena Magazine Top Doctor AMA Inspiration Award Professional Memberships: American Society of Anesthesiologists (ASA), Society of Pediatric Anesthesia (SPA), Society for Education in Anesthesia (SEA), American Medical Association, Alpha Omega Alpha Honor Medical Society, and former Board Member of the Pediatric Anesthesia Program Directors' Association.
Dr. Michèle Tranda-Pittion is a lecturer at the University of Geneva (UNIGE) and an architect affiliated with École Polytechnique Fédérale de Lausanne (EPFL). She specializes in the design and management of territorial projects , actively participating in the Master in Territorial Development (MDT) program by leading student support during the first territorial project workshop. Additionally, she serves as the head of the TOPOS urban planning office in Geneva, focusing on practical applications of urban planning methodologies. Research Interests: Production processes of the ordinary city Complexity in urban and regional planning Context-specific methodological tools Cross-border territorial dynamics Evolving urban planning practices
