Find Academic Supervisors

Yicheng SongView profile

Assistant Professor

• Personalized Recommender Systems
• Digital Consumer Analytics
• Charity Analytics
• +3 more

Yicheng Song serves as an Assistant Professor in the Department of Information & Decision Sciences at the Carlson School of Management, University of Minnesota. His research develops personalization technologies using machine learning and structural economic models to enhance user satisfaction and business revenue through data-driven solutions. His educational background includes: PhD in Business (2017), Questrom School of Business, Boston University PhD in Computer Science (2012), Institute of Computing Technology, Chinese Academy of Sciences BA in Computer Science (2006), Wuhan University Dr. Song's research specializes in Personalized Recommender Systems, Digital Consumer Analytics, and Charity Analytics. He develops advanced machine learning frameworks to address information overload in digital platforms, with applications spanning e-commerce, customer journey optimization, and nonprofit donation systems. His work uniquely bridges technical innovation in AI with practical business impact through structural economic modeling. Analysis of his publication trajectory (2011-2024) reveals consistent focus on personalization algorithms, evolving from multimedia geolocation to reinforcement learning for customer interventions. Recent work emphasizes explainable AI in donor matching systems and multi-category utility modeling, demonstrating increasing sophistication in handling complex consumer behavior data across digital and charitable contexts. His research excellence has been recognized with prestigious awards: Best Paper Award at INFORMS CIST 2018 Best Paper Award at WITS 2014 Runner-up for Best Paper Award at INFORMS CIST 2023 While specific grant details and student advising records aren't publicly listed, his publications indicate active collaboration with researchers in marketing, operations, and computer science. The absence of laboratory information suggests integration within the Carlson School's existing research infrastructure rather than standalone facilities.

Steven O. KimbroughView profile

Professor

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +6 more

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Henning PiezunkaView profile

Associate Professor

• Competition
• Collaboration
• Crowdsourcing
• +3 more

Henning Piezunka is an Associate Professor of Management at The Wharton School, University of Pennsylvania. He holds a PhD from Stanford University, a Master of Science from the London School of Economics, and a Diploma Kaufmann from the University of Mannheim. Before academia, he co-founded and led a web design company for 18 years, growing it to 30+ employees serving 80+ countries before its sale in 2016. His research examines: Competition and collaboration dynamics in entrepreneurial firms, family businesses, and sports contexts Crowdsourcing innovation and platform strategies Organizational decision-making structures and learning processes CEO succession planning and its organizational impacts His recent publications (2019-2025) demonstrate methodological diversity, combining archival analysis of Formula One/soccer data, crowdsourcing platform experiments, and organizational case studies. Key thematic focuses include: how competition manifests in collaborative settings, AI's role in strategic learning, and how organizational structures shape individual behavior. Awards & Honors: Wharton Teaching Excellence Award (2023) Emerging Scholar Award, TIM Division, Academy of Management (2022) Poets & Quants Best 40 Under 40 Professor (2019) Multiple teaching commendations for MBA/EMBA courses (2014-2023) He teaches core entrepreneurship courses including MGMT2300 (Entrepreneurship) and MGMT2310 (Entrepreneurship Launchpad), focusing on venture creation, resource acquisition, and scaling strategies. He also leads doctoral seminars on research design and maintains an active advisory practice through the 'Between the Lines' webinar series featuring business school authors.

Mahir ArzokyView profile

Lecturer

• Artificial Intelligence
• Search Based Software Engineering
• Intelligent Data Analysis
• +4 more

Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.

Xia NingView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +7 more

Xia Ning is a Professor jointly appointed in the Department of Computer Science and Engineering , the Division of Medicinal Chemistry and Pharmacognosy (College of Pharmacy), and the Department of Biomedical Informatics at The Ohio State University . She also holds affiliation with the Translational Data Analytics Institute at OSU. Education: Ph.D. in Computer Science & Engineering, University of Minnesota, Twin Cities (2012) M.S. in Computer Science, University of Minnesota, Twin Cities M.S. in Statistics, University of Minnesota, Twin Cities B.S. in Computer Science, Chu Kechen Honors College, Zhejiang University, China Research Focus : The Ning Lab pioneers data-driven Artificial Intelligence, Machine Learning, and Big-Data analytics with targeted applications in drug discovery, medical informatics, health informatics, and e-commerce . Recent thrusts include generative AI for molecule design, graph neural networks for retrosynthesis, large-language models specialized for chemistry (LlaSMol) and e-commerce (eCeLLM), and reinforcement-learning frameworks for precision-medicine drug selection. The lab’s methodologies are intentionally generalizable, enabling spill-over benefits to domains such as social networks and system monitoring. Publication Trends : Over the past four years Professor Ning has released a steady stream of high-impact articles spanning retrosynthesis planning, LLM instruction tuning for scientific domains, reinforcement learning for drug discovery, and COVID-19 health-analytics . These works repeatedly integrate cutting-edge AI techniques (deep RL, graph Transformers, large-scale instruction datasets) with rigorous experimental validation in chemistry and biomedicine. Scientific Awards & Honors : Sanofi iDEA-TECH Award (2024) 10-Year Highest-Impact Award, International Conference on Data Mining (ICDM, 2020) Grants & Collaborations : Funding includes the Sanofi iDEA-TECH Award and collaborative grants with Amazon Web Services for COVID-19 knowledge graphs. Her open-source datasets (ECInstruct, SMolInstruct, CTKG) and models (G2Retro, LlaSMol, eCeLLM) are publicly released on HuggingFace and GitHub, fostering broad academic and industrial adoption. Labs & Teams : Professor Ning heads the Ning Lab at OSU, a multidisciplinary team focusing on AI/ML methodology and translational applications in health and medicine. The lab actively releases code and interactive web portals to accompany each major publication.

• Built Environments and Health
• Geospatial Analytics
• Physical Activity Research
• +4 more

J. Aaron Hipp is a Professor of Community Health and Sustainability at NC State University, affiliated with the Department of Parks, Recreation and Tourism Management. He serves as the Associate Director of Social and Behavioral Sciences in the Center for Geospatial Analytics and holds a leadership role in the Global One Health Academy. His work focuses on the intersection of built environments, physical activity, and health equity through innovative geospatial and sensor-based methodologies. Professor, Department of Parks, Recreation and Tourism Management CGA Fellow for Social and Behavioral Science Applications Associate Director, Center for Geospatial Analytics Executive Core Member, Global One Health Academy Hipp develops advanced approaches to studying human-environment interactions using webcams, GPS, accelerometers, and crowdsourcing technologies. His research emphasizes: Geospatial decision support systems Workplace health infrastructure Park accessibility and usage patterns Equity-focused community engagement Visualization of health-environment dynamics Rural/urban health disparities Recent publications highlight his work on scaling physical activity studies via technology, curling as a recreational intervention, international nature-prescription frameworks, and child play environment analysis. Collaborative projects include rural action plans and pandemic-era wellbeing evaluations. Professional recognition includes the CGA Fellow title. He actively mentors students in built environment impact studies and advises on geospatial health analytics.

Christopher L H WredeView profile

Professor

• Nuclear Astrophysics
• Experimental Nuclear Physics
• Low-Energy Nuclear Physics
• +3 more

Professor Christopher L H Wrede is a tenured faculty member at the Department of Physics and Astronomy , Michigan State University , and leads experimental research at the Facility for Rare Isotope Beams (FRIB) . His work bridges nuclear physics and astrophysics , focusing on beta decays of proton-rich nuclides to study hydrogen burning in accreting compact stars and isospin-symmetry breaking effects in the Standard Model. Ph.D. in Physics from Yale University (2008) Research areas: Nuclear Astrophysics Low-Energy Nuclear Experiments Isospin Symmetry Detector Instrumentation His group develops advanced detectors like GADGET II , LIBRA , and DSL2 to measure nuclear reactions in novae, neutron stars, and cosmic explosions. Recent work leverages machine learning and MCMC Bayesian analysis for data interpretation. Scientific awards include the DOE Office of Science Early Career Research Program (2016). His students and postdocs contribute to international collaborations and instrumentation projects, often publishing in Physical Review C and Nuclear Instruments and Methods in Physics Research .

Seyyed A. HosseiniView profile

Research Professor

• Multiphase fluid flow in porous media
• Geological carbon storage (CO2 sequestration)
• Reservoir engineering
• +4 more

Seyyed A. Hosseini is a Research Professor at the Bureau of Economic Geology (BEG), Jackson School of Geosciences, University of Texas at Austin . His work focuses on subsurface fluid dynamics with applications to geological carbon storage (CO 2 sequestration) and underground hydrogen storage . Education: Ph.D. in Petroleum Engineering (University of Tulsa, 2008) M.S. in Biotechnology (Sharif University of Technology, 2005) B.S. in Chemical Engineering (University of Isfahan, 2002) Research Interests include: Multiphase fluid flow in porous media Pressure-pulse testing for CO 2 monitoring Reservoir engineering fundamentals Machine learning for subsurface energy projects Geomechanical impacts of fluid injection Environmental risk assessment for CO 2 and hydrogen storage Article Trends reveal his expertise in developing applied methodologies for CO 2 and hydrogen storage, emphasizing machine learning integration , 3D modeling , and fault leakage detection . Recent work explores deep learning workflows and microfluidic experiments for subsurface energy systems. Grants and Funding PI on a $1.5M DOE/NETL grant (2015) to study brine extraction for CO 2 storage pressure management Collaborative Initiatives include the SMART program for machine learning in CCS and partnerships with institutions in Mississippi, Texas, and the Gulf Coast . His research extends to laboratory experimentation following BEG's facility upgrades.

Katharina DobsView profile

Researcher

• Visual Cognition
• Computational Neuroscience
• Face Perception
• +4 more

Dr. Katharina Dobs is a faculty-level researcher at Justus Liebig University Giessen, based in the Department of Psychology and Sports Science and soon moving to the Department of Mathematics & Computer Science. She leads the Visual Cognition & Computational Neuroscience (VCCN) Lab, where she integrates state-of-the-art machine-learning models with human behavioural and neural data to decipher the computations underlying rapid visual scene and face perception. Education & Career: While explicit degree details are not provided, Dr. Dobs currently holds a prestigious LOEWE Start-Professorship and an ERC Starting Grant (DEEPFUNC), underscoring her leading role in computational vision research. Research Interests: Computational principles of human visual perception Functional organisation of the visual cortex Deep neural network models of object and face recognition Integration of facial form and motion cues Temporal dynamics of face perception using MEG and fMRI Across her recent publications, a clear trend emerges toward leveraging deep convolutional networks to explain category-selective responses in the human brain, particularly for faces. Her work repeatedly demonstrates how artificial networks trained on ecologically relevant tasks spontaneously develop brain-like functional specialisation, providing computational evidence against domain-specific expertise hypotheses. Scientific Awards & Funding: ERC Starting Grant “DEEPFUNC” LOEWE Start-Professorship Supervision & Collaboration: Dr. Dobs currently supervises PhD students Elaheh Akbarifathkouhi and Hilal Nizamoglu. She co-leads multiple SFB (Collaborative Research Centre) projects, including Project S “Deep Learning: Unleashing the Potential” with Prof. Roland Fleming and Project C9 “Factors influencing categorical face processing” with Dr. Benjamin de Haas. The VCCN Lab is additionally affiliated with the CRC Cardinal Mechanisms of Perception, the research cluster The Adaptive Mind, the Center for Mind, Brain and Behavior, and the Center for Applied Computer Science and Data Science (ZAD).

Bagrat MailyanView profile

Assistant Professor

• Lightning
• Terrestrial Gamma-ray Flashes
• Gamma-ray Bursts
• +3 more

Dr. Bagrat Mailyan is an Assistant Professor at the Department of Aerospace, Physics and Space Sciences within the College of Engineering and Science at Florida Institute of Technology . His research focuses on Terrestrial Gamma-ray Flashes (TGFs) , Gamma-ray Bursts (GRBs) , and Neutrinoless Double Beta Decay , with a particular emphasis on spectral diversity in TGFs and detector development for particle physics experiments. Expertise : Lightning, Terrestrial Gamma-ray Flashes, Gamma-ray Bursts, Neutrinoless Double Beta Decay, High Energy Astrophysics Contact : mailyanb@fit.edu | F.W. Olin Physical Sciences, 346 | (321) 674-7717 Dr. Mailyan's research spans two primary domains: (1) high-energy atmospheric physics , where he investigates TGFs and their relationship to lightning and thunderstorm dynamics, and (2) high-energy astrophysics , focusing on GRBs and neutrinoless double beta decay. His work on spectral measurements of TGFs has revealed their diversity, contributing to understanding particle acceleration in thunderstorms. In particle physics, he contributes to experiments like AMoRE-I and AMoRE-II, which aim to detect neutrinoless double beta decay and characterize detector backgrounds. Recent publications highlight his involvement in Fermi-GBM observations of GRBs, TGF catalog validation, and detector R&D for cryogenic calorimeters. His collaborations include multi-institutional efforts to link GRBs with gravitational wave events and improve gamma-ray burst localization techniques. He also explores the use of machine learning for lightning classification and TGF detection. Dr. Mailyan's work integrates observational data from space missions (Fermi-GBM, Swift-BAT) and ground experiments (AMoRE, EMBRACE-AGS-Seed) to advance knowledge in both astrophysics and atmospheric radiation phenomena.

Fatih NayebiView profile

Lecturer

• Information Systems
• Data Science
• Machine Learning
• +5 more

Dr. Fatih Nayebi is a Faculty Lecturer in Information Systems at McGill University while also serving as Vice President of Data & AI at the ALDO Group. He bridges academic research with enterprise innovation, focusing on data science, machine learning, and AI systems. Academic Background: Ph.D. in Computer Engineering from École de technologie supérieure M.Sc. in Software Engineering from Boğaziçi University B.Sc. in Computer Engineering from Boğaziçi University Dr. Nayebi's research interests include: Information Systems Data Science Machine Learning Engineering & MLOps Deep Learning Agentic AI Human-Computer Interaction AI in Retail His recent publications focus on AI for retail, mathematical foundations of AI, information integrity in democratic systems, and best practices for technical documentation. Dr. Nayebi also teaches graduate courses at McGill University including: Enterprise Data Science Machine Learning in Production Introduction to AI and Deep Learning Applications and Architectures of Deep Learning Designing and Developing Agentic AI Systems As an active speaker and thought leader, Dr. Nayebi has participated in events such as: World Summit AI Americas RETHINK Retail NRF Nexus 2025 Supply Chain Research Forum JOPT2025 - Annual Conference of Optimization Days He is also the founder of Gradient Divergence, an advisory studio focused on advanced AI solutions for retail and consumer brands.

Fendy SantosoView profile

Academic

• cyber-physical systems
• cybersecurity
• artificial intelligence
• +6 more

Dr. Fendy Santoso is a Visiting Fellow at UNSW Canberra's School of Engineering and Information Technology, where he conducts cutting-edge research at the intersection of cyber-physical systems, cybersecurity, and artificial intelligence. His work focuses on developing robust security mechanisms for autonomous systems, particularly UAVs and robotics operating in adversarial environments. His educational background includes a PhD in Electrical Engineering from UNSW Sydney and a Master of Engineering (Electrical and Computer Systems) from Monash University. Dr. Santoso's research interests span cyber-physical systems security, adversarial machine learning, trustworthy autonomy, and AI-driven cybersecurity for autonomous platforms. His work specifically targets penetration-resistant architectures and secure decision-making frameworks for UAVs and robotic systems operating in dynamic, threat-prone environments. His approach integrates advanced fuzzy logic systems with deep learning techniques to create resilient control mechanisms that can withstand cyberattacks and operational uncertainties. Analysis of his recent publications reveals a consistent focus on applying type-2 fuzzy systems, deep learning, and negative imaginary control theory to solve critical challenges in autonomous systems security and control. His work demonstrates strong interdisciplinary connections between cybersecurity, control theory, and artificial intelligence, with particular emphasis on real-world implementation and experimental validation. Distinguished Early Career Travel Fellowship 2019, University of Wollongong ARC Linkage Project: Robust Defenses Against Adversarial Machine Learning for UAV Systems (2023) CSIRO Next Generation Grant for AgriTwins: Bridging Cyber-Secure Emerging Technologies and Data-Centric Twin Tech for Resilient Agriculture of the Future (2024) Dr. Santoso has secured over AUD 3 million in competitive research funding from prestigious sources including the Australian Research Council, U.S. Army Ground Vehicle Systems Centre, and Defence Science and Technology Group. He leads multi-disciplinary research teams and maintains strategic partnerships with defense and government agencies to translate theoretical advances into practical cybersecurity frameworks. His laboratory work centers around the Autonomous System Laboratory at UNSW Canberra, where he conducts experimental validation of security protocols for military ground robots and UAVs under realistic cyberattack scenarios.

• Architecture
• Artificial Intelligence
• Machine Learning
• +4 more

Martha Tsigkari serves as an Associate Professor at The Bartlett School of Architecture, University College London (UCL), where she bridges architectural practice with cutting-edge computational research. Her position situates her at the forefront of digital transformation in the built environment, with institutional affiliations spanning UCL's Faculty of the Built Environment and direct contributions to UN Sustainable Development Goals 4 (Quality Education), 11 (Sustainable Cities), and 13 (Climate Action). Her research program critically examines the integration of artificial intelligence, machine learning, and cognitive psychology into architectural design processes. Key investigations include spatial and visual connectivity analysis, XR-enhanced collaborative design environments, and AI-driven optimization of building performance. She explores how digital tools reshape creativity, professional identity, and sustainability outcomes in architecture, with particular focus on data commoditization, skills evolution, and human-AI collaboration in design workflows. Her interdisciplinary approach connects architectural theory with computational neuroscience and industrial digitalization trends. Tsigkari's publication trajectory reveals a clear evolution from computational structural analysis (2012-2017) toward AI ethics and professional transformation (2022-2024). Early work established foundations in performance-driven facades and material systems, while recent output confronts existential questions about architectural practice in the AI era. Her scholarship consistently addresses the tension between technological capability and human-centered design values, with growing emphasis on sustainable development frameworks and educational implications. Scientific Awards: No major awards are documented in the available records. Advising and Grants: While specific supervisees and funding mechanisms aren't detailed in current sources, her extensive collaborative network across 30+ publications indicates active mentorship and research leadership. Co-authorship patterns suggest involvement in multi-institutional projects addressing AECO industry digitalization, with potential ties to UK research councils and industry partnerships like RIBA. Labs and Teams: Tsigkari operates within The Bartlett's digital research ecosystem through recurring collaborations with Kosicki, Tarabishy, and Psarras. Her work manifests in experimental toolsets including Glaucon (XR design environment), HYDRA (optimization framework), and SandBOX (conceptual design system), indicating leadership in UCL's computational design labs focused on human-AI interaction and sustainable building technologies.

Christy F. LandesView profile

Professor

• Physical Chemistry
• Analytical Chemistry
• Materials Chemistry
• +5 more

Christy F. Landes is the Jerry A. Walker Endowed Chair in Chemistry and Professor of Chemistry at the University of Illinois Urbana-Champaign, with additional appointments as Professor in Electrical and Computer Engineering and Materials Research Lab, and as an Affiliate in Chemical and Biomolecular Engineering. She joined UIUC in 2023 after serving as the Kenneth S. Pitzer-Schlumberger Chair of Chemistry at Rice University. Education: B.S. in Chemistry, George Mason University (1998) Ph.D. in Chemistry, Georgia Institute of Technology (2003) Postdoctoral positions at University of Oregon and University of Texas at Austin Professor Landes' research focuses on physical, analytical and materials chemistry with emphasis on predictive separations, spectro-electrochemistry, protein dynamics at interfaces, imaging and signal processing, and single-molecule spectroscopy. Her work aims to understand complex structure-function relationships in biological processes to inspire innovation for materials design. The Landes Research Group develops new spectroscopic tools to image chemical dynamics at interfaces at the limit of a single event, creating new models to understand and predict macroscale processes like protein separation and photocatalysis. Her research spans several specific areas including predictive separations, spectro-electrochemistry, protein dynamics at interfaces, computational imaging/AI/data science, and interfacial energy and charge transfer in hybrid nanomaterials. By studying individual molecules rather than ensembles, her group can identify the chemical complexity of nanoscale interfacial dynamics and reveal underlying populations that form ensemble measurements. Scientific Awards: 2023 Fellow of the American Association for the Advancement of Science 2024 Kazuhiko Kinosita Award in Single-Molecule Biophysics 2020 Award for Special Creativity, National Science Foundation 2019 Kavli Fellow, U.S. National Academy of Sciences 2016 Early Career Award in Experimental Physical Chemistry, American Chemical Society 2011 NSF CAREER Award Professor Landes has advised numerous graduate and undergraduate students throughout her career at University of Houston, Rice University, and now at UIUC. Her research has been supported by various grants including NSF funding. Her group has developed innovative methods to break the Abbe diffraction limit, achieving spatial resolutions of just a few nanometers and time resolutions faster than traditional cameras frame times. The Landes Research Group at UIUC continues to develop new spectroscopic tools to image chemical dynamics at interfaces, with specific focus areas including predictive separations, spectro-electrochemistry, protein dynamics at interfaces, computational imaging/AI/data science, and interfacial energy and charge transfer in hybrid nanomaterials.

Ahmed ElkadyView profile

Associate Professor

• Performance-Based Earthquake Engineering
• Collapse Risk and Loss Assessment
• Resilience-based Design
• +4 more

Dr. Ahmed Elkady is an Associate Professor in Structural Engineering at the University of Southampton's Faculty of Engineering and Physical Sciences, Department of Civil, Maritime and Environmental Engineering. His research focuses on structural performance under seismic hazards with specialization in steel and composite structures. He leads the Infrastructure Research Group and actively supervises PhD students while developing innovative computational tools for structural analysis. Elkady's research interests center on Performance-Based Earthquake Engineering, Collapse Risk and Loss Assessment of Steel and Composite Buildings, and Resilience-based design of Existing Structures. His work combines advanced numerical modeling with large-scale experimental testing to develop robust predictive models for structural behavior under extreme loading conditions. He has made significant contributions to the understanding of structural connections, particularly steel endplate and bolted connections. His recent publications demonstrate a strong trend toward integrating machine learning with traditional structural engineering methods, particularly in modeling steel connections and predicting structural behavior. The research spans both fundamental mechanics and practical applications for seismic risk assessment, with several of his 2023-2025 publications focusing on data-driven approaches to structural analysis. Raymond C Reese Research Prize (2022) Multiple Outstanding Reviewer awards from ASCE Journal of Structural Engineering (2019-2020) First Place Award in NIST-ATC Blind Prediction Contest (2018) Alexander Graham Bell graduate scholarship from NSERC Canada (2014) Multiple best presentation awards at engineering conferences (2012-2015) Elkady currently supervises three PhD students (Weiran Li, Zizhou Ding, and Aran Naserpour) and leads the EPSRC-funded project 'Seismic Resilience of Egypt's Built Environment: A GIS-Based Framework for Assessment and Mitigation.' He has also secured funding from Research England for the 'EGYGIS: GIS Mapping in Support of Egypt's Disaster Risk Management' project. His research has resulted in several open-source software tools including EaRL (Earthquake Risk, Loss & Lifecycle Assessment), FM-2D (Frame Modeler 2D), and SCRonED (Semi-Rigid Connections Experimental Database), which are widely used in the structural engineering community for performance-based earthquake engineering.