Dr. Arpit Dua is an Assistant Professor in the Department of Physics at Virginia Tech. Previously, he held positions including a joint Simons-IQIM postdoc at Caltech under Xie Chen and a PhD at Yale University under Meng Cheng and Liang Jiang. His research focuses on theoretical quantum information systems, with emphasis on quantum error correction, topological order, and integrating machine learning principles into physics frameworks. Education: PhD in Physics from Yale University, Postdoctoral research at Caltech. Research Interests: Quantum error correction (developing novel codes using conventional and machine learning methods), thermalization in topological systems, self-correcting models, and applying physics-based insights to AI architecture design. His current projects explore fault-tolerant protocols, fracton orders, and Floquet codes. Publications reflect contributions to topological codes, subsystem symmetries, and fracton physics. His work bridges quantum information theory with condensed matter physics.
Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.
Prof. Maja Horst is a Visiting Professor of Science and Technology Policy at the Department of Science, Technology and Society (STS) at Technical University of Munich. She holds a PhD in Science Communication from Copenhagen Business School (2003) and has held academic roles including Head of the Department of Media, Cognition and Communication at the University of Copenhagen (2011–2014) and Professor of Responsible Technology at DTU Technical University of Denmark (2019). Her research focuses on public communication of science, societal responsibility in technology, and innovation governance. She chairs DTU’s Responsible Technology group and is a member of the Danish Academy of Technical Sciences. Key roles include Chair of the Danish Independent Research Fund Board and President of the European Association for the Study of Science and Technology (EASST). Education: MA in Social Science and Communication, Roskilde University (1996) PhD in Science Communication, Copenhagen Business School (2003) Research Interests: Public communication of controversial technologies Social responsibility in science and technology Innovation governance and ethics Sociology of science and technology Recent Focus: Her work emphasizes interactive installations for science communication and co-creation in energy transitions. Over 20+ publications address topics like gene editing ethics, public debate frameworks, and trust in science. Awards: 2021: Hans Fischer Senior Fellowship (TUM-IAS) 2019: Danish Science Minister’s Communication Prize Leadership & Service: President, European Association for the Study of Science and Technology Chair, Danish Independent Research Fund Board
Thomas Piketty is a Professor of Economics and Economic History at the École des Hautes Études en Sciences Sociales (EHESS) and the Paris School of Economics. He is co-director of the World Inequality Lab and the World Inequality Database, and a leading scholar on economic inequality and historical political economy. Born on May 7, 1971, in Clichy, France. PhD in Economics from EHESS and LSE (1990–1993). Professor at MIT (1993–1995), Research Fellow at CNRS (1995–2000), and Director of Studies at EHESS since 2000. His research focuses on the interplay between economic development, wealth distribution, and political institutions. Key works include Capital in the 21st Century (2014), Capital and Ideology (2020), and A Brief History of Equality (2022). Piketty has pioneered methods to measure long-term inequality using historical data. Publications span articles in top journals like the Quarterly Journal of Economics, Journal of Political Economy, and American Economic Review. His work emphasizes fiscal policies, wealth taxation, and global inequality trends. He co-founded the World Inequality Database (WID.world) to track income and wealth disparities worldwide. Distinctions include the Yrjö Jahnsson Prize (2013), CNRS Bronze Medal (2001), and Best Young Economist Award (2002). He has advised governments and contributed to policy debates on tax reform, climate justice, and European democratization.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Özgür Atalay (ORCID: 0000-0003-1050-0685) is an Associate Professor at the Department of Textile Engineering , Istanbul Technical University . His research focuses on Textile-based Engineering with emphasis on Capacitive , Actuator , and Soft Robotics Engineering . Current projects include TexSoRVA (Textile-Based Soft Robotics for VR Applications), SMARTWASTE (Agricultural/Natural Waste Textiles), and TEXWEAROTS (Self-powered Wearable Soft Robotics). Collaborations span EU-funded initiatives, Turkish Scientific and Technological Research Council (TUBITAK), and industrial partnerships. His work integrates Internet of Things (IoT) with Electronic Textiles (E-textiles) , focusing on applications in Telerehabilitation , Wearable Robotics , and Smart Textiles . Key technologies include Capacitive Sensors , Conductive Yarns , and Fog Computing Frameworks . Recent publications highlight advancements in auxetic sensor design , cloud-controlled textile gloves , and self-powered wearable systems . His research output (58 publications) demonstrates a focus on practical implementations in Therapeutic Applications and Human-Machine Interaction . Özgür leads 6 active research projects, including: TexSoRVA : 04/2025-09/2026, EU-funded SMARTWASTE : 01/2023-12/2026, TTO-funded TEXWEAROTS : 11/2022-10/2027, EU-funded With over 1623 citations (h-index 15), his work contributes to Textile Electronics , Soft Robotics , and Wearable Technology fields.
Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.
Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling
Amilcare Porporato is the Thomas J. Wu '94 Professor of Civil and Environmental Engineering at Princeton University, with joint appointment at High Meadows Environmental Institute. His research integrates hydrology, ecology, and thermodynamics to study soil-water interactions and sustainable resource management. Education includes: Ph.D. Polytechnic of Milan (1996) M.S. Civil Engineering, Polytechnic of Milan (1992) Research explores nonlinear hydrologic systems, soil moisture dynamics, ecohydrology, and environmental complexity using theoretical and experimental approaches. His work advances understanding of water-biota interactions across scales from pore-level processes to landscape evolution. Honors and Awards: Hydrologic Sciences Award, AGU Borland Lecturer American Geophysical Union Fellow Inaugural Landolt Chair, EPFL Teaches courses on Environmental Thermodynamics (ENV 385/CEE 385) and Ecohydrology (CEE 587/ENV 587).
David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.
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.
Dr. Seyyed Hamed Hosseini Nasab is a Lecturer at the Department of Health Sciences and Technology at ETH Zürich, affiliated with the Institute for Biomechanics and the Laboratory for Movement Biomechanics. His research focuses on biomechanical analysis of musculoskeletal systems, particularly knee mechanics, implant design, and ligament behavior in total knee arthroplasty. He integrates experimental, computational, and clinical approaches to improve surgical techniques and prosthetic design. Key research interests include knee joint loading, ligament elongation patterns, and the influence of implant conformity on post-surgical outcomes. He has contributed to standardized methods for measuring tibiofemoral implant loads and kinematics, earning the European Society of Biomechanics SM Perren Award in 2022. His publications emphasize computational modeling, in vivo testing, and finite element analysis to address challenges in orthopedic engineering. Recent work explores artificial neural networks for real-time knee contact force estimation and the biomechanical implications of surgical procedures like posterior cruciate ligament substitution.
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.
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.
