Ashkan Yousefpour is a Computer Scientist with a PhD from the University of Texas at Dallas , where he contributed to the FLOW project. He served as a Lecturer and research assistant at UT Dallas, while also working as a Visiting Researcher at UC Berkeley . His research spans Fog/Edge Computing , Federated Learning , Reinforcement Learning , and Distributed Systems . Current Role: AI Scientist at Meta Academic Affiliation: Department of Computer Science, University of Texas at Dallas Research Interests include: Minimizing IoT service delay through fog offloading Developing failure-resilient distributed neural networks (ResiliNet) Advancing privacy-preserving machine learning (Opacus, Papaya) Optimizing traffic flow with autonomous vehicles via reinforcement learning Advising : Supervised multiple graduate students including Ashish Patil , Harshavardhan Nalajala , and Brian Nguyen . Collaborated with researchers like Professor Alexandre Bayen (UC Berkeley) and Professor Cathy Wu (MIT) on traffic control frameworks such as Flow .
Xiaofei Xie is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He received his PhD from Tianjin University in 2018 and was a postdoctoral researcher at Nanyang Technological University (2018-2021) before joining SMU in 2022. His research focuses on software engineering, AI systems, and cybersecurity. Dr. Xie's primary research areas include program analysis, software testing, vulnerability detection, and quality assurance of AI systems. His work spans: Testing methodologies for autonomous systems and games AI security including backdoor detection and model robustness Automated program repair and code generation Formal methods and semantic code analysis His recent publications demonstrate strong emphasis on AI/ML system testing, cybersecurity applications, and program analysis techniques. Research trends show increasing focus on LLM-based program repair, autonomous system validation, and federated learning security. Major Awards: ACM SIGSOFT Distinguished Paper Awards (ASE'23, ISSTA'22, ASE'19, FSE'16) CCF Outstanding Doctoral Dissertation Award (2019) 3rd place in AI Singapore's Trusted Media Challenge (2022) Wallenberg-NTU Presidential Postdoctoral Fellowship (2019) APSEC Best Paper Award (2020) He currently advises 7 PhD/Master's students including CHENG Mingfei, KONG Jiaolong, and YU Jiongchi. Dr. Xie leads research in software reliability and AI security at SMU's SCIS.
Sanem Sarıel Uzer is a Professor at Istanbul Technical University (ITU) in the Department of Artificial Intelligence and Data Engineering, part of the College of Computer and Informatics. She has been a faculty member at ITU since 2007, progressing from Instructor to Associate Professor in 2016 and achieving full Professor status in 2024. She is the founder and coordinator of the ITU Artificial Intelligence and Robotics Laboratory and leads multiple research initiatives in cognitive robotics, planning, and machine learning. Ph.D. in Computer Engineering, Istanbul Technical University (2002–2007) M.Sc. in Computer Engineering, Istanbul Technical University (1999–2002) B.Sc. in Control and Computer Engineering, Istanbul Technical University (1995–1999) Her research focuses on artificial intelligence, robotics, and machine learning, particularly in enabling cognitive systems and robots to reason, plan, and learn in complex environments. She investigates lifelong learning methods, multi-robot team strategies, and safe robot manipulation using deep reinforcement learning. Her recent work emphasizes failure anticipation, multimodal detection, and knowledge distillation to improve robot safety and autonomy. The trend in her recent publications (2023–2024) shows a strong focus on enhancing the safety and reliability of robotic manipulation through AI techniques such as deep reinforcement learning, adversarial learning, and multimodal perception. Her work spans both theoretical algorithm development and practical applications in service and industrial robotics. Scientific Awards: Siemens Turkey Excellence Award (2004) TÜBİTAK 13th Technology Award – Best Product (2018) TÜBİTAK 13th Technology Award – University-Industry Collaboration Mention (2018) Best Visual Presentation Award, IEEE SIU (2016) ITU Project Performance Award (2020) TÜBİTAK Ufuk 2020 (2019) Necdet Eraslan Project Competition Mention (2007) She has supervised numerous students in RoboCup competitions and led multiple funded research projects, including two TÜBİTAK projects on lifelong learning for cognitive robots and the development of the open-source Violet system for robot vision. She has also served as a consultant on AI projects with companies like Triodor, Artı Teknoloji, and Analitik Bilişim. Her grants include funding from TÜBİTAK, ITU Scientific Research Projects, and the Ministry of Science, Industry and Technology. She leads the ITU Artificial Intelligence and Robotics Laboratory and is actively involved in national and international collaborations, including with Georgia Tech, University of Pennsylvania, and University of South Florida. She also contributes to professional communities such as IEEE, AAAI, RoboCup, and EUCog.
Professor Ying Liu is a Professor and Chair in Intelligent Manufacturing at the School of Engineering, Cardiff University, UK, a position he has held since August 2021. He leads the High-value Manufacturing research group within the Department of Mechanical Engineering. Prior to this, he served as an Assistant Professor at the National University of Singapore (2010–2013) and the Hong Kong Polytechnic University (2006–2010). PhD, Innovation in Manufacturing Systems and Technology (IMST), Singapore-MIT Alliance (SMA), National University of Singapore (2006) MSc, Singapore-MIT Alliance (SMA), Nanyang Technological University (NTU) MEng & BEng, Mechanical Engineering, Chongqing University, China His research spans engineering informatics, digital and intelligent manufacturing, AI and machine learning in engineering design, and advanced ICT in manufacturing. He has published over 160 scholarly articles and contributed to major journals and conferences in the field. His recent work focuses on knowledge graphs, digital twins, human-robot collaboration, and energy modeling in smart manufacturing, often integrating large language models and advanced deep learning techniques. The most recent publications highlight a strong trend toward integrating AI, particularly large language models and knowledge graphs, into smart manufacturing systems. Themes include predictive maintenance, battery state estimation, human fatigue modeling, and sustainable manufacturing. His work increasingly emphasizes human-centric approaches aligned with Industry 5.0 principles. Best Paper Award 2022, CCF Transactions on Pervasive Computing and Interaction ESI Highly Cited Paper and Hot Paper, Research and Application of Machine Learning for Additive Manufacturing 2020 Reviewer of the Year, ASME Journal of Computing and Information Science in Engineering (JCISE) Professor Liu actively supervises postgraduate students and has advised several successful PhD candidates, including Dr. Chong Chen and Mr. Zhouyang Ding. His research is funded by major agencies such as EPSRC (UK), GRF (Hong Kong), MOE (Singapore), A*STAR, and NSF (China), as well as industrial partners. He serves as Associate Editor for ASME JCISE, IEEE T-ASE, and several other journals, and was recently appointed Senior Editor of the Journal of Engineering Design. He also leads special issues and topical collections on AI in engineering. He leads the High-value Manufacturing research group at Cardiff University, focusing on digital transformation in manufacturing. His team works on projects involving digital twins, knowledge graphs, and AI-driven design innovation, often in collaboration with international institutions.
Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.
Kevin Heaslip is a Professor and Director of the Center for Transportation Research in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville. He joined the university in 2022, bringing extensive experience from his prior role as Professor and CACI Faculty Fellow at Virginia Tech. Education: PhD in Civil and Environmental Engineering, University of Massachusetts Amherst, 2007 MS in Civil and Environmental Engineering, Virginia Tech, 2003 BS in Civil and Environmental Engineering, Virginia Tech, 2002 Dr. Heaslip's research focuses on the intersection of transportation engineering, intelligent systems, and cybersecurity. His work spans future transportation concepts such as electrified and automated vehicles, transportation operations including freeway and transit management, and cybersecurity for transportation and critical infrastructure. He applies advanced data analytics, machine learning, and cyber-physical systems modeling to real-world challenges in mobility and national security. The recent articles highlight a strong trend in leveraging big data and artificial intelligence for transportation mode detection, traffic event identification, and security risk assessment. His work increasingly integrates cybersecurity into transportation systems, particularly in autonomous and connected vehicles, reflecting the evolving technological landscape and national priorities in infrastructure protection. Scientific Awards and Recognitions: Virginia Tech G.V. Loganathan Faculty Achievement Award for Excellence in Civil Engineering Education (2019) Virginia Tech Favorite Faculty Award (2017) Outstanding Alumni, University of Massachusetts Institute of Transportation Engineers Student Chapter (2015) Outstanding Young Alumni, Virginia Tech Department of Civil & Environmental Engineering (2013-2014) USU Civil & Environmental Engineering Teacher of the Year (2014) USU Civil & Environmental Engineering Undergraduate Research Mentor of the Year (2013) USU Civil & Environmental Engineering Outstanding Researcher (2011, 2012) USU College of Engineering Undergraduate Research Mentor of the Year (2011) Dr. Heaslip has secured over $25 million in research grants and contracts from federal and state governments as well as industry partners. He has advised numerous students and mentored undergraduate researchers, receiving multiple awards for teaching and mentorship. His professional service includes membership in the American Society of Civil Engineers (ASCE), Institute of Transportation Engineers (ITE), and Transportation Research Board (TRB), as well as serving as an Appointed Member of the Resilient America Roundtable of the National Academy of Sciences from 2014 to 2020. He leads the Center for Transportation Research at UT Knoxville, a multidisciplinary lab focused on advancing transportation technologies and policies. The lab's research is organized around three thrusts: future transportation systems (electrified, connected, and automated vehicles), transportation operations (freeway, transit, and corridor management), and cybersecurity of transportation and critical infrastructure.
Néstor Rodríguez Pérez is an Assistant Professor at the Technological Research Institute (IIT) of Comillas Pontifical University, affiliated with the School of Engineering. His research focuses on smart grids, digitalization of power systems, and the integration of information and communication technologies (ICT) in energy networks. His research interests include sustainable smart grids, IoT in power systems, cybersecurity (particularly MaDIoT attacks), scalability and replicability of digital solutions, TSO-DSO coordination, and edge computing for grid applications. He applies advanced modeling and simulation techniques to evaluate the performance and security of modern power systems. The recent publications reflect a strong trend in analyzing the digital transformation of electricity distribution networks, with emphasis on ICT architecture, cybersecurity threats, and scalability of communication protocols such as Modbus TCP and wireless M-Bus. His work bridges theoretical modeling with practical implementation in European power systems, often in the context of EU-funded projects. He has been involved in significant research grants and projects funded by the European Commission (Horizon 2020 and Horizon Europe), the State Research Agency (AEI), and the Naturgy Foundation. These projects focus on digital twins, grid resilience, flexibility solutions, and transnational smart grid platforms. He has conducted a research stay at the Electrical Sustainable Energy Department of TU Delft and has been active in knowledge dissemination through technical reports, workshops, and invited seminars at IIT Comillas and international forums.
Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.
Dessislava Georgieva Petrova-Antonova is a Professor at Sofia University's Faculty of Mathematics and Informatics , specializing in the Department of Software Technologies . Her work focuses on service-oriented architectures, web services, and big data systems for smart cities. She holds a PhD in Software Technologies from Technical University of Sofia (2007) and an MSc in Computer Systems and Control (2000). PhD: Technical University of Sofia, Faculty of Computer Systems and Control (2007) MSc: Technical University of Sofia, Faculty of Computer Systems and Control (2000) Research Interests: She pioneers methodologies for testing web service compositions (e.g., TASSA ), develops big data platforms for smart governance, and explores digital twin modeling for urban environments. Her projects integrate IoT, quality of service (QoS) frameworks, and data-driven policy tools. Projects: Leads initiatives like National CogniTwin (National Science Fund, 2019–2022) and Big4Smart (NSF, 2017–2020). Collaborates in EU programs such as Digital Twin Cities Centre (2020–2025) and People Network+ (FP7, 2012–2013). Labs & Collaborations: Affiliated with the GATE Center of Excellence and contributes to international teams in big data and smart city research. Her work bridges academia and industry through tools like TESSI (Web Service Testing Tool) and faultInjector (BPEL Fault Injection Tool).
Chris Impey serves as a University Distinguished Professor in the Department of Astronomy at the University of Arizona, with over 450 publications and $20 million secured in NASA and NSF research grants. He previously held the position of Vice President at the American Astronomical Society and has developed massive open online courses (MOOCs) reaching over 420,000 students globally, generating 8 million minutes of video lecture views. His research spans observational cosmology (quasars and galaxy evolution), astrobiology, and transformative astronomy education methodologies. Recent work focuses on combating science misinformation through innovative pedagogical approaches, including the application of large language models for automated writing assessment in online learning environments. He actively explores the societal implications of space exploration and the philosophical dimensions of cosmic discovery. Analysis of his 15 most recent publications reveals a dominant trend in educational technology (70% of works), particularly LLM applications for grading and combating misinformation, alongside sustained contributions to astrobiology and space ethics. Key thematic clusters include AI-enhanced education, pseudoscience analysis, and off-Earth societal development. Notable awards include: Career Education Prize from the American Astronomical Society NSF Distinguished Teaching Scholar designation Carnegie Council’s Arizona Professor of the Year Howard Hughes Medical Institute Professorship Ted and Shirley Taubeneck Superior Teaching Award (awarded in 2022 and 2024) Professor Impey has directed $20 million in grant-funded research while pioneering scalable educational models through MOOCs and digital resources. His mentorship extends to hundreds of thousands of learners globally, with significant contributions to open educational resources including textbooks, online platforms, and multimedia content. Current initiatives focus on AI-driven assessment tools and developing curricula for space ethics and astrobiology education.
Michael Hilton is an Associate Teaching Professor in the Software and Societal Systems Department of the School of Computer Science at Carnegie Mellon University. He also serves as the Associate Department Head for Education and directs both the Software Engineering Minor and Software Engineering Concentration programs. His work bridges academic research with practical software engineering education. Ph.D. in Computer Science, Oregon State University (2017) M.S. in Computer Science, Cal Poly San Luis Obispo (2013) B.S. in Computer Science, San Diego State University (2002) Professor Hilton's research primarily focuses on understanding and improving the developer experience, with particular emphasis on flaky tests, continuous integration practices, and software engineering education. His work combines empirical studies of real-world development practices with educational innovations to enhance how software engineers are trained. He has conducted extensive research on test flakiness, identifying patterns, causes, and potential solutions to this pervasive problem in modern software development. His scholarly contributions reveal a consistent focus on practical software engineering challenges, particularly those affecting developer productivity and software quality. The research trajectory shows increasing attention to educational aspects of software engineering, including team-based learning, structured feedback mechanisms, and the impact of emerging technologies like AI on programming education. Professor Hilton has over 20 years of professional experience in software development, including 9 years at SPAWAR Pacific where he worked on projects for the US Navy, Coast Guard, and White House. This industry background informs his teaching approach, which emphasizes preparing students for real-world challenges they'll face after graduation. He teaches software engineering-focused courses and has developed educational approaches that integrate practical development experience with theoretical foundations. His teaching philosophy centers on providing students with both immediate practical skills and enduring principles that will serve them throughout their careers, with special attention to software engineering in startup environments.
Dr Xiandong Ma is a Reader in Power and Energy Systems at Lancaster University's School of Engineering, where he has been a faculty member since December 2008. His research focuses on intelligent condition monitoring and fault diagnosis of power systems, with particular expertise in wind energy systems and smart grid technologies. His educational background includes: BEng in Electrical Engineering from Jiangsu University (1986) MSc in Power Systems and Automation from Nanjing Automation Research Institute (1989) PhD in Partial Discharge based High-voltage Plant Condition Monitoring from Glasgow Caledonian University (2002) Dr Ma's research spans intelligent condition monitoring and fault diagnosis/prognosis of wind power systems and electrical assets, condition-based operations and maintenance of power and energy systems, modeling, optimization, and control of smart/micro grids with renewable energy resources, power conversion and renewable energy integration, and associated machine learning and AI technologies and digital twin solutions. His work bridges theoretical advances with practical engineering applications in the renewable energy sector. His recent publications demonstrate a strong focus on quantum machine learning applications for wind turbine monitoring, electric vehicle-grid integration challenges, wave energy conversion systems, and nuclear fuel inspection technologies. The research shows a clear trajectory toward more sophisticated AI-driven solutions for energy systems, with increasing emphasis on multi-physics modeling and cross-domain applications. Dr Ma has received several prestigious recognitions: Chartered Engineer Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Higher Education Academy (FHEA) Member of EPSRC Peer Review College KTP Fellowship awarded by University of Technology Sydney (2018) Ranked in the world's top 2% scientists by Stanford University He actively supervises numerous PhD students and postdoctoral researchers, with current projects including the Leverhulme Trust-funded "Self-Aware Power Networks: Autonomous Operation at Scale" and several EPSRC-funded initiatives. Dr Ma has secured significant research funding and collaborates extensively with industry partners to translate research into practical applications. Dr Ma leads research within Lancaster's Energy research group, focusing on the integration of advanced sensing, AI, and control techniques for next-generation power and energy systems. His team works closely with industrial partners including ALSTOM Power and various renewable energy companies to develop innovative solutions for real-world energy challenges.
Professor Nebojsa Mitrović is a distinguished faculty member at the Electronic Faculty of the University of Niš, Serbia, where he serves as a Professor in the Department of Power Engineering. With decades of academic and research experience, he has established himself as a leading expert in electric motor drives and power electronics. His work bridges theoretical research with practical industrial applications, particularly in crane systems and power quality issues. Professor Mitrović's research interests span electric motor drives, power electronics, control systems for electrical machines, induction motors, voltage sag effects on electrical drives, crane applications, renewable energy systems, and electromechanical energy conversion. His work demonstrates a consistent focus on improving the performance and reliability of electrical drive systems, particularly in industrial settings where power quality issues can significantly impact operations. He has made substantial contributions to understanding how voltage sags affect various types of motor drives and has developed innovative control strategies to mitigate these effects. His extensive publication record shows a clear research trajectory focusing on electric motor drives and power electronics. The most recent publications (2020-2023) demonstrate continued innovation in grid-connected converters, microgrid stability, and modern testing methodologies for electric drives. Earlier works (2006-2017) established foundational knowledge in direct torque control, multi-motor drive systems for cranes, and voltage sag effects on industrial drives. His research consistently addresses practical engineering challenges while advancing theoretical understanding in the field. Professor Mitrović has contributed significantly to academic literature through numerous journal articles, conference papers, and book chapters. His 2009 monograph "Implementacija algoritama za upravljanje momentom i fluksom asinhronih motora" (Implementation of Algorithms for Torque and Flux Control of Induction Motors) and the 2012 book chapter "Electrical Drives for Crane Application" in Mechanical Engineering published by InTech represent substantial contributions to the field. He has also co-authored educational materials including solved problem collections and laboratory exercises for electric motor drives courses. Professor Mitrović actively supervises student research and has been involved in numerous technical projects, including the development of laboratory setups for testing vector controlled induction motor drives. His work has practical applications in various industries, particularly in crane systems and industrial drive applications. He has collaborated extensively with colleagues including Vojkan Kostić, Milutin Petronijević, and Bojan Banković on research projects funded by various Serbian research initiatives. His laboratory work includes the development of testing systems for electric drives and the implementation of advanced control algorithms for industrial applications. Professor Mitrović is associated with the Power Engineering Department at the University of Niš, where he teaches courses including Electric Motor Drives, Selected Topics in Electric Motor Drives, Electrical Machines, Electromechanical Energy Conversion, and Modeling of Electrical Machines and Drives. His teaching reflects his research expertise and provides students with both theoretical knowledge and practical skills in electric drive systems.
Natasa Miskov-Zivanov is an Assistant Professor at the University of Pittsburgh where she leads the MeLoDy Lab (Mechanistic, Logical, and Dynamic Modeling). She holds a PhD in Electrical and Computer Engineering from Carnegie Mellon University and conducts interdisciplinary research at the intersection of computational methods and biological systems. Her education includes: PhD in Electrical and Computer Engineering, Carnegie Mellon University (2009) MS in Electrical and Computer Engineering, Carnegie Mellon University (2005) BS in Electrical Engineering and Computer Science, University of Novi Sad (2003) Her research focuses on developing computational frameworks and tools for biological systems modeling. Primary interests include: Automated knowledge extraction from biomedical literature Dynamic network modeling of cellular signaling pathways Development of standardized knowledge representation formats (BioRECIPE) Hybrid modeling approaches for complex biological systems Applications in cancer systems biology and immunology Her publications demonstrate consistent focus on computational biology methods development, with recent work emphasizing: Context-aware knowledge selection systems Automated model assembly from literature Biomedical text mining frameworks Hybrid multi-resolution modeling Standards for executable biological models She leads several funded research initiatives including DARPA's Big Mechanism program (AIMCancer W911NF-17-1-0135) and University of Pittsburgh-supported projects. Her lab develops open-source tools like CLARINET, ACCORDION, and VIOLIN that facilitate biological network modeling and knowledge extraction.
Nikolaos Papaspyrou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and a member of the Software Engineering Laboratory . His research focuses on the theory and implementation of programming languages, including semantics, type systems, compilers, static analysis, and formal verification. Since October 2021, he has been on leave from NTUA, working as a Software Engineer for Google in the memory management team for the V8 JavaScript and WebAssembly engine. He previously served as Director of the Division of Computer Science (2017-2019) and was on sabbatical with Google's compiler group in Munich (2015-2016). His work includes the RELEASE project (EU FP7 STREP) for reliable large-scale server software and uncertainty handling in distributed databases (European Social Fund). Ph.D. and Diploma in Electrical and Computer Engineering from NTUA M.Sc. in Computer Science from Cornell University His research interests span programming languages , software engineering , and formal verification , with recent publications on coinductive proofs in Liquid Haskell, concurrency semantics, and quantum compilation. He has supervised over 50 diploma projects and mentored numerous students now at institutions like MIT, Princeton, and UC Berkeley. Awards include conference organizing and program committee roles, though no formal scientific prizes are listed.
