Emiliya Lazarova is a Professor of Economics and Head of the School of Economics at the University of East Anglia (UEA). She chairs the Royal Economics Society’s Conference of Heads of Departments of Economics. Her research focuses on coalition formation, matching theory, and applied economics, with recent projects analyzing technological innovation via patent data, biodiversity market measurements, and international environmental agreements. She has held academic roles at the University of Birmingham and Queen’s University Belfast, teaching quantitative courses like Applied Econometrics and topics in applied microeconomics. Her research interests include coalition dynamics, social housing allocation, and the political economy of environmental policies. Current projects with Dr. Yuan Gao include developing an ex-ante novelty index for inventions and studying biodiversity valuation mechanisms. Lazarova has secured grants from the Royal Economic Society and British Academy, focusing on property rights and economic development in emerging economies. Her work bridges theoretical models and empirical applications, addressing issues like firm behavior under political pressure, patent innovation cycles, and disability discrimination impacts. Collaborations span institutions globally, reflecting her interdisciplinary approach to economic challenges. Advisory roles include supervising PhD students on topics such as status-seeking in matching markets and conflict resolution via coalition theory. Her teaching expertise complements her research, emphasizing quantitative methods and policy analysis.
Can Firtina is a Lecturer at ETH Zurich's Department of Information Technology and Electrical Engineering and a Senior Researcher in the SAFARI Research Group. His research focuses on accelerating genome analysis through algorithm-architecture co-design, particularly leveraging hardware-software integration for bioinformatics workloads. He holds a PhD in Electrical and Computer Engineering from ETH Zurich and degrees from Bilkent University. As of Fall 2025, he will join the University of Maryland, College Park (UMD) as an Assistant Professor of Computer Science. Education: PhD in Electrical and Computer Engineering (D-ITET), ETH Zurich MSc in Computer Engineering, Bilkent University BSc in Computer Engineering, Bilkent University Research Interests: His work bridges bioinformatics and computer architecture, emphasizing real-time, accurate, and energy-efficient genome analysis. Key areas include raw nanopore signal processing (e.g., RawHash, Rawsamble), hardware-software co-design for bioinformatics, and scalable metagenomic analysis. His algorithms address noise mitigation and accelerate applications like assembly polishing (Apollo) and alignment remapping (AirLift). Labs & Collaborations: He leads research within the SAFARI Group, collaborating with institutions like NVIDIA, AMD, and Huawei. His contributions span tools like GenASM (approximate string matching) and BLEND (fuzzy seed matching). He also organizes workshops on bioinformatics acceleration and serves on review boards for venues like ISMB and RECOMB. Future Directions: Future work includes end-to-end raw signal analysis without basecalling, reference-free genome assembly, and leveraging emerging hardware for real-time field applications. He will expand these efforts at UMD, hiring students in Fall 2025.
Yuanzhu Chen is a Professor in the School of Computing at Queen’s University, affiliated with the Faculty of Arts and Science. He previously served as Professor and Department Head at Memorial University of Newfoundland (2005–2021). His research focuses on computer networking, mobile computing, complex networks, and applied machine learning, emphasizing wireless innovation beyond traditional wired systems. He holds a PhD from Simon Fraser University (2004) and a B.Sc. from Peking University (1999). Education: PhD in Computing Science (Simon Fraser University, 2004); B.Sc. in Computer Science (Peking University, 1999). Earlier roles include Post-doctoral Researcher at Simon Fraser University (2004–2005) and leadership positions at Memorial University, including Department Head (2019–2021). Research Interests: Network Coding and Opportunistic Routing Mobile and Wireless Network Protocols Complex Network Analysis Machine Learning Applications Indoor Positioning Systems Social Network Dynamics Selected Awards: Recipient of Queen’s University President's Award for Distinguished Teaching. Lab Affiliation: Director of the Wireless Networking and Mobile Computing Lab (WineMocol). Active in collaborative projects involving smartphone sensors, community-based environmental monitoring, and stock market prediction using web data.
Todd Collins is a Professor of Political Science and Public Affairs at the University of Western Carolina’s College of Arts and Sciences. He holds a Ph.D. in Political Science from the University of Georgia, a JD from the University of North Carolina at Chapel Hill, and a BA from the same institution. His research focuses on judicial decision-making, legal system biases, and the intersection of religion, politics, and policy. Key research interests include racial and gender disparities in legal contexts, media’s role in shaping public perception of courts, and religious influences on legislative behavior. Collins has extensively studied Supreme Court dynamics, appellate processes, and the interplay between public opinion and judicial rulings. His work bridges empirical legal studies with sociological and political frameworks, addressing issues such as courtroom fairness, attorney collaboration barriers, and the impact of religious affiliations on policymaking. Notable projects include analyses of case salience metrics and the role of precedent in judicial reasoning.
Titu Andreescu is a retired Associate Professor in the Science/Mathematics Education Department at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds a Ph.D., M.S., and B.A. in Mathematics from the University of West Timisoara, Romania. His research focuses on enhancing mathematics education through problem-solving initiatives, particularly via AwesomeMath—a program encompassing summer camps, correspondence courses, and journals for gifted students. He specializes in Diophantine Analysis, particularly quadratic equations and their applications in advanced mathematics. Andreescu has authored numerous publications, including textbooks like Complex Numbers from A to Z and contributed to Olympiad problem design for competitions such as the IMO and W.L. Putnam. He has received prestigious awards, including the Edith May Sliffe Award and led the U.S. IMO team to historic victories, including a first-place finish in 1994. His professional roles include directing the Mathematical Olympiad Summer Program (MOSP) and serving as a coach for national teams in Romania and the U.S. Key projects include keynote speaking at international math camps and consulting for competitions. He has secured grants totaling over $800,000 for mathematics education initiatives, including funding from Akamai Technologies and the U.S. Army Research Office.
Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.
Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.
Miguel Mujica Mota is a Senior Lecturer at the Faculty of Technology, National Autonomous University of Mexico (UNAM), and a member of the Centre of Applied Research Technology. His research focuses on airport operations, multimodal transport systems, and simulation modeling. He has expertise in analyzing capacity challenges in multi-airport systems, particularly in Mexico City, and developing decision support systems for airport security and resource allocation. His work integrates sustainability and efficiency, addressing topics like environmental reporting in airlines and post-pandemic airport recovery strategies. Research Contributions: Dr. Mujica Mota has published extensively on airport capacity optimization, multimodal transport integration, and simulation-based methodologies. Key projects include the X-TEAM D2D initiative for door-to-door travel and the IMHOTEP project for smart passenger flow management. His work often involves collaboration with institutions like Schiphol Airport and the H2020 EU framework. Research Interests: Airport terminal design, air traffic management, simulation modeling, multimodal logistics, and sustainable aviation. Awards: A-BOOST Research Fund (2020) Beste paper award EMM2018 X-TEAM D2D Project Recognition (2020) Activities: Organized conferences like the 2023 EUROSIM Simulation Seminar and served on committees for events such as the 2024 Multilog Conference. Grants & Projects: Involved in EU-funded initiatives like H2020, focusing on multimodal integration and sustainable transport solutions. His research also explores climate change impacts on infrastructure and simulation-based validation approaches.
Elizabeth Lemmon is a Research Fellow within the Health Economics Group of the Edinburgh Clinical Trials Unit at the Usher Institute, University of Edinburgh. Her work focuses on applying econometric methods to healthcare and social care data, particularly in the context of aging populations and long-term care provision. PhD in Economics, University of Stirling (2019) MSc in Economics, University of Edinburgh (2014) BA Hons in Economics, University of Stirling (2013) Her research spans applied econometric analysis of survey and administrative data, economic aspects of aging, unpaid care dynamics, long-term care provision, health and care resource utilization at end-of-life, and policy implications derived from data-driven insights. A key component of her work involves leveraging Scottish and English national health data repositories to evaluate cancer care costs, screening efficiency, and treatment outcomes. Recent publications highlight her expertise in analyzing colorectal cancer economics, end-of-life hospital cost trajectories, and long-term care vulnerabilities during pandemics. She has contributed to the development of the national CORECT-R data repository and has explored international comparisons of care home mortality during the COVID-19 crisis. Elizabeth is actively engaged in public and patient involvement initiatives, ensuring her research informs both policy and clinical practice through the integration of administrative datasets.
Dr. Emre Alp is an Associate Professor at the Environmental Engineering Department of Middle East Technical University (METU) , where he has served since 2014. Previously, he held roles as Assistant Professor (2011-2014), Instructor (2008-2011), and Part-time Instructor (2008). His research spans Watershed Management , Water-Energy-Food Nexus , and Environmental Economics , with a focus on Diffuse Pollution and Water Quality Modeling using GIS and Remote Sensing . Education : Ph.D. in Civil and Environmental Engineering (2006), M.S. in Environmental Engineering (1999), and B.S. in Environmental Engineering (1997) from METU and Marquette University. Research : His work integrates Environmental Risk Assessment with optimization models like SWAT for nutrient load analysis, economic valuation of ecosystems, and urban waterway pollution control. Key projects include Sustainable Stormwater Management on METU campus and Water-Energy Nexus studies in Turkey. Publications : Over 14 refereed journal articles since 2007, focusing on Urban Hydrology , Environmental Economics , and Watershed Modeling . Awards : Post-Doctoral Scholarship (Marquette University, 2006-2008) and Research/Teaching Assistantships (Marquette, 2000-2006). Students : Supervised 2 Ph.D. and 7 M.S. theses on topics like climate change impacts, water allocation, and pollution control.
Yong-Bin Kang is a Senior Data Science Research Fellow at the ARC Centre of Excellence for Automated Decision Making and Society (ADM+S) at Swinburne University of Technology, affiliated with the School of Social Sciences, Media, Film and Education. He holds a PhD in AI from Monash University and leads numerous transdisciplinary research projects applying artificial intelligence to address complex societal challenges. Education: PhD in Faculty of IT, Monash University, Australia Dr. Kang's research focuses on Responsible AI and Society, with specific interests in developing Societal-AI platforms that integrate social data with ethical principles. His work spans healthcare, humanitech, education, financial planning, environmental health, and justice domains. He investigates how AI can enhance decision-making processes while promoting societal well-being, with particular attention to ethical implementation and human-centered approaches. His expertise encompasses AI, natural language processing, machine learning, and decision-making optimization. Analysis of Dr. Kang's recent publications reveals a strong trajectory toward socially responsible AI applications across diverse domains. His work consistently bridges technical AI capabilities with social implications, particularly focusing on ethical frameworks, community-centered design, and addressing societal inequalities through technology. The publications demonstrate increasing collaboration across disciplines including criminology, environmental science, mental health, and education. Dr. Kang is actively involved in significant research funding initiatives, with multiple ongoing projects that address critical societal challenges through AI. His supervision availability includes Doctorate (PhD) candidates, indicating his commitment to mentoring the next generation of researchers in AI and data science fields. Current Flagship Areas: Digital Capability Innovative Society Manufacturing Futures Sustainable Development Goals: Good Health and Well Being (SDG 3) Industry, Innovation and Infrastructure (SDG 9) Affordable and Clean Energy (SDG 7)
Maroš Servátka is a Professor at the Department of Finance, Faculty of Economics and Finance, University of Economics in Bratislava. He previously held academic positions at Macquarie Graduate School of Management (2015–2024), University of Canterbury (2007–2015), and University of Mannheim (2006). His research spans experimental economics, behavioral economics, game theory, and finance, with a focus on decision-making, procrastination, market design, and trust. Education: PhD in Economics, 2015, Macquarie Graduate School of Management PhD in Economics, 2006, University of Arizona Master’s in Quantitative Methods and Information Systems, 2000, School Glówna Handlowa Research Interests: Servátka’s work explores behavioral and experimental economics, particularly how psychological factors and institutional designs affect economic decisions. He has investigated topics like tax compliance, charitable giving, project scheduling accuracy, and market equilibrium adjustments. Article Trends: His recent publications (2021–2023) emphasize behavioral nudges, scheduling efficiency, and institutional designs to mitigate economic inefficiencies. Earlier works focus on game theory, fairness, and market mechanisms. Grants and Projects: Commonwealth Bank of Australia (2022–2026): Financial Decision Making of Young Australians VEGA (Slovak Ministry, 2021–2025): Psychological Nudges and Tax Compliance Slovak Research and Development Agency (2020–2024): Evidence-Based Policy Making Czech Science Foundation (2020–2022): Unintended Consequences of Promotions IFREE (2019–2020): Natural-Resource Windfalls and Accountability Organizational Experience: He is a member of the University of Alaska Anchorage Experimental Economics Laboratory (since 2019), FAME Council (Group for Research in Applied Economics, since 2020), the Slovak Economic Association (since 2013), and serves on the editorial board of the Journal of Behavioral and Experimental Economics (since 2013).
Jerry Zeyu Gao is a Professor in the Department of Computer Engineering at San Jose State University, part of the Charles W. Davidson College of Engineering. He maintains active office hours and has a strong presence in both academic and industry domains, combining over 15 years of academic experience with more than 10 years in software engineering and IT development management. His research spans a wide range of cutting-edge areas in computing, including: Cloud Computing and Services Software as a Service (SaaS) and Testing as a Service (TaaS) Test Automation Mobile Computing and Mobile Cloud Technologies Software and Service Engineering Mobile Sensor Technologies Smart Cities infrastructure Dr. Gao has published over 180 papers in top-tier IEEE and ACM journals and conferences, and has co-authored three technical books while editing several others in software engineering and mobile computing. His scholarly output reflects a strong focus on practical, scalable solutions in cloud, mobile, and service-oriented systems. The publications show consistent themes in automation, service delivery, distributed architectures, and real-world software validation. He has played a leadership role in the international research community, having served as conference chair, program co-chair, and workshop co-chair for numerous prestigious events such as IEEE MobileCloud, IEEE SOSE, SEKE, and others between 2004 and 2015. These roles highlight his influence and recognition in the software engineering and cloud computing communities. Dr. Gao advises students and contributes to graduate and undergraduate education, although specific advisees are not listed. He is involved in research projects and likely secures external funding given his publication and conference leadership activities, though specific grants are not mentioned. He is associated with research initiatives related to mobile systems, cloud services, and smart city technologies.
Professor Chun-Hung Chen is a distinguished academic at George Mason University ’s Volgenau School of Engineering , where he holds the rank of Professor in the Department of Systems Engineering and Operations Research . He has also held professorships at National Taiwan University and visiting roles at institutions like University of Pennsylvania and Microsoft Research Asia . Education: PhD in Decision and Control, Harvard University (1994) MS in Electrical Engineering, National Taiwan University (1989) BS in Control Engineering, National Chiao-Tung University (1987) Research Interests focus on Stochastic Simulation Optimization , particularly his pioneering Optimal Computing Budget Allocation (OCBA) methodology. OCBA enhances simulation efficiency by dynamically allocating computational resources to critical design alternatives, reducing computation time by orders of magnitude. Applications span air transportation , healthcare , power grids , and semiconductor manufacturing . His 15 most recent articles (2022–2025) explore intersections of simulation optimization , artificial intelligence , reinforcement learning , and personalized medicine , emphasizing computational efficiency and stochastic systems in domains like microgrids and organ transplant logistics . Scientific Awards include: IEEE Fellow (2015) K.D. Tocher Medal (2017) Best Paper Awards at IEEE CASE (2019), LOGMS (2019), and IEEE ICC (2021) Harvard’s Eliahu I. Jury Award (1994) Advisory roles include editorial leadership in IIE Transactions , Journal of Simulation , and IEEE Transactions series. He has coordinated graduate programs at George Mason (2006–11, 2015–19) and led conferences like INFORMS International Meeting (2025) and Harvard Control Workshop (2024). His work is funded by organizations such as the National Science Foundation , National Institutes of Health , and Department of Energy , with applications in healthcare logistics and microgrid control .
Avi Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study, Princeton. He is a leading authority in theoretical computer science, particularly computational complexity theory. Wigderson organizes the Computer Science and Discrete Mathematics (CSDM) program at the Institute, fostering interdisciplinary research at the intersection of mathematics and computer science. Wigderson earned his Ph.D. (1983), M.A. (1982), and M.S.E. (1981) from Princeton University. Prior to his current position, he held appointments at The Hebrew University of Jerusalem (1986-2003), Princeton University (1990-1992), Mathematical Sciences Research Institute, Berkeley (1985-1986), IBM Research (1984-1985), and University of California, Berkeley (1983-1984). Wigderson's research spans computational complexity theory, randomness and computation, algorithms and optimization, circuit complexity, proof complexity, quantum computation and communication, and cryptography. His work explores fundamental questions like whether mathematical creativity can be automated (P vs NP problem), the security of electronic commerce, the role of randomness in computation, and the potential of quantum mechanics to enhance computation. He has made significant contributions to understanding the power and limitations of efficient computation. Analysis of Wigderson's recent publications reveals a strong focus on optimization, complexity theory, and their mathematical foundations. His work connects diverse areas including non-commutative algebra, geometric complexity, graph theory, and quantum computing. A recurring theme is exploring whether fundamental computational problems like P vs NP can be addressed through optimization techniques such as gradient descent. His research shows increasing interdisciplinary connections between theoretical computer science, mathematics, and physics. ACM A.M. Turing Award (2023) Abel Prize (2021) Donald E. Knuth Prize (2019) Gödel Prize (2009) American Mathematical Society's Levi L. Conant Prize (2008) Rolf Nevanlinna Prize (1994) Yoram Ben-Porat Presidential Prize for Outstanding Researcher (1994) Bergman Fellowship (1989) Member, American Academy of Arts and Sciences Member, National Academy of Sciences While specific details about Wigderson's students are not provided in the source material, his extensive lecture series, workshops, and program organization suggest significant mentorship activities. His book "Mathematics and Computation" published by Princeton University Press serves as an educational resource for students and researchers. Wigderson has organized major programs at the Institute for Advanced Study including "Lower Bounds in Computational Complexity" (2018) and "Pseudorandomness" (2017), creating research opportunities for numerous scholars. Wigderson leads the Computer Science and Discrete Mathematics (CSDM) program at the Institute for Advanced Study, which brings together researchers from mathematics and computer science to explore fundamental questions in computation. His work with collaborators across multiple institutions has established connections between theoretical computer science and diverse fields including quantum information theory, algebraic geometry, and optimization. Recent projects focus on non-commutative optimization and its applications to computational complexity problems.
