Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .
Cheng Yuhan is an Assistant Professor at Shandong University's School of Management, serving as director of the Center for Artificial Intelligence and Digital Finance and recognized as a Taishan Scholar Young Expert in Shandong Province. His interdisciplinary research bridges artificial intelligence with finance, accounting, and economics through collaborations with MIT and Tsinghua University. His educational background includes: Bachelor of Science in Mathematical Sciences from Beijing Normal University Double Degree in Economics from Peking University National School of Development PhD in Finance from Tsinghua University PBC School of Finance Cheng's research focuses on AI applications in accounting, auditing, and finance, particularly large language models for financial regulation, asset pricing, and macro-finance. He integrates computational methods to solve complex financial problems using industrial-grade computing infrastructure, enabling novel approaches to traditional economic analysis. His recent publications demonstrate a clear trend toward generative AI in financial modeling, with emphasis on stock factor generation, predictive analytics, and economic forecasting. These works showcase how language models transform financial analysis through automated insight extraction and data-driven decision frameworks. Key honors include: Taishan Scholar Young Expert award Best Paper Award at 2024 China International Risk Forum Outstanding Paper Awards at 13th International Conference on Futures and Derivatives Membership in Shandong Provincial Philosophy and Social Sciences Young Talent Team Cheng mentors students across Master of Accounting, Master of Auditing, and MBA programs while securing competitive grants including National Natural Science Foundation of China Youth Fund. His lab supports students in academic exchanges, with former research assistant Dou Yun admitted to University of Chicago Economics Master's with scholarship. His research lab features industrial-grade hardware including NVIDIA H100, Huawei Ascend 910b, A100, and A800 processors, providing near-tech-company computing power for large-scale AI/finance research and industrial application development.
Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.
Oya Celiktutan is a Reader (Associate Professor) at King’s College London in the Department of Engineering, leading the Social AI & Robotics (SAIR) Lab within the Centre for Robotics Research. She holds a BSc in Electronics Engineering from Uludag University, and an MSc and PhD in Electrical and Electronics Engineering from Bogazici University, Turkey. Her doctoral work included a visiting research period at the National Institute of Applied Sciences in Lyon, France. She has held postdoctoral positions at Queen Mary University London, the University of Cambridge, and Imperial College London before joining King’s College in 2018. Her research focuses on multimodal machine learning for autonomous agents, addressing challenges in human-robot interaction, social awareness, and navigation. Key areas include human behavior analysis, generative models, and continual learning. Her work is supported by EPSRC, The Royal Society, EU Horizon, and industry partners like Toyota and NVIDIA. Notable awards include the EPSRC New Investigator Award (2021) and a Best Paper Award at IEEE Ro-Man 2022. Dr. Celiktutan’s lab explores socially assistive robotics, extended reality systems, and ethical AI. Recent projects include the LISI initiative to enable robots to learn social interactions from human-human data and the CL-HRI project advancing continual learning in human-centric robotics. Her research has led to datasets like MHHRI and RICA, and collaborations with companies like SoftBank Robotics. She teaches modules on Sensing and Perception, and Sensors and Actuators, emphasizing practical applications in robotics and mechatronics. Current research interests span socially-aware navigation, algorithmic fairness, and multimodal data fusion for robotics in healthcare and urban environments.
Guang Tian, Ph.D. , is an Assistant Professor of City and Metropolitan Planning at the University of Utah and a faculty member at the Scientific Computing and Imaging Institute . His research bridges land use-transportation planning , travel behavior , and urban data science , with a focus on sustainability , climate adaptation , and equitable transit-oriented development . He previously founded the Center for Equitable Transit-Oriented Communities at the University of New Orleans as an Associate Professor. Education : Ph.D. in City & Metropolitan Planning (University of Utah, 2016) Professional Affiliations : Faculty, Scientific Computing and Imaging Institute (2025–present) His research leverages machine learning and GIS to analyze VMT reduction , active transportation , and the built environment’s impact on mobility . Key findings include the superior performance of random forest models over traditional methods in predicting mode choice and the role of polycentric urban structures in reducing auto dependency. Scientific Awards : Rising Scholar Award (2024, Association of Collegiate Schools of Planning) Grants include funding from the US Department of Transportation for equitable transit communities and multiple Louisiana Transportation Research Center projects on VMT modeling, rail infrastructure, and truck parking efficiency. His teaching centers on GIS applications in urban planning and transportation analysis.
Brian Leung is an Associate Professor at McGill University, jointly affiliated with the Department of Biology and the Bieler School of the Environment . He holds the prestigious UNESCO Chair for Dialogues on Sustainability and serves as Director of the McGill Neotropical Environment Option (NEO) , a collaborative program with the Smithsonian Tropical Research Institute. His work bridges ecological theory, computational modeling, and environmental policy. Dr. Leung earned his PhD in Biology from Carleton University and completed postdoctoral research at the University of Cambridge and the University of Notre Dame. His academic journey at McGill began in 2004 as an Assistant Professor, advancing to Associate Professor in 2010. His research centers on predictive ecology , particularly modeling biological invasions and sustainability challenges . He develops and applies mathematical, statistical, and computational models to understand invasion dynamics across terrestrial, aquatic, and marine systems. His recent work includes the Panama Research and Integrated Sustainability Model (PRISM) , a spatially explicit framework for sustainability science in the Global South. His research spans scales from local to global and integrates ecological, economic, and social factors. His recent publications show a strong focus on invasion risk assessment , species distribution modeling , economic costs of invasions , and ecological forecasting . He frequently publishes in top journals such as Nature , Ecology Letters , and Global Ecology and Biogeography , emphasizing data-driven decision-making and policy relevance. Dr. Leung has received significant recognition through invitations to contribute to major reports and has co-edited influential works on invasive species economics. While specific named awards are not listed, his leadership roles and publication record reflect high scientific esteem. He actively mentors a dynamic research group, supervising multiple Ph.D. and M.Sc. students on projects related to invasion modeling, mangrove conservation, forest pest dynamics, and urban ecology. His lab emphasizes quantitative skills and interdisciplinary collaboration. He has secured research funding to support these projects, though specific grants are not detailed in the text. He leads the Leung Lab , which focuses on predictive modeling in ecology and sustainability. The lab collaborates with institutions such as the Smithsonian Tropical Research Institute and environmental firms like Habitat. Current projects include multi-species connectivity modeling, mangrove ecosystem services, and forecasting forest pest outbreaks.
Prof. Dr. Harald Wehnes serves as a Professor at the University of Würzburg within the Faculty of Mathematics and Computer Science, specifically affiliated with the Institute of Computer Science's Chair of Computer Science III (Communication Networks). His office is located in room A206 at the Hubland campus, with contact email wehnes@informatik.uni-wuerzburg.de. His research centers on project management methodologies, with particular emphasis on the Project Excellence Model and its practical applications. Prof. Wehnes has developed significant expertise in applying project management frameworks to complex IT infrastructure initiatives and healthcare systems. His work demonstrates how theoretical project management concepts translate into real-world implementation, especially in cross-organizational contexts. Analysis of his publication history reveals a consistent focus on practical project management applications, particularly the NIMBUS project case study which documented the consolidation of 12 data centers into a single state data center. His publications demonstrate evolving expertise from foundational programming work (evidenced by his 1981 book "Strukturierte Programmierung mit FORTRAN 77" which went through seven editions) to sophisticated project management frameworks. Prof. Wehnes has maintained active engagement with the German Project Management Association (GPM), presenting at numerous forums and events. His international reach is evident through presentations at institutions including the University of the United Arab Emirates and the University of Canterbury in New Zealand. From 2013-2020, he taught specialized courses on professional project management (Spezialvorlesung aus der Praxis: Professionelles Projektmanagement) at the University of Würzburg, sharing his extensive practical experience with students. His work environment within the Chair of Computer Science III connects his project management expertise with research areas including 5G & 6G network technologies, network and service management, and green communication networks.
Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.
Robert Fletcher is a Researcher at the University of Cambridge, affiliated with the Department of Zoology and the C-CLEAR Doctoral Training Partnership . His work focuses on applied ecology and conservation science, utilizing landscape and population ecology to address biodiversity challenges globally. Research Areas : Conservation biology, population ecology, landscape ecology, environmental informatics Collaborations : Partners in North America, Europe, Africa, and Southeast Asia Key Themes : Species extinction prevention, landscape conservation prioritization, and rapid biodiversity data delivery Email : rf497@cam.ac.uk Fletcher's interdisciplinary approach integrates fieldwork (e.g., Everglades endangered species, African elephants) with advanced modeling of habitat loss, fragmentation, invasive species, and climate change impacts. His recent work emphasizes: Drivers of species decline and recovery strategies Landscape management and restoration techniques Interdisciplinary collaborations with engineers, social scientists, and computer scientists His publications span topics like savanna ecosystem dynamics, community science applications, and conservation forecasting, reflecting a commitment to actionable science for global biodiversity preservation.
Zita Vale is a Full Professor at the Institute of Engineering (ISEP) of the Polytechnic of Porto (IPP), where she holds the first Full Professor position since 2017. She is a co-founder of GECAD (1999) and coordinates GECAD's Power and Energy (PES) activities. GECAD is recognized by FCT since 2004 and classified as Excellent. She has served as GECAD director (2010-2017), vice-director (1999-2009, 2017-present), and is a member of the administration board. She is also co-founder and member of the coordination board of the National Associated Laboratory on Intelligent Systems. Her educational background includes a PhD (1993) and Agregação/Habilitation (2003) in Electrical and Computer Engineering from the University of Porto. She began her academic career at the University of Porto as a Teaching Monitor (1985), Assistant (1985-1993), and Professor (1993-1998) before moving to ISEP in 1998. Zita Vale's research focuses on the design and development of artificial intelligence-based models for Power and Energy Systems. Her work spans knowledge-based systems, multiagent systems, machine learning, metaheuristics, and semantics, with applications in smart grids, energy management, electricity markets, and renewable energy integration. She has an extensive international network and has participated in 80 R&D projects, raising over 22 million Euros for GECAD. Her recent publications demonstrate a strong emphasis on optimization techniques, explainable AI, energy storage systems, and the integration of distributed energy resources in power systems. She serves as Editor-in-Chief of Applied Energy (Elsevier), a leading journal in the field with an Impact Factor of 11.2. Her citation metrics are impressive, with over 17,000 citations on Google Scholar and an H-index of 64. Editor-in-Chief of Applied Energy (Elsevier) Over 17,000 citations on Google Scholar H-index of 64 Zita Vale has supervised 29 PhD students (25 completed) and 72 MSc students (66 completed), demonstrating her strong commitment to academic mentorship. She has also been involved in numerous international and national evaluation processes, including project proposals, faculty positions, and PhD juries across 15+ countries. She has contributed to over 225 evaluation processes from 2018-2023, including 150+ project proposals/execution, 50+ Faculty/Researchers positions, 2 Habilitation juries, and 25+ PhD juries. She leads GECAD's involvement in several major research initiatives, including the National Associated Laboratory on Intelligent Systems and various European projects such as IoTalentum, TRADERES, DOMINOES, and EcoRural-IoT from Horizon 2020, as well as PRODUTECH EU DIH from Horizon Europe. Her leadership extends to international organizations where she serves as President of Intelligent Systems Applications in Power (ISAP) and Technical Committee Program Chair of IEEE PES Analytic Methods for Power Systems Committee.
Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.
Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.
Allaudeen Hameed is the Tang Peng Yeu Professor in Finance at the National University of Singapore (NUS) Business School , where he has been a Professor since 2006. He also holds editorial roles at several leading finance journals and has previously held visiting positions at the Chinese University of Hong Kong, University of North Carolina at Chapel Hill, and University of Texas at Austin. Education: Ph.D. in Finance, University of North Carolina at Chapel Hill Bachelor of Business Administration (Honours), Second Class Upper Division, National University of Singapore Research Interests: His research spans a wide range of topics in finance, including return-based trading strategies , stock return co-movement , liquidity , the role of financial analysts , and international financial markets . These interests are deeply rooted in empirical asset pricing, market microstructure, and behavioral finance. His work often explores how market frictions, investor behavior, and institutional features affect asset prices and trading strategies, with a strong focus on cross-country and emerging market contexts. Scientific Awards & Honors: Asian Finance Conference Best Paper Award – 2024 Pacific Basin Finance Journal Best Paper Award – 2024 UM Distinguished Visiting Scholar, University of Macau – 2024 Best Paper of PERC Award – 2023 Tun Ismail Mohamed Ali Distinguished Chair, Universiti Kebangsaan Malaysia – 2022–2024 Teaching Excellence Team Award, NUS Business School – 2020 Best Paper Awards, FMA – 2016 & 2018 Outstanding Researcher Award, NUS Business School – 2015 & 2003 University of North Carolina Kenan-Flagler Alumni Merit Award – 2011 Professional Service: He serves as Editor of the International Review of Finance and Associate Editor of the Journal of Financial and Quantitative Analysis and Pacific-Basin Finance Journal . He is also a Senior Fellow at the Asian Bureau of Financial and Economic Research (ABFER) and a former Council Member of the Society for Financial Studies. Leadership Roles: He is currently Chair of the Faculty Promotion & Tenure Committee (FPTC) and Chair of the Faculty Promotion in Educator Track Committee (FPEC), both from 2025–2026.
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Giuliano Di Baldassarre is a Professor of Hydrology and Environmental Analysis at the Department of Earth Sciences, Uppsala University , Sweden. He serves as Head of Division for LUVAL (Air, Water and Landscape Sciences) and directs the Centre of Natural Hazards and Disaster Science (CNDS) (2016–2025). His work bridges water, environment, and society through interdisciplinary methods , focusing on disaster risk reduction, climate adaptation, and sustainable development. Education : Details not explicitly provided in the text. His research examines feedbacks between human activities and hydrological processes , including floods, droughts, and reservoir management . Key themes include social-ecological systems , inequalities in water crises , and policy implications of hydrological extremes . He has pioneered sociohydrology and human-water system modeling . Recent articles highlight global drought-flood interactions , urban water inequality , climate service maladaptation , and sociohydrological modeling . His work spans Nature Sustainability , Science Advances , and Hydrological Sciences Journal . Scientific Awards : International Hydrology Prize (Volker Medal) Plinius Medal (EGU) Witherspoon Lecture Award (AGU) European Research Council Consolidator Grant He led Panta Rhei - Everything Flows (2013–2022), IAHS’s global initiative on water-society interactions. Current efforts include transdisciplinary praxis and climate risk reduction frameworks .
