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Constantinos Chamzas is an Assistant Professor in Robotics Engineering at Worcester Polytechnic Institute (WPI), with an affiliated appointment in the Computer Science Department. He leads the Efficient Learning for Planning and Intelligent Systems (ELPIS) Lab, which focuses on developing autonomous robotic systems capable of reasoning about and interacting with the physical world. His research interests span robotics, artificial intelligence, motion planning, machine learning, and autonomous systems, with specific focus on planning efficiency, planning under uncertainty, and planning from visual inputs. The ELPIS Lab integrates classical planning algorithms with state-of-the-art machine learning techniques to develop efficient and robust robotic agents. Prof. Chamzas has received notable recognition including an NSF-GRFP fellowship during his doctoral studies and a recent NSF-CRII grant for $175k. His publication record includes papers at top robotics conferences such as RSS, IROS, and HSCC, with research focusing on motion planning algorithms, robot learning, and efficient computational methods for robotics. His lab actively advises both Master's and Ph.D. students, with recent graduates and new students joining regularly. The ELPIS Lab maintains strong connections with the robotics research community and participates in major conferences and workshops. NSF-GRFP fellowship recipient NSF-CRII grant awardee ($175k) Multiple publications at top robotics conferences (RSS, IROS, HSCC) Prof. Chamzas received his Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki in 2017 and completed his Ph.D. in Computer Science at Rice University in 2023 under the supervision of Dr. Lydia Kavraki and Dr. Anshumali Shrivastava.

• Robotics
• Artificial Intelligence
• Motion Planning
• +6 more

Dr. Archie Singh Khuman serves as Associate Head of Education and Senior Lecturer at De Montfort University's School of Computer Science and Informatics. An award-winning academic and proud DMU alumnus (BSc 2009, MSc 2011, PhD 2017), he specializes in Artificial Intelligence, Computational Intelligence, and Uncertainty Modelling with extensive leadership in curriculum development and Universal Design for Learning. His educational background demonstrates deep institutional commitment: PhD in Computer Science (2017, De Montfort University) MSc in Intelligent Systems (2011, De Montfort University) BSc (Hons) in Computer Science (2009, De Montfort University) Dr. Khuman's research pioneers hybrid uncertainty frameworks, notably the R-fuzzy grey analysis framework (RfGAf), bridging Grey Theory, Fuzzy Logic, and Rough Sets. His work focuses on quantifying perception-based uncertainty for applications in healthcare diagnostics, robotic swarm intelligence, and risk assessment systems, emphasizing real-world implementation through industry partnerships. His publication trajectory reveals increasing interdisciplinary convergence, particularly in healthcare AI applications since 2021. Recent outputs integrate Grey-Fuzzy methodologies for medical diagnostics (diabetes, heart disease), sentiment analysis of pandemic communications, and adaptive systems in automotive/industrial contexts, demonstrating strong translation from theoretical foundations to societal impact. Major recognitions include: Five consecutive Students' Choice Teaching Awards (2018-2024) De Montfort Teacher Fellowship (2022) Senior Fellow of the Higher Education Academy (2022) Leverhulme Trust-funded international Grey Systems network (£124,997) Best Paper Award at Grey Systems and Uncertainty Analysis (2016) As academic lead for the Data Analytics Project, he mentored students in developing machine learning solutions that generated £2.5m savings for retail partners. His grant portfolio includes Royal Society funding for IoT-based scheduling (£12,000) and leadership of the Race Equality Network. Current initiatives focus on transnational education partnerships and SI-PASS peer learning programs. Dr. Khuman co-leads DMU's Institute of Artificial Intelligence research group and serves as School Governor for Inglehurst Junior School, maintaining active collaborations with institutions in China, Canada, Romania, and Spain through the International Network on Grey Systems.

• Artificial Intelligence
• Computational Intelligence
• Machine Learning
• +5 more

Dr. Yueqing Li serves as an Associate Professor in the Department of Industrial and Systems Engineering within Lamar University's College of Engineering. His expertise spans neuroergonomics, human-computer interaction, and data analytics with significant contributions to transportation safety and assistive technologies. Dr. Li's educational foundation includes: Ph.D. in Industrial Engineering from North Carolina State University M.S. in Industrial Engineering from University of Arkansas M.S. in Economics from Nanjing University of Aeronautics and Astronautics B.S. in Electronics Engineering from Zhengzhou University His research program integrates neuroergonomics (BCI systems, neurocognitive processing), human-computer interaction (haptic interfaces, AR-based systems), human factors engineering (driving safety, occupational ergonomics), and data science (machine learning applications for safety analytics). Current projects focus on UAV security systems, pipeline leak detection, and transportation resilience in extreme weather. Analysis of Dr. Li's 15 most recent publications reveals dominant themes in transportation safety (32% of works), neuroergonomics applications (28%), and machine learning for safety engineering (24%). His Southeast Texas-focused crash severity studies demonstrate methodological innovation through CART and fuzzy systems, while his BCI research bridges rehabilitation engineering and human factors. Key recognitions include: Engineering Faculty Fellow (Lamar University College of Engineering, 2017-2020) Eric Malstrom Endowed Memorial Scholarship (University of Arkansas) Multiple university-level teaching and research awards across three institutions Dr. Li actively mentors graduate students (11 primary advisees since 2017) and secures substantial research funding, including $516,031 from NSF for high-performance computing infrastructure and $33,172 from the Center for Advances in Port Management for autonomous freight safety. His $600,000+ grant portfolio addresses critical challenges in port security, pipeline monitoring, and transportation resilience. The Human Factors and Ergonomics Laboratory under Dr. Li's direction features 16-channel EEG systems, BIOPAC data acquisition suites, wireless EMG/ECG modules, driving simulators, and Gazepoint eye tracking technology. His research team collaborates with TEES and the Texas Department of Transportation on interdisciplinary projects spanning neuroergonomics, transportation safety, and assistive technology development.

• Human Factors and Ergonomics
• Neuroergonomics
• Brain-Computer Interfaces
• +4 more

Đorđe Žikelić is an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University (SMU) in Singapore. He completed his PhD in 2023 at the Institute of Science and Technology Austria (ISTA) under Krishnendu Chatterjee and Petr Novotný, receiving both Outstanding PhD Thesis and Outstanding Scientific Achievement awards. Prior to his doctorate, he earned bachelor's and master's degrees in mathematics from the University of Cambridge. His educational background includes: PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023 Bachelor's and Master's in Mathematics, University of Cambridge Dr. Žikelić's research focuses on advancing formal methods to ensure software and AI systems are correct, safe, and trustworthy. His work bridges theoretical aspects of formal reasoning about probabilistic systems with practical automated verification methods. His primary research interests span three interconnected areas: Program Analysis and Verification: He develops techniques for analyzing probabilistic programs, numerical programs, and efficient quantifier elimination methods, addressing fundamental challenges in verifying complex software systems. Trustworthy AI and Safe Autonomy: He creates formal verification frameworks for learning-enabled control systems and neural networks, ensuring AI operates safely in uncertain environments through methods like runtime monitoring and certificate repair. Probabilistic System Verification: He explores broader applications including bidding games on graphs and blockchain protocol analysis, extending formal methods to novel domains beyond traditional finite-state verification. His publication trajectory shows a consistent progression from theoretical foundations to practical applications, with recent work increasingly focused on integrating formal verification with machine learning. His 2024-2025 publications demonstrate growing expertise in verifying learning-based systems and developing practical tools like PolyQEnt for quantified entailment solving. His scientific achievements have been recognized with: Outstanding PhD Thesis Award at ISTA Outstanding Scientific Achievement Award at ISTA Distinguished Paper Award at FM 2024 Dr. Žikelić serves on program committees for major conferences including TACAS, PLDI, AAAI, and CAV. He actively mentors through the Programming Languages Mentoring Workshop (PLMW) at PLDI 2025. His research group at SMU focuses on developing novel algorithms for verifying correctness of programs and AI systems, with current projects spanning formal methods, artificial intelligence, and programming languages.

• Formal Methods
• Program Analysis and Verification
• Trustworthy AI
• +5 more