Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.
Ooi Beng Chin is a Professor at the School of Computing , National University of Singapore (NUS). He holds concurrent roles as an adjunct Chang Jiang Professor at Zhejiang University, Visiting Distinguished Professor at Tsinghua University, and Director of NUS AI Innovation and Commercialization Centre in Suzhou, China. He earned his B.Sc. (1st Class Honours, 1985) and Ph.D. (1989) from Monash University, Australia. His research spans database systems, blockchain, machine learning, and large-scale analytics , focusing on system architectures, security, and cross-domain applications. Notable contributions include initiating the Apache SINGA distributed deep learning platform and developing Blockbench, the first blockchain benchmarking system. He also co-founded MZH Technologies (2018) for healthcare analytics. Key publications highlight his work in blockchain-database integration, AI for healthcare/finance, and 5G-enabled data systems. Awards include the ACM SIGMOD EF Codd Innovation Award (2020), Singapore President's Science Award (2011), and fellowships from SNAS, IEEE, ACM , and SAEng (2023). He leads the Singapore Blockchain Innovation Programme (SBIP) and contributes to industry collaborations with healthcare institutions and financial organizations. Fellow, Singapore National Academy of Science (SNAS) Fellow, IEEE Fellow, ACM Singapore President's Science Award, 2011 IEEE Kanai Award, 2012 NUS Outstanding Researcher Award, 2013 ACM SIGMOD EF Codd Innovation Award, 2020 Foreign Member, Chinese Academy of Sciences, 2023
Prof. Mohamed El Moursi is a Professor in the Electrical Engineering Department at Khalifa University, Abu Dhabi, UAE. He serves as Director of the Advanced Power and Energy Center (APEC) and Theme Director for renewable energy Integration at the Virtual Research Institute (VRI) for Sustainable Energy Production, Storage, and Utilization (funded by ASPIRE). An IEEE Fellow (Class of 2024) and Distinguished Lecturer of IEEE Power and Energy Society, he holds leadership roles in UAE's scientific community including membership in the MOHAMED BIN RASHID Scientists Council. His academic credentials include: BSc in Electrical Power Engineering from Mansoura University, Egypt (1997) MSc in Electrical Power Engineering from Mansoura University, Egypt (2002) PhD in Electrical Engineering (Power Systems/Power Electronics) from University of New Brunswick, Canada (2005) El Moursi's research pioneers renewable energy integration in modern power systems, focusing on hybrid AC/DC grid stability, AI-driven grid management, and transportation electrification. His work bridges theoretical innovation with industrial applications, particularly in grid stability assessment for high-renewable penetration systems. Key contributions include developing operational platforms like the SAVE software for UAE's national grid and REMS tools for international energy management. He has secured $27,027,129 in research funding from global sources including Europe, North America, GCC nations, and South Korea. Major projects encompass the ASPIRE-funded Virtual Research Institute, TRANSCO/Manitoba Hydro's SAVE Tool, and MIT-collaborative initiatives on grid resilience. To date, he has graduated 10 PhD and 35 MSc students, many receiving national thesis awards. His distinguished recognition includes: IEEE Fellow (2024) Khalifa Award for Education (2022) UAE Ministry of Energy R&D Award (2023) Mission Innovation Champion (2019) Abu Dhabi Technology Development Committee Gold Medal (2015) Multiple editorial awards from IEEE Transactions As APEC Director, El Moursi leads a multidisciplinary team developing cutting-edge solutions for grid modernization. His center maintains strategic partnerships with TRANSCO, ELIA GRID International, and Dubai Electricity and Water Authority, translating research into real-world grid stability and energy management systems through tools like SAVE and REMS.
Sunday Oyadiji is an Associate Professor in the Department of Mechanical and Aerospace Engineering at The University of Manchester. He holds a BSc (First Class) and PhD in Mechanical Engineering from the same institution (1979 and 1983). Before joining the University of Manchester in 1991, he served as a Lecturer at Obafemi Awolowo University, Nigeria, and conducted postdoctoral research at Heriot-Watt University and the University of Manchester. His research focuses on viscoelasticity, smart materials for vibration isolation, structural fault identification, and composite materials. Key areas include fracture mechanics, multibody dynamics, and biomechanics. He contributes to the Aerospace Research Institute and Digital Futures platforms, advancing aerospace engineering and structural fire safety. Recent work involves stress-intensity factor analysis using 3D-DIC and finite element methods, constitutive modeling of elastomeric foams, and graphene-based energy storage systems. His publications span high-impact journals like Engineering Fracture Mechanics and Polymer .
Professor Matthew Jonathan Rosseinsky holds the Chair of Inorganic Chemistry at the University of Liverpool, a position he has occupied since October 1999. His career includes significant appointments at the University of Oxford (1992-1999) and Bell Laboratories in New Jersey (1990-1992), following his DPhil at Merton College, Oxford. As a Fellow of the Royal Society and recipient of numerous prestigious awards, Professor Rosseinsky maintains an active research program and leadership roles in the international chemistry community. Professor Rosseinsky's educational background includes a First Class Honours degree in Chemistry with Quantum Chemistry from the University of Oxford (1987) and a DPhil in "Physical Properties of Superconducting Oxides and Radical Cation Salts" completed in 1990 under Professor P. Day FRS. His research focuses on the synthesis of new materials with applications in energy storage and generation, communications, separation, and catalysis. The Rosseinsky Group employs a broad range of synthesis and characterization techniques, including neutron and synchrotron X-ray diffraction, combined with computational methods in collaboration with Dr. George Darling. Current research areas include Dynapore, CO2 fuels, SOLBAT, and CATMAT projects that target specific material challenges. Professor Rosseinsky's publication record is exceptional, with 304 papers including 11 in Nature, 6 in Science, and 3 in Nature Materials, accumulating over 15,000 citations and an h-index of 56 as of 2012. His work demonstrates consistent excellence across materials chemistry, with particular emphasis on porous frameworks, electronic materials, and solid-state chemistry. Among his numerous accolades are the Harrison Memorial Prize (1991), Corday-Morgan Medal (2000), Royal Society Wolfson Research Merit Award (2002), De Gennes Prize (2009), and the prestigious Hughes Medal from the Royal Society (2011). He also holds an ERC Advanced Investigator Grant and has delivered distinguished lectures worldwide. Professor Rosseinsky has served in numerous editorial and advisory capacities, including as Associate Editor for Chemical Sciences, membership on the Royal Society Conference and Travel Grant Committee since 2007, and as a member of the International Advisory Board for the Max Planck Institut for Solid State Research since 2011. His professional activities extend to international review committees for research institutions in France, South Korea, and Saudi Arabia. The Rosseinsky Group operates within the Department of Chemistry at the University of Liverpool, collaborating extensively with researchers including Dr. John Claridge, Professor Andrew Cooper, and Professor Paul Chalker. The group maintains strong international partnerships and utilizes advanced facilities for materials synthesis and characterization to drive innovation in functional materials development.
Dr. Philip A. Rea is a Professor of Biology and Rebecka and Arie Belldegrun Distinguished Director of the Life Sciences & Management Program at the University of Pennsylvania's School of Arts and Sciences. With over 100 publications and two co-authored books, his work spans fundamental biochemical research and science communication, focusing on life sciences implementation challenges. Education: D.Sc. (2020) and D.Phil. in Plant Biochemistry (University of Oxford), B.Sc. First Class Honors in Biological Sciences (University of Sussex) Dr. Rea's research explores transport phenomena and cellular detoxification, including vacuolar proton pumps and ABC transporters in plants, yeast, and nematodes. His work has significant applications in phytoremediation and combating parasitic diseases. His recent publications and book Managing Discovery in the Life Sciences (2018) analyze the transition from laboratory discovery to market success through case studies like statins, ivermectin, and metformin. These works highlight serendipity in biomedical innovation and investor-driven discovery management. Scientific Awards: Jesse H. Neal Award (2025) for technical/scientific content AAAS Fellow (2013) for membrane transport research National Academies Cozzarelli Prize (2010) for original research Multiple teaching honors including Lindback Foundation Award (2014) and Ira H. Abrams Award (2009) Society for Experimental Biology President's Medal (1990) Teaching: Proseminar in Management and the Life Sciences (LSMP 1210) Biochemistry (BIOL 2810) Focus on problem-solving with incomplete datasets
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
Dr. Assela Pathirana is a Professor at the IHE Delft Institute for Water Education, specializing in water infrastructure asset management (WIAM), climate resilience of Small Island Developing States (SIDS), and sustainable urban water systems. His work bridges academia, policy, and practice, focusing on digitalization, data-driven decision-making, and nature-based solutions. Key roles include Chief Technical Advisor for the Maldives’ water systems and leadership of a MOOC on SIDS climate adaptation. He holds a BSc (First Class Honours) from the University of Peradeniya and advanced degrees from the University of Tokyo, specializing in hydrology and water resources engineering. Education: Bachelor of Science in Civil Engineering (First Class Honours), University of Peradeniya, Sri Lanka Master’s and Doctoral Degrees in Civil Engineering (Hydrology and Water Resources), University of Tokyo, Japan Research Interests: Dr. Pathirana’s work emphasizes climate resilience, SIDS adaptation, urban flood risk management, and sustainable infrastructure. He develops decision-support tools for flood forecasting, evaluates land-use changes in Jakarta, and promotes equity in water rationing systems. His interdisciplinary approach integrates hydrological modeling, open-source software development, and policy analysis. Advising & Capacity Building: He leads training-of-trainers programs and capacity-building initiatives, enhancing postgraduate education in technical disciplines. His efforts focus on didactics and pedagogy, ensuring graduates gain both theoretical knowledge and practical expertise. Key Projects: Developed the WIAM curriculum at IHE Delft MOOC on SIDS climate adaptation and water security Consultancy for the Maldives’ Ministry of Environment and UNDP Labs & Collaborations: Engages in cross-disciplinary teams addressing urban water challenges, including sponge cities in China and flexible adaptation planning in Melbourne and Pune. His work with UNESCO-ICHARM and UNU underscores global water security and disaster risk reduction.
Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .
Margaret Garcia is an Associate Professor at the School of Sustainable Engineering and the Built Environment , Arizona State University (ASU). She is affiliated with multiple research centers, including the Center for Behavior, Institutions and the Environment (CBIE) , Central Arizona-Phoenix Long Term Ecological Research , Earth Systems Science for the Anthropocene , Water Institute , and Global Futures Scientists and Scholars . Education: Ph.D., Civil and Environmental Engineering, Tufts University (2017) M.S., Civil and Environmental Engineering, University of California-Los Angeles B.S., Civil and Environmental Engineering and B.A., International Studies, Lafayette College (2004) Her research focuses on the sustainability and resilience of urban water systems , using systems analysis to study feedbacks in coupled human-hydrological systems . Key themes include reservoir operations , adaptive infrastructure , water policy , and real-time flood monitoring . Recent work explores equity in water management , institutional dynamics , and green infrastructure optimization . Her 15 most recent publications span topics in socio-hydrology , water policy analysis , climate change adaptation , and urban flood modeling , with applications to the Western Water Network , Colorado River Basin , and transboundary regions like Ambos Nogales. Collaborative projects include NSF-funded initiatives on adaptive reservoir operations , cross-scale interactions , and community-based flood awareness . Scientific Awards: NSF CAREER Grant 1942370 (2020) She mentors students in Ph.D. programs (e.g., Ashish Shrestha , Behshad Mohajer ) and Master’s programs (e.g., Dillon Nys , Krista Lawless ). Her teaching includes graduate courses on research, reading, and thesis advising, alongside undergraduate hydrology classes.
Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.
Robert Furbank is a Professor and Centre Director at the Australian National University (ANU), leading the ARC Centre of Excellence for Translational Photosynthesis. He specializes in enhancing crop yields through improving photosynthesis and abiotic stress tolerance in cereals like wheat and rice. His work spans plant phenomics, genetic manipulation, and high-throughput phenotyping techniques. He co-leads the C4 Rice Consortium, aiming to introduce C4 photosynthesis into rice to boost productivity. Furbank holds a BSc (Hons) from the University of Wollongong (1979) and a PhD from ANU (1982). He has received prestigious awards, including the Queen Elizabeth II Research Fellowship (1987) and the CSIRO Plant Industry Leadership Award (2014). Research interests include C3/C4 photosynthesis mechanisms, carbon allocation, and developing tools for plant phenomics. He collaborates internationally with organizations like CIMMYT and IRRI, focusing on translational research to bridge experimental findings with crop improvement. His recent projects address heat tolerance in wheat and satellite-based phenotyping for crop analysis. Education: Bachelor of Science (First Class Honours), University of Wollongong, 1979 PhD, Australian National University, 1982 Research Highlights: Combining molecular genetics and phenomics to understand genetic variation in photosynthesis; developing CO2-concentrating mechanisms in rice; improving wheat yield via high-throughput measurement tools. Awards: Queen Elizabeth II Research Fellowship (1987) ACT ICT Innovation Award (2013) CSIRO Plant Industry Leadership Award (2014) Grants and Collaborations: Leads major initiatives like the ARC Centre and participates in global consortia such as the International Wheat Yield Partnership. His work integrates advanced imaging and machine learning for crop trait prediction.
Anna Corinna Cagliano is a Full Professor at the Department of Management and Production Engineering (DIGEP) , Polytechnic University of Turin . She is a member of the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility, and serves as Deputy Coordinator of the Doctoral College in Management and Production Engineering . Research Interests: Industrial and manufacturing engineering, logistics, supply chain management, project risk analysis, healthcare logistics, and ICT applications in logistics. Scientific Affiliation: AIDI Italian Association of Industrial Plant Teachers since 2016. Research Trends: Her publications focus on the intersection of Industry 4.0 , lean manufacturing , and sustainability in logistics. Key areas include automated storage systems , digital twins in intra-logistics , and COVID-19 impacts on supply chains . She emphasizes ICT tools and risk management across industrial and healthcare contexts. Teaching Roles: She lectures on Industry 4.0 for Production Systems and Plants and Manufacturing Systems at the Master’s and Bachelor’s levels in Automotive and Management Engineering. She also supervises PhD students in Management and Production Engineering . PhD Students: Simone Preziosa (2024–ongoing) Abror Hoshimov (2019–2023) Mahsa Mahdavisharif (2019–2023)
Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.
Dr. Changsheng Wu is a Professor at the National University of Singapore (NUS), leading the Lab for Intelligent Sensing, Harvesting and Actuation (LISHA). He holds a Bachelor's from NUS and a PhD from Georgia Tech, with postdoctoral research at Northwestern University. His work focuses on wireless wearables, bioelectronics, energy harvesting, and advanced manufacturing for sustainable solutions. Education: Bachelor in Engineering Science (First Class Honours), NUS PhD in Materials Science and Engineering, Georgia Institute of Technology Postdoctoral Research, Querrey Simpson Institute for Bioelectronics, Northwestern University Research Interests: Wireless bioelectronics for clinical health monitoring Energy harvesting via nanogenerators Soft skin-electronics interfaces using metastructures Programmable materials for adaptive systems Advanced manufacturing techniques for wearable devices Key Achievements: Over 50 publications and 5 patents Recipient of TechConnect 2018 Innovation Award and 56th R&D 100 Award Developed wireless implantable sensors for tissue monitoring and bioresorbable medical devices Teaching: MLE5220: Finite Element Method in Materials MLE5238: Bioelectronics Laboratory: His LISHA lab pioneers innovations in self-powered systems, wearable health monitoring, and biohybrid robots. Current projects include metamaterial-based sensors and sustainable energy conversion materials.
