جست‌وجوی استاد و پژوهشگر

• Internet Engineering
• Computer Security
• Applied Cryptography
• +۷ مورد دیگر

Althaff Irfan Cader Mohideen is a Lecturer at the School of Computing, Engineering and Physical Sciences. His research focuses on Internet engineering, computer security, and applied cryptography, with expertise in security protocols, authentication mechanisms, and trust management. He has contributed to interdisciplinary projects such as the NEAT Horizon2020 initiative and the SMILE project funded by Thales Alenia Space. His work includes developing network access methodologies for rural broadband, optimizing web performance via satellite, and exploring IoT security challenges. Mohideen holds a PhD in authentication mechanisms for e-assessments and collaborated on the award-winning 'Intelligent Keyboard' project. He currently serves as an External Examiner at Staffordshire University and advocates for multidisciplinary collaboration. Research Areas: IoT Security, Network Protocols, Authentication Mechanisms, Trust Management Key Projects: NEAT (Web Performance), SMILE (Streaming Optimization), Rural WiFi Deployment His research emphasizes practical solutions grounded in formal analysis, balancing theoretical rigor and real-world applicability. He actively seeks collaborations across academia and industry, particularly in securing constrained IoT devices and improving trust-based access control systems. Awards: Queen’s Award (2011), Recognition from Google’s 'Making the web Faster' Community Mohideen’s future work targets IoT authentication protocols, trust management in distributed systems, and scalable security solutions for constrained devices. He is committed to advancing sustainable development goals through secure, accessible digital infrastructure.

• Social Media Analysis
• Sentiment Analysis
• Computational Journalism
• +۶ مورد دیگر

Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.

Wei Pangمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۷ مورد دیگر

Wei Pang is a Professor of Computer Science and Bicentennial Research Leader at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. He leads the BCML Lab and is affiliated with the Edinburgh Centre for Robotics and National Robotarium. His expertise spans bio-inspired computing, machine learning, and AI applications in healthcare, robotics, and sustainability. Pang holds a PhD in Computing Science from the University of Aberdeen, with prior roles including Senior Lecturer at the University of Aberdeen and research fellowships in systems biology. Affiliations: Heriot-Watt University, Edinburgh Centre for Robotics, National Robotarium Education: PhD in Computing Science (2009), MEng (by research), BSc (Jilin University, China) Research Interests: Bio-inspired computing (e.g., artificial immune systems, swarm intelligence), machine learning (deep learning, explainable AI), healthcare applications (medical imaging, disease detection), and interdisciplinary projects in robotics and environmental science. His work addresses challenges in robust AI, fairness, and accountable machine learning. Recent Projects: EPSRC-funded RAIns and MI projects, CRUK-funded Endo.AI, and PRIME project on minority ethnic communities' digital experiences. His research has secured over £10M in grants, including £3.5M institutional funding. Awards: Scottish Crucible Award (2015), ADMA Best Paper Runner-Up (2016), EPSRC PRIME Award (2024) Grants/Advising: Supervised 12 PhD completions; contributed to £10M+ external funding. Labs/Teams: BCML Lab (focusing on bio-inspired AI), collaborations with Oxford, Cambridge, and industrial partners like Weather2 and Data2Text.

Yuntian Dengمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.

Guanhong Taoمشاهده پروفایل

استادیار

• AI Security
• Machine Learning Security
• Adversarial Generative AI
• +۳ مورد دیگر

Guanhong Tao is an Assistant Professor at the Kahlert School of Computing, University of Utah. His research focuses on the security and safety of AI-enabled systems, particularly addressing adversarial attacks on machine learning models and large language models (LLMs). He has received notable awards, including the NVIDIA Academic Grant Award (2025) and the Maurice H. Halstead Memorial Award (2023). Educational Background: He earned his Ph.D. in Computer Science from Purdue University under Dr. Xiangyu Zhang’s supervision. His work spans adversarial generative AI, LLM agent security, and machine learning for security applications. Research Interests: Tao’s research emphasizes securing AI systems against adversarial threats, including backdoor attacks, alignment loss in LLMs, and privacy-preserving techniques. His projects have been published in top venues like IEEE S&P, USENIX Security, and NeurIPS. Recent Contributions: Key publications include 'Alleviating the Fear of Losing Alignment in LLM Fine-tuning' (S&P 2025) and 'BAIT: Large Language Model Backdoor Scanning' (S&P 2025). His work often bridges cybersecurity and machine learning, addressing real-world vulnerabilities in AI systems. Grants & Awards: In addition to his NVIDIA grant, Tao has received the ACM SIGPLAN Distinguished Paper Award (2019) and multiple best-paper recognitions. His research is funded by leading industry and academic partnerships. Advising & Teaching: He advises students like Shih-Chieh Dai and co-advises Kang Yang (with Dr. Jun Xu). He teaches courses such as 'Machine Learning Security' at the University of Utah and has guest-lectured at institutions like Purdue and Rutgers. Professional Service: Tao serves on program committees for top conferences, including IEEE S&P, ACM CCS, NeurIPS, and CVPR. He chairs workshops like BANDS (ICLR) and AISCC (NDSS), fostering collaborative research in AI security.

Erisa Karafiliمشاهده پروفایل

دانشیار

• Cybersecurity
• Formal Methods in Security
• IoT Threat Models
• +۴ مورد دیگر

Erisa Karafili is an Associate Professor in Cybersecurity at the University of Southampton. She leads Teaching Methods Innovation at the GCHQ/EPSRC Academic Centre of Excellence for Cyber Security Education (ACE-CSE) and is a Champion in Security by Design at ACE-CSR. A Fellow of the Higher Education Academy, she joined the University in 2020 after roles including a Marie Curie Fellowship at Imperial College London, where she investigated cyber-attack attribution techniques. Her research focuses on formal methods applied to security, IoT threat models, and secure data sharing frameworks. Education: PhD in non-classical logics applied to multi-agent systems security from the University of Verona. Previous positions include PostDoc at Technical University of Denmark and Researcher at Imperial College London. Research Interests: Cyber-attack attribution, IoT security, formal methods in cybersecurity, data privacy, and argumentation-based reasoning for security. Awards: Higher Education Academy Fellowship. Current PhD Students: Betul Gokkaya, Mohammed Homaid Alquliti, Peter Geoffrey Williams, Steve Johnson. Active Projects: Heterogeneous Material Integrated MEMS/NEMS-Photonics Platform for Secure Communication (collaborative with Jize Yan and others).

Vahid Yazdanpanahمشاهده پروفایل

استادیار

• Multiagent Systems
• Artificial Intelligence
• Responsibility of AI Systems
• +۳ مورد دیگر

Vahid Yazdanpanah is an Assistant Professor (Lecturer) of Computer Science at the University of Southampton and a Visiting Lecturer at the University of Twente. He holds a PhD from the University of Twente (2019), an MSc in Artificial Intelligence from Utrecht University (2015), and an MBA from the University of Greenwich (2012). His research focuses on multiagent systems, AI responsibility, and circular economy applications. He leads the Agents, Interaction and Complexity (AIC) research group and is an RRI Champion for the UKRI CDT in AI for Sustainability (SustAI). Research interests include agent-based computing, formal logics for multiagent decision-making, and socio-technical systems. Notable awards include the Best Paper Award at PRIMA 2016 and recognition at AAMAS 2021. He teaches courses such as COMP2211 (Software Engineering) and supervises PhD students in AI and sustainability. Active projects include the ARGOS (AI Resilience Governance) and AutoTrust (Internet of Vehicles) initiatives. He has served on program committees for AAAI, IJCAI, and AAMAS, and reviews for journals like AI & Society and Annals of Operations Research. His work bridges technical AI advancements with ethical, legal, and societal challenges in responsible AI deployment.

Baishakhi Rayمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Baishakhi Ray is an Associate Professor at Columbia University, specializing in improving software reliability and developers' productivity for both traditional and AI-driven systems. She leads the ARiSE Lab, focusing on interdisciplinary research at the intersection of software engineering and artificial intelligence. Her research interests include software testing for AI systems, adversarial robustness, automated testing of autonomous systems, and leveraging AI techniques such as neural networks for dynamic analysis and fuzzing. Notable projects include DeepTest for autonomous car testing and NEUZZ for efficient fuzzing. Awards: VMware Early Career Faculty Award (2020), IBM Faculty Award (2019), NSF CAREER Award (2019), and multiple best paper awards including EAPLS FASE (2020) and ACM Distinguished Papers (FSE 2017, MSR 2017). Grants: NSF CAREER grant (2019-2024) for deep learning testing, NSF grants for workshops and security bug detection, and collaborative grants on persistent memory and SSL/TLS implementations. Her recent work emphasizes advancing code generation with large language models (LLMs), evaluating model robustness under data contamination, and developing tools like CodeSense and CrashFixer for code semantics and kernel debugging. The ARiSE Lab also explores causal performance debugging and transfer learning for configurable systems.

Coral Dandoمشاهده پروفایل

استاد

• Forensic Cognition
• Eyewitness Memory
• Deception Detection
• +۷ مورد دیگر

Professor Coral Dando is a Professor of Psychology at the University of Westminster, leading research in forensic cognition and investigative interviewing. With a background as a London Police Officer, she completed a BSc (Hons) Psychology and PhD in Applied Forensic Cognition. She is a Chartered Psychologist, National Teaching Fellow, and Registered Forensic Psychologist. Her research focuses on eyewitness memory, deception detection, and cross-cultural interviewing, with a particular emphasis on improving investigative methods in security and forensic contexts. Her academic roles include teaching forensic psychology modules (e.g., detecting deception, investigative interviewing) and supervising PhD students researching topics like virtual environments in interviews and neurodivergent credibility. She has secured significant research funding from entities like the Home Office, FBI, and CPNI, totaling over £2M across projects addressing insider threats, county lines exploitation, and aviation security. Key Research Themes: Eyewitness reliability, cognitive interviewing techniques, cross-cultural persuasion, and insider threat detection. Grants: Includes a 2024 €97,000 COST grant for implementing the Mendez Principles and a 2016 $469,000 FBI-funded study on intelligence interviewing. Professor Dando’s work bridges academia and practice, training professionals from police forces, security agencies, and NGOs. She contributes to policy development and has authored over 100 peer-reviewed publications, including seminal works on the cognitive interview and WAFA (Witness-Aimed First Account) for neurodivergent individuals. Her research emphasizes ethical, context-aware methodologies to enhance justice outcomes.

Rongxing Luمشاهده پروفایل

استاد مدعو

• Applied Cryptography
• Privacy Enhancing Technologies
• IoT Security
• +۴ مورد دیگر

Rongxing Lu is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick (UNB), Canada, since August 2016. Previously, he held positions at Nanyang Technological University (NTU), Singapore (2012–2016) and the University of Waterloo, Canada (PhD in 2012). His research focuses on applied cryptography, privacy enhancing technologies, and IoT-big data security. He has over 7,500 citations and received prestigious awards like the Governor General’s Gold Medal (2012) and the IEEE ComSoc Asia Pacific Outstanding Young Researcher Award (2013). He is an IEEE senior member and serves on editorial boards of journals like IEEE Network. **Education**: PhD in Electrical & Computer Engineering, University of Waterloo (2012), awarded Governor General’s Gold Medal Postdoctoral Fellow at University of Waterloo (2012–2013) **Research Interests**: Developing cryptographic protocols for IoT and big data systems Privacy-preserving techniques for distributed systems Secure communication in 5G/6G networks and vehicular systems **Awards and Recognition**: Recipient of multiple best paper awards in IEEE conferences 2016–2017 Excellence in Teaching Award at UNB **Editorial and Leadership Roles**: Symposium co-chair at IEEE Globecom’16 Secretary of IEEE ComSoc CIS-TC Organized special issues on fog computing security (Elsevier) and big data security (IEEE IoT Journal) **Key Contributions**: Pioneered privacy-aware data reporting schemes for vehicular networks Designed lightweight IoT authentication protocols Advanced secure machine learning frameworks with privacy guarantees

Joseph Alejandro Gallego Mejiaمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۱۲ مورد دیگر

Joseph Alejandro Gallego Mejia is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He holds a PhD with meritorious distinction in Systems and Computing Engineering from the National University of Colombia, along with a Master’s and dual Bachelor’s degrees in Systems and Computing Engineering and Industrial Engineering. PhD in Systems and Computing Engineering, National University of Colombia (Meritorious Distinction) Master of Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Industrial Engineering, National University of Colombia His research focuses on artificial intelligence, machine learning, computer vision, quantum machine learning, natural language processing, and cybersecurity. He explores robustness estimation, anomaly detection, incremental learning, and scalable software architectures for AI systems. His work bridges theoretical foundations and practical applications in health, remote sensing, and edge computing. The recent publications reflect a strong trend in interdisciplinary AI research, combining machine learning with quantum computing, cybersecurity, and natural language understanding. His work spans domains such as satellite imagery analysis, medical diagnostics, IoT security, and conversational AI, demonstrating a commitment to scalable and robust intelligent systems. Keywords across publications include Computer Science, Machine Learning, Quantum Computing, and Cybersecurity, with subfields ranging from adversarial robustness to hybrid quantum-classical models. Scientific distinctions include: PhD with meritorious distinction, National University of Colombia Postdoctoral fellow, Frontier Development Lab (Trillium), supported by NASA and ESA He has served as a reviewer for top-tier journals and conferences including Neurocomputing, IEEE Access, Radioscience, NeurIPS, and NLDL. Though no formal grants are listed, his postdoc was funded by NASA and ESA, indicating significant external support. He teaches courses in programming, data science, machine learning, deep learning, NLP, and software engineering. He founded the tech company Sammu and mentors students through instruction and research supervision. He is actively involved in research and teaching, contributing to innovative programs in AI and computing education. His lab and team affiliations are not explicitly stated, but his work suggests collaboration with AI, quantum computing, and cybersecurity research groups.

• Business Process Management
• Process Mining
• Explainable AI
• +۵ مورد دیگر

Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.

• Machine Learning
• Deep Learning
• Anomaly Detection
• +۵ مورد دیگر

Peter Karsmakers serves as Associate Professor at KU Leuven's Department of Computer Science within the Faculty of Engineering Technology, based at the Geel Campus. He coordinates the Declarative Languages and Artificial Intelligence (DTAI) research group and holds leadership roles including coordinator of Research and Education for Computer Science across Geel and Diepenbeek Campuses. Karsmakers earned his PhD in Engineering Science in May 2010, focusing on kernel-based learning algorithms for sparse modeling and efficient predictions from large datasets. His doctoral work established foundations for his current research trajectory in resource-constrained machine learning systems. His research integrates machine learning with signal processing for real-time sensor data interpretation, specializing in anomaly detection from acoustic, radar, and accelerometer signals on embedded devices. Current projects address industrial condition monitoring, elderly care systems, and livestock facility monitoring through three main tracks: acoustic monitoring (e.g., SINS, WATCHDOG), radar-based systems (e.g., FARADAY, NextPerception), and smart electronics for power converters. Recent publications demonstrate strong trends in constraint-guided deep learning architectures for industrial applications, cross-environment robustness in sensor systems, and domain-knowledge integration to reduce data requirements. His work consistently bridges theoretical machine learning with practical implementations in resource-constrained environments. No scientific awards or fellowships were mentioned in the provided materials. Karsmakers supervises over 10 master's theses annually and coordinates a research team of 10 PhD students and a post-doc within DTAI-ADVISE. He has secured approximately 2.3 million euros in funding through VLAIO, EU-ECSEL, and bilateral industry contracts, including 10 active projects such as AutoEdgeML (2024-2028) and Fault Tolerant Neural Networks for Space Applications (2024-2027). He leads the DTAI-ADVISE research group focused on developing software that attaches semantics to sensor data on resource-constrained devices. The team operates across multiple campuses with specialized labs for acoustic monitoring (Geel), radar-based systems (in collaboration with ESAT-TELEMIC), and smart electronics (with Electrical Engineering department), maintaining strong industry partnerships with companies in healthcare, manufacturing, and agriculture sectors.

Enkelejda Kasneciمشاهده پروفایل

استاد

• Human-Centered Technologies
• Artificial Intelligence in Education
• Multimodal Interaction
• +۴ مورد دیگر

Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.

Nguyen Dangمشاهده پروفایل

مدرس

• combinatorial optimisation
• algorithms
• machine learning
• +۵ مورد دیگر

Nguyen Dang is a Lecturer at the School of Computer Science, University of St Andrews, actively supervising PhD students and teaching AI-related modules including Artificial Intelligence (CS3105), Artificial Intelligence Practice (CS5011), Machine Learning (CS5014), and Uncertainty in Artificial Intelligence (CS5016). He leads the Centre for Interdisciplinary Research in Computational Algebra and maintains an active research profile with numerous publications in top conferences. University of St Andrews, School of Computer Science Lecturer (equivalent to assistant professor) Supervising PhD students including Tai Nguyen Teaching multiple AI and Machine Learning courses Dr. Dang's research focuses on the intersection of machine learning and optimization, particularly automated algorithm configuration and design. His work centers on leveraging machine learning techniques to automate the development of optimization algorithms, with special emphasis on deep reinforcement learning for Dynamic Algorithm Configuration and integrating machine learning into constraint programming. His research has significant applications across various domains, especially in automated constraint modeling. The publications reflect strong activity in combinatorial optimization, algorithm selection, and benchmark instance generation. His recent publications demonstrate consistent output in top venues including Artificial Intelligence Journal, GECCO, FOGA, and CP conferences, with notable achievements including Best Paper Awards at GECCO'2025 and GECCO'2022. The research spans theoretical foundations of parameter control, practical applications in constraint programming, and innovative approaches to algorithm configuration. Best paper award at GECCO'2025 Best paper award at GECCO'2022 Nomination for best paper award at FOGA'2023 Best paper award at GECCO'2017 Dr. Dang holds a Leverhulme Early Career Fellowship (2020-2023) worth £90,000 for his project on constraint-based automated generation of synthetic benchmark instances. He has secured additional funding including EPSRC High Performance Computing grants totaling over 2.2 million CPU hours and a COST Action grant. His research group actively develops tools and frameworks for automated algorithm configuration and benchmark instance generation, with several open-source datasets available on GitHub. He is involved with multiple research groups including the Centre for Interdisciplinary Research in Computational Algebra and collaborates extensively with researchers at University of St Andrews and internationally, including at Université de Paris I Panthéon-Sorbonne where he conducted visiting research.