Dr. Wibowo Hardjawana is a Senior Lecturer in Telecommunications Engineering at the School of Electrical & Computer Engineering , University of Sydney. He holds a PhD from the University of Sydney and serves as an ARC DECRA Research Fellow. His research focuses on wireless network softwarisation, enabling programmable radio interfaces to address traffic elasticity in 5G/6G systems. Education : PhD (University of Sydney) Grants : ARC DP210100744 (2021), ARC DECRA DE140101114 (2014) His work spans 5G/6G network architectures , machine learning for wireless systems , and open radio interfaces . Key contributions include graph representation learning for interference management, Bayesian neural network detectors for OTFS modulation, and NOMA decoding techniques . Recent publications analyze ultra-reliable low-latency communications , UAV-enabled networks , and stochastic geometry in wireless systems . He has collaborated with institutions in China, Indonesia, and UAE, and engaged with industry partners like Telstra and Ausgrid.
Professor Jennifer Whyte is a Professor and Director of the John Grill Institute for Project Leadership at the University of Sydney's School of Project Management (Faculty of Engineering). Her research focuses on project leadership, systems integration, digital transformation in construction, and future-making practices. She previously led the School of Project Management (2021-23) and holds a retained Professorship at Imperial College London's Department of Civil and Environmental Engineering. She is a Policy Fellow of the Institution of Civil Engineers, contributing to industry policy and advisory boards such as the UK Construction Leadership Council. Education: Holds a PhD and is a Fellow (FICE) of the Institution of Civil Engineers. Her work bridges academia and industry, emphasizing practical impact through tools like digital twins and visualization technologies. Current projects include the Alan Turing Institute's Data-Centric Engineering Programme (Grand Challenge III) and the EPSRC-funded VENTURA Project's Virtual Decision Room initiative. Research interests span infrastructure projects, innovation ecosystems, and leadership in complex environments. She actively supervises doctoral students exploring areas like risk management in construction and digital transformation in public institutions. Awards include recognition for policy contributions and leadership in project-based organizations. Grants include funding for Big Data-driven stakeholder engagement in mega-projects and collaborations on new energy technologies. Her global links include Imperial College London and ongoing partnerships with institutions in the UK and Australia.
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.
Soufiene Djahel is a Professor at the Centre for Future Transport and Cities (CFTC) at Coventry University, UK. His research focuses on connected and autonomous vehicles (CAVs), unmanned aerial vehicles (UAVs), cyber security, and smart cities. He holds a PhD in Secure Routing and Medium Access Protocols from Université des Sciences et Technologies de Lille (2010), and has held academic positions including Senior Lecturer at the University of Huddersfield and Manchester Metropolitan University. His research interests include CAV coordination protocols, cyber-physical security solutions, and intelligent transportation systems. Djahel leads projects such as the £1.2M AeroPharma Logistics initiative and has secured funding from the Newton Fund and JSPS. He is a recipient of the 2021 JSPS Invitational Fellowship and has published extensively in IEEE journals and conferences. Current projects explore UAVs-as-a-service, digital twins for CAVs, and B5G/6G for smart infrastructure. He advises PhD students on topics like AI-based threat mitigation and transport electrification. Djahel also serves as an external examiner and editorial board member for journals like IEEE Transactions on Intelligent Transportation Systems.
Janki Bhimani is a Professor and Director of the Data Management Research Lab (DaMRL) at the School of Computing and Information Science, Florida International University (FIU). Her research focuses on Memory and Storage Systems, Cloud Computing, Performance Modeling, and Applied Machine Learning. She holds a Ph.D. in Computer Engineering from Northeastern University (2019), an M.S. in Electrical and Computer Engineering (2016), and a B.S. in Electrical and Electronics Engineering from GITAM University (2013). Prior to FIU, she taught at Northeastern University and collaborated with Samsung Semiconductor Research Labs on flash-based SSDs. Her research interests include emerging memory technologies, high-performance computing, and datacenter reliability management. She leads innovative projects like Heimdall (machine learning for storage I/O optimization) and MoKE (modular key-value storage emulation). Awards include FIU Top Scholar and KFSCIS Excellence in Applied Research. Teaching highlights include CIS 3530 (Data Structures), CIS 5346 (Storage Systems), and EECE 2560 (Engineering Algorithms). Her work emphasizes bridging theory and practice, with patents on storage system optimization and machine learning integration.
Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.
Manxi Wu is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, specializing in societal networks and game-theoretic approaches to system design. Her research develops computational models for strategic learning and incentive mechanisms in socio-technical systems, with applications to transportation networks and digital platforms. Education: B.S. Applied Mathematics, Peking University (2015) M.S. Transportation, Massachusetts Institute of Technology (2017) Ph.D. Social and Engineering Systems, Massachusetts Institute of Technology (2021) Her research integrates game theory, optimization, and machine learning to address challenges in autonomous services, traffic management, and decentralized decision-making. Current investigations focus on adaptive incentive structures, spatial resource allocation, and equilibrium analysis in complex networked environments. Publication analysis reveals consistent emphasis on game-theoretic frameworks applied to urban mobility systems, with recent work exploring multi-agent reinforcement learning, congestion pricing equity, and electric fleet management. Methodological innovations include novel convergence proofs for decentralized algorithms and computational approaches to fairness constraints. Awards and Honors: Hammer Fellowship UTC Milton Pikarsky Memorial Award Siebel Scholarship EECS Rising Star recognition No information is currently available regarding student advising, research grants, or laboratory affiliations.
Jean-Louis Scartezzini is an Honorary Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Solar Energy and Building Physics Laboratory (LESO-PB). His research focuses on natural/artificial lighting, solar energy systems, and building technology, with a strong emphasis on energy efficiency and sustainability. Director of LESO-PB since 1994 Founded and led several institutes, including the Institute for Infrastructure, Resources, and Environment (2002–2009) Doctorat in Physics from EPFL (1986) Extensive international collaborations, including visiting roles at NUS (2009) and LBNL/UCLA (1988) Research interests include: - Daylighting and lighting control systems - Passive/active solar technologies - Urban microclimate and energy systems - Stochastic simulation and predictive control Recent work addresses climate change impacts on energy systems, urban sustainability, and machine learning applications in energy optimization. Key publications span lighting health impacts, renewable integration, and microclimate modeling Awards include the European Solar Prize (2001/2002) and Walsh-Weston Bronze Medal (1998) Mentored over 20 PhD students, many leading in academia and industry (e.g., Marilyne Andersen at EPFL, Flavio Foradini at E4Tech).
Ed Hopkins is a Professor of Economics at King's College London and the Interim Head of the Economics Department within the King’s Business School. He holds a PhD from the European University Institute in Florence, Italy, and has held positions at the University of Edinburgh and as a visiting scholar at institutions including the California Institute of Technology and the University of British Columbia. His research focuses on game theory, behavioral economics, and social economics, with particular emphasis on topics such as tournament models, inequality, learning dynamics, and experimental economics. Key areas of interest include status concerns, marriage matching, and the implications of non-standard preferences in strategic interactions. Ed has contributed extensively to experimental economics, testing theoretical predictions in laboratory settings. He is actively involved in PhD supervision, accepting students for doctoral research. His work spans theoretical and empirical analyses, with notable contributions to understanding cyclical behavior in strategic situations, the role of information in decision-making, and the interplay between economic inequality and risk-taking.
Baike She is a Postdoctoral Fellow at the School of Electrical and Computer Engineering, Georgia Institute of Technology. Their research focuses on interdisciplinary topics at the intersection of control theory, network science, and epidemiological modeling. Key areas include epidemic spread analysis, distributed systems optimization, and privacy-preserving algorithms for networked models. Research interests emphasize mathematical frameworks for analyzing complex systems, including compositional control approaches (e.g., LQR analysis via category theory), robust epidemic control strategies, and leveraging differential privacy in sensitive data computations. Work spans both theoretical developments and applied methodologies for real-world systems such as SIR/SIS epidemic models and infrastructure networks. Recent publications (2022-2025) highlight contributions to distributed reproduction number computation, optimal epidemic mitigation under uncertainty, and the integration of opinion dynamics with vaccination strategies. Methodologies include Gaussian process regression, dissipativity theory, and model predictive control frameworks. No specific awards or grants are explicitly listed in the provided texts. Advising roles and laboratory affiliations remain unspecified based on available information.
Andrew Ho is the Charles William Eliot Professor of Education at Harvard University's Graduate School of Education (HGSE). He holds a Ph.D. in Educational Psychology and an M.S. in Statistics from Stanford University. His research focuses on improving educational assessment design, particularly in measuring educational progress and inequality. He developed the Stanford Education Data Archive (SEDA), a national repository of student achievement data, and advocates for low-stakes assessment use in policy. Ho has held leadership roles including Immediate Past President of the National Council on Measurement in Education and trustee of the Carnegie Foundation. He advises seven U.S. states' testing programs and teaches graduate courses in statistics and psychometrics at HGSE. His work emphasizes the importance of accurate assessment during crises like the pandemic, advocating for standardized testing as part of a multi-measure 'census' approach to identify learning disparities and allocate resources effectively. Education: Ph.D. in Educational Psychology (Stanford, 2005), M.S. in Statistics (Stanford) Affiliations: Harvard Graduate School of Education, Technical Advisory Committees for 7 states Key Projects: SEDA, Assessment Literacy initiatives, pandemic-era testing advocacy His research bridges psychometrics with policy, emphasizing equitable assessment practices. Notable contributions include frameworks for interpreting test scores in multi-measure systems and critiques of 'learning loss' terminology favoring actionable 'learning lag' perspectives. Ho's recent work addresses pandemic-era education challenges through rigorous data analysis and policy recommendations.
Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.
Andrea Iannelli is a Tenure-Track Assistant Professor at the Institute for Systems Theory and Automatic Control (IST) , University of Stuttgart, Germany. He also serves as a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and participates in the Cluster of Excellence Data-Integrated Simulation Science (SimTech) . His research focuses on reconciling model-based and data-driven approaches for robust and adaptive control of uncertain dynamical systems. Ph.D. : Control and Dynamical Systems, University of Bristol (UK), 2019 Postdoctoral Researcher : ETH Zürich (Switzerland), 2019–2022 Harnessing the intersection of control theory, optimization, and machine learning , Iannelli’s work addresses data-driven modeling, uncertainty quantification, and robust control with applications in energy systems, intelligent transportation, and industry 4.0 . His recent publications highlight trends in LPV frameworks, online convex optimization, and hybrid control systems , emphasizing safety and efficiency. He contributes to the academic community as an Associate Editor for the International Journal of Robust and Nonlinear Control and as a member of international conference IPCs. His group, Trustworthy Autonomy for Smart Adaptive Systems (TASAS) , mentors PhD students in projects spanning adaptive control, uncertainty quantification, and reinforcement learning .
Sean Ehlman is an Assistant Professor in the Department of Biological Sciences at the University of South Carolina's McCausland College of Arts and Sciences. He leads the Ehlman Lab, which focuses on evolutionary behavioral ecology using integrated lab, field, and theoretical approaches. His research examines how ecological and evolutionary processes shape animal behavior development, with specific interests in: Phenotypic plasticity in response to environmental changes Origins and maintenance of behavioral individuality Aquatic ecosystem dynamics using Poeciliid fish models Application of tracking technology and AI in behavioral mapping Recent publications (2023-2024) demonstrate his interdisciplinary approach, combining behavioral ecology with data science to study: Developmental impacts of predator exposure Reproductive individuality in controlled environments Genotype-phenotype relationships in evolution Big data applications in behavioral research
Prof. Vahid Jamali is an Assistant Professor and Head of the Resilient Communication Systems Group at the Technical University of Darmstadt, Germany. His research focuses on resilient communications, 6G wireless systems, bio-inspired molecular communication, and reconfigurable intelligent surfaces (RIS). He holds a Doctoral Degree from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, and has served as a postdoctoral researcher at Princeton University and FAU. Education PhD in Communication Systems, FAU (2019) Visiting Researcher at Stanford University (2017) Research Assistant at FAU's Institute for Digital Communications (2013-2019) Research Interests Resilient Networks : Emergency networks, RIS-based systems, and resilience-by-design architectures. Wireless Innovations : 6G technologies, holographic MIMO, and joint communication-sensing systems. Bio-inspired Systems : Molecular communication modeling using biological principles like diffusion and chemical reactions. Recent Work Trends His 2024-2025 publications emphasize RIS optimization (e.g., temperature-aware phase shifts, fast beam switching) and molecular communication (e.g., Poisson channel identification, bio-inspired receiver designs). Emerging themes include AoI-based RIS reconfiguration and integrated sensing-communication-powering (ISCAP) for IoT. Lab Activities He leads the Resilient Communication Systems Group, exploring cutting-edge RIS hardware (e.g., liquid crystal implementations) and theoretical foundations for future wireless systems.
