Dr. Dima Alhadidi is an Associate Professor in the School of Computer Science at the University of Windsor. His research focuses on Cybersecurity, Data Privacy, Machine Learning, and their applications in Health Informatics, Cloud Computing, and Smart Grids. He holds a PhD in Computer Science and Software Engineering from Concordia University (2010). His research interests include secure federated learning frameworks, privacy-preserving techniques for genomic and health data, and adversarial machine learning defenses. Notable contributions include Trustformer (2025), secure aggregation methods in federated learning, and hybrid malware classification using deep learning. Recent work emphasizes mitigating membership inference attacks and developing privacy-preserving analytics for distributed systems. Dr. Alhadidi actively advises graduate students on topics like social network clustering (NICASN 2022) and federated learning security. No scientific awards are explicitly listed. His research spans theoretical frameworks (e.g., λ_AOP calculus) to applied systems in smart grids and healthcare informatics.
Martin Hairer is a Professor of Pure Mathematics at Imperial College London and EPFL (École Polytechnique Fédérale de Lausanne). He has held significant academic positions, including Regius Professor at the University of Warwick and Professor at the Courant Institute, NYU. His research focuses on stochastic analysis, probability theory, and partial differential equations. Education: B.Sc., M.Sc., and Ph.D. in Physics from the University of Geneva (1998-2001) Martin Hairer’s research interests span Stochastic Partial Differential Equations (SPDEs), Rough Path Theory, and Regularity Structures, with applications in mathematical physics and statistical mechanics. His recent publications emphasize stochastic analysis and SPDEs, particularly the development of Regularity Structures and Rough Path Theory, enabling the rigorous understanding of singular SPDEs and nonlinear dynamics. Scientific Awards: Breakthrough Prize in Mathematics (2021) Fields Medal (2014) Fermat Prize (2013) Royal Society Wolfson Research Merit Award (2009) LMS Whitehead Prize (2008) Martin Hairer actively contributes to education through lecture notes and seminars, including courses on SPDEs and stochastic analysis. He also develops mathematical software.
Hari Subramonyam is an Assistant Professor (Research) at Stanford University's Graduate School of Education with a courtesy appointment in Computer Science. He serves as the Ram and Vijay Shriram Faculty Fellow at Stanford's Institute for Human-Centered AI (HAI) and is a core faculty member of Stanford HCI. Subramonyam earned his PhD in Information from the University of Michigan under advisor Eytan Adar. His research focuses on the intersection of Human-Computer Interaction (HCI) and Learning Sciences, specifically developing AI systems to augment human learning through cognitively informed design, co-design with educators, and transformative learning experiences. His work prioritizes ethical AI, responsible design practices, and human values in technology creation. Research spans generative AI for education, human-AI interaction paradigms, and accessible learning technologies. Subramonyam's publications demonstrate strong focus on human-centered AI systems for education, visualization, and creative applications. His recent work (2023-2025) concentrates on generative AI interfaces for writing assistance, educational tools, and collaborative systems, while maintaining consistent exploration of visualization techniques and AI transparency frameworks. Awards & Honors: Best Paper Award at CHI (2025, 2020, 2019) Honorable Mention Award at CHI (2025) Best Paper Award at IUI (2021) Ram and Vijay Shriram Faculty Fellow HAI Hoffman Yee Grant (2024) Cover Story in Interactions Magazine (2024) Advising & Grants: Leads 27 students including PhD advisee Neha Rajagopalan (co-advised) and diverse MS/BS researchers. Received HAI Hoffman Yee Grant (2024) for "Integrating Intelligence: Building Shared Conceptual Grounding for Interacting with Generative AI" as co-investigator. Teaches courses on data visualization (CS 448B) and educational technology design (EDUC 432). Labs & Leadership: Core faculty at Stanford HCI group, directing research on human-centered AI systems. Organizes workshops including "Tools for Thought" (CHI 2025) and "Human–AI Coevolution" (ICLR 2025). Maintains collaborations with National University of Singapore and University of Michigan.
Dr. Andrew Logsdail is a Reader in Catalytic and Computational Chemistry at Cardiff University’s School of Chemistry, part of the Cardiff Catalysis Institute (CCI). He holds a PhD in Chemistry (University of Birmingham), an MRes in Materials and Nanochemistry, and a BSc in Natural Sciences. His research focuses on computational modeling of catalytic materials, software development (e.g., ChemShell), and heterogeneous catalysis with applications in energy and sustainability. He is a Fellow of the Higher Education Authority and a Chartered Chemist with the Royal Society of Chemistry. Key roles include UKRI Future Leaders Fellow (2020–2024) and leadership in international organizations like the IUPAC Division II. His work is funded by UKRI, EPSRC, and industry partners like BP and Johnson Matthey. Research interests span computational catalysis, nanomaterials, and data-driven materials discovery. Notable projects include QM/MM simulations for catalytic systems, development of the ChemShell software, and studies on zeolites, palladium catalysts, and CO₂ reduction. He supervises PhD students and contributes to teaching at undergraduate and postgraduate levels. Dr. Logsdail’s achievements include over 100 peer-reviewed publications and significant contributions to software development in computational chemistry. His awards include the UKRI Future Leaders Fellowship and leadership roles in national and international scientific committees. He actively engages in outreach, promoting chemistry education and catalysis research.
J. Alex Halderman is the Bredt Family Professor of Computer Science & Engineering at the University of Michigan, directing both the Center for Computer Security and Society and the Michigan CSE Systems Lab. His work critically examines the societal impacts of security and privacy technologies through empirical research and policy engagement. Halderman's research spans computer security and privacy with emphasis on election integrity, censorship resistance, and the intersection of technology with law and policy. He investigates real-world vulnerabilities in systems ranging from voting infrastructure to encrypted communications, prioritizing measurable societal impact through forensic analysis and large-scale measurement studies. His publication record demonstrates consistent focus on high-stakes security challenges, particularly in democratic processes and user privacy. Notable contributions include internet-wide scanning tools (ZMap), forensic investigations of election systems, and foundational work on cryptographic vulnerabilities affecting global infrastructure. Scientific recognitions include: USENIX Security Best Paper Award (2024) USENIX Security Best Paper Award (2022) USENIX Security Best Paper Award and Internet Defense Prize (2022) IEEE Symposium on Security and Privacy Best Student Paper Award (2020) Pwnie Award for Best Crypto Attack (2016) ACM CCS Best Paper Award (2015) ACM IMC Applied Networking Research Prize (2015) ACM IMC Best Paper Award (2014) USENIX Security Best Paper Award and Test of Time Award (2012) USENIX Security PET Award Runner-up (2011) USENIX Security Best Student Paper Award (2008) Halderman advises a dynamic research group including current members Braden Crimmins, Erik Chi, and Dhanya Narayanan, with over two dozen alumni who have advanced the field. His leadership extends to developing practical security solutions like Let's Encrypt and conducting court-admissible forensic analyses of election systems. He directs the Michigan CSE Systems Lab, which pioneers research in computer systems security, and the Center for Computer Security and Society, which bridges technical research with policy impact through cross-disciplinary collaboration.
Tom Verhoeff is an Assistant Professor at the Faculty of Mathematics and Computing Science of Eindhoven University of Technology (TU/e) , working within the Software Engineering & Technology group. His research focuses on Model-Driven Engineering (MDE) , Domain-Specific Languages (DSLs) , and the intersection of mathematics, computing, and the arts . He teaches courses in data analytics, programming, algorithms, theoretical computer science , and logic . Verhoeff earned both his MSc and PhD in Technical Science (Mathematics and Computer Science) from TU/e. He is actively involved in promoting mathematics and informatics through initiatives like the annual Bridges conference , and serves as board member and treasurer of the Dutch Mathematics Olympiad , as well as chair of the Koos Verhoeff MathArt foundation . He has also held roles as guest lecturer in Lithuania and Finals Director for the ACM International Collegiate Programming Contest . Research Interests: Verhoeff’s work spans Model-Driven Engineering , domain-specific language development , and 3D geometric modeling . His scholarship often explores symmetry, recursion, and mathematical visualization , particularly through computational art and algorithmic puzzles . Recent publications highlight 3D rotation methods , knot theory , and mathematical art using lattice paths and geometric transformations . Scientific Awards: ACM ICPC European Founders Award (2004) IOI Distinguished Service Award (2007) Second Place in the 2022 Wolfram Computational Art Contest Notable Collaborations and Affiliations: He is affiliated with the Esprit Working Group on Asynchronous Circuit Design (ACiD-WG) , WIRE (TUE Mathematics Alumni) , ACM (Senior Member) , CSTA , IEEE Computer Society , and Royal Dutch Mathematical Society (KWG) .
Guanghan Meng is an Assistant Professor at the University of California, Berkeley , with dual appointments in the Herbert Wertheim School of Optometry and Vision Science and the Department of Electrical Engineering and Computer Science (EECS) . He leads the Visionary Optical Imaging Lab (VOILA) , focusing on interdisciplinary research combining optical physics and computational science to develop advanced microscopy technologies for eye and brain imaging. Education : PhD (2021, UC Berkeley), BE (2015, Shanghai Jiao Tong University) PhD Programs Affiliated With : Vision Science, Applied Science & Technology (AS&T), EECS His research integrates optical physics , computational biology , and artificial intelligence to create cutting-edge imaging tools. Recent work includes differentiable wave-optics libraries (Chromatix), super-resolution microscopy techniques, and high-speed neural imaging systems. Publications highlight applications in neuroscience (cerebral circulation, synaptic activity) and biomedical imaging (OCT, two-photon microscopy). VOILA is a highly interdisciplinary team spanning physics , engineering , and biology . In 2025, the lab will welcome 2 PhD students and 1 postdoc, though funding is currently at capacity for new members. Meng is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) and Berkeley Center for Computational Imaging (BCCI) .
Gary L. Miller is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. His research focuses on Spectral Graph Theory, Algorithms, Computational Geometry, and Scientific Computing. He has developed influential methods in graph partitioning, mesh generation, and numerical linear algebra solvers. Teaching includes advanced courses like Spectral Graph Theory, Algorithms, and Computational Geometry. Active in publishing, recent work involves weighted Cheeger inequalities, exact manifold metric computations, and adaptive graph sketching techniques. Projects include Orasis, 3D Meshing Software, Tumble, and Sangria. His work bridges theoretical foundations with applications in machine learning, image processing, and scientific computing. Current projects emphasize efficient graph algorithms and scalable numerical methods.
Alexandre Manuel de Castro Passos de Almeida is an Assistant Professor in the Department of Information Science and Technology at the University Institute of Lisbon (ISCTE-IUL), where he also contributes to the School of Technology and Architecture. He is an Associate Researcher at the Institute of Telecommunications - IUL, actively involved in the Radio Systems Group, focusing on telecommunications and sensor-based environmental monitoring. PhD in Telecommunications, ISCTE-IUL, 2012 His research interests span telecommunications, wireless sensor networks, air quality monitoring, computer architecture, and robotics. He has led and contributed to innovative projects such as ExpoLis, which uses mobile sensor networks on public buses to map urban air pollution. His work bridges engineering and environmental science, aiming to influence urban policy and public health through real-time data systems. The most recent publications highlight a strong trend toward deploying low-cost, mobile sensor networks for environmental monitoring, particularly in urban settings. His work integrates computer systems, signal processing, and sustainable technology, with applications in smart cities, public health, and robotics. Topics like air quality mapping, energy harvesting, and noise-aware robot navigation reflect a multidisciplinary approach to solving real-world problems. He has advised seven Master’s students at ISCTE-IUL, guiding research in sensor networks, air pollution, database performance, robotic navigation, and personal photography assistants. While no specific scientific awards are listed, his contributions to funded research projects and consistent scholarly output indicate recognition in his field. He has held leadership roles in academic governance, including Vice President of the Pedagogical Council, underscoring his institutional engagement. His teaching portfolio includes core courses such as Fundamentals of Computer Architecture, Operating Systems, and Big Data Processing, delivered across multiple undergraduate and postgraduate programs. He is involved in research projects that combine academic innovation with practical urban applications, particularly through the deployment of scalable, open-source environmental sensing systems.
Jenine Brown is Associate Professor of Music Theory and Coordinator of Ear Training at The Peabody Conservatory of The Johns Hopkins University. She joined the faculty in 2015 and teaches the undergraduate ear training core curriculum and a graduate seminar on music cognition and analysis. Her work bridges empirical research and music-theoretical inquiry, with a strong focus on listener expectation and aural skills pedagogy. Ph.D. in Music Theory, Eastman School of Music Degrees from University of Michigan–Ann Arbor Executive Leadership Training, Johns Hopkins Carey Business School Her research takes an empirical approach to understanding how listeners perceive musical structure, especially in tonal and post-tonal contexts. She investigates pre-compositional structures and collaborates on projects exploring the pre-dominant function with Dr. Daphne Tan and others. Her pedagogical scholarship focuses on aural skills instruction, including reviews of digital tools and contributions to major publications such as The Routledge Companion to Aural Training in Music Education . She has published in Music Theory Spectrum , Music Perception , Frontiers in Psychology , and Journal of Music Theory Pedagogy . Her recent publications and presentations reflect a strong interdisciplinary trend, combining music theory, cognitive science, and educational research. Topics include listener expectation, aural pedagogy, Suzuki method applications, and empirical modeling of musical cognition. She frequently presents at major conferences such as the Society for Music Theory, Society for Music Perception and Cognition, and the International Conference on Music Perception and Cognition. Her scientific awards include: Johns Hopkins University Catalyst Award (2023) Peabody Conservatory CARES Award (2020) COVID-19 Research Accelerator Grant (2021) Brown is actively involved in academic service and mentoring. She has served as Associate Editor of Music Theory Online , President of the Music Theory Society of the Mid-Atlantic (2022–2024), and is a key contributor to the College Board’s AP Music Theory program, where she has served as Visiting Fellow and currently sits on the Test Development Committee. She mentors students through collaborative research, resulting in co-authored publications in Frontiers in Psychology and Research Studies in Music Education . Her leadership extends to hosting the 20th-anniversary MTSMA conference at Peabody in 2023. Brown leads initiatives in music cognition and aural skills development at Peabody, collaborating with colleagues across disciplines. She is central to curriculum development in ear training and integrates cognitive research into pedagogical practice. Her lab-like collaborative environment fosters student involvement in empirical research and conference presentations.
Dr. YANG, Renchi is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University, Faculty of Science. He earned his BEng in Software Engineering from Beijing University of Posts and Telecommunications and his PhD in Computer Science from Nanyang Technological University, followed by a postdoctoral fellowship at the National University of Singapore. His research is centered on developing efficient algorithms and systems for large-scale data management and analysis. His research interests include: Big Data Management and Analysis Graph Learning and Network Embedding Databases and Data Management (especially graph query processing and similarity search) The Web and Information Retrieval (search, ranking, recommendation, web mining) Data Mining and Machine Learning (social network analysis, text mining, large language models) Dr. Yang’s recent publications span top conferences such as KDD, SIGMOD, WWW, ICDE, and AAAI, focusing on scalable graph clustering, network embedding, GNNs, and LLM integration. His work emphasizes algorithmic efficiency, scalability, and practical applications in real-world graph data. Scientific honors include: VLDB 2021 Best Research Paper Award 2022 ACM SIGMOD Research Highlight Award Best Paper Award Nominee in WWW 2022 Honorable mention as best PC member in WWW 2022 Dr. Yang actively mentors PhD and research students, currently supervising several RPg students including LIN Xiaoyang, LAI Yurui, and ZHENG Haoran. He has secured research funding enabling PhD scholarships and research assistant positions. He serves on the program committees of major conferences like VLDB, KDD, WWW, and SIGIR, and reviews for journals including TKDE and VLDBJ. He is a key member of the Database Research Group at HKBU, which has published extensively in top venues, including 8 papers at SIGMOD 2023. His research lab, the LAGAS Group, focuses on large-scale graph analytics and systems. The team is actively working on projects involving graph clustering, embedding, GNNs, and integration with large language models. Dr. Yang is currently recruiting PhD students for 2026 and research assistants for 2025, indicating active and expanding research operations.
Merlyna Lim is a Full Professor at the School of Journalism and Communication, Carleton University, and a former Canada Research Chair in Digital Media and Global Network Society. She is the founder and director of the ALiGN Media Lab, an interdisciplinary research lab focusing on digital media, data, and civic engagement. Her research spans the interplay between technology, society, and politics, with a regional focus on Southeast Asia. Education: PhD in Science and Technology Studies, University of Twente, Netherlands Bachelor’s in Architecture, Institute of Technology Bandung (ITB), Indonesia Bachelor’s in Architectural Engineering, University of Parahyangan (Unpar), Indonesia Her research interests include digital media, political communication, social movements, algorithmic politics, critical data studies, privacy, surveillance, and religion and media. She takes an interdisciplinary approach, drawing from communication, sociology, urban studies, religious studies, and computer science. Her work critically examines how digital platforms shape civic engagement, dissent, and social change, particularly in the Global South. Her recent publications explore themes such as affective sociability in Indonesian social media, algorithmic enclaves, digital disconnection, and the evolution of activist media in Southeast Asia. The articles reflect a strong focus on the political and cultural implications of algorithms, disinformation, and platform governance in non-Western contexts. Scientific Awards and Honors: Royal Society of Canada’s New College of Scholars, Artists, and Scientists (2016) Carleton University Top 10 Women Leaders and Researchers (2020) Faculty Graduate Mentoring Award (2019) Best Publication Award in Information Systems (2012) One of 100 Most Inspiring Indonesian Women (2011) One of 100 Most Prominent Alumni of ITB (2020) One of 25 Notable Alumni of University of Twente (2023) Lim has received major research grants from SSHRC, CFI, NSF, Ford Foundation, and others. She has delivered over 250 talks globally and has been featured in media such as The Guardian, Al Jazeera, and CBC News. She has held visiting positions at Princeton University, Arizona State University, and the University of Southern California. She also leads the a cappella group The Edoens and is an accomplished visual artist.
Dr. Paul Ralph is a Professor in the Faculty of Computer Science at Dalhousie University , where he leads the Dalhousie Software Engineering Lab (DalSEL) . His work bridges software engineering, human-computer interaction, and project management, with a focus on empirical research and social sustainability in software development. Education: PhD in Computer Science, University of British Columbia BSc in Computer Science, Memorial University BComm in Business, Memorial University Dr. Ralph's research centers on the sociotechnical aspects of software engineering , particularly how team dynamics, ethics, and human factors influence software success. He rejects pseudoscientific models like Waterfall and SDLC, and avoids AI/ML/data science, instead emphasizing rigorous qualitative and quantitative human-participant studies. His lab is known for its work on socially sustainable software engineering and evidence standards in computing research. His recent publications reflect a strong trend toward methodological rigor, ethical computing, and human-centered practices . Themes include empirical standards, agile methods, requirements engineering, and the social impact of technology. He publishes in top venues like IEEE TSE and ICSE , and has authored over 80 scholarly works. Scientific Awards and Recognition: Award-winning scientist (multiple unspecified awards) Editor-in-Chief, SIGSOFT Empirical Standards for Software Engineering Research Dr. Ralph is actively involved in mentoring and funding graduate students , particularly through external scholarships like NSERC, Killam, and Banting. He prioritizes applicants from underrepresented groups and emphasizes original, non-AI-generated work. His lab offers strong industry connections, professional development, and support for tenure-track aspirations among postdocs. Labs and Research Groups: Dalhousie Software Engineering Lab (DalSEL) : Focuses on empirical, human-centered software engineering research, with active projects in social sustainability and evidence standards.
Simon Ruffieux is a Senior Researcher and Lecturer at the Department of Computer Science, University of Fribourg, and a member of the Human-IST Institute. He currently leads the HIP-Initiative (Human-IST x SwissPost Initiative) and coordinates academic projects related to Swiss Post. His academic roles include Lecturer and Senior Assistant , reflecting his active engagement in teaching and research. His research focuses on leveraging advanced technologies to support individuals, particularly those with special needs. Key areas include: Machine Learning and Data Science for urban systems (e.g., bike-sharing optimization) Human-Computer Interaction (HCI), especially gesture recognition and multimodal interfaces Augmented and Virtual Reality applications in rehabilitation and assistance Development of smart glasses for visually impaired users Physiological signal analysis for workload classification The 15 most recent publications reveal a strong trend in applying AI and data science to real-world challenges, particularly in assistive technologies and urban mobility. His work often involves interdisciplinary collaboration, integrating computer science with psychology, rehabilitation, and industrial applications. There is a consistent emphasis on user-centered design and real-world usability. Simon Ruffieux has not been mentioned as receiving specific scientific awards in the provided text. He has advised or collaborated with several researchers, including Nicolas Spycher, Samuel Torche, and Nicolas Ruffieux, on projects related to forecasting, AR, and gesture recognition. While no formal grant details are listed, his leadership of the HIP-Initiative suggests involvement in externally funded academic projects. His work is closely tied to the Human-IST Institute, where he contributes to interdisciplinary research in human-centered computing. He is actively involved in research teams focused on assistive technologies, gesture interaction, and data-driven urban solutions. The Human-IST Institute serves as the primary hub for his collaborative efforts, particularly through the HIP-Initiative with Swiss Post.
Professor Michael Clarke is a distinguished academic at the University of Huddersfield , serving as Director of IRiMaS (Interactive Research in Music as Sound) and holding leadership roles including former Dean of the School of Music, Humanities and Media and Dean of the Graduate School . His career spans over three decades at Huddersfield, where he has pioneered innovative software for music composition, pedagogy, and analysis. Education: PhD in Music (Durham University), MTC in Teaching (UCL Institute of Education) Clarke's research focuses on composition , particularly in live interactive works , and the development of software like Max/MSP for musicological analysis and sound synthesis. His work intersects with UN Sustainable Development Goals , emphasizing technological innovation in education and cultural preservation. Recent publications highlight his contributions to interactive aural analysis and fluid corpus manipulation tools. Clarke has secured major funding, including a €2.5m ERC Advanced Grant for IRiMaS and AHRC grants for collaborative projects. Awards include the National Teaching Fellowship (2011) and multiple European Academic Software Awards . Scientific Awards: National Teaching Fellowship (2011) European Academic Software Awards (record 3 wins) As a Principal Investigator , he has led projects with Prof Peter Manning and Dr Frédéric Dufeu, while actively supervising PhD students and contributing to REF assessments.
