Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago, where he researches data ecology, a concept he created to study how data shapes our world and how we can shape it back. He is the faculty co-lead of the Data Science Institute's Data Ecology Research Initiative and a member of ChiData, the data systems research group at the University of Chicago. He is also co-founder and Chief Research Officer at invocate and co-runs Chicago Data Night, a forum connecting industry and academia in Chicago. Castro Fernandez's research focuses on data ecology, data discovery, data markets, and data integration. He develops both theory and systems that help people and organizations find, evaluate, and use data effectively. His work often uses techniques from data management, statistics, and machine learning. He has pioneered concepts in data market design, understanding the economics of data, and building platforms to support markets of data. His research on data ecology frames how data moves through and transforms technological, economic, and social systems—and how to design interventions to make those ecosystems more valuable, equitable, and resilient. His publications reveal a strong focus on data markets, data discovery, and LLM applications for data management. Recent work includes Pneuma (leveraging LLMs for tabular data), Solo (data discovery using natural language), and Nexus (correlation discovery for spatio-temporal data). His research spans theoretical foundations of data value to practical systems for data sharing and discovery. SIGMOD Test of Time Award (2023) NSF CAREER Award (2024) Sloan Research Fellowship (2025) Castro Fernandez has advised numerous PhD, Master's, and undergraduate students who have gone on to pursue PhDs at institutions like University of Washington and Stony Brook, joined companies like Google, Anthropic, and Citadel, or founded startups. His teaching includes courses on The Value of Data, Ethics in Data Science, and Introduction to Databases. He serves on program committees for major conferences including SIGMOD, VLDB, and CIDR, and has been recognized as a Distinguished Reviewer by multiple venues.
Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.
Ram Bala is an Associate Professor of AI & Analytics at Santa Clara University’s Leavey School of Business. He holds a Ph.D. in Operations Research from UCLA Anderson School of Management and a Mechanical Engineering degree from IIT Bombay. His research focuses on pricing strategies, marketplace design, supply chain dynamics, and the integration of AI into business operations. He leads the Prometheus Lab on AI and Business and is Co-founder/Chief AI Scientist of Samvid, a generative AI startup for logistics. Additionally, he co-founded the MS-SCMA program and holds leadership roles in academic governance committees. Education: Ph.D. in Operations Research, UCLA Anderson School of Management Bachelor's in Mechanical Engineering, Indian Institute of Technology Bombay Research Interests: Ram’s work bridges optimization, game theory, and machine learning to address dynamic market challenges. He explores the transformative adoption of AI-driven autonomous systems in organizations, particularly in supply chains and healthcare logistics. His recent projects include pandemic response platforms for PPE distribution and AI tools for humanitarian aid via Project Stanley. Leadership & Ventures: Founder & President of Project Stanley (non-profit applying data science to humanitarian issues) Co-founder and Director of MS-SCMA program Past roles: Chief Data Scientist at GrandCanals (acquired by C.H. Robinson) and leadership at Andela Labs & Teams: Co-leads Prometheus Lab on AI and Business at Leavey School, focusing on enterprise AI adoption and generative AI applications in supply chain management.
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.
Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.
Maarten de Rijke is a Professor at the University of Amsterdam's Informatics Institute, leading the Information Retrieval Lab (IRLab). He specializes in information retrieval, machine learning, and recommendation systems, focusing on neural ranking models, fairness, and conversational search. His work bridges theory and practice, addressing challenges in reproducibility, robustness, and ethical AI. He supervises numerous PhD students and postdocs, including recent defenses by Barrie Kersbergen, Antonis Krasakis, and Vera Provatorova. His lab collaborates internationally, organizing events like SIGIR workshops and the Search Engines Amsterdam (SEA) meetup. Key awards include the Best Reproducibility Paper Award (2025) and Best Paper at WSDM 2021. Research interests span generative retrieval, adversarial robustness, and fairness in ranking. Notable projects include the FULTR dataset, FairDiverse toolkit, and studies on empathetic conversational systems. He actively promotes open science through reproducible methodologies and community-driven benchmarks.
Parinaz Naghizadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Design Lab. She holds a PhD from the University of Michigan and has prior roles at Ohio State University and postdoctoral positions at Purdue and Princeton. Her research focuses on network economics, game theory, AI ethics, optimization, and cybersecurity. She received the NSF CAREER Award (2022), Rising Stars in EECS (2017), and Barbour Scholarship (2014). Education: PhD in Electrical Engineering (University of Michigan), M.Sc. in Mathematics and Electrical Engineering (University of Michigan), B.Sc. in Electrical Engineering (Sharif University of Technology, Iran). Research Interests: She develops mathematical models to analyze decision-making in complex networks, with emphasis on AI ethics, multi-agent systems, and cybersecurity. Recent work explores biases in AI, strategic classification, and incentive mechanisms for security investments. Article Trends: Her recent publications (2023-2025) address strategic classification challenges, multiplex network equilibria, federated learning fairness, and robust control in cyber-physical systems. Themes include ethical AI, game-theoretic security design, and optimization under uncertainty. Awards: NSF CAREER Award (2022), Rising Stars in EECS (2017), Barbour Scholarship (2014) Advising & Grants: No student advisees listed, but active in securing research grants (e.g., NSF CAREER). Works with interdisciplinary teams in UCSD's Design Lab. Labs/Teams: Affiliated with UCSD's Design Lab, focusing on innovative engineering solutions for societal challenges.
Akhtar Hussain serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Laval University, Quebec. His research centers on AI-driven optimization of power and energy systems, with emphasis on microgrid resilience, distributed energy resource integration, and electric vehicle-grid interactions. He actively contributes to advancing grid reliability through innovative resource allocation and consumer satisfaction frameworks. Ph.D. in Electrical Engineering, Incheon National University, South Korea (2019) M.Sc. in Electrical Engineering, Myongji University, South Korea (2014) B.Sc. in Electrical Engineering, National University of Sciences and Technology, Pakistan (2011) Dr. Hussain's research spans power systems resilience, smart grid technologies, and equitable energy access. His work integrates artificial intelligence with traditional power engineering to address challenges in microgrid operation, electric vehicle integration, and renewable energy management. Key focus areas include developing algorithms for optimal resource utilization, enhancing grid stability during contingencies, and designing frameworks for fair energy distribution in diverse communities. His recent publications (2023-2025) reveal a strong trajectory toward AI-enhanced grid management, with recurring themes of resilience optimization, equity-focused resource allocation, and electric vehicle-grid synergies. The research demonstrates increasing sophistication in handling uncertainty through machine learning while addressing socio-technical dimensions of energy transition. Dr. Hussain currently supervises one Master's student and has guided five Ph.D. candidates to completion. His research is funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant ($160,000/year) for the project 'Grid Condition and Resilience-Aware Incentivization and Deployment of Distributed Energy Resources' (2024-2029), supplemented by a Springboard to Discovery award ($40,000) for 2024-2025. As an IEEE member, he collaborates with industry partners on real-world grid modernization initiatives, focusing on practical implementation of resilience strategies through microgrids and mobile energy resources.
Gaétan de Rassenfosse is an Associate Professor at EPFL's School of Management of Technology (CDM), specializing in Science, Technology, and Innovation Policies. He joined EPFL in 2014 and holds a PhD in Economics from the Solvay Business School (Université Libre de Bruxelles). His research focuses on crafting evidence-based policies for knowledge economies, particularly in intellectual property, intangible capital measurement, and 'science of science.' He has published in top journals like Journal of Industrial Economics and Research Policy , secured over CHF 2.5M in research grants, and advised governments and international organizations such as the European Commission and Swiss Federal Office for Education and Research (SERI). He teaches courses on innovation economics and intellectual property management. Professional roles include membership in the CDM Management Board, the Conference of Section Directors (CDS), and doctoral program committees. His advising includes guiding 5 PhD students focusing on policy impacts in clean technology, innovation incentives, and science organization. Key research themes span patent systems, innovation policy efficacy, and cross-border knowledge flows. He actively engages with media and policymakers to translate academic insights into actionable public strategies.
Christos Nicolaides is an Assistant Professor at the Department of Business and Public Administration within the School of Economics and Management at the University of Cyprus (UCY), holding a secondary appointment as a Digital Fellow at MIT's Initiative on the Digital Economy. Previously, he spent three years as a James McDonnell Foundation-funded Postdoctoral Fellow at MIT Sloan School of Management. His educational background includes a PhD in Engineering from Massachusetts Institute of Technology (2014), SM from MIT (2011), MSc in Applied Mathematics from Imperial College London (2009), and BSc in Physics from University of Thessaloniki (2008). Nicolaides' research applies mathematical, statistical, and computational tools to large-scale empirical questions in social influence mediated by digital technologies. His work spans Data Science , Machine Learning , Social Networks , and Computational Social Science , with significant contributions to understanding human mobility patterns, disease transmission dynamics, and social contagion effects. His research has established novel methodologies for analyzing complex network structures in mobility data and social interactions. Analysis of his 15 most recent publications reveals a consistent focus on applying network science to real-world problems, particularly in pandemic response (12 publications), human mobility analytics (9 publications), and social contagion dynamics (7 publications). His work demonstrates increasing interdisciplinary integration, combining computer science, epidemiology, and organizational behavior since 2020. Marie S. Curie Fellow Two Highly Cited Papers by Web of Science (2017, 2020) Best Paper Award by Risk Analysis Society (2019) Professor of The Week by Poets & Quants (2020) As principal institutional investigator, Nicolaides has secured over €1 million in research funding from the European Commission, industry partners, Cyprus Innovation and Research Foundation, and Cyprus Ministry of Health. His current teaching includes Social Networks and Entrepreneurship, Introduction to Operations Management, and Quantitative Methods in Management. Media coverage of his work spans major outlets including The New York Times, CNN, Nature, and Science, with significant impact on public health policy discussions during the COVID-19 pandemic.
Associate Professor Nalin Arachchilage is a leading academic in Cyber Security at RMIT University's School of Computing Technologies. He holds an honorary role at the University of Warwick and advises DEFSAFE Cyber Security Inc. His research focuses on usable security, privacy engineering, and AI-driven cybersecurity solutions. Key roles include revamping RMIT's Master of Cyber Security program and leading the Usable Security Engineering group at UNSW ADFA. Education: PhD in Cyber Security (Brunel University), Postdocs at Oxford and UBC. Research spans interdisciplinary areas including HCI, machine learning for threat modeling, and serious games for security education. He has published extensively in top venues like ACM CCS and SOUPS, contributing to global standards like OWASP. Leadership: Previously Assistant Head of School (Research) at the University of Auckland, and Director of the MProfStuds in Digital Security. Active in program committees for major conferences (ACM CCS, SOUPS). Current supervision includes 5 PhD students across RMIT and Auckland. Media Impact: Featured in Sky News Australia, ABC, and TVNZ. Collaborations with HP and Facebook. Awards include impactful contributions to security frameworks and standards. Teaching: Coordinates courses like INTE2625 (Cyber Security) and COSC2738 (Human-Centric Cyber Security). Supervises projects on topics like blockchain security and responsible AI. Non-Academic Roles: Chair of the Academic Board at Canberra Business & Technology College (2020-2021). Advisor to DEFSAFE, focusing on novel cyber-security products.
Hedyeh Beyhaghi is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst , affiliated with the Manning College of Information and Computer Sciences. She holds a PhD in Computer Science from Cornell University and completed postdoctoral research at the Toyota Technological Institute at Chicago , Northwestern University , and Carnegie Mellon University . Research Interests : Her work focuses on algorithmic game theory , mechanism design , machine learning theory , and algorithms under uncertainty . She investigates strategic agent behavior, fairness in algorithmic systems, revenue maximization in auctions, and optimization under stochastic constraints. Recent Publications address topics like the Strategic Perceptron , Pandora’s Box Problem , and Fair Incentive Design , reflecting trends in strategic learning , multi-agent optimization , and fairness-aware algorithms . These studies often intersect economics , machine learning , and theoretical computer science . Teaching : She teaches COMPSCI 611 - Advanced Algorithms , covering randomized algorithms, approximation techniques, and computational complexity. Weekly quizzes and biweekly assignments emphasize collaboration policies and academic integrity in algorithm design. PhD Advisee : Amirmahdi Mirfakhar. No scientific awards are currently documented.
Brent Lagesse is an Associate Professor at the University of Washington - Bothell , affiliated with the Division of Computing & Software Systems under the School of Science, Technology, Engineering & Mathematics . His research focuses on security in emerging environments , particularly secure machine learning and privacy in sensor-rich systems . Ph.D. in Computer Science from the University of Texas at Arlington (2009) Research Interests include: Detecting and locating hidden webcams Scalable AI/ML defense mechanisms Privacy-preserving video sharing AI systems for air quality prediction Automated yeast cell analysis CRISPR/CAS9 guide-donor libraries Article Trends : Recent publications emphasize secure machine learning for smart city applications, privacy-preserving technologies , and resource-constrained security in crowdsensing environments . Collaborative work spans cybersecurity education , environmental monitoring , and context-aware systems . Scientific Awards : Cybersecurity Fulbright Scholar (University of Cambridge, 2018) Johann-von-Spix International Guest Professorship (University of Bamberg, 2019-20) Advising & Grants : Advises current research students Neil Prakasam and Nicholas Handaja NSA grant ($96k) for GenCyber curriculum development (2022) NSF grant ($300k) for AI-enhanced cybersecurity workforce studies (2021) T-Mobile grants for ML security metrics and dataset anonymization (2020-2022) Laboratory : Leads the Security of Emerging Environments (SEE) Lab , developing practical and theoretical frameworks for smart city security and privacy-preserving technologies .
Dr. Wei Song is a Professor and the Coordinator of Software Engineering at the Faculty of Computer Science, University of New Brunswick (UNB) in Fredericton, New Brunswick, Canada. She has been with UNB since 2009, after completing her postdoctoral studies at UC Berkeley, and has established herself as a leading researcher in mobile networking and wireless communications. Her office is located in room ID419 and she can be reached at wsong@unb.ca. Education Ph.D. in Electrical and Computer Engineering, University of Waterloo (2003-2007) Postdoctoral Fellow, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2008-2009) Research Focus Dr. Song's research spans multiple cutting-edge areas in mobile and wireless networking, with a strong emphasis on integrating artificial intelligence and machine learning techniques. Her work addresses fundamental problems in mobile social networks, Internet of Things, vehicular networks, and mobile cloud computing. She explores how cooperative intelligence and distributed AI can enhance network performance while addressing practical constraints such as energy efficiency and user incentives. Her recent work particularly focuses on intelligent edge computing, mobile crowdsensing with deep reinforcement learning, and social-aware data dissemination through device-to-device communications. She investigates how to turn decentralized mobile "crowds" into coherent working groups and how social connections can be leveraged to improve data dissemination efficiency. Publication Trends Dr. Song's recent publications (2016-2023) demonstrate a clear evolution from traditional wireless networking to AI-driven approaches. While her earlier work focused on fundamental problems in device-to-device communications and resource allocation, her recent publications increasingly incorporate deep reinforcement learning, graph neural networks, and other AI techniques to solve complex optimization problems in mobile crowdsensing and edge computing. This shift reflects broader trends in the field toward intelligent, adaptive networking solutions. Scientific Recognition Best Paper Award from IEEE ICC (2018) UNB Merit Award (2014) Best Student Paper Award from IEEE CCNC (2013) Top 10% Award from IEEE MMSP (2009) NSERC postdoctoral fellowship (2008) Best Paper Award from IEEE WCNC (2007) Professional Service and Mentoring Dr. Song serves as Senior Member of IEEE and has held significant leadership roles, including Chair of the Joint Computer and Communications Chapter of IEEE New Brunswick Section (2014-2020). She has chaired symposia at major conferences including IEEE VTC Fall 2023, 2017, and 2016. As a supervisor, she mentors graduate students in areas including intelligent edge computing and deep learning for networking, and is currently recruiting students for Winter 2024 and Fall 2025.
