Liadh Kelly is an Assistant Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. She supervises PhD students in applied artificial intelligence, focusing on intelligent search, ubiquitous computing, and multimodal information access. She is affiliated with the ADAPT SFI Research Centre, SFI Centre for Research Training in Foundations of Data Science, and Human Health Institute. BSc in Computer Science MSc (Research) in Computer Science PhD in Computer Science Her research explores context-sensitive retrieval and evaluation methodology in AI-driven systems. Key areas include ubiquitous computing for personal data analysis, deep learning classification for mental wellness indicators, and multimodal lifelogging integration. Recent publications focus on urban mental wellbeing classification , contextual cue analysis , and AI-driven health search systems . Articles address smart city applications , consumer health search , and cross-lingual medical retrieval . Professional roles include Doctoral Consortium Chair at ECIR 2023 and Programme Committee member for SIGIR and ICWSM conferences. She leads grants for 4-year PhD studentships with stipend and fee coverage.
Dr. Mohammad Iftekhar Husain is a Professor and Graduate Coordinator in the Department of Computer Science at California State Polytechnic University, Pomona (Cal Poly Pomona). He serves as the Inaugural Director of the PolySec Cyber Lab, a federally funded center for cyber security and forensics education, research, and outreach (~$2.5M in grants), and directs the university's Virtual Reality Lab. His career spans over a decade of leadership in cyber security program development, extramural funding, and academic governance. Education: B.S., Computer Science, Yamagata University (Japan) M.S. & Ph.D., Computer Science and Engineering, SUNY-Buffalo Dr. Husain's research focuses on data privacy in social networks, neurophysiological cyber security solutions, and blockchain applications. He has secured $18.4M in principal investigator grants, including NSF SFS, EAGER, and REU Site projects, and trained students placed in top institutions like UC campuses, MIT Lincoln Lab, and government agencies such as NSA and DHS. His work on brainwave authentication earned a US patent (USPTO 10,198,566) and media coverage in Time Magazine and PC Magazine . Scientific Awards: 2016 College of Science Distinguished Teaching Award Early Promotion and Tenure (2016) 2020 Faculty Learning Community for Leadership Pipeline Development Cohort As academic leader, he founded the Cal-Bridge CS Ph.D. pathway program for underrepresented students, chairs the CPP Academic Senate Academic Programs committee, and led university IT initiatives including Cyber Security Cluster Hiring and High-Performance Computing Lab development.
Bhuvan Urgaonkar is a Professor in the Department of Computer Science and Engineering at Penn State University's College of Engineering. His research centers on optimizing cloud computing systems through innovative approaches to resource allocation, cost efficiency, and energy management. Current research focuses on Burstable Instance Scaling Serverless Computing Optimization Distributed Storage Systems Multi-resource Fair Allocation Cloud Economics Recent publications highlight advancements in autoscaling techniques, serverless architecture design, and trace modeling for high-load scenarios. These works emphasize practical solutions for cost-effective resource utilization in public cloud environments. Scientific Awards: CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud (NSF, 2022-2025) CNS Core: Small: Principled Methodologies for Automated Cost-Effective Service Blending (NSF, 2021-2024) PPoSS: Cross-Layer Design for HPC in the Cloud (NSF, 2020-2022) CSR: Burstable Instances for Cost-Efficacy (NSF, 2017-2020) CSR: Student Travel Support for SIGMETRICS (NSF, 2016-2017)
Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.
Dr. Daniel M. Dowden is an Assistant Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University. He holds a PhD in Civil Engineering (Structural Engineering) from the University at Buffalo, along with MS and BS degrees in Civil Engineering from the University of Wyoming and Washington State University, respectively. Prior to academia, he worked as a structural engineer in Seattle for 10 years, which influenced his research focus on earthquake engineering and resilient infrastructure design. Education: PhD, Civil Engineering – Structural Engineering, University at Buffalo (2014) MS, Civil Engineering – Structural Engineering, University of Wyoming BS, Civil Engineering – Structural Engineering, Washington State University His research emphasizes innovative solutions for earthquake-resistant structures, particularly self-centering systems and low-damage frameworks to mitigate seismic impacts. He has conducted extensive experimental testing using shake-tables, pseudo-dynamic systems, and static methods. Key areas include steel plate shear walls, post-tensioned connections, and resilient friction dampers. Teaching focuses on earthquake engineering, structural dynamics, steel/concrete design, and structural analysis. He joined Michigan Tech in 2017 after serving as Structural & Testing Engineer at UB’s Structural Engineering and Earthquake Simulation Laboratory (SEESL), where he contributed to seismic qualification testing. Recent publications highlight advancements in numerical modeling of friction dampers, shake-table testing methodologies, and analytical investigations of rocking wall systems.
Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.
Sarah Hernandez is an Associate Professor in the Civil Engineering Department at the University of Arkansas , specializing in transportation systems engineering. Her research focuses on advanced data collection and analysis for freight planning, and she teaches graduate courses in transportation planning and data analysis. Ph.D. in Civil and Environmental Engineering, University of California, Irvine M.S. in Civil Engineering, University of California, Irvine B.S. in Civil Engineering, University of Florida Her research integrates Intelligent Transportation Systems (ITS) technologies to address freight data gaps, including: Development of tools for freight performance measures Fusion of GPS, WIM, and lock performance data Weather impact on freight traffic Lidar-based truck classification Key trends in her publications include: Advancing sensor technologies for freight analytics Improving long-range infrastructure planning Addressing data gaps in commercial vehicle operations Enhancing freight network efficiency through modeling Scientific awards: Private Sector Applicability Award, TRB Intermodal Freight Committee (2018) As founder of the Freight Transportation Data Research Lab , she leads initiatives on unbiased freight planning and workforce diversity. Her outreach includes mentoring middle and elementary school STEM programs.
Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.
Erik Koch is an apl. Prof. Dr. (Associate Professor) and head of the Research Group Computational Materials Science at the Institute for Advanced Simulation (IAS) , Jülich Supercomputing Centre (JSC) , within Forschungszentrum Jülich , Germany. His research is part of the Helmholtz Program-oriented Funding (PoF IV) under 'Engineering Digital Futures', focusing on enabling computational- and data-intensive science and engineering. He is actively engaged in both research and teaching, leading a group dedicated to understanding quantum materials with strong electronic correlations. His research interests center on the theoretical and computational challenges of strongly correlated electron systems . He investigates phenomena such as orbital ordering , employing advanced numerical techniques including Lanczos diagonalization and analytic continuation . His group develops and applies methods to tackle the many-body problem, particularly using Dynamical Mean-Field Theory (DMFT) to bridge the gap between model systems and real materials, aiming to understand and design novel quantum materials with emergent functionalities. Prof. Koch is deeply involved in academic education. He teaches core and elective courses for the MSc in Simulation Sciences (MSc SiSc), including Applied Quantum Mechanics , Correlated Electrons , Density Functional Theory & Practice , and Solid State Theory . He is the primary organizer of the renowned annual Autumn School on Correlated Electrons , which has been a key international forum for over a decade, covering topics from Kondo physics to quantum topology and entanglement. This demonstrates his significant role in training the next generation of computational physicists. He has no listed scientific awards in the provided text. Prof. Koch leads the Computational Materials Science research group, which collaborates with the Strongly Correlated Systems group at PGI-2/IAS-3. His work is fundamentally tied to the high-performance computing resources at the Jülich Supercomputing Centre, utilizing massively parallel simulations. While specific grants are not mentioned, his research is funded through the Helmholtz Association's Program-oriented Funding. The group's research area is focused on using analytical methods and large-scale simulations to understand and design quantum materials with strong electronic correlations.
Dr. David Evans is a Royal Society University Research Fellow at the University of Southampton with expertise in paleoclimate reconstruction and marine geochemistry. His research focuses on understanding climate-Earth surface interactions through geochemical proxies, experimental culturing of marine organisms, and numerical modeling. He leads several major projects funded by the Royal Society and UKRI aimed at refining paleoclimate reconstructions. Dr. Evans investigates Cenozoic warm periods (particularly the early Eocene) to evaluate climate model performance and understand climate-biosphere interactions. His work centers on: 1) Reconstructing boundary conditions affecting geochemical proxies, especially seawater composition; and 2) Understanding biomineralization processes in key archives like foraminifera to ensure accurate proxy applications through geologic time. His publications (2024-2025) predominantly focus on marine biomineralization, paleoclimate proxies, and carbonate geochemistry, with frequent applications of isotope techniques and experimental approaches. Research consistently emphasizes methodological improvements in geochemical analyses and proxy validation. He currently supervises four PhD students across biological sciences and oceanography. His research group is part of the Geochemistry unit and Southampton Marine and Maritime Institute. Major active projects include Horizon Europe's AMOEBA initiative and a correlative cryo-analytical center development.
Edwin Romeijn holds the Jill Stewart Archer Family Chair and Professor position in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He served as School Chair from 2015-2024, overseeing the nation's top-ranked industrial engineering program. Previously, he held faculty positions at the University of Michigan, University of Florida, and Erasmus University Rotterdam, and served as Program Director at the National Science Foundation. Education: Ph.D. in Operations Research (1992), Erasmus University Rotterdam M.S. in Econometrics (1988), Erasmus University Rotterdam Romeijn's research centers on optimization theory and applications , with dual focus areas in radiation therapy treatment planning and supply chain management . His radiation therapy work develops algorithms for cancer treatment planning and clinic scheduling, while his supply chain research addresses integrated optimization of production, inventory, and transportation under demand flexibility, resource constraints, perishability, and uncertainty. His methodologies bridge theoretical operations research with real-world healthcare and logistics systems. His publication portfolio demonstrates consistent contributions to optimization methods across diverse application domains, with recent work spanning healthcare systems, renewable energy, sports analytics, and unconventional logistics. The research exhibits strong methodological continuity in stochastic programming, network optimization, and decision-making under uncertainty. Scientific Awards: Fellow of IISE and INFORMS (2017) Richard C. Wilson Faculty Scholar (2012-2013) Multiple best paper awards in industrial engineering conferences Pierskalla Best Paper Award (2003) Young Investigator’s Award at ICCR (2004) Romeijn has advised numerous graduate students and secured significant research funding through NSF and other agencies. His leadership extends to program direction at NSF and chairing Georgia Tech's Industrial and Systems Engineering school. He maintains active collaborations with healthcare institutions and manufacturing enterprises, translating theoretical advances into practical solutions for radiation oncology and supply chain resilience.
Dr. Chenhao Ma is an Assistant Professor at the School of Data Science , The Chinese University of Hong Kong, Shenzhen , where he works on large-scale data management and data mining. Previously, he was a Postdoctoral Fellow at the University of Hong Kong (2021–2022) and earned his PhD in Computer Science from the University of Hong Kong (2021) and B.Eng. from Shandong University (2017). Current research focuses on graph computing (dense subgraph discovery, motif analysis, graph learning), AI+DB (Text-to-SQL, vector search), and traffic data mining (trajectory analysis, outlier detection). He has published over 40 papers in top venues including SIGMOD, PVLDB, KDD and received the ACM SIGMOD Research Highlight Award 2021 and Best of SIGMOD 2020 (4/458). Scientific Awards : ACM SIGMOD Research Highlight Award 2021 Best of SIGMOD 2020 (4/458) Presidential Young Fellow at CUHK-Shenzhen (2023) Hong Kong and China Gas Scholarship (2019-2020) Reaching Out Award (2019) HKU Postgraduate Scholarship (2017-2021) ACM-ICPC Gold Medal (2015) National Scholarship (2014, 2015) Advising and Research Team : He leads a team including Postdoc Dr. Yuanyuan Zeng, PhD students Lujie Ban, Yuwei Xu, and MPhil students Yi Yang, Yuyang Liang. Former mentees like Yichen Xu (PhD at Berkeley) and Jiayang Pang (Master at UC San Diego) have achieved academic placements. Professional Service : He has served as PC member/reviewer for VLDB, KDD, ICDE, WWW, NeurIPS, TKDE , and guest editor for Applied Sciences and Frontiers in Big Data . He chairs sessions at ICDE and VLDB.
Prof. Markus Axer is a Professor and Deputy Head of the Structural and Functional Organisation of the Brain (INM-1) at the Institute of Neuroscience and Medicine (INM) within Forschungszentrum Jülich. His research focuses on connectomics, neuroimaging technologies (e.g., 3D-Polarized Light Imaging), and high-performance computing applications in brain architecture analysis. He leads the 'Fiber Architecture' working group, advancing microscopy techniques like scattered light imaging and MRI-histology correlation for studying brain microstructure. His work bridges experimental neuroscience with computational methods, aiming to decode brain organization at meso- and macroscales. Key achievements include developing the HippoMaps atlas of the human hippocampus and improving fiber orientation mapping in brain tissue. Awards include Fellowship in the Royal Netherlands Academy of Arts and Sciences (2024). Research emphasizes cross-modal data integration, with applications in Alzheimer’s disease biomarker validation and primate brain evolution studies. He collaborates with academic institutions like the University of Wuppertal and contributes to international initiatives like the BigBrain Analytics Learning Laboratory.
Dr. Joseph Cassady serves as the South Dakota Corn Endowed Dean of the College of Agriculture, Food and Environmental Sciences at South Dakota State University (SDSU). Previously, he held the rank of Professor and Head of the Department of Animal Science at SDSU (2013–2022) and North Carolina State University (2001–2013). His research focuses on food animal genetics and genomics, particularly improving production efficiency in swine and beef cattle. Education: B.S. in Animal Science (Iowa State University, 1993), M.S. and Ph.D. in Animal Science (University of Nebraska-Lincoln, 1995 and 1999). Professional roles include past-president of the National Swine Improvement Federation and executive director of the Beef Improvement Federation (2009–2015). Research emphasizes genetic and genomic advancements to enhance livestock productivity, with notable work on heat stress mitigation, swine behavior, and genomic selection in cattle. Awards include the 2019 Harold and Barbara Bailey Award for Academic Leadership and recognition for service to the Beef Improvement Federation. Administrative responsibilities include leading the College of Agriculture, Food and Environmental Sciences, overseeing accreditation processes, and participating in industry boards such as the South Dakota Poultry Industries Association and American Angus Foundation. Key research themes span animal genomics, livestock environmental adaptation, and precision agriculture. Over 150 peer-reviewed publications address topics like swine feeding behavior, cattle genetics, and genomic tools for improving meat quality and production efficiency.
