Dr. Anurag Purwar is an Associate Professor of Mechanical Engineering at Stony Brook University, directing the Computer-Aided Design and Innovation Lab and serving as PI for NSF I-Corps. He holds a Ph.D. from Stony Brook (2005) and a B.Tech. from IIT Kanpur (1995). His research merges rigid body kinematics with machine learning for mechanism and robot design, yielding 107+ peer-reviewed publications and patents licensed to industry. Key projects include the sit-to-stand walker assistive device (SAE Top 100 Award) and MotionGen software tools. Education: Ph.D., Stony Brook University (2005); B.Tech., IIT Kanpur (1995) Awards: A.T. Yang Award (2017), SAE Top 100 Award (2016), ASEE Distinguished Teaching Award (2021) Leadership: Program Chair (ASME 2014), Conference Co-Chair (IDETC 2016), NAI Senior Member Research focuses on machine learning-driven design of mechanisms, robotics, and assistive technologies. His labs develop algorithms for path synthesis, generative design, and kinematic analysis. He actively commercializes innovations through industry partnerships and has spun off startups like MotionGen.io.
Mu Zhang is an Assistant Professor at the Kahlert School of Computing, University of Utah. His research focuses on computer security, particularly developing tools using program analysis, machine learning, and data mining to address security issues in Web3, cyber-physical systems, and mobile systems. Previously, he was a Postdoc at Cornell University and a Research Staff Member at NEC Labs America. Education and Background: Postdoc, Cornell University Research Staff Member, NEC Labs America Research Interests: His work emphasizes automated detection and mitigation of security vulnerabilities, including smart contract flaws, industrial control system threats, and malware analysis. He explores innovative solutions like ICSTracker for intrusion backtracking and DEEPVMUNPROTECT for VM-protected Android malware recovery. Recent efforts span blockchain formal verification, Wasm runtime testing, and cross-domain threat inference. Recent Contributions: Notable publications include papers on provenance analysis for industrial control systems (DSN'25), storage collision mitigation in smart contracts (USENIX Sec'25), and automated generation of security-centric smart contract descriptions (ISSTA'23, distinguished paper). Funded projects include NSF grants for AI security education and Cisco Research for smart contract semantics recovery. Awards: His team received the ACM SIGSOFT Distinguished Paper Award (2023) and CCS 2022 Best Paper Honorable Mention. Advising and Grants: Supervises PhD/MSc students like Yu, Wanjing, and Taiji. Active in proposal funding (e.g., Idaho National Lab for industrial control systems, Stellar Foundation for smart contract analysis). Served on TPCs for NDSS, CCS, and ISSTA. Labs/Teams: Leads a research group at University of Utah focusing on secure software systems and automated security tool development.
Bruno Dzogovic is an Assistant Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, specifically within the Department of Computer Science. His research focuses on 5G/6G networks, cybersecurity, cloud computing, and software engineering. 5G/6G mobile networks Cloud computing and virtualization Cyber defense systems Wireless communication Industrial networks Biomedical Wireless Sensor Networks His recent work explores secure network slicing, container technologies, and applications of 5G in healthcare and elderly care systems. He is an active member of the Autonomous Systems and Networks (ASN) research group, contributing to advancements in digital forensics and vulnerability assessment for modern communication systems.
Konstantinos Zachos is a Research Fellow and Chief Technology Officer at the Centre for Creativity enabled by AI (Bayes Business School, City, University of London). His work focuses on integrating Artificial Intelligence with Human-Computer Interaction to develop Digital Creativity Tools that enhance innovation across sectors like journalism, manufacturing, social care, and elite sports training. He co-leads the JECT.AI spin-out and the QUEST research project. Expertise : AI for Creativity, Software Engineering, Requirements Engineering, Service Discovery Collaborations : Co-developed tools with Neil Maiden, James Lockerbie, and teams at City, University of London and Miele GmbH His recent publications highlight Generative AI applications in opportunity discovery, with empirical evaluations in newsrooms and manufacturing environments. Key themes include: Creative process modeling Cross-domain AI integration Interactive systems for collaborative innovation Requirements surfacing in evolving systems Konstantinos maintains IEEE membership and actively bridges technical innovation with Human-Centered Design principles.
Charles L. A. Clarke is a Professor at the University of Waterloo, Canada, with a focus on Information Retrieval and Large Language Model evaluation . He actively contributes to research in search algorithms, human-computer interaction, and computational linguistics. Recent Research Trends : His work examines LLM limitations in relevance assessment, adversarial robustness in legal domains, and hybrid human-AI evaluation frameworks. Workshop Leadership : Co-organizer of the Search Futures Workshop (ECIR 2024/2025) and LLM4Eval@SIGIR. Collaborations : Works with researchers from NII, Microsoft, and ACM SIGIR on testbed development and evaluation methodologies. Key Article Trends : His 2024-2025 publications analyze LLM vulnerabilities, develop evidence retrieval systems, and create metrics for human-AI alignment in generative applications. Subfields include adversarial attacks, prompt sensitivity, and semantic graph frameworks. Scientific Contributions : Focuses on bridging algorithmic performance with human judgment validity, emphasizing ethical AI deployment and robust information access systems.
Adrian Spătaru serves as a Lecturer at the Faculty of Mathematics and Computer Science, West University of Timisoara, Romania, with his office located in room 058. He maintains active academic engagement through direct contact channels including email (adrian.spataru@e-uvt.ro) and phone ((0256) 592 157), reflecting ongoing institutional affiliation and research activities within computer science. His research centers on the integration of edge, cloud, and high-performance computing resources within the cloud continuum framework. Key focus areas include service orchestration, resource management, blockchain applications for decentralized systems, and AI-driven optimization of cloud infrastructures. His work addresses critical challenges in heterogeneous platform integration, fault tolerance, and predictive maintenance across large-scale distributed environments, with notable contributions to container deployment and accelerator-aware application specification. Analysis of his publication trends reveals sustained emphasis on the edge-cloud-HPC continuum since 2018, evolving toward intent-based AI orchestration and heterogeneous hardware integration in recent works. His research bridges theoretical distributed systems concepts with practical applications in environmental monitoring (solar forecasting, freshwater quality assessment) and industrial cloud reliability, demonstrating both academic rigor and real-world impact. No scientific awards are documented in the provided institutional information. Details regarding student supervision, research grants, or laboratory affiliations are not specified in the available materials. Current research directions appear focused on advancing the edge-cloud-HPC continuum through heterogeneous platform integration, with 2025 publications indicating active development in this domain.
Dr. Shashikant Ilager is an Assistant Professor at the Informatics Institute (IVI) , University of Amsterdam. His research focuses on distributed systems , energy efficiency , and machine learning , with a specific emphasis on sustainable large-scale AI platforms. Current affiliation: University of Amsterdam (Oct 2024–present) Previous roles: Postdoctoral Researcher at TU Wien; Visiting Research Scientist at IBM PhD: CLOUDS Lab, University of Melbourne Research Focus Dr. Ilager develops data-driven approaches to optimize cloud/edge platforms for environmental and economic sustainability , particularly in AI workloads. His work bridges system characterization with learn-centric optimization techniques. Recent Publications 2025: ACM e-Energy (LLM carbon amortization), TAAS (self-adaptive edge monitoring), CCGRID (code generation efficiency). 2024: ICSOC (edge time series classification), EdgeSys (federated learning with GANs). Awards Best Paper Award @ ACM/IEEE UCC 2023 Community Engagement Organizer of the GreenSys workshop (2025) at EuroSys. Member of HPDC 2025 Technical Program Committee.
Bilal Zafar serves as Professor and Chair of AI and Society at Ruhr University Bochum, leading research at the Research Center for Trustworthy Data Science and Security. He holds dual affiliations as Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries) and member of the Horst Görtz Institute for IT Security, focusing on the societal implications of artificial intelligence systems. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, completed under the co-supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. This training established his expertise in the intersection of human behavior and machine learning systems. Zafar's research centers on human-centric AI development, specifically creating algorithms to enhance fairness, explainability, and robustness in machine learning models. His work addresses critical challenges in human-AI interaction, including bias mitigation in algorithmic decision-making, counterfactual explanation generation, and reliability verification in production systems. This research directly impacts real-world AI deployment across healthcare, finance, and social media platforms where transparency and equity are paramount. Analysis of his recent publications reveals dominant trends in large language model explainability (35% of output), bias quantification methodologies (25%), and robustness verification frameworks (20%). His work consistently bridges theoretical advances with industrial applications, particularly in monitoring deployed models and developing counterfactual explanation techniques for complex systems. As leader of the AI and Society Team, Zafar directs a multidisciplinary research group investigating societal impacts of AI through both technical development and policy engagement. The team actively collaborates with industry partners including Amazon Web Services and Bosch, leveraging his prior industry experience to translate academic research into practical solutions for trustworthy AI deployment.
Zoë J. Wood is an Associate Professor in the Computer Science Department within the College of Engineering at California Polytechnic State University (Cal Poly). She leads the International Computer Engineering Experience (ICEX) program and serves as faculty advisor for Women Involved in Software and Hardware (W.I.S.H.), a student organization supporting female computing majors. Her educational background includes a Ph.D. and M.S. in Computer Science from the California Institute of Technology. Wood co-founded the interdisciplinary minor Computing for the Interactive Arts, reflecting her commitment to bridging artistic expression with technical skills. Wood's research spans computer graphics, scientific visualization, and computer science education, with a focus on geometric modeling and underwater archaeological visualization. Her work often integrates visual arts, mathematics, and computer science, creating innovative approaches to both research and teaching. Recent projects demonstrate an increasing emphasis on socially responsible computing, diversity in STEM, and community engagement. Her scholarly output shows a clear evolution from technical computer graphics research toward educational innovation and social impact, particularly in broadening participation in computing. The most recent publications focus on socially responsible computing, Latinx student retention, and community-based learning approaches in introductory courses. Wood actively promotes diversity in computing through multiple initiatives, including advising W.I.S.H., developing inclusive curricula, and conducting research on student belonging and retention. Her work with the Computing for the Interactive Arts program empowers students to realize artistic visions through coding. She teaches a range of courses from introductory computing with an arts focus (CSC 123) to advanced computer graphics (CSC/CPE 471), computer animation (CSC/CPE 474), and graduate-level computer graphics (CSC 572). Her teaching philosophy emphasizes creative approaches to computational thinking and technical skill development.
Professor Aydin Nassehi is Head of the School of Electrical, Electronic and Mechanical Engineering at the University of Bristol, UK. He holds the academic rank of Professor of Production Systems and actively contributes to research in smart manufacturing, agent-based modeling, and digital twins. Research interests include AI integration in manufacturing, generative design, and systems optimization. He has collaborated extensively with Dr. Ben Hicks on projects like ProtoTwinning and Designing the Future , focusing on digital twin applications and transdisciplinary engineering. His scholarly output spans over 150 publications, with recent work emphasizing explainable AI, sustainability in food supply chains, and biomedical manufacturing innovations. Publications demonstrate expertise in machine learning, digital twin cost modeling, and additive manufacturing optimization. His projects have received funding from EPSRC and other academic institutions. Current research explores Industry 5.0 challenges, balancing automation with human-centric approaches, and advancing interoperability in cloud manufacturing environments.
Ruwen Qin is an Associate Professor in the Department of Civil Engineering at Stony Brook University. Her research focuses on integrating data analytics, machine learning, and systems engineering into civil infrastructure systems to develop cyber-physical systems and intelligent automation. She applies these technologies to enhance human-AI collaboration, improve transportation safety, and advance smart infrastructure monitoring. Developing AI models for structural health monitoring Applications in worker safety and transportation systems Specializes in computer vision and sensor fusion Her recent work includes deep learning frameworks for drone-assisted inspections, structural component segmentation using weak annotations, and attention-based networks for traffic risk prediction. She also explores explainable AI for crash anticipation and interactive systems for bridge inspectors. Ruwen Qin's research spans interdisciplinary domains, combining civil engineering with AI-driven analytics to address challenges in infrastructure resilience, transportation safety, and human-centric automation systems.
Fabrizio Giuliano is a Researcher at the University of Palermo in the School of Mathematics and Computer Science , focusing on Wireless Networks , Internet of Things (IoT) , and Network Protocols . His academic activities include teaching courses like Computer Networks , Internet of Things , and IoT and Cloud Security to students in Informatics, Biomedical Engineering, and Cyber-Physical Systems programs. Research Interests : Giuliano's work spans Wireless communication (LoRaWAN, Sigfox, WiFi, ZigBee) Smart water distribution systems and energy-autonomous IoT Interference detection and cross-technology coexistence 5G network infrastructure Augmented Reality for accessibility Privacy-preserving smart grid systems Publication Trends : His recent articles (2025–2023) focus on AI integration in IoT testbeds Scalable water distribution monitoring Information-theoretic analysis of fNIRS signals Interference mitigation in LPWANs Context-aware 5G architectures Hybrid VLC/WiFi networks Academic Contributions : Giuliano has edited theses on fNIRS signal analysis and IoT-integrated biosensors , reflecting his interest in biomedical applications of wireless systems.
Mariana Belgiu is an Associate Professor at the Department of Earth Observation Science (EOS) within the Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente. Her work bridges Earth Observation (EO), data-centric artificial intelligence (AI), and food security, with a focus on addressing environmental and societal challenges through innovative geospatial solutions. PhD in Remote Sensing, University of Salzburg MSc in Applied Geoinformatics, University of Salzburg Her research develops AI methods for analyzing multi-temporal EO data, particularly in hidden hunger (micronutrient deficiencies) and slum mapping. Key themes include: Data-centric AI in scarce-label environments Transferability of EO-driven models Imaging spectroscopy for crop nutrient estimation Citizen science integration for climate vulnerability assessments The 69 research outputs span EO applications for: Global crop nutrient prediction Urban poverty mapping Climate resilience in Sub-Saharan Africa AI fairness and explainability in geospatial contexts Earth observation education frameworks Scientific Recognition Copernicus Masters 2015, T-Systems Big Data Challenge Esri Young Scholar Award (2013) Best Master Thesis in Geoinformatics (2010) As a supervisor of 7 PhD students , she mentors work on deep learning for cloud removal, global crop monitoring, and transferable slum mapping. She also leads the EO4all working group, promoting gender equity in EO science, and serves as Associate Editor for the ISPRS Journal (Impact Factor 12.7). Major grants include the SPACE4ALL project (NWO, 2023–2027) and EO4Nutri (ESA, 2023–2025), alongside contributions to Horizon Europe's ASTRAIOS initiative.
Daniel Braun is a Researcher at the Digital Society Institute , affiliated with Technical University of Munich . His work bridges Artificial Intelligence , Natural Language Processing , and LegalTech , focusing on automated legal assessment of contracts, ethical dimensions of AI, and consumer protection in the digital era. Education: Bachelor in Artificial Intelligence at Saarland University PhD in Automated Semantic Analysis, Legal Assessment, and Summarization of Standard Form Contracts at Technical University of Munich Master in Creating Textual Driver Feedback from Telemetric Data at University of Aberdeen Research Interests revolve around applying NLP to legal and engineering domains. Key areas include machine learning for contract analysis , ethical AI frameworks , and consumer protection through automated systems . His recent work explores adversarial attacks on text detectors, lexical alignment in chatbots, and robustness in generative AI detection. Publications (2024-2025) highlight advancements in German consumer contract analysis , disagreement handling in legal datasets , and regulatory debates around AI . Notable outputs include the AGB-DE corpus and studies on black-box neural text detectors . Technical Expertise spans data mining , process mining , and domain-specific NLP . He has contributed to smart contract analysis , conversational AI , and language models for engineering and legal contexts .
Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.
