Xiaotian Zheng is an Assistant Professor of Statistics at the University of Georgia. Previously, they were a Postdoctoral Research Fellow with the Australian Research Council Special Research Initiative Securing Antarctica's Environmental Future at the University of Wollongong, working under Professor Noel Cressie and Associate Professor Andrew Zammit-Mangion. They earned their Ph.D. in Statistical Science from the University of California, Santa Cruz, advised by Professors Athanasios Kottas and Bruno Sansó. Their research focuses on developing statistical and machine learning methods for analyzing complex, dependent data, particularly in ecological and environmental contexts. Key areas include spatial/spatio-temporal statistics, probabilistic downscaling, data integration, transfer learning, and statistical deep learning. Xiaotian's publications reflect their work on mixture transition distribution models, nearest-neighbor mixture models, and geostatistical frameworks for discrete-valued processes. These contributions emphasize Bayesian inference, computational efficiency, and real-world applications in environmental science and biodiversity modeling.
Wajih Ul Hassan is an Assistant Professor of Computer Science at the University of Virginia (UVA), affiliated with the School of Data Science. He leads the DART Lab, focusing on system intrusion detection, forensic investigation, and cybersecurity through machine learning and data provenance techniques. His research has earned prestigious awards including the NSF CAREER Award (2024) and Symantec Research Labs Graduate Fellowship. Dr. Hassan holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (2021). He has collaborated with NEC Labs and Symantec Research Labs to integrate defensive strategies into commercial security products. His work emphasizes practical solutions for networked systems and scalable security mechanisms. He teaches courses such as CS 6501: Machine Learning in Systems Security, CS 4630: Defense Against the Dark Arts, and DS 6559: Machine Learning in Systems and Network Security. His research interests span intrusion detection, provenance analytics, and automated threat response, with over 20 peer-reviewed publications since 2017. Award Highlights: NSF CAREER Award (2024) ACM SIGSOFT Distinguished Paper Award Young Researcher at Heidelberg Laureate Forum Service Activities: He has served on program committees for top-tier conferences (e.g., IEEE S&P, ACM CCS) since 2018 and reviewed grants for the NSF and Commonwealth Cyber Initiative. Labs & Teams: The DART Lab at UVA focuses on advancing enterprise security through data-driven approaches. Dr. Hassan actively recruits students for research projects in cybersecurity and systems security.
Saqib Javed is a doctoral researcher and Researcher at the Computer Vision Laboratory (CVLab) at EPFL, supervised by Prof. Pascal Fua and Dr. Mathieu Salzmann. His research focuses on energy-efficient deep networks, 3D reconstruction, quantization-aware training, and domain generalization. He holds a master’s degree from TU Munich and ETH Zurich. He is affiliated with the School of Computer and Communication Sciences (IC) and the Department of Computer Science at EPFL. His current projects include compressed Gaussian splatting for dynamic scenes, quantized diffusion models, and reducing inference time for vision-language models. He has been awarded the EPFL IC Distinguished Service Award and is a Global Leaders PhD Fellow. His work spans theoretical research and practical applications in low-power device optimization. Teaching roles include serving as a Teaching Assistant for courses like Introduction to Machine Learning (CS-233) and Probability and Statistics (MATH-232). He has supervised multiple students, including Chengkun Li and Ahmad Jarrar Khan, on projects related to quantization and 3D pose estimation. His research also involves collaborations with industry partners like BMW, Siemens, and Intel, focusing on hardware-friendly neural networks. Awards include the EPFL IC Distinguished Service Award (2024) and recognition for his contributions to efficient deep learning. His recent publications address domain generalization, Gaussian splatting, and modular quantization techniques, reflecting his interdisciplinary approach to advancing machine learning efficiency and applicability.
David Thesmar is the Franco Modigliani Professor of Financial Economics and Professor of Finance at MIT Sloan School of Management. He holds a BA in physics and economics from École Polytechnique and a PhD from the Paris School of Economics. Thesmar has served on the council of economic advisors to the French prime minister (2007-2013) and is a scientific adviser to the European Systemic Risk Board. His research examines corporate finance, financial intermediation, entrepreneurship, and behavioral economics, with recent focus on financing constraints and systemic risk in banking. He has received the Brattle Group Distinguished Paper Prize for his contributions to financial economics.
Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.
Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.
Agostino Capponi is a Professor of Industrial Engineering and Operations Research at Columbia University, affiliated with Columbia Engineering and the Data Science Institute (DSI). He holds academic fellowships at the Luohan Academy (Alibaba Group) and the Fintech@Cornell Center. His research focuses on systemic risk, financial technology, blockchain economics, and machine learning applications in finance. He has authored a best-selling book on machine learning in financial markets and received prestigious awards including the NSF CAREER Award and the JP Morgan AI Faculty Research Award. Education: Master's and PhD in Computer Science and Applied & Computational Mathematics from Caltech (2006-2009). Professional roles include Editor of Management Science , co-editor of Mathematics and Financial Economics , and leadership positions in the Bachelier Finance Society and INFORMS Finance Section. His research has been funded by NSF, DARPA, J.P. Morgan, Ethereum Foundation, and others. Research interests span blockchain governance, decentralized finance protocols, and systemic risk mitigation in financial networks. Notable contributions include work on liquidity risk, crypto-economic systems, and causal inference in financial modeling. Media coverage includes American Banker, Vox, and Chicago Booth Review. He holds a patent in military network tracking and served as a visiting scholar at the Federal Reserve Bank of New York.
Luis Amaral is an Associate Professor at the School of Engineering, University of Minho, where he has served since 1998. He holds a PhD in Computer Science (Information Systems) from the University of Minho (1995) and has extensive experience in teaching, research, and administrative leadership. His research focuses on Information Systems in social and organizational contexts, particularly in public administration, with over 400 publications including books, journal articles, and conference papers. Key roles include Vice-Rector for Organizational Transformation and Administrative Simplification, Director of the Information Systems Department (2005–2006, 2010–2012), and leadership in projects like the Virtual Campus (e-UM). He has held numerous administrative positions, including President of the School of Engineering’s Council (2013–2016) and Pro-Rector (2006–2009). His work emphasizes e-government, digital transformation, and public procurement systems. Recent research trends include digitalization of public services, regulatory compliance (e.g., GDPR in higher education), and optimization of renewable energy systems. He has contributed to international initiatives such as the ICEGOV conference and projects in Mozambique and Timor-Leste. His publications highlight innovation in administrative processes, citizen engagement, and institutional digital readiness. Luis Amaral has coordinated postgraduate programs, including the Master’s in Information Systems (2001–2002, 2016–2017), and led research centers like IDITE Minho. His career reflects a balance between academic rigor and practical impact in technology-driven governance and organizational change.
Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.
Dr. Vagelis Papalexakis is an Associate Professor and Ross Family Chair in the Computer Science & Engineering Department at the University of California, Riverside. His research focuses on data science, machine learning, and tensor methods, with applications in multi-aspect/multi-modal data analysis. He holds a Ph.D. from Carnegie Mellon University and a Diploma/M.Sc. from the Technical University of Crete. Affiliations: Ross Family Chair, Bourns College of Engineering, UCR Education: Ph.D. in Computer Science, Carnegie Mellon University M.Sc./Diploma in Electronic & Computer Engineering, Technical University of Crete His work emphasizes interpretable insights from complex datasets, including tensor-based defenses against adversarial attacks, graph representation learning, and scalable algorithms for high-dimensional data. Notable awards include the NSF CAREER Award (2021), IEEE DSAA Next Generation Award (2021), and ICDM Tao Li Award (2022). Grants include NSF funding for railway safety (CISE MSI: RPEP CPS), USDOT transportation research, and NVIDIA GPU grants. He leads projects in AI ethics, misinformation detection, and gravitational wave analysis. His lab collaborates with industry (e.g., Cisco, Instacart) and national labs (e.g., Lawrence Livermore).
Lirong Xia is a Professor of Computer Science at Rutgers University - New Brunswick and Deputy Director of DIMACS (Center for Discrete Mathematics and Theoretical Computer Science). He holds a Ph.D. in Computer Science from Duke University, an M.A. in Economics from Duke, and a B.E. in Computer Science and Technology from Tsinghua University. His research focuses on the intersection of artificial intelligence, machine learning, and social choice theory, addressing challenges in voting systems, fair division, privacy, and multi-agent systems. Key research areas include algorithmic fairness, computational social choice, and mechanism design. Recent work explores equitable voting rules, privacy-preserving mechanisms, and strategic behavior analysis. Notable publications include advancements in computational social choice and privacy in voting systems. Xia has been recognized with prestigious awards such as the NSF CAREER Award and IEEE’s “AI’s 10 to Watch.” Education: Ph.D. Computer Science, Duke University (2011) M.A. Economics, Duke University (2010) B.E. Computer Science and Technology, Tsinghua University (2004) Awards: NSF CAREER Award Simons-Berkeley Research Fellowship 2018 Rensselaer James M. Tien’66 Early Career Award IEEE Intelligent Systems “AI’s 10 to Watch” Advising: Supervised over 30 students, including PhDs and master’s candidates in AI, algorithms, and social choice theory.
Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.
Damon Clark is an Associate Professor (with tenure) in the Department of Economics at the University of California, Irvine, within the School of Social Sciences. He is also affiliated with several prestigious research institutions, including the National Bureau of Economic Research (NBER), IZA Institute of Labor Economics, and the Institute for Fiscal Studies (IFS) in London. Research Interests: His primary research focuses on the economics of education, with additional expertise in labor economics and public economics. His work explores school choice, educational policy, the signaling value of credentials, and the long-term impacts of education on health and economic outcomes. He employs rigorous empirical methods, including field experiments and quasi-experimental designs, to evaluate educational reforms and policies. The most recent publications reflect a consistent focus on education policy evaluation, school effectiveness, peer effects, and human capital formation. His research often uses large-scale administrative datasets and natural experiments to identify causal effects, contributing significantly to debates on equity, accountability, and efficiency in education systems. Scientific Awards and Honors: UC Irvine Faculty Mentoring Award (2015–2016) Excellence in Refereeing Award, Journal of the European Economic Association (2003) Excellence in Refereeing Award, American Economic Review (2012) Excellence in Refereeing Award, Quarterly Journal of Economics (2011) National Academy of Education/Spencer Post-Doctoral Research Fellow (2007–2008) European Economic Association Young Economist Award (2005) Advising and Grants: While specific student names are not listed, his role as a tenured associate professor and principal investigator on multiple grants indicates active mentoring of graduate students. He has secured significant external funding from agencies such as the National Institutes of Health (NIH), the Institute of Education Sciences (IES), the WT Grant Foundation, and the Nuffield Foundation, supporting research on test-based retention, school access, and intergenerational education transmission. Labs and Research Teams: Clark collaborates extensively with researchers at institutions like NBER, IZA, IFS, and universities across the U.S. and Europe. His work is often conducted through collaborative research networks rather than a single lab, reflecting the interdisciplinary and policy-oriented nature of his scholarship.
José António Ferreira Machado is a Full Professor at the Nova School of Business and Economics, Universidade Nova de Lisboa. He currently serves as Vice-Rector of the university and previously held director roles at the Nova School of Business and Economics (2005-2015) and Angola Business School (2010-2015). His academic career includes consultancy at the Bank of Portugal (1992-2015) and teaching Econometrics, Statistics, and Macroeconomics. Research Interests: Machado's work focuses on Econometrics, Quantile Regression, Wage Distributions, Firm Size Analysis, and Macroeconomic Modeling. His most cited paper (2005) introduced counterfactual decomposition methods for wage distribution analysis. Recent publications examine quantile regression extensions, trade margins, and moment-based statistical inference. His research spans both theoretical and applied economics, with collaborations including J. M.C. Santos Silva and Roger Koenker.
H. Scott Asay is an Associate Professor of Accounting at the Tippie College of Business, University of Iowa, where he holds the Tippie Children Professorship in Accounting and serves as Director of the RSM Institute of Accounting Education and Research. He received his PhD and MS from Cornell University's Samuel Curtis Johnson Graduate School of Management. Research Interests: His research focuses on financial accounting, disclosure practices, and the judgment and decision-making processes of managers and investors. He investigates how narrative disclosures, presentation formats, and information environments influence financial reporting and investor behavior. His work often employs experimental methods to test behavioral theories in accounting contexts. The recent publications of H. Scott Asay span top journals such as The Accounting Review , Journal of Accounting and Economics , and Journal of Management Accounting Research . The articles reflect a consistent theme in behavioral accounting, with strong emphasis on disclosure format, investor cognition, managerial communication, and information asymmetry. Key trends include the psychological effects of numerical precision, platform design on investor perception, and strategic use of language in financial reporting. Scientific Awards & Honors: Outstanding Reviewer Award - Contemporary Accounting Research, 2024 Midyear Meeting Connecting to Practice Award - American Accounting Association, FARS, 2024 ABO Outstanding Service Award - American Accounting Association, 2021 David and Lois Gardner Faculty Award for Mid-Career Excellence - Tippie College of Business, 2020 Doctoral Fellowship - Deloitte Foundation, 2012 Advising and Service: While no formal advisees are listed, he frequently collaborates with junior researchers and PhD students. He has held significant editorial roles, serving on the boards of Contemporary Accounting Research , The Accounting Review , and Journal of Financial Reporting , indicating strong engagement with the academic community and mentorship through peer review. Labs and Research Centers: As Director of the RSM Institute of Accounting Education and Research, he leads initiatives focused on advancing accounting education and supporting empirical and experimental research in accounting. This role underscores his commitment to academic development and institutional leadership in the accounting discipline.
