Anh Tuan Le is an Associate Professor at the Department of Electrical Engineering, Chalmers University of Technology. He holds a PhD in Power Systems from Chalmers (2004) and a Master's in Energy Economics from the Asian Institute of Technology (1997). Specializes in power grid planning, electricity market modeling, and renewable energy integration Active in electric vehicle-grid interaction and battery storage systems Develops voltage stability solutions and decentralized control strategies His recent research focuses on: Flexibility markets for congestion management Machine learning applications in load forecasting Real-time security margin control using AI Key projects include: DigiRES (2024-2027): Digital integration of multi-energy flexibility POTENT-X (2024-2027): Port energy transition hubs FLEXIGRID (2019-2023): Distribution grid flexibility solutions
Marco Caccamo is a Professor at the Technical University of Munich (TUM) , holding the Chair of Cyber-Physical Systems in Production Engineering within the Faculty of Mechanical Engineering. He is also a Principal Investigator and Professor at the Department of Computer Science, with courtesy appointments in Electrical and Computer Engineering, Coordinated Science Lab (CSL), and Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research spans Embedded Systems , Real-Time Systems , and Cyber-Physical Systems (CPS) , focusing on resource management, reinforcement learning architectures, and 6D pose recognition for robotics. University of Pisa (B.Sc., 1997) Scuola Superiore Sant'Anna (Ph.D., 2002) Research highlights include predictable resource management on heterogeneous platforms, security frameworks for AI-based controllers , and UAV testbed development . His work integrates deep learning and real-time constraints in industrial applications like avionics, farming, and automotive systems. His 15 most recent publications emphasize cache optimization , memory bandwidth regulation , and reinforcement learning for CPS , with a focus on multi-core processors and DNN inference . Awards include the IEEE Fellow (2018), Alexander von Humboldt Professorship (2018), and multiple Best Paper Awards at RTSS, RTNS, and RTAS. NSF CAREER Award (2003) IEEE Fellow (2018) Alexander von Humboldt Professorship (2018) Best Paper Awards (RTSS 2024, RTNS 2023, ECRTS 2019) He has advised numerous Ph.D. students and postdocs, with a track record in UAV development and industrial collaborations . His lab, the Real-Time and Embedded System Laboratory , focuses on real-time OS and predictable computing .
David De Roure is Professor of e-Research at the University of Oxford and Academic Director of both the Digital Scholarship initiative and the Laboratory for AI Security Research. He is also an Honorary Research Professor at the Royal Northern College of Music (RNCM), where he serves as Technical Director of the Centre for Practice & Research in Science & Music (PRiSM). His work bridges computer science, digital humanities, cybersecurity, and music through his distinctive interdisciplinary approach. De Roure received his PhD in 1990 supervised by David W Barron and Peter Henderson, with research in Lisp and distributed systems. Prior to joining Oxford in 2010, he was Professor of Computer Science at the University of Southampton and Director of the Centre for Pervasive Computing in the Environment. His career spans multiple institutions and research domains, reflecting his commitment to interdisciplinary work. De Roure's research focuses on new methods of digital scholarship, innovation in knowledge infrastructure, cybersecurity, and computational approaches to music. His work uniquely combines humanities (digital musicology), social sciences (social machines and web science), engineering (Internet of Things), and computer science (distributed systems, AI). A key theme is empowering human creativity through technology rather than replacing humans with AI. He emphasizes co-creation between humans and machines, particularly in music composition where he explores how algorithms can generate fragments for human assembly. His recent publications reveal a strong focus on AI security in IoT systems, digital scholarship methods, and the intersection of music with computational approaches. There's a clear trajectory from foundational work in social machines and web science toward current applications in cybersecurity and music-AI co-creation. His publications consistently bridge technical domains with humanistic inquiry, demonstrating his commitment to interdisciplinary scholarship that addresses real-world challenges. Fellow of the British Computer Society (FBCS) Fellow of the Institute of Mathematics and its Applications (FIMA) Fellow of the Royal Society of Arts (FRSA) Chartered IT Professional (CITP) Turing Fellow at The Alan Turing Institute (2018-2024) De Roure has co-founded three major interdisciplinary initiatives: PETRAS National Centre of Excellence for IoT Systems Cybersecurity (the world's largest socio-technical research center focused on IoT security), the Software Sustainability Institute (dedicated to improving research software), and PRiSM at RNCM. He was Director of the Oxford e-Research Centre from 2012-17 and has led numerous research projects including SOCIAM (The Theory and Practice of Social Machines), FAST (Fusing Audio and Semantic Technologies), and Transforming Musicology. The Laboratory for AI Security Research, which he directs, took its first PhD students in 2024. At Oxford, De Roure chairs the Digital Research Cluster at Wolfson College and oversees the Laboratory for AI Security Research. The PRiSM team at RNCM has produced numerous musical works and performances, including six premieres in New York in 2024. He has been involved in designing gesture recognition software used in many performances and has collaborated on public engagement projects including the Science Together project which released a Hip Hop album. His current work includes exploring Chladni Plates for new musical instrument design and developing algorithmically enhanced instruments.
Ayse Coskun is a Professor in the Electrical and Computer Engineering Department at Boston University's College of Engineering. She serves as Director of the Center for Information and Systems Engineering (CISE) and as interim Associate Dean for Research and Faculty Development. Her research focuses on the intersection of computer systems, energy efficiency, and AI. Dr. Coskun received her PhD from the University of California, San Diego in 2009. Prior to joining academia, she worked at Sun Microsystems (now Oracle). Her research spans energy-efficient computing, cloud computing, high performance computing, computer architecture, and embedded systems, with recent work focusing on AI's impact on data center energy demands. Her publication record shows consistent innovation across multiple domains, with recent work emphasizing AI applications for improving cloud security (through frameworks like DeltaSherlock and Praxi) and transforming data centers into grid-responsive assets (Emerald AI project). Her research bridges theoretical advances with practical applications, resulting in tools adopted by industry partners including IBM. IBM Faculty Award (2020) Ernest S. Kuh Early Career Award (2017) NSF CAREER Award (2012-2017) Multiple best paper and artifact awards at top conferences As an educator, Dr. Coskun teaches courses including EC327 Introduction to Software Engineering, EC535 Introduction to Embedded Systems, and EC713 Advanced Computing Systems and Architecture. She has advised numerous PhD students including Mert Toslali, Anthony Byrne, and Burak Aksar. Her lab maintains strong industry partnerships with IBM, Intel, AMD, and Oracle, and collaborates with academic institutions worldwide including Brown University, MIT, EPFL, and CEA-Tech in France. Dr. Coskun leads the Coskun Lab, which secured a $500K grant from Sandia National Labs for AI-based analytics in high performance computing systems, demonstrating the practical impact of her research on critical computing infrastructure.
Professor Tim Dodwell holds a personal chair in Machine Learning at the University of Exeter, spanning the Department of Mechanical Engineering and the Institute of Data Science and AI. He leads the Data Centric Engineering Group and serves as co-founder and CTO of digiLab, a deep tech startup. His prestigious appointments include a 5-year Turing AI Fellowship from the Alan Turing Institute and the Romberg Visiting Professorship at Heidelberg University in Scientific Computing. His academic foundation includes a 1st class BSc in Mathematics from the University of Bath (2004-2008) and a PhD in Applied Mathematics from the Bath Institute of Complex Systems (2009-2012), where he researched variational models for complex materials under Professors Giles Hunt and Mark Peletier. Dodwell's research pioneers the intersection of applied mathematics, probabilistic machine learning, and high-performance computing, with signature contributions to Multilevel Methods in Bayesian Inverse Problems , Generative Hybrid Modelling , and Machine Learning in Safety Critical Engineering . His work bridges theoretical data science with industrial applications across nuclear fusion, aerospace materials, air traffic control, nuclear decommissioning, water treatment, and urban solar energy systems. His major recognitions include: Turing AI Fellowship (2019-2024) Romberg Visiting Professorship at Heidelberg University Visiting Professorship at MIT Prize Fellowship in Engineering Mathematics (2013-2015) Pro Vice Chancellors Fellowship (2015-2018) Through competitive fellowships and digiLab initiatives, Dodwell secures funding for uncertainty quantification research while driving real-world impact in sustainability sectors. His dual academic-industry roles enable rapid translation of theoretical advances into engineering solutions, particularly through digiLab's twinLab platform which delivers 60,000x acceleration in simulation workflows. He directs the Data Centric Engineering Group at Exeter and co-founded digiLab's multidisciplinary team comprising AI specialists, domain experts, and educators. The organization operates through three synergistic pillars: developing AI solutions for critical infrastructure, building the twinLab platform for industrial ML deployment, and running an ML academy for practitioner training through datacamps, internships, and specialized courses.
Prof. Dr. Peter Gomber is a Professor of e-Finance at the Faculty of Economics and Business Administration , Goethe University Frankfurt, since 2004. He co-chairs the Data Science Institute (efl) and holds adjunct professorships at the University of Bamberg (2004), Mannheim (2009), and Luxembourg (2018). His roles include board memberships at the Frankfurt Stock Exchange, Clearstream Banking AG, and advisory positions for European regulatory bodies. Education : Diplom-Kaufmann in Economics, University of Gießen (1999), PhD in Business Informatics. Research Interests focus on market microstructure , FinTech , electronic trading , and regulatory impacts on financial markets. His work explores algorithmic trading , liquidity dynamics , and AI-driven compliance . Publication Trends (15 most recent) emphasize digital finance , market fragmentation , regulatory analysis , and AI applications in trading and compliance. Key subfields include blockchain , high-frequency trading , and news-driven liquidity shocks . Scientific Awards : Reuters Innovation Award (2000) Hochschulpreis des Deutschen Aktieninstituts (1999) IBM SUR Grant (2007) Best Paper Awards (multiple conferences) Best Information Systems Publications Award (2020) Advising & Grants : Teaches in executive programs (Goethe Business School, Amsterdam Institute of Finance). Secured grants from public/private institutions, including a U.S. patent for market model innovation. Labs & Teams : Leads the e-Finance professorship and contributes to the Data Science Institute (efl) , fostering industry-academia collaborations with Deutsche Börse, Capveriant, and others.
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
Salim El Rouayheb is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. He leads the Coding and Securing Information (CSI) Lab, which focuses on information-theoretic security and privacy in distributed systems. His research spans multiple areas including secure machine learning, private information retrieval, and data synchronization. Dr. El Rouayheb received his Ph.D. in Electrical Engineering from Texas A&M University in 2009. Prior to joining Rutgers, he was an Assistant Professor at the Illinois Institute of Technology (2013-2017), a Research Scholar at Princeton University (2012-2013), and a Postdoctoral Researcher at UC Berkeley (2010-2011). His research interests focus on information-theoretic security in distributed systems, private information retrieval and search, secure machine learning algorithms, and data synchronization in distributed systems. He has made significant contributions to developing frameworks that provide information-theoretic privacy guarantees in various contexts including federated learning, genomic data analysis, and decentralized networks. His work often bridges theoretical foundations with practical applications, particularly in the areas of secure distributed computing and privacy-preserving algorithms. His recent publications demonstrate a strong trend toward applying information-theoretic principles to address privacy and security challenges in machine learning systems, particularly in federated and decentralized settings. Many of his papers explore random walk approaches for decentralized learning, secure matrix multiplication techniques, and privacy mechanisms that can be toggled "on and off" based on correlation patterns in data. His work spans both theoretical contributions in information theory and practical implementations for real-world systems. Dr. El Rouayheb has received several prestigious awards including the NSF CAREER Award (2016), Google Faculty Research Award (2018), and the Rutgers University Walter Tyson Junior Faculty Chair (2019). He has successfully secured multiple research grants including NSF SaTC, NSF CAREER, Google Faculty Research Awards, and Army Research Lab funding. His lab, the Coding and Securing Information (CSI) Lab, currently includes postdoc Xingran Chen, PhD student Zonghong Liu, and undergraduate researchers. The CSI Lab maintains an active research agenda with regular publications in top-tier venues and hosts the Shannon Channel, a series of online talks related to information theory. Dr. El Rouayheb is also involved in organizing workshops on coding theory and information security.
AI Xin is a Lecturer in the School of Computing at the National University of Singapore (NUS), specializing in Artificial Intelligence and Data Science. She teaches courses such as machine learning, deep learning, and data mining, including advanced modules like CS4225 and CS5425. Education: Ph.D. in Electrical and Computer Engineering from NUS; B.Eng. from Xidian University, China. Her research spans Game Theoretical Modelling , Optimization Methods , Algorithm Design , and Wireless Networks . She has contributed to multi-agent systems, algorithmic game theory, and wireless community networks, focusing on robust and distributed solutions. Her recent publications highlight trends in game theory for wireless networks , distributed coverage algorithms , and optimization for network efficiency , with a strong emphasis on theoretical and practical applications in AI and networking. Scientific Awards: Teaching Excellence Award (NUS, 2024). She has taught courses on Big Data Systems for Data Science and Computational Thinking , bridging academic rigor with industry relevance through her prior experience in risk management, supply chain, and sales at BHP Billiton Marketing Asia.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Roberto Tamassia is the James A. and Julie N. Brown Professor of Computer Science and Chair of the Computer Science Department at Brown University. He is also Director of Brown's Center for Geometric Computing. His research focuses on information security, cryptography, algorithms, graph drawing, and computational geometry. He has authored six textbooks and over 250 publications, and his work has been funded by ARO, DARPA, NATO, NSF, and industry sponsors. Education: PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. Research Interests: Cryptography and Secure Systems Algorithm Design and Optimization Graph Drawing and Geometric Computing Encrypted Database Security Awards and Honors: IEEE Fellow Technical Achievement Award from IEEE Computer Society Listed among the 360 most cited computer science authors by ISI Grants and Funding: His research has been supported by major agencies and organizations including ARO, DARPA, and NSF, as well as industry partnerships. Labs and Affiliations: Directs Brown's Center for Geometric Computing, a hub for interdisciplinary research in computational geometry and graph algorithms.
Prof. Nikos Mamoulis is a Professor at the Department of Computer Science & Engineering, University of Ioannina, and a Lead Researcher at Archimedes Research Unit, ATHENA RC. He holds a PhD from Hong Kong University of Science and Technology (HKUST) and a Diploma from the University of Patras. His research focuses on spatial data management, big data analytics, data privacy, and uncertain data systems. Education : Ph.D., Computer Science, Hong Kong University of Science and Technology (2000) 5-year Diploma, Computer Engineering and Informatics, University of Patras (1995) Research Interests : Complex data management (spatial, spatio-temporal, time-series, text, graphs), big data analysis, privacy preservation, and uncertain data systems. He develops scalable algorithms and systems for modern data management challenges, with applications in transportation, social networks, and geospatial analytics. Projects & Grants : MESA: In-memory Spatial Analytics Made Scalable (HFRI-funded, PI) MORE: Real-time Energy Data Management (H2020, Senior Researcher) Smart City Bus Platform (ERDF-funded, PI/Coordinator) Awards : Outstanding Young Researcher Award (2008-2009, HKU) Best Paper Award at SSTD 2015 Test of Time Award at MDM 2022 Labs & Collaborations : Leads the Archimedes Research Unit at ATHENA RC, collaborating with institutions like HKUST and Uppsala University on spatial and big data systems. Active in organizing top-tier conferences like SIGMOD, EDBT, and ICDE.
YingLi Tian is a CUNY Distinguished Professor in the Department of Electrical Engineering at The City University of New York. Their work focuses on computer vision, machine learning, and medical imaging. Key areas include sign language recognition, medical image analysis, and AI-driven healthcare solutions. Research Interests: Artificial Intelligence applications in healthcare 3D point cloud and scene understanding Self-supervised learning and domain adaptation Sign language recognition systems Medical imaging segmentation and diagnosis Human-robot interaction and assistive technologies Notable Projects: Developed AI systems for American Sign Language recognition using RGB-D data Pioneered self-supervised feature learning techniques in medical imaging Created virtual contrast enhancement tools for CT scans Advanced sea ice motion prediction using deep learning Labs & Teams: Leads the Media and Information Technology Lab at CCNY, focusing on multimodal AI and healthcare technology innovations.
Jeanna Matthews is a Full Professor of Computer Science at Clarkson University and an affiliate at Data and Society. She holds a PhD from UC Berkeley (1999) and teaches courses ranging from operating systems to cybersecurity. Her research focuses on algorithmic transparency, AI ethics, and societal impacts of automated systems. Matthews is a prominent ACM leader, serving on multiple committees including the Technology Policy Subcommittee on AI Accountability. She has pioneered work on forensic software analysis in criminal justice systems and delivered DEF CON presentations on virtualization security and adversarial testing. Awards include ACM Distinguished Speaker and Fulbright Specialist roles. Her work emphasizes open-source tools and critical thinking in education, extending to global service learning programs in the Dominican Republic and Brazil. Education: PhD in Computer Science (UC Berkeley, 1999), B.S. in Math/Computer Science (Ohio State, 1994), B.A. in Spanish (SUNY Potsdam, 2016). Research Interests: Cybersecurity vulnerabilities, algorithmic accountability frameworks, automated decision systems in justice contexts, and ethical AI design. Recent projects include investigating bias in DNA forensic software through a Brown Institute grant and analyzing political polarization on social platforms. Awards & Recognition: ACM Distinguished Speaker (2018-present) Fulbright Specialist (2018-present) 2018-2019 Brown Institute Magic Grant ACM SIGOPS Chair (2011-2015) ACM Special Interest Group Governing Board Chair (2016-2018) Teaching & Outreach: Designed courses integrating open-source tools and critical inquiry, including abroad programs in Mexico, Brazil, and the Dominican Republic. Advocates for lifelong learning strategies and questioning underlying assumptions in computing systems. Key Projects: Forensic Software Accountability: Examining discrepancies in DNA analysis tools Algorithmic Transparency: Frameworks for auditing automated systems Cybersecurity Education: Adversarial testing methodologies for justice software
Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.
