Abolfazl Hashemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, directing the MINDS Group. He holds a B.Sc. from Sharif University of Technology (2014), and M.S.E. and Ph.D. degrees from The University of Texas at Austin (2016, 2020). His research focuses on Large-Scale Optimization for AI/ML, Learning at the Edge, and Decision-Making under Uncertainty, with applications in Federated Learning, Medical Image Analysis, and Cyber-Physical Systems. He leads the MINDS Group and collaborates with EnCORE and ICON centers. Key research areas include optimizing algorithms for machine learning, robustness in distributed systems, and adversarial learning. He has developed algorithms with mathematical guarantees for efficient deployment under resource constraints. Teaching includes Optimization for Deep Learning (graduate) and undergraduate courses like ECE 20001. He advises the Purdue RoboMaster robotics team and has mentored students through programs like SURF and Summer Stay Scholars. Outreach activities include fostering diversity through robotics competitions and research fellowships. His work bridges theoretical optimization with practical AI applications, emphasizing equitable and robust solutions in federated and decentralized learning.
Yung-Hsiang Lu is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on mobile/cloud computing, energy-efficient computing, and image/video processing. He holds a BSEE from National Taiwan University (1992), an MSEE (1996), and a PhD (2002) from Stanford University. Dr. Lu's academic background includes significant contributions to VLSI and circuit design, with primary emphasis on computer engineering. His work spans theoretical and applied domains, including optimizing neural networks for edge devices, securing deep learning models, and leveraging large language models for software development. Recent research trends in his articles emphasize energy efficiency in AI systems, interdisciplinary applications of transformers (e.g., music analysis), and challenges in model interoperability and security. His publications also highlight innovations in global camera networks and real-time visual data analysis. While no specific grants or awards are explicitly mentioned, his extensive list of publications reflects sustained academic engagement. His educational contributions include developing C programming resources and teaching large-scale image processing using global camera networks. Dr. Lu's professional address is at Purdue's Materials and Electrical Engineering Building in West Lafayette, Indiana, where he maintains an active research lab focused on embedded systems and low-power computing innovations.
Ben Collier is an Assistant Teaching Professor of Business Analytics at the Tepper School of Business , Carnegie Mellon University. He holds a PhD in Information Systems and Organizational Behavior from Carnegie Mellon and has extensive experience in data science leadership roles in industry. PhD, Information Systems and Organizational Behavior (2012), Carnegie Mellon University MS, Information Systems and Organizational Behavior (2009), Carnegie Mellon University MBA, Information Systems (2007), University of Wisconsin-Madison BBA, Management Computer Systems and Mathematics (2004), University of Wisconsin-Whitewater His research focuses on data mining for business , data visualization , and large-scale experimental design , with applications in healthcare analytics, online community dynamics, and gender equity in technology. His recent work includes monetization data science for Duolingo's $6.5 billion IPO and developing UPMC's CognitiveRx analytics engine. Ben's publications span topics including gender gaps in Wikipedia , leadership in open collaboration communities , and conflict resolution in crowdsourced platforms . He has served on CMU committees for curriculum review and summer summit planning, and actively advises MSBA capstone projects.
Weining Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on probability theory, stochastic processes, stochastic networks, and queueing systems. She holds a Ph.D. in Mathematics from the University of California, San Diego (2005). Her work emphasizes fluid models for many-server queues, stochastic networks with abandonment, and reflected diffusions. Notable contributions include analyzing nonlinear Volterra equations in queueing systems, equivalence of fluid models for Gt/GI/N+GI queues, and stationary distribution characterizations for reflected diffusions. She collaborates frequently with experts like K. Ramanan and G. Pang on stochastic network dynamics and performance analysis. Recent publications (2023-2007) explore long-time limits of measure-valued equations, submartingale problems for diffusions, and diffusion approximations for input-queued switches. Her work bridges theoretical stochastic analysis with practical applications in operations research and network engineering. While no formal awards are listed, her extensive peer-reviewed publications and collaborative research highlight her contributions to stochastic systems analysis. She advises on fluid model methodologies and has contributed to ACM Sigmetrics and SIAM journals.
Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.
Jonathan Rodden is a Professor of Political Science at Stanford University and a Senior Fellow at the Hoover Institution and Stanford Institute for Economic Policy Research. His research focuses on the comparative political economy of institutions, particularly federalism and fiscal decentralization, with significant contributions to understanding the urban-rural political divide and its implications for representation and policy. PhD from Yale University BA from the University of Michigan Fulbright student at University of Leipzig His research spans several domains: Comparative Federalism: Analyzed fiscal decentralization mechanisms, budgetary transfers, and institutional design in federations globally. Political Geography: Developed computational methods for redistricting analysis and demonstrated how geographic voter distribution creates inherent electoral biases. Policy Uncertainty: Co-authored foundational work linking economic uncertainty to political polarization and fiscal cycles. Public Health: Conducted epidemiological studies on firearm-related mortality and suicide patterns in California. Scientific awards include: Gregory Luebbert Prize for best book in comparative politics (2007) Michael Wallerstein Award for best paper in political economy (2015) He has advised PhD students like Jowei Chen and directed policy projects with international institutions including the World Bank, IMF, and OECD. His work on redistricting simulations has been cited in U.S. Supreme Court amicus briefs and congressional testimony.
Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.
Vikrant Vaze is the Stata Family Career Development Associate Professor and Executive Director of the Master of Engineering Management Program at Dartmouth's Thayer School of Engineering. He leads research in transportation systems, aviation optimization, and healthcare analytics, developing data-driven solutions for complex logistics challenges. His work integrates game theory, statistical modeling, and large-scale optimization. Research spans sustainable urban mobility, airline disruption recovery, multimodal pricing alliances, and healthcare operations. Articles consistently focus on optimization algorithms for real-world transportation and healthcare systems, with recent emphasis on electric aerial mobility and pandemic-responsive logistics. Major Awards: INFORMS Aviation Applications Best Paper (2024, 2023) AGIFORS Best Innovation Award (2024) NSF CAREER Award (2018) President of India Gold Medal As founding co-director of the Operations Research Group, he collaborates with industry partners like Multivariate Systems to translate academic research into deployable solutions.
Giovanna Tinetti is a Professor of Astrophysics and Vice Dean (Research) at King's College London's Faculty of Natural, Mathematical & Engineering Sciences. She leads the European Space Agency's Ariel mission, a space telescope surveying exoplanet atmospheres, set to launch in 2029. As co-founder of the London Centre for Space Exochemistry Data and Blue Skies Space Ltd, she pioneers satellite technology for scientific data collection. She holds a PhD in Theoretical Physics from the University of Turin, with prior affiliations at Caltech/JPL, the Institute of Astrophysics in Paris, and University College London (UCL), where she was a Royal Society University Research Fellow. Her research focuses on exoplanetary atmospheres, molecular spectroscopy, and advanced data science techniques. With over 300 publications, her 2019 paper on water vapor in K2-18b's atmosphere achieved the highest altmetric score in Physical Sciences that year. She has delivered over 350 international talks and lectures. Education: PhD in Theoretical Physics (University of Turin) Affiliations: King's College London, UCL (past), ESA's Ariel Mission, Blue Skies Space Ltd Research Interests: Exoplanet atmospheres, molecular spectroscopy, space science, data-driven analysis methodologies, and atmospheric modeling. Her work bridges observational astronomy with computational chemistry to interpret exoplanet compositions and climates. Awards: Royal Society University Research Fellow Highest Altmetric Score (2019 Physical Sciences) Grants & Projects: Principal Investigator for ESA's Ariel mission Co-leader of the Ariel Data Challenge 2025 Labs/Teams: London Centre for Space Exochemistry Data, Blue Skies Space Ltd technical team, and the international Ariel collaboration network.
Michael C. Johnson is a Professor in the Department of Civil and Environmental Engineering at Utah State University (USU), affiliated with the Utah Water Research Laboratory (UWRL). He holds a PhD in Fluid Mechanics and Hydraulics from USU (1996), with research focused on hydraulic modeling, flow meter performance, valve analysis, and dam/spillway design. His work bridges physical and numerical methods, addressing challenges in water resources engineering and infrastructure. Education: PhD, Fluid Mechanics and Hydraulics (Groundwater, Hydrology, Water Resources), Utah State University, 1996 MS, Fluid Mechanics and Hydraulics, Utah State University, 1994 BS, Civil and Environmental Engineering, Utah State University, 1992 Research Interests: Johnson specializes in hydraulic structures, physical and computational fluid dynamics, flow meter calibration, and energy dissipation systems. His work emphasizes practical applications like spillway design, low-head dam safety, and turbine testing. Notable projects include the Lower Baker Dam Spillway model and Archimedes Turbine evaluations. His research often combines laboratory experiments with CFD simulations to optimize water infrastructure performance. Recent Articles Trends: His publications (2019–2025) highlight advancements in CFD modeling for pressure recovery, flow meter accuracy under disturbed conditions, and valve performance optimization. Key themes include improving meter testing protocols, mitigating turbulence effects, and enhancing hydraulic infrastructure resilience. Awards and Recognition: Outstanding Graduate Mentor, 2020 Multiple AWWA Best Paper Awards (2007, 2010, 2012) Outstanding Teacher Awards (2002–2003) Advising & Labs: Johnson has mentored over 25 graduate students, guiding research in areas like valve performance, spillway hydraulics, and meter calibration. He leads the UWRL’s hydraulics team, overseeing projects such as the Orville Dam Spillway model and large-scale physical modeling initiatives. His lab facilities support both experimental and computational approaches to water engineering challenges.
Kartik Sreenivasan is Associate Professor of Psychology and Associate Program Head for Undergraduate Studies in Psychology at New York University Abu Dhabi (NYUAD), with an affiliation in Biology. He is also a Global Network Associate Professor of Psychology, reflecting his role across NYU’s global campuses. His research is centered on the neurobiological basis of working memory and goal-directed cognition. His educational background includes a BA in Psychology from Yale University and a PhD in Neuroscience from the University of Pennsylvania under Dr. Amishi Jha. He completed postdoctoral training at UC Berkeley with Dr. Mark D’Esposito and joined NYUAD in 2014. Sreenivasan’s research focuses on how the brain maintains and manipulates information in working memory. Key interests include dynamic neural coding, feature binding, effective connectivity, and the transformation of memory into action. His lab employs fMRI, MEG, EEG, TMS, and behavioral paradigms to study both healthy and clinical populations. The most recent publications highlight trends in distributed neural systems, phase-coding mechanisms in working memory, and the role of subcortical structures. His work increasingly emphasizes network-level interactions, oscillatory dynamics, and the flexibility of memory representations under interference and attentional demands. Sreenivasan has received no explicitly mentioned scientific awards in the provided text. He actively mentors PhD students (Ying Zhou, Shanshan Li, Hannah Chu) and supervises undergraduate capstone projects. His lab has been supported by institutional funding, though specific grants are not listed. He teaches core courses such as Biopsychology, Cognitive Neuroscience, and Capstone Research in Psychology and Biology. The Sreenivasan Lab at NYUAD is a vibrant research group focused on understanding the neural underpinnings of cognition. It includes postdocs, research assistants, PhD students, and undergraduates, and has produced numerous publications in high-impact journals. The lab investigates topics such as working memory organization, neural connectivity, and the interplay between perception and memory.
Wiebke Meesenburg is an Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Thermal Energy. She is actively involved in research on large-scale heat pump systems, district heating integration, and digital twin applications for energy optimization. Her research focuses on sustainable thermal energy systems, particularly the design, monitoring, and optimization of heat pumps in district heating networks. Key areas include dynamic modeling, real-time adaptation, fouling mitigation, and the integration of renewable energy sources. She contributes to advancing energy efficiency and sustainability in urban infrastructure. The recent publications highlight a strong trend toward digitalization and optimization of thermal systems, with an emphasis on model-based monitoring, digital twins, and operation scheduling using advanced algorithms. Her work bridges mechanical engineering, energy systems, and computational modeling to improve system performance and reliability. She has supervised PhD research and contributed to major projects such as the implementation of digital twins for heat pump systems and EnergyLab Nordhavn. Collaborations involve key figures in energy research at DTU, including Professor Brian Elmegaard. While no formal awards are listed, her active participation in conferences and project leadership demonstrates recognition in her field. Wiebke Meesenburg has been involved in organizing and presenting at international events, including the 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems and workshops on Modelica and flexible heat supply. Her work is embedded in interdisciplinary teams focused on future energy infrastructures and smart urban energy systems.
Dr. Panagiotis Andriotis is a Lecturer in Computer Science at the School of Computer Science, University of Birmingham, within the College of Engineering and Physical Sciences. He is also a GIAC Certified Forensic Examiner (GCFE, GASF) and a Senior Fellow of the Higher Education Academy (SFHEA). His interdisciplinary research spans Cyber Security, Human Factors, and Mobile and Ubiquitous Computing. He teaches courses in Computer Science, Cyber Security, and Digital Forensics. His educational background includes a PhD in Computer Science from the University of Bristol (2016), an MSc with Distinction in Computer Science from the same institution (2011), and a BSc in Mathematics from the National and Kapodistrian University of Athens (2004). Dr. Andriotis’s research interests focus on user-centered security, particularly in mobile environments. He investigates how users interact with Android’s permission systems, develops novel authentication mechanisms like Bu-Dash, and explores adversarial machine learning in cybersecurity. His work bridges technical and human aspects, aiming to improve both system robustness and user experience. His recent publications reflect a strong trend in adversarial machine learning, mobile malware detection, usable privacy, and the societal implications of AI in education. He has contributed to high-impact journals such as IEEE Transactions on Cybernetics, ACM Transactions on Privacy and Security, and Elsevier’s Journal of Information Security and Applications. Best Paper Award at HCI International 2020 Impact Award, UWE Bristol Student Union GIAC Certified Forensic Examiner (GCFE) GIAC Advanced Smartphone Forensics (GASF) SANS Lethal Forensicator Coin Dr. Andriotis has advised PhD students, including Andrew McCarthy, and has been involved in funded research projects such as those related to fuzzing, software security, and critical infrastructure protection in collaboration with Airbus. He has served as an External Examiner at Cardiff Metropolitan University and is currently on the editorial boards of Digital Threats: Research and Practice (ACM) and the Journal of Responsible Technology (Elsevier). He has held visiting roles at the National Institute of Informatics in Tokyo, including as a JSPS Fellow and Toshiba Fellow. He leads research in digital forensics and security, with a lab focus on mobile ecosystems, behavioral modeling, and AI-driven threat detection. His team explores both technical and human dimensions of cybersecurity, contributing to tools and frameworks that enhance mobile security and user awareness.
Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.
Jingrui He is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology, with primary research focuses on statistical machine learning and large-scale data mining. Her work spans theoretical algorithm development and practical applications in diverse domains. University: Stevens Institute of Technology Department: Computer Science Department Academic Rank: Assistant Professor Research interests include: Heterogeneous machine learning techniques Rare category analysis and detection Social network analysis Public safety applications Traffic analytics Multimedia processing Virtual metrology in semiconductor manufacturing Publishing trends show consistent contributions to machine learning, data mining, and graph-based methods across multiple domains including semiconductor manufacturing, social networks, and multimedia. She has collaborated with researchers from Stevens Institute of Technology, IBM, and Carnegie Mellon University on both theoretical and applied problems.
