José Cano Reyes is a Senior Lecturer (Associate Professor) at the University of Glasgow's School of Computing Science, leading the Glasgow Intelligent Computing Lab (gicLAB) and serving as deputy Head of the GLAsgow Systems Section (GLASS). His academic career includes postdoctoral roles at the University of Edinburgh (2014-2018) and Universitat Politècnica de Catalunya (2012-2013), with a PhD and engineering degree from Universitat Politècnica de Valencia (2004-2012). He has held visiting and guest lecturer positions at Edinburgh and Glasgow across computer architecture, compilers, and embedded systems topics. Research focuses on hardware-software co-design for edge AI, including DNN acceleration (FPGA/GPU), encrypted AI systems, and secure mission-critical SoCs. Key projects include EU's dAIEDGE, EPSRC IDEAL, and UKRI AppControl. He leads over 15 research staff and students in areas like quantization, sparsity exploitation, and robust AI deployment. Notable contributions span 100+ peer-reviewed publications across top venues (ISCA, IJCNN, IEEE TPDS) and 3 authored books on ad hoc networks and embedded systems. Academic service includes organizing 20+ conferences (ISPASS, Euro-Par, ASPLOS) and serving on editorial boards for ACM TACO and IEEE TPDS. His educational efforts include teaching Computer Architecture (Year 4), Computer Systems (Year 1), and supervising over 20 PhD/MSc students since 2017.
Tomaž Dobravec is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. He is actively involved in research and teaching in computer science, with a focus on system software, algorithms, and distributed computing. His work is supported by national research programmes and structural funds projects. His research interests include: Parallel and Distributed Systems Graph Optimization and Big Data Combinatorial Scientific Computing Mobile and Industrial Operating Systems Interactive Educational Software Development The trend in his research projects indicates a strong emphasis on algorithmic efficiency, large-scale data processing, and practical software solutions for both industrial and educational contexts. His involvement in long-term ARRS programmes (e.g., P2-0095) highlights sustained contributions to core computing disciplines. He has participated in several research and development projects, including: P2-0095 - Parallel and distributed systems (2020–2026) N2-0171 - Graph Theory and Combinatorial Scientific Computing (2021–2023) PKP6 - Mobile Operating System for Industrial Use (MobiIOS) (2020) Graph Optimisation and Big Data (2016–2019) Development of the Visual Assistant project (2016–2017) Design and programming of interactive electronic textbooks (2017) He teaches key undergraduate courses such as System Software Programming 2 , Algorithms and Data Structures 2 , and The C Programming Language , indicating a strong foundation in core computer science education. While no formal advisees are listed, his role in research projects suggests mentorship of students and junior researchers. There is no mention of scientific awards in the provided text.
Dr Thomas Scott is an early career teaching academic and researcher at the University of Adelaide's School of Chemical Engineering within the Faculty of Sciences, Engineering and Technology. He holds a Bachelor of Engineering (Honours) in Chemical and a PhD in Engineering (Process and Resource Engineering), both from the University of Adelaide. His research expertise centers on hydrothermal liquefaction with key interests in process design and optimisation. He has expanded his research portfolio to include minerals processing, winery sustainability, brewery engineering, and water & wastewater engineering. As the teaching laboratory coordinator for the School of Chemical Engineering, he oversees the Unit Operations Laboratory course and coordinates practical components in core chemical engineering courses. Dr Scott is eligible to supervise Masters and PhD students as a co-supervisor, reflecting his growing role in academic mentorship despite being early in his career. His professional activities demonstrate a strong commitment to both research innovation and hands-on chemical engineering education.
Shervan Babamohammadi is a Research Fellow in the Chemical Engineering Department at the University of Wolverhampton , specializing in CO2 capture technologies, blue hydrogen production, and gas separation. His expertise includes process modelling and optimization for carbon capture systems, development of novel solvents, and supervision of PhD/undergraduate students. He previously held a postdoctoral position at Brunel University London (2022) and earned his PhD from the University of Malaya (2021). Education PhD in Chemical Engineering (University of Malaya, 2021) MSc in Chemical Engineering (University of Technology of Malaysia, 2012) B.Eng. in Chemical Engineering (University of Mazandaran, 2008) Research Interests: Focused on sustainable CO2 capture methods using novel solvents, hydrogen production optimization, environmental engineering for renewable energy integration, and mass transfer behavior in gas absorption systems. His work involves lab-scale and pilot-scale experiments. Publications: His recent works highlight advancements in hydrogen production with machine learning integration (2023), design of experiments for blue hydrogen systems (2023), bio-based sorbents for carbon capture (2022), and solvent solubility studies (2015-2023). Professional Memberships: Graduate Engineer, Board of Engineers Malaysia (BEM) since 2020 Associate Member, Institute of Chemical Engineers (IChemE) since 2020 Graduate Member, American Institute of Chemical Engineers (AIChE) since 2020 Full Member, Iranian Association of Chemical Engineers (IAChE) since 2020 Early Career Researcher Member, UKCCSRC since 2022 Industry Experience: Engaged in freelance consultancy projects related to chemical engineering and carbon capture.
Francis Chi Chung Ling serves as an Associate Professor in the Department of Physics within the Faculty of Science at The University of Hong Kong (HKU). His academic journey began at HKU where he earned his B.Sc., M.Phil., and Ph.D. degrees. He maintains professional affiliations as C.Phys., M.IEEE, and F.Inst.P., and conducts research at Room 417, Chong Yuet Ming Physics Building. Office: CYP 417 Contact: 3917 5248 / 2559 9152 Email: ccling@hku.hk ORCID: https://orcid.org/0000-0003-4757-1065 Professor Ling's research spans multiple critical areas in semiconductor physics and materials science. His primary focus centers on defect characterization and engineering in semiconductors, particularly examining how defects influence electrical, optical, and magnetic properties. His work with positron annihilation spectroscopy provides atomic-scale insights into vacancy defects, while his investigations into deep level transient spectroscopy and carrier transport mechanisms advance fundamental understanding of semiconductor behavior. His laboratory maintains international collaboration with the positron beam line at Helmoltz Zentrum Dresden Rossendorf in Germany. Analysis of his 207 publications reveals a strong concentration on zinc oxide (ZnO) systems, silicon carbide (SiC), and other wide bandgap semiconductors. His recent work shows increasing focus on energy applications including photovoltaics, neuromorphic computing elements, and battery materials. The publications demonstrate consistent methodology combining experimental characterization with theoretical analysis, particularly emphasizing defect engineering approaches to modify material properties. Faculty Excellent Service Award: Faculty of Science, HKU (2008) Best Poster Award: ICMAT 2007, Singapore Faculty Knowledge Exchange Award 2024 for SiC Power Devices collaboration Professor Ling has supervised over 35 research postgraduate students across MPhil and PhD programs, with recent graduates focusing on SiC devices, ZnO-based materials, and 2D semiconductor structures. His grant portfolio includes significant funding as Principal Investigator on 17 projects totaling over HK$8 million, with recent major awards including HK$1.26 million for 'Bipolar Degradation in SiC Devices Suppressed by Proton Implantation' (2023) and HK$804,000 for 'Optimising the Performance of SiC Devices Through the Control of Atomic Scale Deep Level Defect' (2021). He leads the Material Physics Laboratory within HKU's Experimental Condensed Matter and Material Science Group, specializing in defect characterization and engineering of functional materials. The laboratory maintains specialized equipment for deep level transient spectroscopy, temperature-dependent Hall measurements, and luminescence spectroscopy, with international access to positron beam facilities in Germany.
Bo Zhao is an Assistant Professor in the Department of Computing at Xi’an Jiaotong-Liverpool University , with a joint affiliation to the University of Oxford where he continues collaborative research in demographic sciences and population health. His career trajectory spans post-doctoral research at Oxford’s Nuffield Department of Population Health and earlier roles as Research Associate at the University of Leicester’s NIHR Respiratory Biomedical Research Unit. Education: PhD in Computer Science and Informatics, Cardiff University (2011–2016) BEng in Communication Engineering, Wuhan University of Technology (2004–2008) Research Interests: Dr Zhao’s work sits at the intersection of computer science and population health . He develops computational methods for high-dimensional metabolomic data—especially breathomics—using advanced two-dimensional gas chromatography and mass spectrometry. A second major strand examines socio-economic determinants of respiratory disease, leveraging large-scale observational data to quantify how prepayment energy meters influence emergency hospital admissions and health inequalities. Publications Overview: His 2015–2024 portfolio reveals a clear evolution from foundational bioinformatics tooling (KneeTex NLP system, LabPipe data-management suite) through methodological advances in GC×GC data processing, and finally to high-impact epidemiological studies linking deprivation metrics with respiratory health outcomes. The translational 2022 Science Translational Medicine paper showcases breath-metabolite signatures for acute cardiorespiratory diagnosis, illustrating the clinical relevance of his analytical innovations. Grants, Awards & Students: No specific grants, awards, or doctoral students are listed in the provided text. Laboratories & Teams: While no dedicated laboratory name is mentioned, his affiliations with the Nuffield Department of Population Health at Oxford and collaborative links to the Department of Sociology and Leicester’s NIHR Respiratory Unit imply active participation in multidisciplinary teams spanning epidemiology, analytical chemistry, and computational biology.
Andreas Miroslaus Wichert is an Associate Professor at the University of Lisbon, affiliated with INESC-ID (GAIPS research group). His academic journey includes studies in computer science at the University of Saarland and a PhD in computer science from the University of Ulm (2000). He has taught courses like Artificial Intelligence, Machine Learning, Deep Learning, and Quantum Artificial Intelligence at institutions including University of Lisbon and Technical University Munich. Born in Wrocław, Poland, with a grandfather who was a Polish officer (Katyn massacre memorialized) Current affiliations: University of Lisbon, INESC-ID, GAIPS research group Research Interests Andreas specializes in Artificial Intelligence , Machine Learning , Quantum Artificial Intelligence , and Neural Networks . His work explores intersections between quantum computing and AI, as detailed in his 2024 book Quantum Artificial Intelligence with Qiskit . He also investigates cognitive systems and high-dimensional data indexing through the HEIDI project. Scientific Awards Recipient of multiple teaching excellence awards for courses in: Logic for Programming (24/25) Machine Learning (21/22, 23/24, 24/25) Decision Support Systems (14/15) Intelligent Multimedia Databases (14/15) Advising Currently supervises PhD students Maria Osorio, Jose Miguel Penedo Ramos, and Joaquim Domingos Mussandi. Past advisees include Dr. Luis Tarrataca, Dr. Angelo Cardoso, Dr. Joao Sacramento, Dr. Catarina Pinto Moreira, and Dr. Luis Sa-Couto. Projects & Labs Lead developer of the HEIDI project (High-Dimensional Indexing), and active member of the GAIPS research group since 2009.
Dr Nam Nghiep Tran is a Senior Lecturer in the School of Chemical Engineering at the University of Adelaide, serving as Associate Dean of Internationalisation (Southeast Asia) within the Faculty of Sciences, Engineering and Technology (SET). His work focuses on fostering academic collaborations between Australia and Southeast Asia, particularly Vietnam. Education: BEng (Chemical Engineering) from Can Tho University, MEng (Chemical System Engineering) from the University of Tokyo, and PhD from the University of Adelaide. Dr. Tran's research interests span process control, renewable energy, reaction engineering, thermal/non-thermal plasma technologies, and ESG principles. He plays a key role in promoting sustainable technologies through interdisciplinary research. He co-founded the Adelaide University Vietnamese Students Association (AVA) and leads the South Australia chapter of the Vietnam-Australia Scholars & Experts Association (VASEA), enhancing academic networks between Australia and Vietnam. Additionally, he serves as Associate Editor-in-Chief of Green Processing and Synthesis (GREENPs).
Dr. Edgar Galván is an Associate Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He is a leading expert in Genetic Programming (GP) and Evolutionary Algorithms, with a focus on semantic-based approaches, neutrality, and multi-objective optimization. His work spans applications in combinatorial optimization, gaming (e.g., Carcassonne), and software engineering, including neuroevolution for deep learning architectures. Current Affiliation: Maynooth University Previous Roles: Senior Researcher at University College Dublin, Trinity College Dublin, and INRIA Paris-Saclay Research interests include: Semantic-based Genetic Programming Multi-objective Evolutionary Algorithms Monte Carlo Tree Search Circular Economy Applications Privacy-Preserving Optimization Neuroevolution in Autonomous Systems His recent publications analyze semantic diversity in GP, neural architecture search, and privacy-aware swarm optimization. Key awards include being ranked among the top 1% of GP researchers by University College London (2020), a Marie Curie Fellowship (2014), and a Best Paper Award at ECTA 2015. Current Projects: REBUILD (Circular Economy Buildings, 2024-2027), VISION (Circular Business Models, 2023-2026) Previous Grants: Stochastic Bio-inspired Algorithms (2014-2017, €267k), circAI (2022-2023, €142k) Dr. Galván serves on program committees for IEEE, ACM, and Springer conferences, and as Scientific Adviser for institutions in Ireland, France, and Mexico. His work bridges theoretical GP analysis with real-world applications in energy optimization and AI.
Sven De Sutter is a Visiting Professor (Paid guest professor) in the Mechanics of Materials and Constructions research group at Vrije Universiteit Brussel (VUB) , Belgium. His affiliation with VUB centres on advancing structural engineering solutions for lightweight, high-performance composite and concrete systems. Research Focus: Fracture mechanics and damage characterisation of cementitious and composite materials Acoustic emission monitoring and digital image correlation for real-time structural health assessment Development of hybrid textile- or fibre-reinforced concrete beams for lightweight construction His investigations integrate experimental validation with analytical modelling, aiming to optimise safety and durability while reducing material usage in civil infrastructure. Publication Trends: Across 26 outputs captured between 2013 and 2019, De Sutter’s work consistently targets composite-concrete hybrid beams , employing acoustic emission as a primary non-destructive evaluation tool. Key themes include fracture monitoring, damage source identification, and the structural performance of carbon- and textile-reinforced lightweight systems, reflecting a trajectory towards sustainable, resilient construction technologies. Scientific Recognition: While the provided text does not list any specific awards or honours, his publications have attracted 230 citations (Scopus) and an h-index of 6, indicating growing impact within the engineering community. Supervision & Grants: De Sutter has co-supervised at least one Master’s thesis (2012) entitled “Analyse en haalbaarheidsstudie van een innovatief lichtgewicht composiet bekistingssyteem voor betonnen balken” . Further doctoral supervision or grant details are not disclosed in the text. Laboratory & Collaboration: He conducts research within the VUB Mechanics of Materials and Constructions laboratories, collaborating closely with colleagues such as Prof. T. Tysmans, Dr. S. Verbruggen, Dr. D. Angelis, and others, forming an active network around advanced composite and concrete experimentation.
Dr. Andrew Ward is an ARC Early Career Industry Fellow at the Australian Centre for Water and Environmental Biotechnology (ACWEB) within the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. He leads research projects focused on wastewater treatment, nutrient recovery, and microalgae biotechnology, with significant industrial experience working with water utilities to scale research to pilot and demonstration levels. Dr. Ward holds a PhD from the School of Chemical Engineering at the University of Adelaide, where his thesis focused on the optimisation of halophilic anaerobic digestion of algal biomass. He also earned a Bachelor (Honours) degree from Flinders University. His educational background provides a strong foundation in chemical and environmental engineering principles applied to water and wastewater treatment. Dr. Ward's research primarily focuses on innovative approaches to wastewater treatment and resource recovery. His work spans nutrient recovery via electrodialysis, Anammox processes for domestic and agricultural wastewater treatment, and the development of algae-bacterial aggregated flocs (ABAF) for wastewater remediation. He investigates microalgae's role in energy and nutrient recovery from a circular economy perspective, aiming to transform wastewater treatment facilities into resource recovery centers. His research integrates biological, chemical, and engineering approaches to create sustainable solutions for water management challenges. Dr. Ward has extensive experience scaling laboratory research to pilot and demonstration levels, working closely with industry partners including Urban Utilities. Dr. Ward's publication record demonstrates a consistent focus on advancing wastewater treatment technologies, with recent articles examining deep learning applications, nutrient recovery systems, and sustainable biogas production. His work shows a clear progression toward more integrated, circular economy approaches to water management, emphasizing resource recovery alongside treatment. ARC Early Career Industry Fellowship (2024-2027): "Circular Economy", via renewable energy and resource recovery ARC Industry Fellowship Advance Queensland Industry Research Fellowship (2020-2023): Aggregated algal/bacterial flocs for wastewater treatment and algae industries Dr. Ward serves as lead investigator for Urban Utilities' wastewater microalgae research program and manages multiple research projects with industry partners. He currently supervises PhD students working on "Recovery of high-value coloured organic compounds from wastewater" and previously supervised research on "Algae Bacteria Aggregated Flocs in the Enhanced Treatment of Wastewater." His research has attracted significant funding from the Australian Research Council, Queensland government, and industry partners, demonstrating the practical relevance and impact of his work. Dr. Ward works within the Australian Centre for Water and Environmental Biotechnology (ACWEB), collaborating with researchers across engineering, microbiology, and environmental science disciplines. His team focuses on developing and scaling technologies that address real-world water treatment challenges while recovering valuable resources from wastewater streams.
Bilal Ahmad serves as a Teaching Fellow in the Department of Mechanical and Aerospace Engineering at the University of Strathclyde, United Kingdom. His academic role combines teaching responsibilities with research in advanced materials joining technologies, focusing on computational modeling of welding processes for structural and polymeric materials. Education PhD in Mechanical Engineering from University of Strathclyde (awarded March 2019) with thesis titled Numerical Optimisation of low alloy steel friction stir welding supervised by Professors Galloway and Toumpis Dr Ahmad's research centers on friction stir welding (FSW) and laser-assisted variants, employing numerical modeling to optimize weld quality in structural steel and polyethylene applications. His work addresses critical challenges in material flow dynamics, thermal management, and mechanical property enhancement during joining processes, contributing to advancements in manufacturing efficiency for automotive and aerospace sectors. Analysis of his 2019-2023 publications reveals consistent focus on computational optimization of welding parameters, with significant citation impact (25+ Scopus citations) for his structural steel research. His work bridges theoretical modeling with practical industrial applications, particularly in joining dissimilar materials where conventional techniques face limitations. Dr Ahmad actively contributes to departmental quality initiatives as Academic Staff on the EGCF project (active since June 2024), developing standardized exam guidelines and quality assurance frameworks for Mechanical and Aerospace Engineering programs. He also engages in educational outreach through activities like the MAE Egg Drop Program, demonstrating commitment to engineering education at multiple levels.
Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.
Nicole Borth is Associate Professor (associate Univ.Prof.) at the University of Natural Resources and Life Sciences, Vienna (BOKU) and Deputy Head of the Institute of Animal Cell Technology and Systems Biology . Her work sits at the intersection of cell engineering, systems biology and biopharmaceutical manufacturing, with CHO and HEK293 cells as primary platforms. Research in a nutshell: Genome-wide CRISPR/Cas deletion and activation screens to map essential loci and boost recombinant protein titres. Epigenetic and synthetic-biology toolboxes (dCas9-DNMT, synthetic promoters, RNA devices) for multiplexed gene-control. Glyco-engineering and biomarker discovery to optimise critical quality attributes of monoclonal antibodies. Low-cost, animal-component-free media design and microfluidic single-cell cloning to shorten development timelines. Between 2022-2025 her group released a rapid succession of papers exploiting nanopore Cas9-targeted sequencing to pinpoint transgene integration sites, unveiled novel stress-biomarkers for difficult-to-express mAbs, and provided public-domain glyco-analytics for the NIST CHO reference line. Parallel projects apply similar tool-chains to AAV production in HEK293 and characterise human diamine oxidase biopharmaceuticals. Awards & funding: Specific prizes not enumerated in supplied text; however, the volume and recency of high-impact publications indicate sustained competitive funding. Contact: nicole.borth@boku.ac.at | Tel +43 1 47654-79064 | Muthgasse 11, 1190 Vienna, Austria.
Nicolai Siim Larsen is an Assistant Professor (Tenure track) in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in statistical modeling with applications in finance and healthcare analytics. His work bridges theoretical statistics and real-world data challenges. Education : Ph.D. in Stochastic Financial Models based on Matrix-Analytic Methods, Technical University of Denmark (2022) Dr. Larsen's research centers on multivariate phase-type distributions and matrix-analytic methods , with significant contributions to financial risk modeling and healthcare analytics . His expertise spans survival analysis, sensor data processing, and price optimization frameworks, addressing complex stochastic phenomena in insurance and disease progression monitoring. Analysis of his publications reveals a cohesive focus on advancing numerical computational methods for multivariate data structures. His 2022 Ph.D. thesis established foundations for joint density functions and infinitely divisible distributions, with subsequent work directly applied to commercial pricing strategies and edema patient monitoring systems. Statistical analysis and price optimisation of DJ rental services (2022) AI Denmark: Monitoring disease progression in Oedema patients (2022) KomDigital: Optimal pricing strategies for price monitoring (2022) Stochastic Financial Models based on Matrix-Analytic Methods (PhD project 2018-2023) He actively contributes to academic service as an internal examiner for Statistical Genetics (2025) and instructor for R-based data science workshops, demonstrating commitment to both research leadership and pedagogical development within DTU's Statistics and Data Analysis section.
