Shengchao Liu | Computer Science | Research Excellence Award

Dr. Shengchao Liu | Computer Science | Research Excellence Award

The Chinese University | Hong Kong

Shengchao Liu is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, whose research lies at the intersection of machine learning, geometry, and scientific discovery. His work focuses on developing foundation models and physics-inspired learning frameworks for molecules, proteins, and materials, with the long-term goal of accelerating discovery in chemistry, biology, and materials science. By integrating multi-modal data, symmetry principles, and domain knowledge, his research bridges theoretical advances in AI with real-world experimental impact. A central theme of Dr. Liu’s research is geometric and symmetry-informed representation learning. He has pioneered group-equivariant and manifold-constrained generative models that respect the underlying physical laws of molecular and material systems. His contributions include SE(3)-invariant pretraining methods, group-symmetric stochastic differential equation models, and rigid flow matching techniques, which have significantly improved the fidelity and interpretability of molecular generation and dynamics modeling. These methods form a unifying framework for learning across molecules, proteins, and crystalline materials, as demonstrated in his influential works at ICLR, ICML, NeurIPS, and AISTATS. Dr. Liu’s work is deeply collaborative and interdisciplinary. He has worked closely with leading researchers across academia and industry, including Mila, UC Berkeley, NVIDIA Research, and national laboratories. As a Principal Investigator, he has led NERSC-supported projects on foundation models for material discovery, leveraging large-scale GPU resources to push the frontier of generative AI for science. His research has also contributed widely used open-source resources, including geometric graph learning benchmarks and toolkits adopted by the broader AI-for-science community.

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Featured Publications


Pre-training Molecular Graph Representation with 3D Geometry

– International Conference on Learning Representations , 2021 | Cited by 574


N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

– Advances in Neural Information Processing Systems, 2019 | Cited by 295


A text-guided protein design framework

– Nature Machine Intelligence, 2025 | Cited by 225

 

Christos Bouras | Computer Science | Research Excellence Award

Prof. Christos Bouras | Computer Science | Research Excellence Award

Prof. Christos Bouras | University of Patras | Greece

Professor Christos Bouras is a distinguished academic leader and renowned computer engineering expert, currently serving as Professor in the Department of Computer Engineering and Informatics and Rector of the University of Patras, Greece. He holds a Diploma and a PhD in Computer Engineering and Informatics from the University of Patras. Over the course of his career, he has made substantial contributions to advanced networking technologies, digital communications, and distributed systems while leading major academic, administrative, and international initiatives. His research expertise spans and Beyond Networks, performance analysis of networking and computer systems, mobile and wireless communications, telematics, QoS and pricing mechanisms, e-learning technologies, and networked virtual environments. As an active member of IEEE and ACM, Professor Bouras has built a global reputation for innovative contributions and collaborative research. He has also held several prestigious roles, including Honorary Professor at the College of Information Engineering, Sichuan Agricultural University, China, and President of the University of Patras Property Utilization & Management Company. His long-standing academic leadership is matched by a major international presence in scholarly events. Professor Bouras has participated extensively in international conference committees for more than three decades, contributing to global research dialogue in computing, networking, and educational technologies. His committee roles span top-tier conferences such as ACM STOC, ICALP, IEEE ICALT, ICL, ICWN, ICOMP, GRID Computing, and numerous specialized workshops across Europe, Asia, and North America. His involvement includes organizing committees, program committees, keynote speaking, and advisory roles in areas such as distributed algorithms, multimedia systems, web-based learning, virtual environments, mobile technologies, simulation and modeling, and entertainment computing. Widely respected for his research excellence, international collaboration, and academic leadership, Professor Bouras continues to advance global innovation in computer networks, digital systems, and technology-enhanced learning.

Profiles: Google Scholar

Featured Publications

Jurgelionis, A., Fechteler, P., Eisert, P., Bellotti, F., David, H., Laulajainen, J. P., Bouras, C., … (2009). Platform for distributed 3D gaming. International Journal of Computer Games Technology, 2009(1), Article 231863.

Bouras, C., Kollia, A., & Papazois, A. (2017). SDN & NFV in 5G: Advancements and challenges. In 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) (pp. xx–xx). IEEE.

Bouras, C., & Tsogkas, V. (2012). A clustering technique for news articles using WordNet. Knowledge-Based Systems, 36, 115–128.

Bouras, C., & Tsiatsos, T. (2006). Educational virtual environments: Design rationale and architecture. Multimedia Tools and Applications, 29(2), 153–173.

Bouras, C., Philopoulos, A., & Tsiatsos, T. (2001). e-Learning through distributed virtual environments. Journal of Network and Computer Applications, 24(3), 175–199.

Bouras, C., Ntarzanos, P., & Papazois, A. (2016). Cost modeling for SDN/NFV based mobile 5G networks. In 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) (pp. xx–xx). IEEE.

Bouras, C., Konidaris, A., & Kostoulas, D. (2004). Predictive prefetching on the web and its potential impact in the wide area. World Wide Web, 7(2), 143–179.

Mr. Hussm Rostum | Computer Science | Best Researcher Award

Mr. Hussm Rostum | Computer Science | Best Researcher Award

Miskolc University, Institute of Automation and Info-communication, Hungary.

Hussam Rostum is a PhD candidate and researcher at the University of Miskolc in Hungary, specializing in computer vision for autonomous drone navigation. With a strong background in telecommunications and electronics, he blends academic excellence with hands-on experience as a part-time software engineer at FIEK. Hussam is known for developing cutting-edge solutions in industrial automation, biomedical imaging, and human–machine interfaces. Fluent in Arabic and English, he brings international insight into interdisciplinary research projects, merging software innovation with engineering systems.

Profile

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🎓 Education

Hussam holds a BSc and MSc in Telecommunication and Electronic Engineering, equipping him with deep theoretical and practical knowledge in signal processing, system design, and electronics. Currently, he is pursuing a PhD in Information Science at the University of Miskolc, focusing on AI-based vision systems for autonomous drone operations.

💼 Experience

Hussam serves as an Assistant Researcher and Part-time Software Engineer at FIEK, where he builds C# monitoring software, implements PLC-to-PC communications, and automates data workflows using Linux, Docker, and Excel. His professional journey includes work as a Full Stack Developer and Telecom Engineer, with experience in GUI development, DevOps collaboration, and .NET technologies.

🔬 Research Interests

📸 Computer Vision & Image Processing

🤖 Autonomous Systems & Drone Navigation

🩺 Biomedical Imaging & Oxygen Saturation Estimation

🔬 Optical System Design (Zemax)

⚙️ Industrial Automation & Data Visualization

🧠 Human–Machine Interfaces & Sensor Integration

📚 Selected Publications

Enhancing Machine Learning Techniques in VSLAM for Robust Autonomous Unmanned Aerial Vehicle Navigation
📅 2025-04-02 | 📰 Electronics
📌 Focus: Improving Visual SLAM with machine learning for UAVs in complex environments.
🔗 DOI: 10.3390/electronics14071440
👥 Co-author: József Vásárhelyi

Comparing the Effectiveness and Performance of Image Processing Algorithms in Face Recognition
📅 2024-05-22 | 📚 Conference Paper
📌 Focus: Evaluation of various image processing techniques for face recognition applications.
🔗 DOI: 10.1109/ICCC62069.2024.10569864
👥 Co-author: József Vásárhelyi

FPGA Implementation in Mobile Robot Applications: State of the Art Review
📅 2023-12-20 | 📰 Multidiszciplináris Tudományok
📌 Focus: Overview of FPGA-based systems in robotics.
🔗 DOI: 10.35925/j.multi.2023.2.21
👥 Co-authors: Omar M. Salih, Noha Hammami

An Overview of Energies Problems in Robotic Systems
📅 2023-12-14 | 📰 Energies
📌 Focus: Challenges in energy management for robotic systems.
🔗 DOI: 10.3390/en16248060
👥 Co-authors: József Vásárhelyi, Omar M. Salih, Rabab Benotsname

A Review of Using Visual Odometry Methods in Autonomous UAV Navigation in GPS-Denied Environments
📅 2023-12-01 | 📰 Acta Universitatis Sapientiae, Electrical and Mechanical Engineering
📌 Focus: Use of visual odometry for UAVs in GPS-denied settings.
🔗 DOI: 10.2478/auseme-2023-0002
👥 Co-author: József Vásárhelyi

 

 

 

 

Assist. Prof. Dr. Constantinos D. Zeinalipour-Yazdi | Ammonia Synthesis | Best Researcher Award

Assist. Prof. Dr. Constantinos D. Zeinalipour-Yazdi | Ammonia Synthesis | Best Researcher Award

Northeastern University London, United Kingdom.

Dr. Constantinos D. Zeinalipour-Yazdi is an Assistant Professor of Chemistry at Northeastern University London. He is a distinguished researcher known for his contributions to catalysis, materials science, and computational modeling. His work focuses on understanding chemical reaction mechanisms and developing novel catalysts and materials for diverse applications. His research employs density functional theory (DFT) and other computational methods to investigate reaction pathways and material behaviors. He has led multiple high-impact projects and contributed significantly to advancing theoretical chemistry and material science.

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🎓 Education

Dr. Constantinos D. Zeinalipour-Yazdi earned his Ph.D. in Chemistry from the University of California, San Diego & San Diego State University in 2006. Following his doctorate, he held prestigious research fellowships and academic appointments at leading institutions worldwide. He was a Research Fellow at University College London (UCL), where he contributed to advancements in computational chemistry and catalysis. He also held positions at the University of Cambridge and Imperial College London, focusing on density functional theory (DFT) and materials science applications. His extensive experience spans collaborations with top-tier universities and research centers, making significant contributions to theoretical and computational chemistry.

💼 Experience

Dr. Constantinos D. Zeinalipour-Yazdi is an Assistant Professor of Chemistry at Northeastern University London, where he focuses on computational and theoretical chemistry. He previously served as a Research Fellow under the Cyprus Research Promotion Foundation (RPF), contributing to groundbreaking research in molecular modeling and catalysis. As a Lead Investigator at the Environmental Molecular Sciences Laboratory (EMSL) at the Pacific Northwest National Laboratory (PNNL), he spearheaded studies on chemical reaction mechanisms and materials science applications. Additionally, he has been an active participant in multiple European Union and Engineering and Physical Sciences Research Council (EPSRC)-funded projects, collaborating on cutting-edge advancements in energy materials, catalysis, and computational chemistry.

🔬 Research Interests

Catalysis & Reaction Mechanisms: Investigating ammonia and hydrazine synthesis mechanisms on metal nitrides.

Materials Science: Developing and optimizing catalysts for reactions such as the Water-Gas Shift reaction.

Computational Chemistry: Utilizing DFT and multiscale simulations to study material properties and reaction pathways.

🏆 Awards & Recognitions

Multiple grants and research fellowships from EPSRC, EMSL, and EU Framework Programmes

Recognized for high-impact computational studies in catalysis and materials science

International Invention Awards recipient

📚 Selected Publications

A DFT assessment of the activation barrier for concerted proton transfer in cyclic water clusters (H₂O)ₙ where n = 3–8
Computational and Theoretical Chemistry (Feb 2025)
DOI: 10.1016/j.comptc.2024.115061
Co-authors: Numair Elahi

Emerging Trends in Palladium Nanoparticles: Sustainable Approaches for Enhanced Cross-Coupling Catalysis
Catalysts (Feb 2025)
DOI: 10.3390/catal15020181
Co-authors: Jude I. Ayogu, Numair Elahi

A study using physical sphere-in-contact models to investigate the structure of close-packed nanoparticles supported on flat hexagonal, square, and trigonal lattices
Chemical Physics (Jan 2025)
DOI: 10.1016/j.chemphys.2024.112464

3D Printed Sphere-in-Contact Models of Carbon Materials
Preprint (2024)
DOI: 10.2139/ssrn.4985077
Co-authors: Toby Chai, David Pullman, Deborah Gater, James Kneller

A computational study of H-bonded networks in cyclic water clusters, (H₂O)ₙ (n = 3–12)
Journal of Molecular Modeling (2024)