Find Academic Supervisors

Siegfried HandschuhView profile

Professor

• Semantic Technologies
• Information Linguistics
• Information Extraction
• +4 more

Professor Siegfried Handschuh holds dual academic appointments as Associate Professor at the University of Passau and Ordinary Professor for Data Science at the University of St. Gallen. His research spans semantic technologies, information linguistics, and artificial intelligence applications in the humanities, with significant funding from the EU, Science Foundation Ireland, and Enterprise Ireland. His research focuses on making big data workable for humanities scholars through innovative AI approaches. Handschuh's work addresses critical challenges in digital humanities including inconsistent and non-integrated data with varying quality. His research interests encompass semantic technologies, information linguistics, information extraction, and distributional semantics, with particular emphasis on applying AI to art history and cultural heritage. Handschuh has coordinated numerous international research projects including the Digital Aristotle project initiated by Microsoft founder Paul Allen, which aimed to develop AI-powered educational tools. His Neoclassica project teaches machines to recognize neoclassical objects through formal language processing, while the PACE initiative establishes the Passau Centre for eHumanities as a research hub. Other significant projects include EU-Project MARIO for healthy aging with care robots and various studies on human-machine interaction. His scientific contributions focus on developing new tools for humanities researchers that leverage artificial intelligence to uncover novel contexts and relationships in cultural data. Handschuh's work bridges computer science with humanities disciplines, creating practical applications for semantic technologies in cultural heritage analysis.

Baris KasikciView profile

Associate Professor

• Computer Systems
• Reliability
• Security
• +8 more

Baris Kasikci is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Previously (2017-2023), he was a Morris Wellman Assistant Professor in the Electrical Engineering and Computer Science Department at the University of Michigan. His research focuses on building efficient and trustworthy computer systems through innovative combinations of approaches from systems, computer architecture, and programming languages. Dr. Kasikci received his PhD in Computer Science at EPFL and has held research positions at Microsoft Research Cambridge, Google, Intel, and VMware. His work addresses critical challenges in system reliability, security, and performance in increasingly complex software ecosystems. His research interests center on improving the efficiency of datacenter applications and machine learning systems, analyzing and fixing failures, and enhancing hardware security. His lab develops techniques for automated bug detection, formal verification of distributed systems, and building systems support for heterogeneous hardware architectures. Recent projects include Whisper (profile-guided branch misprediction elimination), Huron (taming false sharing), and Agamotto (automatic detection and repair of bugs in persistent memory applications). Analysis of his recent publications shows a strong trend toward optimizing large language model serving, hardware security, and performance optimization for modern heterogeneous architectures. His work bridges traditional systems research with emerging AI infrastructure needs, particularly in efficient LLM serving, security vulnerabilities in modern hardware, and performance optimization for heterogeneous computing environments. NSF CAREER award Microsoft Research Faculty Fellowship Intel Rising Star Award VMware Early Career Faculty Grant Google Faculty Award Roger Needham PhD Award (best PhD thesis in computer systems in Europe) Patrick Denantes Memorial Prize (best PhD thesis at EPFL) Best Paper Award at OSDI'18 Best Paper Award at MICRO'22 Dr. Kasikci has advised numerous PhD students who have gone on to prestigious positions in academia and industry, including Tanvir Ahmed Khan (Assistant Professor at Columbia University), Akshitha Sriraman (Assistant Professor at CMU), and Jiacheng Ma (AMD). His research has been supported by significant grants from NSF, DARPA, Intel, Google, Microsoft, VMware, and Amazon. His lab, the EfesLab, focuses on building tools and techniques that make computer systems more reliable, secure, and efficient. The EfesLab, led by Dr. Kasikci, brings together postdocs, PhD students, and undergraduate researchers to tackle fundamental challenges in systems reliability and performance. The lab has developed numerous influential tools including Whisper, Huron, and Agamotto that address critical performance and reliability issues in modern computing systems. Current research directions include efficient LLM serving, security of emerging hardware technologies, and automated debugging techniques.

Sairaj DhopleView profile

Professor

• Power Systems
• Power Electronics
• Renewable Energy Integration
• +3 more

Sairaj Dhople is the Oscar A. Schott Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on renewable energy systems, particularly modeling and control of grid-connected inverters, power-system reliability, and distributed energy resources. University: University of Minnesota Department: Electrical and Computer Engineering Academic Rank: Professor His work spans power systems, power electronics, and control theory, with recent publications examining grid-forming inverters, stability analysis, and hybrid computing solutions for optimization problems. Key research themes include: Equivalent-circuit modeling for renewable systems Large-signal stability assessment inverter-based resources Grey-box system identification of power networks Interoperability standards for grid-forming technologies Scientific awards include the Institute for Advanced Study Faculty Fellowship (2018). Current projects funded by the National Science Foundation and U.S. Department of Energy explore analog/hybrid computing and universal interoperability for grid-forming inverters (UNIFI Consortium). His Dhople Research Group investigates power-system architecture and sustainability challenges.

• Structural Health Monitoring
• Civil Infrastructure
• Machine Learning for Engineering
• +7 more

Dr. Andy Nguyen is a Senior Lecturer in Structural Engineering at the University of Southern Queensland, within the School of Engineering. He is an active researcher and educator, specializing in the Structural Health Monitoring (SHM) of critical civil infrastructure such as bridges, buildings, and transport tunnels. Bachelor of Engineering (BEng), NUCE, 1999 Master of Engineering (MEng), NUCE, 2003 Doctor of Philosophy (PhD), Queensland University of Technology (QUT), 2014 Dr. Nguyen's research is at the forefront of integrating advanced technologies into civil engineering. His primary focus is on developing and deploying sophisticated SHM systems that utilize sensors, data analytics, and machine learning to provide real-time insights into the structural integrity of ageing infrastructure. His work aims to enable proactive maintenance, extend the lifespan of structures, and enhance public safety. He has successfully implemented monitoring systems on major bridges and high-rise buildings in Queensland and New South Wales, with systems capable of even detecting distant earthquake events. His research interests span Structural Health Monitoring, Machine Learning for Engineering, Damage Detection, Finite Element Model Updating, Sustainable Building Materials like bamboo, and the application of AI for automated condition assessment of transport infrastructure. The analysis of his recent publications reveals a strong and consistent research trajectory centered on the application of data-driven and AI methods to solve practical problems in civil infrastructure. His work frequently combines signal processing techniques (like Stockwell Transform) with deep learning models for tasks such as crack detection in concrete and pavement. He also conducts significant research on model updating for complex structures like cable-stayed and arch bridges, using vibration data and optimization algorithms. The integration of machine learning for overload classification and the development of cost-effective, automated monitoring systems are key trends in his recent output. Advanced Queensland Fellow (2024-2027) Dr. Nguyen is actively involved in research supervision and collaboration. He is currently supervising several postgraduate students on projects related to AI-powered condition assessment, bamboo as a sustainable building material, and railway track design. He receives research funding from the Queensland Government through his Advanced Queensland Fellowship. His research has direct practical applications, as evidenced by his public engagement, such as writing for The Conversation on safeguarding ageing bridges, and his work with the Australian Network of Structural Health Monitoring. Dr. Nguyen's work embodies the development of a next-generation 'Living' Laboratory for engineering education, where research, teaching, and real-world infrastructure monitoring are integrated. His current projects involve creating smart, automated fault detection systems and advancing 'digital twin'-based monitoring platforms for infrastructure.

Nickolaos KoroniotisView profile

Senior Lecturer

• Cybersecurity
• Artificial Intelligence
• Internet of Things
• +4 more

Dr Nickolaos Koroniotis is a Senior Lecturer at the School of Professional Studies, University of New South Wales (UNSW) Canberra, focusing on cybersecurity, artificial intelligence, and Internet of Things (IoT) research. His work bridges network forensics with deep learning, particularly in smart environments like airports and battlefield systems. BSc in Informatics and Telematics (2014) MSc in Web Engineering and Applications (2016) PhD in Cybersecurity (2020) His research emphasizes network-based attack detection and deep learning-driven vulnerability assessment for IoT security. Key areas include smart airports , Internet of Battlefield Things , and federated learning for digital forensics. His recent articles (2025-2019) highlight trends in AI-powered IoT security , federated learning frameworks , and explainable intrusion detection . Topics span from smart environment defense to generative AI for vulnerability discovery. Email: n.koroniotis@unsw.edu.au

• Energy Systems
• Machine Learning
• Reinforcement Learning
• +12 more

Marco Badami is a Full Professor at the Department of Energy (DENERG) , Polytechnic of Turin. He serves as Scientific Director for national and EU-funded research projects in energy systems and has been a Course Lecturer for Energy Systems and Industrial Use of Energy since 2010. He supervises PhD students in Energetics and Electrical Engineering . Research Interests: His work spans energy systems optimization, machine learning applications for industrial energy efficiency, smart grids, cogeneration scheduling, blockchain-based energy data immutability, and predictive maintenance algorithms for photovoltaic plants. Current projects focus on AI-driven energy audits, optimized control systems for industrial microgrids, and digital twin architectures. Collaborations: He works with Trigenia Srl, Stogit SpA, and international institutions on commercial research contracts. His scientific contributions include 15+ publications on topics like LSTM forecasting for solar energy, deep reinforcement learning for multi-energy systems, and decentralized peer-to-peer energy trading platforms.

Zhenghao ChenView profile

Lecturer

• Computer Vision
• Natural Language Processing
• Machine Learning
• +3 more

Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.

• Telecommunication Networks
• Distributed Machine Learning
• Edge Computing
• +5 more

Falko Dressler is a Full Professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, Technische Universität Berlin. He holds a Ph.D. and M.Sc. in Computer Science from Friedrich-Alexander University of Erlangen-Nuremberg (1998-2003). His research focuses on next-generation wireless systems , distributed machine learning , edge computing , and applications in Internet of Things (IoT) , cyber-physical systems , and internet of bio-nano-things . Editorial roles: IEEE Trans. on Mobile Computing, Elsevier Computer Communications, IEEE/ACM Trans. on Networking Conference leadership: IEEE INFOCOM, ACM MobiSys, IEEE VNC Textbooks: Self-Organization in Sensor and Actor Networks (Wiley), Vehicular Networking (Cambridge) Recent publications highlight trends in Edge Computing Resilience and 6G Network Architecture , with a strong emphasis on Molecular Communication , Terahertz Band Synchronization , and Federated Learning in vehicular environments. Scientific contributions include multiple IEEE Fellow , ACM Fellow , and VDE ITG Prize 2023 recognitions. Advisory and professional activities include membership in the German National Academy of Science and Engineering (acatech), IEEE COMSOC Conference Council, and ACM SIGMOBILE Executive Committee. His work spans cooperative driving, ultra-low power sensor networks, and security in nano-communication systems.

• Business Informatics
• Artificial Intelligence
• Business Intelligence
• +10 more

Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.

Dominic AbramsView profile

Professor

• Group Processes
• Intergroup Relations
• Social Cohesion
• +7 more

Professor Dominic Abrams is a leading academic in Social Psychology and Director of the Centre for the Study of Group Processes at the School of Psychology, University of Kent . His research focuses on group processes , intergroup relations , and social cohesion , with applications to prejudice reduction , ageism , and organizational identity . Current PhD students: Kimberly Sequeira, Jon Bowman, Alessia Bacigalupo Past PhD students: Ben Davies, Katie Goodbun, Maria Heering, Zafer Ozkan, Hilal Özkececi His work spans social identity theory , age stereotypes , and collective action , often intersecting with policy through collaborations with UK government bodies (DWP, MHCLG, Cabinet Office) and charities (Age UK, Belong, Anne Frank Trust). Recent publications analyze volunteering’s role in social cohesion , vaccine hesitancy , and honor values as identity content . Key grants: £500,000 (British Academy), £71,000 (Nuffield Foundation), €166,294 (European Commission FP7) Awards: OBE (2023), British Psychological Society Presidents’ Award, SPSSI Distinguished Contribution Award He co-edits the journal Group Processes & Intergroup Relations and leads the British Academy’s Understanding Communities and Social and Cultural Infrastructure programs, alongside advising on national surveys for ONS and the Equality and Human Rights Commission.

Lili QiuView profile

Professor

• Internet Networking
• Wireless Networking
• Mobile Systems
• +6 more

Lili Qiu is a Professor in the Department of Computer Science at The University of Texas at Austin, where she has been a faculty member since January 2005. She is an active member of the Wireless Networking and Communications Group (WNCG) and has made significant contributions to the field of networking research. Dr. Qiu previously spent 2001-2004 as a researcher at Microsoft Research in Redmond, WA, before joining UT Austin. Dr. Qiu's research spans Internet and wireless networking with a current focus on wireless network management and content distribution in mobile networks. Her work extends into diverse applications including acoustic imaging, metasurface applications, healthcare sensing technologies, and AI systems. She has pioneered research in areas such as acoustic motion tracking, passive RFID sensing, and wireless network optimization. Her research demonstrates a consistent pattern of innovation that bridges theoretical networking concepts with practical real-world applications, particularly in mobile and wireless systems. Her extensive publication record shows a clear evolution from fundamental networking research to increasingly interdisciplinary work that combines wireless systems with healthcare applications, AI, and novel sensing technologies. Recent publications demonstrate growing integration of machine learning techniques with traditional networking problems, as well as expansion into healthcare applications like Parkinson's disease modeling and non-invasive glucose monitoring. ACM Fellow IEEE Fellow National Academy of Inventors (NAI) Fellow ACM Distinguished Scientist NSF CAREER award Google Faculty Research Award Best paper award at ACM MobiSys'18 Best paper award at IEEE ICNP'17 Dr. Qiu has supervised numerous students, including a PhD dissertation that won the SIGMOBILE best dissertation award in 2020. She has served in significant leadership roles including chair of ACM SIGMOBILE, General co-chair for ACM MobiCom 2025, and various conference chair positions for IEEE ICNP, ACM CoNEXT, and other major networking conferences. Her research has been supported by substantial grants from NSF, Google, and other organizations, enabling her to lead cross-disciplinary research teams. As a member of the Wireless Networking and Communications Group at UT Austin, Dr. Qiu leads research efforts that combine networking expertise with innovations in sensing technologies, metasurfaces, and AI systems. Her lab has produced numerous influential results in mobile networking, wireless sensing, and network management, with applications spanning healthcare, consumer electronics, and communication infrastructure.

Md. Zoheb HassanView profile

Assistant Professor

• Spectrum sharing and management
• Wireless communication systems
• Communications and network control and regulation systems
• +1 more

Md. Zoheb Hassan serves as an Assistant Professor in the Department of Electrical Engineering and Computer Engineering at Laval University, where he leads cutting-edge research in wireless communications and spectrum management. His academic role includes graduate recruitment and active participation in the university's research ecosystem, particularly through the Establishment of the Next Generation of Professors program funded by FRQNT. Dr. Hassan's research centers on spectrum sharing and management, wireless communication systems, and communications network control systems. He pioneers the integration of digital twin technology and machine learning to solve critical challenges in next-generation networks, including interference management in 5G/6G aerial corridors, Internet of Vehicles, and satellite-terrestrial integration. His work emphasizes practical implementations such as proof-of-concept demonstrations for tactical networks and proactive resource allocation in dynamic environments. Analysis of his 2024-2025 publications reveals a dominant trend toward AI-driven wireless resource optimization, with 12 of 15 recent papers featuring digital twins for interference management, spectrum sharing, and energy efficiency. Key thematic clusters include vehicular communications (4 papers), underwater IoT networks (2 papers), and hardware-impairment resilient designs (3 papers), demonstrating his focus on bridging theoretical advances with real-world deployment challenges across diverse network topologies. Dr. Hassan has secured significant competitive funding for his research initiatives: Digital Twin-Enhanced Interference Management for Next-Generation Radio Access Networks in the FR3 Band (FRQNT, 2025-2027) Center for Radio Frequency and Communications Systems, Technologies and Applications (FRQNT, 2024-2030) Context-Aware Spectrum Sharing and Management for Next Generation Wireless Networks (NSERC, 2024-2029) Development of innovative technologies for modeling predictive systems in urban mobility (MITACS, 2022-2026) Springboard to Discovery supplement for Context-Aware Spectrum Sharing (NSERC, 2024-2025) He actively mentors doctoral candidates, currently supervising Mahima Karim (PhD in Electrical Engineering, expected 2025) and Mohammadamin Parhizgar (PhD in Electrical Engineering, expected 2024). His supervisory approach combines theoretical rigor with practical problem-solving, focusing on spectrum management algorithms and digital twin implementations for next-generation networks. While specific laboratory affiliations aren't detailed in the source material, his projects indicate strong alignment with Laval University's wireless research infrastructure and the Center for Radio Frequency and Communications Systems.

David ColonView profile

Lecturer

• Propaganda
• Information Warfare
• Political History
• +4 more

David Colon is a Tenured Lecturer at the Centre for History (CHSP) of University of Paris Institute of Political Studies, specializing in propaganda, communication, and digital history. His research focuses on mass persuasion mechanisms, information warfare, and media influence from historical and contemporary perspectives. Position: Tenured Lecturer Institution: Sciences Po (University of Paris Institute of Political Studies) Research Themes: • Propaganda and psychological manipulation • Digital history and modern information warfare • Political communication and social movements Scientific Awards: Prix Akropolis 2019 Prix Jacques Ellul 2020 Teaching & Publications: Holder of the agrégation in history, Colon contributes to graduate studies and has published extensively on propaganda, including works on Edward Bernays, cognitive warfare, and artificial intelligence's role in information manipulation.

Waël JaafarView profile

Professor

• Wireless Communications
• 5G/6G Networks
• Space Telecommunications
• +7 more

Waël Jaafar is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ETS), a constituent school of the Université du Québec system in Montreal, Canada. His research spans multiple critical domains in modern communications and computing infrastructure, with a particular focus on next-generation wireless networks and intelligent systems. Dr. Jaafar holds a B.Eng. from Sup'Com Tunisie, and both M.Sc.A. and Ph.D. degrees from Polytechnique Montréal. His academic background provides a strong foundation for his interdisciplinary research that bridges theoretical concepts with practical engineering solutions. His research interests center around wireless communications systems, with particular emphasis on 5G/6G networks, UAV communications, space telecommunications, and machine learning applications for networking. He has developed significant expertise in federated learning techniques for distributed networks, cybersecurity applications for next-generation mobile systems, and edge computing architectures. His work frequently explores the intersection of communication theory, artificial intelligence, and network security, with applications ranging from industrial IoT to public safety communications. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with wireless networking infrastructure, particularly focusing on UAV-assisted communications, federated learning approaches for distributed networks, and security enhancements for 5G/6G systems. His research demonstrates increasing emphasis on practical implementation challenges including energy efficiency, communication overhead reduction, and reliability in non-ideal network conditions. As an academic supervisor, Dr. Jaafar actively mentors numerous graduate students across various projects. He currently supervises doctoral candidates working on blockchain-enhanced security for 5G networks, green network slice orchestration, and federated learning approaches for Open RAN architecture. His master's students are engaged in diverse topics including LiDAR-based power line monitoring, multimodal behavioral authentication, and 5G/6G security using AI techniques. Dr. Jaafar is affiliated with two prominent research laboratories at ETS: LASI (Computer System Architecture Research Laboratory) and LACIME (Communications and Microelectronic Integration Laboratory). At LASI, he contributes to research in AI-based systems engineering, resource orchestration in edge/cloud environments, and intelligent network design. Through LACIME, he engages with broader communications research spanning from microelectronic components to complex communication systems, with particular focus on wireless networks and signal processing applications.

• Operations Research
• Artificial Intelligence
• Decision-Making
• +10 more

Duong Nguyen serves as an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. His research integrates operations research, artificial intelligence, economics, and engineering to develop mathematical models for decision-making in large-scale networked systems including cloud/edge computing, smart grids, and crowdsourcing. He directs the NEMO research group focused on building intelligent multi-agent platforms through optimization and market design. His educational credentials include: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2020) M.Sc. in Telecommunications from INRS, University of Quebec (2014) B.Sc. in Electronic and Telecommunications from Hanoi University of Science and Technology (2011) Dr. Nguyen's research spans Operations Research, Artificial Intelligence, Decision-Making, Market Design, and Optimization with applications in edge computing, power systems, and network economics. His work emphasizes robust algorithms for uncertain environments and secure multi-agent platforms, recently expanding into quantum machine learning and privacy-preserving distributed systems. Current projects address decentralized federated learning, dynamic pricing, and EV charging network design. Analysis of his publication record reveals consistent focus on distributed optimization techniques for edge/cloud systems, with increasing integration of game theory and quantum computing. His work demonstrates strong methodological innovation in handling spatio-temporal uncertainty while addressing practical challenges in energy flexibility and secure genomic computation. His scientific recognition includes: Finalist for Best Student Paper Award at American Control Conference (ACC) 2024 Finalist for Best Paper Award at International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) 2023 Dr. Nguyen actively mentors Ph.D. students including Jiaming Cheng and Long Vu, with student-led research achieving significant recognition. His NEMO group collaborates with institutions including ETH Zurich on projects spanning autonomous driving, edge AI, and quantum optimization. Current research directions emphasize fair resource allocation, privacy-preserving learning, and dynamic pricing frameworks for next-generation networked systems.