جست‌وجوی استاد و پژوهشگر

Hui Luمشاهده پروفایل

استادیار

• Operating Systems
• Systems Virtualization
• Cloud Computing
• +۶ مورد دیگر

Dr. Hui Lu is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2023. Prior to joining UTA, he was an Assistant Professor at SUNY Binghamton from 2017 to 2023. His academic journey includes a Ph.D. in Computer Science from Purdue University (2017), and Master’s and Bachelor’s degrees in Electronic Engineering from Shanghai Jiao Tong University. Ph.D., Computer Science, Purdue University, 2017 M.S., Electronic Engineering, Shanghai Jiao Tong University, 2009 B.S., Electronic Engineering, Shanghai Jiao Tong University, 2006 Dr. Lu's research centers on systems software with a focus on operating systems, virtualization, cloud computing, file and storage systems, and computer networks. His work emphasizes performance optimization and security in cloud-native environments. He has collaborated with leading industrial research labs including HPE Labs, IBM Research, Microsoft Research, AT&T Labs, and NEC Labs. His recent publications span top-tier venues such as OSDI, SOSP, USENIX ATC, and VLDB. The article trends reflect a strong emphasis on secure container technologies, memory tiering, packet processing optimization in virtualized networks, and efficient cloud storage systems. His work increasingly integrates hardware-aware optimizations and lightweight security mechanisms. NSF CAREER Award (2023) UT System Rising STARs Award (2023) Summer Faculty Fellowship, Air Force Research Lab (2019) Dr. Lu has successfully advised multiple Ph.D. students, including Jiaxin Lei, who is now an Assistant Professor at Kean University. His research is supported by major grants from the National Science Foundation (NSF) and the Air Force Research Lab (AFRL), focusing on secure containers, non-volatile memory management, and cloud-native virtualization. He has served as Principal Investigator (PI) on multiple funded projects, demonstrating strong leadership in research and innovation. He is actively involved in teaching core courses such as Operating Systems and advanced topics in systems and architecture. He mentors a growing group of Ph.D. students and welcomes motivated individuals to join his research group.

Tim J. Nyeمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Operations Research
• Process Modelling
• +۴ مورد دیگر

Tim J. Nye is an Associate Professor in the Department of Mechanical Engineering at McMaster University's Faculty of Engineering. He holds a Ph.D. in Mechanical Engineering (1997) from the University of Waterloo, following an M.Sc. (1989) at Ohio State and B.A.Sc. (1987) at Waterloo. His research focuses on applying operations research techniques to manufacturing systems, with specific expertise in optimization algorithms for sheet metal processes, hydroforming reliability, and adaptive control in forging. Education: Ph.D. Mechanical Engineering, University of Waterloo (1997) M.Sc. Mechanical Engineering, Ohio State (1989) B.A.Sc. Mechanical Engineering, University of Waterloo (1987) Research interests span multiple dimensions of advanced manufacturing: developing decision models for production investment, creating novel lot-sizing algorithms incorporating work-in-process costs, exact solutions for 2D nesting problems, and agent-based systems for reliability prediction using warranty data. His work bridges theoretical operations research with practical metal forming applications. Recent publications demonstrate consistent contributions to manufacturing optimization, with particular focus on stamping processes, sheet metal design, and hydroforming reliability. These align with McMaster's research clusters in Advanced Materials & Manufacturing and Infrastructure. Scientific awards include the 2002 CSME Best Student Paper competition win for machine vision research with S. Dworkin. He maintains active collaborations with industry partners, as evidenced by his research on industry-university R&D ventures. Current projects explore intelligent open die forging as a solid freeform fabrication method, demonstrating his commitment to both traditional manufacturing improvement and emerging rapid prototyping technologies.

Guenter J. Hitschمشاهده پروفایل

استاد

• Quantitative Marketing
• Industrial Organization
• Consumer Choice Modeling
• +۷ مورد دیگر

Günter J. Hitsch is the Kilts Family Professor of Marketing at the University of Chicago Booth School of Business, where he has been a faculty member since 2001. His academic leadership extends to editorial roles as Co-Editor of the Journal of Quantitative Marketing and Economics and Associate Editor at Marketing Science and Management Science. Hitsch's educational journey includes an undergraduate degree from the University of Vienna (1995), followed by master's degrees in economics (1997, 1998), and a PhD in economics from Yale University (2001). This strong foundation in economics informs his approach to marketing research. His research program focuses on quantitative marketing and industrial organization, with particular emphasis on dynamic models of firm and consumer decision-making. Key areas include advertising effectiveness, pricing strategies, sequential learning and experimentation, and intertemporal consumer choice. Hitsch is pioneering in applying causal inference and machine learning to solve practical marketing problems such as optimal customer targeting. His work on dating and marriage markets demonstrates innovative application of economic theory to social phenomena. Hitsch's publication trajectory shows evolution from foundational work on consumer choice and switching costs to more recent applications of machine learning in marketing contexts. His research spans theoretical development and practical application, examining everything from private label demand during economic recessions to television advertising effectiveness across hundreds of brands. Co-Editor, Journal of Quantitative Marketing and Economics Associate Editor, Marketing Science Associate Editor, Management Science Hitsch's editorial leadership has significantly shaped the direction of quantitative marketing research. His commitment to methodological rigor and generalizable results ensures his work provides reliable inputs for both marketing practitioners and academic researchers. As an educator, Hitsch teaches advanced courses in quantitative marketing and business analytics, with scheduled courses for 2024-2026. He emphasizes that 'good marketing isn't fluffy,' challenging students to develop analytical approaches to marketing problems. His research philosophy prioritizes providing generalizable results that apply beyond specific case studies, serving as inputs for both practitioner decision-making and academic advancement.

• Hyperdimensional Computing
• Neuromorphic Hardware
• In-Memory Computing
• +۳ مورد دیگر

Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.

Scott L. Nyklمشاهده پروفایل

استاد

• Computer Vision
• Real Time 3D Computer Graphics
• Sensor Fusion
• +۱۲ مورد دیگر

Dr. Scott L. Nykl is a Professor in the Department of Computer Science at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering & Management. He is a leading researcher in computer vision, real-time 3D graphics, and autonomous aerial systems, with a focus on automated aerial refueling and navigation in GPS-denied environments. Education: Ph.D. in Computer Science, Ohio University (2008–2013), Summa Cum Laude, GPA: 4.0/4.0 M.S. in Computer Science, Ohio University (2011–2012), Summa Cum Laude, GPA: 4.0/4.0 B.S. in Software Engineering, University of Wisconsin–Platteville (2002–2006), Summa Cum Laude, GPA: 3.94/4.0 Dr. Nykl's research interests include computer vision, sensor fusion, interactive virtual worlds, and real-time 3D graphics, with applications in aerospace and defense. His work bridges simulation and real-world deployment, particularly in autonomous aerial refueling using stereo and monocular vision. He has pioneered techniques in pose estimation, occlusion mitigation, and sim-to-real transfer learning. His recent publications and projects show a strong trend toward robust, vision-based navigation systems for unmanned and manned aircraft, with emphasis on reliability, accuracy, and real-time performance. His work frequently appears in IEEE, AIAA, and ION venues, reflecting its high technical and operational relevance. Scientific Awards and Recognitions: 2024 Harold Brown Award – Highest U.S. Air Force scientific honor 2024 General Bernard A. Schreiver Award 2025 AETC Airmen of the Year Multiple Air Force Outstanding Scientist/Engineer Awards (2017–2023) Best Paper Award, ACM SIGGRAPH i3D 2013 Forbes' The Greatest Young Inventors in America (2012) NSF GK-12 Fellow (2006) Dr. Nykl has advised numerous graduate students and collaborated extensively on projects involving automated aerial refueling, 3D reconstruction, and cyber education. He has secured significant research funding, including a $100,000 Ohio Third Frontier grant. His work has led to multiple patents and technology transfers. He leads research integrating virtual worlds, digital twins, and augmented reality for both research and pedagogy. Laboratories and Research Teams: His work is conducted within AFIT’s research ecosystem, involving collaborations with the Air Force Research Laboratory (AFRL), Boeing, and academic partners. He leads projects under the Aerial Refueling Systems Advisory Group (ARSAG) and presents regularly at ION, AIAA, and IEEE conferences.

Mohsen Heidariمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Theoretical Machine Learning
• +۳ مورد دیگر

Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints

Fabrizio Lombardiمشاهده پروفایل

استاد

• Fault-tolerant computing
• VLSI CAD
• Testing
• +۷ مورد دیگر

Fabrizio Lombardi is the ITC Endowed Professor at Northeastern University's Department of Electrical and Computer Engineering, part of the College of Engineering. He previously held faculty positions at Texas Tech University, University of Colorado-Boulder, and Texas A&M University. He earned his B.Sc. from the University of Essex (1977), M.Sc. and Ph.D. from the University of London (1982). His research focuses on fault-tolerant computing, VLSI CAD, quantum computing, and configurable computing systems. He has led major projects like the NSF-funded Neural-Network-based Stochastic Computing Architectures for Machine Learning . He holds leadership roles including President of the IEEE Nanotechnology Council (2022-2023), IEEE Computer Society Vice President (2021), and IEEE PSPB member. His 200+ publications span IEEE Transactions on Computers, Nanotechnology, and Design & Test. Awards include IEEE Fellow, Søren Buus Outstanding Research Award, and multiple research fellowships. His work bridges theory and application, emphasizing defect-tolerant nanosystems and energy-efficient computing hardware. Recent innovations include approximate computing methodologies and secure PUF-based hardware designs.

• Power Electronics
• Building Automation
• Smart Grids
• +۷ مورد دیگر

Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.

• Bayesian Statistics
• Nonparametric Methods
• Machine Learning
• +۷ مورد دیگر

David B. Dunson is the Arts and Sciences Distinguished Professor of Statistical Science at Duke University, with a joint appointment in the Department of Mathematics. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His research bridges theoretical statistics with practical applications across multiple scientific domains, focusing on developing new tools for probabilistic learning from complex data. Dr. Dunson earned his Ph.D. from Emory University in 1997 and his B.S. from Pennsylvania State University in 1994. Dr. Dunson's research focuses on developing statistical methods directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, and criminal justice/fairness. His methodological work spans models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes), Bayesian inference frameworks, and models for "object data" (trees, networks, images, spatial processes). His approach emphasizes creating practical tools that scientists and decision makers can use routinely. Dunson's recent publications demonstrate a strong focus on advancing Bayesian methodology for complex data structures across applications in biodiversity mapping, brain connectomics, environmental health, and infectious disease modeling. His work shows consistent innovation in nonparametric Bayesian methods, computational efficiency, and the handling of high-dimensional and structured data, always with an eye toward solving real-world scientific challenges. Dr. Dunson has received numerous prestigious awards including: IMS Medallion Lecturer (2019) Mitchell Prize from the International Society of Bayesian Analysis (2018) Carnegie Centenary Professorship (2018) DeGroot Prize (2017) COPSS Award: President's Award (2010) Fellow of the Institute of Mathematical Statistics (2010) His extensive publication record with numerous co-authors suggests an active research group mentoring graduate students and postdocs. His research on projects like biodiversity mapping (funded by a European Research Council Grant) and brain connectomics indicates well-funded research programs addressing significant scientific challenges across multiple domains. Dr. Dunson's work involves collaborations across multiple labs and teams, particularly through his affiliation with the Duke Institute for Brain Sciences. His research on biodiversity mapping, brain connectomics, and environmental health suggests involvement in large, interdisciplinary teams addressing complex scientific questions that require sophisticated statistical approaches.

Erik Rodnerمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Data Science
• +۵ مورد دیگر

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

Christian Zemanمشاهده پروفایل

مدرس

• Climate Modeling
• Weather Simulation
• Deep Convection
• +۳ مورد دیگر

Dr. Christian Zeman serves as a Lecturer at the Department of Environmental Systems Science, ETH Zurich, based at the Institute for Atmospheric and Climate Science (CHN N 17.1) in Zurich, Switzerland. His research focuses on high-resolution atmospheric modeling with emphasis on kilometer-scale climate simulations and deep convection processes. Zeman's work centers on advancing regional climate modeling capabilities through rigorous model verification techniques and convection-resolving simulations. His research addresses critical challenges in simulating complex phenomena like island-induced vortex streets, tropical climate dynamics, and the impacts of reduced floating-point precision on model accuracy. Key methodologies include ensemble-based statistical verification and systematic model calibration for improved predictive reliability. Analysis of his 2020-2025 publications reveals a consistent trajectory toward higher-resolution climate modeling, with significant contributions to model intercomparison frameworks and computational efficiency optimization. His research bridges theoretical atmospheric science with practical applications in regional climate projection, particularly for island systems and tropical regions. Zeman operates within Prof. Christoph Schär's research group at ETH Zurich, contributing to cutting-edge developments in the Consortium for Small-scale Modeling (COSMO) and Integrated Forecasting System (IFS) frameworks. His work supports ETH Zurich's leadership in high-resolution climate simulation and verification methodologies.

Hao Yangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Transportation Safety
• Transportation Equity
• +۴ مورد دیگر

Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.

Wei Gaoمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• On-Device AI
• Mobile and Embedded Computing
• +۷ مورد دیگر

Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.

Lilly Shanahanمشاهده پروفایل

دانشیار

• Developmental Psychology
• Clinical Psychology
• Health Psychology
• +۴ مورد دیگر

Lilly Shanahan is an Associate Professor at the Department of Psychology , University of Zurich (UZH), and an Adjunct Professor of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. She leads the Risk & Resilience Research Area and co-directs the z-Proso International Research Network . Her affiliations include the Jacobs Center for Productive Youth Development , the International Max Planck Research School on the Life Course , and Duke University's Center for Developmental Epidemiology . Her research focuses on: Social and biological risk factors for depression and anxiety Long-term developmental consequences of childhood mental/physical health issues Intersection of mental and physical health in adolescence Gender differences in developmental psychopathology Substance use and decision-making dynamics Recent publications analyze maternal mental health impacts, cannabis use trajectories, hormonal biomarkers, and digital mental health interventions. Scientific awards include a NICHD Postdoctoral Fellowship. She collaborates extensively with longitudinal cohort studies like the Zurich Project on Social Development and Great Smoky Mountains Study.

Sameer Mulaniمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Random Vibrations
• Multi-disciplinary Optimization
• +۱۲ مورد دیگر

Dr. Sameer Mulani is an Associate Professor, Associate Department Head, and Director of Graduate Programs in the Department of Aerospace Engineering and Mechanics at the University of Alabama's College of Engineering. He leads the Stochastic Mechanics and Multi-Disciplinary Optimization Laboratory (SMO Lab) and is an integral part of the Remote Sensing Center and Alabama Materials Institute. Dr. Mulani's research spans uncertainty quantification, random vibrations, multi-disciplinary optimization, and composite structures' multi-scale analysis and design. His work combines computational methods with machine learning to develop innovative solutions for aerospace engineering challenges. He has made significant contributions to self-healing composite materials, uncertainty quantification techniques, and optimization of composite structures. His research group has published extensively on topics including polynomial chaos expansion for uncertainty quantification, self-healing composites, stochastic buckling analysis, and machine learning applications in structural mechanics. The publications demonstrate a strong trend toward integrating probabilistic methods with traditional engineering analysis to improve reliability and safety of aerospace structures. AIAA Associate Fellow, Class of 2025 2025 Department of the Air Force Summer Faculty Fellowship Program 2024 Department of the Air Force Summer Faculty Fellowship Program MSC Software Contest Winner (2011) Night on the Town: General Electric Award (2007) DAAD Fellowship (1999-2000) Dr. Mulani has advised numerous graduate students who have gone on to successful careers at institutions including Los Alamos National Laboratory, Cirrus Aircraft, L3Harris, and Lockheed-Martin. His lab collaborates with various research centers including the Remote Sensing Center where they work on antenna design, manufacturing, and integration for aircraft systems. The SMO Lab utilizes advanced software including MSC NASTRAN/PATRAN, ANSYS Mechanical/FLUENT, ABAQUS, SOLIDWORKS, and CATIA for their simulations and analyses.