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Cicero Eduardo De Sousa WalterView profile

Assistant Professor

• Innovation Management
• Business Management
• Organizational Behavior
• +3 more

Cicero Eduardo De Sousa Walter is an Assistant Professor of business and management at the Federal Institute of Education, Science and Technology of Piauí (Brazil) , with affiliations to the University of Aveiro (Portugal) through the GOVCOPP research unit (Governance, Competitiveness, and Public Policy). He holds a Ph.D. in Business and Economics from the University of Aveiro and Master's and Specialization degrees in Management from the Polytechnic Institute of Bragança (Portugal). His research focuses on Innovation Management, Business Management , and Organizational Behavior , with a particular interest in innovation dynamics in small enterprises, consumer behavior, and education-related studies. He has received scientific awards for his research. His work spans institutions in Brazil and Portugal, emphasizing empirical analyses of innovation drivers, licensing models, and educational outcomes. Recent publications highlight trends in machine learning applications for patent licensing, Generation Z's privacy concerns , post-pandemic retail strategies , and innovation policy . Collaborations with researchers like Manuel Au-Yong-Oliveira and Cláudia Veloso reflect cross-institutional partnerships. His affiliations include the Competitiveness, Innovation, and Sustainability (CIS) research group and profiles in SCOPUS, Web of Science, and other academic indexers. Scientific awards include recognition for research contributions in business management and innovation. His work bridges quantitative and qualitative methodologies , with a focus on Portuguese and Brazilian contexts .

Marcus HandteView profile

Researcher

• Computer Science
• Pervasive Computing
• Sustainable Mobility
• +3 more

Dr. Marcus Handte is a Senior Researcher at the University of Duisburg-Essen, focusing on networked embedded systems, context-aware computing, and sustainable mobility. His academic journey includes a Habilitation in Computer Science (2013) and a PhD in Natural Sciences (2009) from Universität Stuttgart, alongside a Master's degree from Georgia Institute of Technology (2002). Research Interests Context-aware applications Localization and location-based systems Sustainable mobility solutions Internet of Things (IoT) Smart city infrastructure Privacy-preserving technologies His recent work involves developing platforms for multimodal mobility analysis (MOBYDEX), wireless EV charging systems (TALAKO, FAIR), and innovative approaches to indoor localization. Publications span journals like Machine Vision and Applications and conferences in pervasive computing. Scientific Recognition Best Poster Award at ACM KMIS 2023 Dr. Handte has contributed to projects such as ATMo2, INNAMORUHR, and GAMBAS, and maintains active collaborations across institutions. His expertise in adaptive middleware and distributed systems continues to shape research in smart mobility and ambient intelligence.

• Incentive-Aware Machine Learning
• Social Computing
• Online Learning
• +7 more

Chara Podimata is an Assistant Professor of Operations Research and Statistics at the MIT Sloan School of Management and a Lead Researcher at Archimedes/Athena RC. She holds the Class of 1942 Career Development Professorship and focuses on the intersection of Theoretical Computer Science, Economics, and Machine Learning, particularly in incentive-aware machine learning, social computing, online learning, and mechanism design. PhD in Computer Science from Harvard FODSI Postdoctoral Fellow at UC Berkeley Diploma from National Technical University of Athens Her research explores the social aspects of computing, including how humans adapt to machine learning algorithms used in consequential decision-making. Recent work investigates policy questions related to AI and recommendation systems, adversarial robustness, and fairness in revenue management. She has received funding from Amazon, MacArthur Foundation, Google, and MIT GenAI Consortium. Key trends in her publications include strategic classification, contextual search, multi-armed bandits with evolving preferences, and incentive-compatible mechanism design. These works bridge theoretical foundations with practical applications in responsible AI and user behavior modeling. Amazon Research Award (2023) Google Research Scholar Award (2025) MacArthur x-grant Microsoft Dissertation Grant Siebel Scholarship As an advisor, Podimata collaborates with PhD students at MIT ORC, EECS, and MBAn capstone students. She is affiliated with the MIT Operations Research Center and previously worked with Microsoft Research and Google. Her personal page details her advising philosophy and research collaborations.

• Human-Computer Interaction
• Ubiquitous Computing
• Computational Materials
• +3 more

Sai Swaminathan is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee. He earned his PhD from Carnegie Mellon University's Human-Computer Interaction Institute, advised by Scott Hudson. His research focuses on computational materials, ubiquitous computing, and cyber-physical systems, emphasizing low-power, embedded technologies in physical infrastructure. He leads the EPIC Lab, exploring interactive systems integrated into structural materials like concrete and carbon fiber. Education: PhD from Carnegie Mellon University (Human-Computer Interaction Institute). Prior to this, he completed a dual master’s degree at Technical University of Berlin and Universite Paris-Sud XI. Professional experience includes roles at Oak Ridge National Lab, Microsoft Research, and Xerox Research. Research Interests: Developing battery-free interactive devices using non-silicon materials (e.g., concrete, carbon fiber), integrating sensing/actuation into architectural structures. Current projects include OptiStructures (optical sensors in building materials), Duco (large-scale circuit writing), and FiberWire (composite electronics). Notable Awards: Best Paper Honorable Mention at CSCW 2019 for 'Learn2Earn: Using Mobile Airtime Incentives to Bolster Public Awareness Campaigns'. Labs/Teams: Director of the Energy, Printed Infrastructure and Interactive Computing (EPIC) Lab, collaborating with interdisciplinary teams in HCI, ECE, CEE, and Materials Science.

Dejun YangView profile

Associate Professor

• Networking
• Mobile Sensing
• Edge Computing
• +5 more

Dejun Yang is an Associate Professor in the Department of Computer Science at Colorado School of Mines. He directs the NEMOS (Networking and Mobile Sensing) Lab, focusing on advancing networking, mobile sensing, and edge computing through interdisciplinary approaches involving game theory, optimization, machine learning, and algorithm design. His work addresses challenges in resource allocation, security, and privacy across domains like IoT, quantum internet, and blockchain-based systems. Education: B.S. in Computer Science, Peking University (2007) Ph.D. in Computer Science, Arizona State University (2013) Research Interests: Networking and satellite communications Edge computing security and SLA enforcement Incentive mechanisms for crowdsourcing and blockchain Quantum internet resource distribution Privacy-preserving mobile sensing Recent Article Trends: His 2023-2025 work emphasizes blockchain payment networks (e.g., fee-aware routing, crypto capital incentives) and edge computing SLA markets. Earlier studies focused on crowdsourcing mechanisms and spectrum auctions. Scientific Awards: Recipient of Best Paper Awards at GLOBECOM’15, ICC’12, ICC’11, MASS’11, and a Best Paper Runner-Up at ICNP’10 (2010-2015). Lab & Collaboration: Leads the NEMOS Lab, which develops innovative solutions for mobile sensing, edge computing, and secure distributed systems. Active in interdisciplinary collaborations involving industry partners and academic institutions.

Erica MyersView profile

Associate Professor

• Energy Economics
• Environmental Economics
• Climate Policy
• +3 more

Erica Myers is an Associate Professor in the Department of Economics at the University of Calgary and holds the Canada Research Chair in Environmental, Energy and Resource Economics. She is affiliated with the National Bureau of Economic Research (NBER), Abdul Latif Jameel Poverty Action Lab (J-PAL), and the E2e initiative. Her research focuses on energy and environmental economics, examining policy effectiveness in decarbonization, consumer decision-making, and market mechanisms for sustainability. Her work bridges theoretical and applied economics, addressing topics like energy efficiency program targeting, time-of-use pricing impacts, and behavioral nudges in energy conservation. Myers serves as Co-Editor at the Journal of Environmental Economics and Management and holds editorial roles at multiple journals, reflecting her leadership in environmental economics research. Key achievements include identifying policy gaps in heating bans, analyzing unintended consequences of EV pricing structures, and demonstrating machine learning's role in optimizing energy interventions. Her Canada Research Chair underscores recognition of her innovative contributions to climate policy analysis. Editorial roles: Journal of Environmental Economics and Management , Land Economics , Journal of the Association of Environmental and Resource Economists Affiliations: NBER, J-PAL, E2e initiative Research themes: Climate policy design, energy markets, behavioral economics

Mohammad Mahdi KhaliliView profile

Assistant Professor

• Machine Learning
• Theoretical Machine Learning
• Applied Machine Learning
• +6 more

Mohammad Mahdi Khalili is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University's College of Engineering. He also serves as a part-time Research Scientist at Yahoo! Research, focusing on theoretical and applied machine learning with emphasis on robustness, interpretability, and model compression for large-scale systems including LLMs. Dr. Khalili earned his Ph.D. in Electrical Engineering and Computer Science and MSc in Applied Mathematics from the University of Michigan, Ann Arbor. Prior to OSU, he was a research scientist at Yahoo Research and a postdoctoral researcher at UC Berkeley. His research spans mechanistic interpretability of foundation models , privacy-aware model compression , and fairness in sequential decision-making . Current projects include counterfactual reasoning for fair ML, physiological signal analysis for worker health monitoring, and corruption analysis in model mechanisms. His work bridges theoretical guarantees with practical healthcare and security applications. Recent publications reveal strong focus on model compression techniques (block-wise sparsity, quantization) and interpretability methods for LLMs, with growing emphasis on physiological signal processing applications. The 2024-2025 papers show increasing integration of healthcare domains like ECG analysis and construction worker monitoring. Dr. Khalili currently advises four PhD students: Zhiqun Zuo: Counterfactual Reasoning Zhongteng Cai: Privacy-Aware Model Compression (UAI travel grant recipient) Ding Zhu: Trustworthy Model Compression & Time Series Analysis Vishnu Chhabra: Mechanistic Interpretability His research is supported by two NSF grants (health monitoring systems and dynamic environment robustness), a Translational Data Analytics Institute grant for medical AI, and a College of Engineering grant for large-scale systems. The lab recently acquired a dedicated GPU server for intensive computations. He actively contributes to the ML community through invited talks (Midwest Machine Learning Symposium) and publications in top venues including NeurIPS, ICML, and UAI.

Xiangyang LiView profile

Teaching Professor

• Information Security
• Security Intelligence
• Human Security Informatics
• +5 more

Xiangyang Li is an Associate Teaching Professor in the Department of Computer Science at Johns Hopkins University, specializing in information security, security intelligence, and human security informatics. His research focuses on computer and system security, user modeling, intelligent systems, simulation and modeling, and knowledge discovery. Education: PhD from Arizona State University Li's work integrates computational cognitive modeling to analyze user security behaviors, phishing classification decisions, and behavioral biometric vulnerabilities. He has developed high-interaction honeypots, explored adversarial techniques against spam detection models, and studied monetary incentives in phishing scenarios. His funded projects include contributions to cybersecurity from DARPA, Air Force Research Laboratory, University of Michigan, and Rensselaer Polytechnic Institute. Li has received recognition including Best Paper Nomination at HICSS-52 and First Place Award at SIG-KM International Research Symposium. At JHU, he teaches courses such as Introduction to Information Security and Computer Intrusion Detection. His research is affiliated with the Information Security Institute.

Haoqi ZhangView profile

Associate Professor

• Social Computing
• Crowdsourcing
• Human-Computer Interaction
• +7 more

Haoqi Zhang is an Associate Professor at Northwestern University in Computer Science and Design, with offices at 2133 Sheridan Rd., Evanston, IL. He directs the Design, Technology, and Research (DTR) program and co-directs the Delta Lab with interdisciplinary collaborators. His work bridges Social Computing , Human-Computer Interaction , Artificial Intelligence , and Learning Science to create socio-technical systems advancing human values. Education: PhD in Computer Science from Harvard University (2012) BA in Computer Science and Economics from Harvard University (2007) Research interests focus on Computational Ecosystems that interweave community processes, social structures, and intelligent systems to promote values like self-direction , metacognition , and belonging . His projects include agile research studios for training researchers, opportunistic collective experiences for remote collaboration, and flexible coordination in crowdsourcing systems. Recent publications examine metacognitive learning in CS education, dialectical activities in HCI, and trust dynamics in crowdfunding. Supported by National Science Foundation grants in Cyber-Human Systems and Cyberlearning, plus Microsoft FUSE Labs and Northwestern institutional awards. Awards include Microsoft FUSE Labs Research Award Northwestern Office of the Provost Award for Digital Learning Notable Paper Award at HCOMP 2013 Best Paper Honorable Mention at CHI 2018 Mentors PhD students Kapil Garg and Gobi Dasu , while fostering over 100 students through DTR. Runs a Junior Faculty Support Group since 2022, providing emotional and professional support for early-career academics.

• Wireless Communications
• Mathematics
• Artificial Intelligence
• +5 more

Keivan Navaie is a Professor of Intelligent Networks at Lancaster University’s School of Computing and Communications. He serves as a member of the Independent Scientific Advisory Committee at the Alan Turing Institute, overseeing the £100 million BridgeAI programme, and previously as Principal AI Technology Advisor to the UK Information Commissioner’s Office (ICO). He is recognized with Fellowships from the Institution of Engineering and Technology (IET), Chartered Engineer status in the UK, Senior Fellowship of the Higher Education Academy (HEA), and the IEEE Young Investigator Award. Research Focus: Wireless communications, mathematics, artificial intelligence, 6G networks, blockchain technology, edge computing, cognitive radio networks, and non-orthogonal multiple access (NOMA). Supervision: Actively supervises PhD students in areas like wireless communications and mathematical modeling. Projects: Involved in distributed learning, blockchain integration, 6G research, and spectrum sharing systems. Awards: IEEE Young Investigator Award, Fellow of IET, Chartered Engineer, Senior Fellow of HEA.

• Incentive-Aware Machine Learning
• Social Computing
• Online Learning
• +5 more

Chara Podimata is the Class of 1942 Career Development Assistant Professor of Operations Research and Statistics at MIT Sloan School of Management. She holds a joint appointment as a Lead Researcher at Archimedes/Athena Research Center. Her work focuses on incentive-aware machine learning, strategic interactions in algorithms, and the societal impacts of AI systems. Prior to MIT, she was a FODSI Postdoctoral Fellow at UC Berkeley and earned her PhD from Harvard University under Yiling Chen’s supervision. Her research is supported by grants from Amazon, Google, MacArthur Foundation, and MIT’s GenAI Consortium. Education: PhD in Computer Science (Harvard University, 2020), Diploma in Electrical & Computer Engineering (National Technical University of Athens, Greece). Key research areas include algorithmic economics, strategic learning, and mechanism design for online platforms. She has advised over 10 students across PhD, MBA, and undergraduate programs, emphasizing ethical and societal implications in her mentorship. Research Themes: Incentive-aware learning, strategic user interactions, fairness in algorithms, AI policy, and recommendation systems Awards: Amazon Research Award (2023), Google Faculty Award (2025), NeurIPS Spotlight Recognition (2023), AAAI Best Paper Shortlist (2019) Her recent work explores how machine learning systems can be designed to account for human adaptation, including studies on recommendation systems, revenue management fairness, and causal mechanisms in strategic classification. She teaches courses on data modeling (15.060) and operations research (15.090) at MIT, blending theoretical rigor with practical industry applications. Podimata’s interdisciplinary approach bridges computer science, economics, and management science, with publications in top venues like NeurIPS, EC, COLT, and ICML. She actively engages in policy discussions around AI governance through her research on algorithmic transparency and societal impacts.

Zubin JelvehView profile

Assistant Professor

• Data Science
• Public Policy
• Criminal Justice
• +3 more

Zubin Jelveh is an Assistant Professor at the University of Maryland’s College of Information Studies (iSchool). He specializes in data science for public policy, record linkage, and the science of science. Prior to joining UMD in 2021, he served as a Research Director at Crime Lab New York, where he collaborated with civic leaders to design interventions targeting violence reduction and criminal justice reform. Dr. Jelveh holds a BA in Economics from the University of Chicago, an MA in Quantitative Methods in the Social Sciences from Columbia University, and a PhD in Computer Science from New York University. His research bridges machine learning and social sciences, focusing on prediction models in criminal justice (e.g., domestic/gun violence) and record linkage algorithms tailored for justice data. He also examines scientific incentives and how research is presented. His work emphasizes ethical AI applications, particularly in judicial systems, and includes projects like assessing pretrial risk in Maryland using CLUE data and targeting interventions for high-risk domestic violence victims. He explores themes such as algorithmic fairness, data quality in randomized experiments, and the impact of specialized prosecution strategies. Before academia, Jelveh was a journalist covering economics for outlets like The New York Times. His interdisciplinary approach integrates computational methods with societal challenges, aiming to improve policy outcomes through rigorous data analysis.

• Data Management
• Database Systems
• Graph Data Management
• +4 more

Professor Chengfei Liu is a faculty member at Swinburne University of Technology, leading the Web and Data Engineering research group and serving as focus area leader for Knowledge and Data Intensive Systems in the SUCCESS Centre. His research focuses on web data management, advanced database systems, graph data, and workflow models. He has held academic positions at the University of South Australia, University of Technology Sydney, and the University of Queensland's DSTC. His work spans data management, distributed systems, and information systems, with notable contributions to cohesive subgraph discovery and efficient algorithms. Research interests include keyword queries, uncertain data, graph databases, and artifact-centric workflows. His awards include the Vice-Chancellor's Research Excellence Award (2007) and multiple best paper/demo awards at top conferences. Professor Liu supervises numerous PhD students and holds grants from the Australian Research Council (ARC), including projects on dynamic networks, heterogeneous graphs, and information resilience. He has authored over 297 publications, with recent work addressing topics like Bitcoin transaction prediction, federated subspace clustering, and multimodal eating disorder detection. His professional roles include program committee memberships at SIGMOD, ICDM, and CIKM, reflecting his influence in data science and database research.

Michele AlbanoView profile

Associate Professor

• Internet of Things (IoT)
• Energy Engineering
• Quality of Service (QoS)
• +5 more

Affiliations & Roles Michele Albano is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design, focusing on research in IoT, Cyber-Physical Systems, and Edge Computing. He leads the Productive4.0 project (2017–2020), funded by Horizon Europe, addressing Industry 4.0 challenges in product lifecycle management. His work integrates formal verification tools like Uppaal with real-world applications in robotics, energy systems, and blockchain-based platforms. Research Interests Albano's research spans IoT architecture optimization , energy-efficient systems , and model-driven engineering . He develops tools for autonomous exploration algorithms (MAES), edge-cloud resource orchestration, and fault-tolerant computation offloading. His work bridges theoretical models (e.g., Uppaal SMC) with practical implementations in smart grids and robotic systems. Recent projects include blockchain-based crowdsourcing for machine learning and energy-aware thermal dynamics estimation in buildings. Collaborations & Impact He collaborates with the European Industry 6.0 community, contributing to the Arrowhead Framework for interoperable IoT systems. His research outputs include 112 publications, with 2025 highlights in human-inspired robotics and cognitive cloud frameworks. Media coverage in 2024–2025 highlights his work on green IT and secure API generation. Albano advises students on system modeling (e.g., ACSmt plugin development) and edge computing optimization. Labs & Teams His research group focuses on Cyber-Physical Systems and Smart Grids , with contributions to tools like RoutesMobilityModel and FlexHousing. He actively participates in workshops on New Trends in Software Architecture (SATrends '24) and IEEE conferences on Industrial Informatics.

• Accountability Reforms
• Performance Management
• Technology Policy
• +2 more

James R. Harrington is an Associate Professor of Public and Nonprofit Management at the University of Texas at Dallas. He serves as Program Head (2022-Present) and holds affiliate faculty appointments in Political Science, Public Policy, and Political Economy. His work bridges public administration with labor economics, education finance, and technology regulation. Education : PhD in Public Affairs from the University of Missouri's Truman School (2013) Research interests focus on accountability reforms, performance management, technology policy, and education governance. His recent studies examine pension systems, tax incentives, and the application of machine learning in regulatory contexts. Recent publications (2025-2022) explore interdisciplinary topics such as Social Security tax impacts on education workforces, pension reform effects on labor supply, flood risk awareness in vulnerable communities, and antitrust challenges in blockchain technology. These works span public economics, institutional analysis, and data-driven policy evaluation. Contact : Email: james.harrington@utdallas.edu Phone: 972-883-3513 Office: Green Hall GR 3.218