Jordi McKenzie is an Associate Professor in the Department of Economics at Macquarie University. His research focuses on industrial organization, cultural economics, and digitization, with notable contributions to digital piracy, film industry economics, and generative AI in media. He holds degrees from the University of Sydney (PhD, MEc Hons) and the University of Tasmania (BEc Hons). Education: PhD in Economics, University of Sydney MEc (Hons) in Economics, University of Sydney BEc (Hons) in Economics, University of Tasmania Research interests include AI ethics in content creation, cultural trade patterns, and the economic impact of streaming services. His work has been published in journals like Poetics and Journal of Cultural Economics . He has contributed to policy discussions on digital piracy, music industry recovery post-pandemic, and author rights in AI-generated content. Recent projects include studying the transformation of data into trusted data products, digital piracy policy impacts, and subscription video on demand’s effect on legal/illegal consumption. He received the Mallen Lifetime Achievement Award in 2014 for contributions to film industry economics. McKenzie has engaged in media commentary on topics like Netflix’s viewing metrics, music collaboration impacts, and talent show biases. His research often bridges empirical evidence and policy implications, emphasizing the cultural and economic dimensions of digitization.
Prof. Tansu Alpcan is a Professor and Reader in the Department of Electrical and Electronic Engineering at The University of Melbourne, Australia. He holds a PhD from the University of Illinois at Urbana-Champaign (UIUC) and has held academic positions at Technical University Berlin and Deutsche Telekom Laboratories. His research focuses on AI/ML applications in engineering, game theory, cybersecurity, Industry 4.0, quantum machine learning, smart grids, and communication networks. Education: PhD in Electrical and Computer Engineering (UIUC, 2006); MSc (UIUC, 2003); BEng (Bogazici University, 1999). Research interests include adversarial machine learning, cybersecurity games, quantum computing, and renewable energy systems. Authored over 200 papers and two books, including Network Security: A Decision and Game Theoretic Approach (Cambridge, 2011). Recipient of IEEE Senior Membership (2012) and multiple best paper awards. He leads the WILAB and has secured grants such as the ARC Training Centre in Optimisation Technologies. Current projects include quantum machine learning, adversarial reinforcement learning, and smart grid modeling. Supervised 17 PhD and 3 Master’s students.
Dr. Kenneth Edwin Barker is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary, where he also serves as Director of the Institute for Security, Privacy and Information Assurance (ISPIA). His academic career spans several decades with significant contributions to database systems and privacy research. Dr. Barker earned his B.S. and M.S. in Computer Science from the University of Calgary in 1982 and 1984 respectively, followed by a Ph.D. in Computer Science from the University of Alberta in 1990. His educational background established the foundation for his extensive research career in database systems and information security. His primary research interests focus on Privacy Preserving Data Repositories , with specific attention to protecting privacy in mobile applications, understanding privacy's impact on data analytics, and architecting database management systems that inherently respect user privacy. His work also extends to distributed database environments, integration of legacy systems, and multidatabase environments. Dr. Barker's research bridges theoretical foundations with practical applications, making significant contributions to how privacy is implemented in real-world systems. An analysis of his recent publications reveals a strong trend toward practical privacy-preserving techniques for cloud data, social networks, and location-based services. His work consistently addresses the tension between data utility and privacy protection, developing innovative methods to maintain data value while safeguarding personal information. The publications span multiple subfields including encrypted search, graph privacy, high-dimensional data privacy, and privacy metrics. Best Paper Award at DBSec 2012 Best Paper Award at CODASPY 2012 Best Paper at BNCOD 2009 Dr. Barker has been instrumental in establishing privacy research infrastructure at the University of Calgary through his leadership of ISPIA. His research has attracted significant funding from various sources supporting privacy and security initiatives. While specific grant details aren't provided in the text, his extensive publication record indicates sustained research funding throughout his career. He has collaborated extensively with researchers both within and outside the University of Calgary, particularly with R. Alhajj and other colleagues on numerous projects. As Director of ISPIA, Dr. Barker oversees a research environment focused on advancing security and privacy technologies. The institute serves as a hub for interdisciplinary research, bringing together computer scientists, social scientists, and legal experts to address complex privacy challenges. His leadership has positioned the University of Calgary as a significant player in privacy research within Canada.
Refik Soyer is a Professor of Statistics at The George Washington University. His research focuses on Bayesian statistics, reliability modeling, decision analysis, and time series analysis. He has made significant contributions to the application of Bayesian methods in reliability engineering, queueing systems, and adversarial risk analysis. Education: D. Sc. in Statistics (1985), George Washington University His recent publications highlight advancements in Bayesian reliability analysis, adversarial decision frameworks, and computational methods for time series and queueing systems. Areas of emphasis include dynamic INAR processes, accelerated life testing, and software failure modeling. Soyer's work bridges theoretical statistics with practical applications in call centers, healthcare fraud detection, and risk management.
Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.
Prof. Geert-Jan P.M. Houben is a Professor at Delft University of Technology's Web Information Systems Department within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on AI ethics, machine learning, data integration, and decision support systems. He has published over 165 works and supervised 24 students. Notable contributions include frameworks for meaningful human control in AI systems and methodologies for bias mitigation in data-driven systems. Editorial roles: Editor for CEUR-WS, Springer, and other publishers since 2012 Awards: Royal Honours from TU Delft (2025) Research emphasizes ethical AI, data engineering, and scalable systems. Recent work addresses AI governance, bias in analytics, and interactive data discovery in modern data ecosystems. He actively contributes to conferences like ACM Web Science and VLDB.
Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.
I. Safak Bayram is a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Glasgow, UK. He joined Strathclyde in 2020 as a Chancellor's Fellow, following his role as an Assistant Professor and Scientist at Hamad Bin Khalifa University, Qatar. His research focuses on advancing sustainability and efficiency in intelligent power grids and transportation networks through system-level modeling, control, and management frameworks. Education: PhD in Electrical and Computer Engineering, North Carolina State University (2014) MSc in Telecommunications, University of Pittsburgh (2010) BSc in Electrical and Electronics Engineering, Dokuz Eylul University, Turkey (2007) His research interests center on the integration of electric vehicles (EVs), renewable energy, and energy storage systems into the grid to decarbonize transportation and electricity sectors. He specializes in smart charging, demand-side management, harmonics, power quality, and V2G technologies. His recent publications (2024–2025) emphasize experimental and modeling approaches to EV smart charging impacts on transformers, phase imbalance, and grid compatibility, reflecting a strong focus on real-world deployment and grid resilience. Scientific Awards: Best Paper Award, IEEE SmartGridComm (2024) Best Paper Award, IEEE Workshop on Renewable Energy and Smart Grid (2015) Best Paper Award, IEEE SmartGridComm (2018) Best Readings in Smart Grid Communications (2014) Adjunct Faculty Member Appointment (2020) Dr. Bayram is actively involved in research leadership and academic service. He has secured multiple research grants as Principal Investigator, including projects on V2G hubs and off-grid EV charging. He serves as an Associate Editor for IEEE Transactions on Transportation Electrification and IET Electrical Systems in Transportation, and has organized special issues and conferences such as IEEE SmartGridComm. He regularly delivers tutorials and participates in international conferences, contributing to the global smart grid and electrification community. Labs and Research Teams: His work is supported by active collaborations with industry (e.g., Arnold Clark Automobiles Limited) and research institutions. He leads research on modular EV charging (BumblebeeEV), smart charging algorithms, and grid integration projects, often involving experimental validation and field data analysis.
Martin Nordal Petersen is an Associate Professor at the Department of Electrical and Photonics Engineering , Technical University of Denmark (DTU) . His work spans Internet of Things (IoT) , optical networking , and wireless communication systems, with notable contributions to LoRa , NB-IoT , and LPWAN technologies. He actively supervises PhD projects on topics such as machine learning in IoT edge devices , secure 5G communication , and smart community architectures . Active projects (2024–2027): Machine Learning in IoT Edge Devices , Deterministic and Secure 5G Communication Finished projects (2021–2024; 2018–2021; 2015–2018): Reliable M2M/IoT Communication , Smart Communities , IoT 100% , Network Slicing His research explores: IoT Reliability : Multi-RAT communication, backup systems, and signal propagation Optical Networks : Alien wavelength integration, SDN control, and network emulation platforms Wireless Innovation : GPS-free geolocation, maritime NB-IoT use cases, and multimode fiber distribution Current collaborations emphasize cross-disciplinary applications of IoT in healthcare , industrial ergonomics , and smart environments .
Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.
Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.
Theodore Lim is an Associate Professor at the University of British Columbia's School of Community and Regional Planning (SCARP). He holds a PhD in City & Regional Planning from the University of Pennsylvania, an MS in Environmental Science and Engineering from Tsinghua University, and a BA in Immigrant Studies from Swarthmore College. PhD: University of Pennsylvania (City & Regional Planning) MS: Tsinghua University (Environmental Science and Engineering) BA: Swarthmore College (Immigrant Studies) Theo's research focuses on urban climate adaptation and environmental justice , particularly through participatory action research with marginalized communities. His work bridges environmental science, data modeling, and community engagement to address issues like urban heat resilience using both grassroots and technical approaches. His recent publications span interdisciplinary topics in climate adaptation , participatory modeling , and urban analytics . Key themes include integrating diverse knowledge systems into environmental governance, addressing power dynamics in socio-ecological systems modeling, and developing trauma-informed approaches to heat resilience planning. NSF CAREER Grant for computer model governance in sustainable water quality $1M NSF CIVIC Innovation award for heat resilience in Roanoke, VA Theo mentors students in urban analytics and policy research, with former advisees including PhD student Ayda Kianmehr and undergraduate researcher Jack Carroll. He also serves as Social Media Editor for Planning Theory and Practice .
Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases
Arash Asadpour Rahimabadi is an Associate Professor at the N. P. Loomba Department of Management within the Zicklin School of Business at Baruch College, CUNY . His research bridges Operations Research and Management Science , focusing on algorithmic design, dynamic pricing, and optimization in gig economy platforms. Education: Ph.D. in Operations Research, Stanford University (2010) BSc in Computer Engineering, Sharif University of Technology (2004) His research interests include stochastic optimization , submodular maximization , marketplace stability , and fair allocation . Recent work explores dynamic pricing in extreme value regimes , shared ride sustainability , and regulation of gig economy platforms . His scientific contributions span algorithmic game theory, combinatorial optimization, and resource allocation, with key publications in Management Science and Operations Research . Current projects analyze escrow payment mechanisms , shared mobility efficiency , and hotel reservation systems . Scientific Awards Best Paper Award, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010 1st Rank in Iran’s National Graduate Entrance Exam in Computer Engineering, 2004 Silver Medals in Iranian National Olympiads in Informatics, 1999–2000 He serves on graduate and PhD committees at CUNY and has reviewed for journals including Management Science and Operations Research . His teaching includes courses like Decision Models and Analytics and Advanced Discrete Optimization .
Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.
