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Yunji ZhangView profile

Assistant Professor

• Mesoscale Meteorology
• Tropical Cyclones
• Ensemble Data Assimilation
• +5 more

Yunji Zhang is an Assistant Professor at the Department of Meteorology and Atmospheric Science within The Pennsylvania State University . He also serves as the Assistant Director of the Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT) and is affiliated with the Alliance for Education, Science, Engineering and Design with Africa (AESEDA) . Research Focus: Dr. Zhang investigates the dynamics and predictability of convectively driven severe weather , including tropical cyclones, mesoscale convective systems, and severe thunderstorms. His work emphasizes ensemble-based data assimilation techniques using satellite and radar observations to enhance numerical weather prediction models. Current projects involve improving forecasts of hurricanes like Harvey and Midwest derecho events through advanced assimilation of all-sky microwave and infrared radiances. Publications Trends: His recent studies (2021-2025) highlight advancements in Ensemble data assimilation of dual-polarization radar and satellite data Predictability of extreme rainfall events in Zhengzhou and China Multi-sensor approaches for convection initiation forecasting Microphysical parameterization impacts on hurricane prediction Automated boundary layer depth estimation techniques Global-to-regional nested modeling for tropical cyclones Advising: Dr. Zhang mentors current graduate students Zhu Yao and Abhisek Das, while former advisees include Ph.D. recipient Keenan Eure and M.S. graduate Paul Mykolajtchuk . He actively invites students interested in M.S. or Ph.D. research to contact him via email. Labs & Centers: His work is supported by the Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT) and collaborations with the Alliance for Education, Science, Engineering and Design with Africa (AESEDA) .

• Adhesion and Cohesion
• Energetic Materials
• Polymer Adsorption
• +5 more

Stephen Beaudoin is a Professor of Chemical Engineering at Purdue University and serves as Director of the Purdue Energetics Research Center. He joined Purdue in 2003 and holds affiliations with the Davidson School of Chemical Engineering. B.S. in Chemical Engineering from MIT (1988) M.S. in Chemical Engineering from University of Texas-Austin (1990) Ph.D. in Chemical Engineering from North Carolina State University (1995) His research focuses on adhesion/cohesion in energetic materials, polymer adsorption onto crystals, and enhanced centrifuge-based powder characterization. Supported by the Department of Homeland Security Science and Technology Directorate and the Department of Education, his work bridges fundamental studies of interfacial phenomena with applications in security and pharmaceuticals. Recent publications highlight advancements in atomic force microscopy techniques for Hamaker constant estimation, trace explosive detection, and pharmaceutical powder adhesion mechanisms. His team develops novel sensors, swabs, and computational models to address challenges in energetic materials and drug delivery systems. Teaching Academy Fellow (2018) NSF CAREER Award (2000) University Faculty Scholar (2006-2011) Multiple awards for undergraduate instruction and mentorship Beaudoin's research group includes current graduate students and has produced alumni now employed at Intel, Microsoft, Pfizer, and academic institutions. His laboratory combines experimental and computational approaches to study particle-scale interactions, surface functionalization, and material behavior under extreme conditions.

• Control Engineering
• Process Control
• Optimal Control
• +11 more

Torsten Wik is a Professor in Control Engineering at Chalmers University of Technology. He leads the Control Engineering research group and focuses on process control with theoretical and applied methodologies. Institution: Chalmers University of Technology Department: Control Engineering His research spans optimal control, model reduction, and systems with model uncertainties. Applications include energy-saving systems, environmental improvement, biological systems (water purification, recirculating fish farms, LED greenhouse lighting), and battery estimation/modeling/control. Recent work emphasizes battery degradation diagnosis, state estimation, fast charging, and reconfigurable systems. Key methodologies include physics-informed frameworks, machine learning integration, entropy-based predictive algorithms, and hypergraph modeling. Applications extend to electric vehicles, photovoltaic systems, fuel cells, and biofilm reactors. Publications highlight collaborations across engineering domains, focusing on control theory, electrochemical modeling, and real-time optimization. His work bridges theoretical advancements with industrial applications in energy systems, transportation, and sustainable agriculture.

Tami Brown-BrandlView profile

Professor

• Precision Livestock Farming
• Animal Behavior Monitoring
• Computer Vision in Agriculture
• +3 more

Tami Brown-Brandl is Professor and holder of the Dr. William E. and Eleanor L. Splinter Chair in Biological Systems Engineering at the University of Nebraska-Lincoln, where she conducts pioneering research at the intersection of agricultural engineering and animal science. Her research program focuses on: Computer vision systems for animal behavior monitoring Vibration-based vital signs detection in livestock Digital twin development for agricultural buildings RFID and sensor fusion for precision livestock farming Swine and beef cattle production optimization Individual animal identification using deep learning Recent publications (2023-2025) demonstrate a clear trajectory toward real-time, non-invasive monitoring systems using multi-modal sensor fusion. Her work integrates computer vision with ambient vibration analysis for piglet nursing behavior assessment, develops depth-image-based posture classifiers for sows, and creates digital twin frameworks that harness real-time data for precision livestock management. These innovations address critical challenges in animal welfare assessment and production efficiency through advanced engineering solutions. Dr. Brown-Brandl's leadership in precision livestock farming is evidenced by her extensive publication record on sensor systems development, with particular emphasis on practical applications for swine production systems including shoulder lesion detection, feeding behavior monitoring, and farrowing pen optimization.

James SchnableView profile

Professor

• Plant Genomics
• Crop Phenomics
• Quantitative Genetics
• +3 more

James Schnable serves as the Charles O. Gardner Professor of Agronomy in the Department of Agronomy and Horticulture at the University of Nebraska-Lincoln. His research integrates genomic, phenomic, and environmental data to advance crop breeding methodologies for maize and sorghum, with particular emphasis on climate-resilient varieties. Dr. Schnable's research program spans plant genomics , high-throughput phenomics , and quantitative genetics , focusing on genetic dissection of nitrogen use efficiency, photosynthetic traits, and stress tolerance mechanisms. His laboratory pioneers UAV- and satellite-based phenotyping systems, machine learning applications for image analysis, and genomic prediction models that bridge genotype-phenotype gaps under variable environmental conditions. Key innovations include nighttime fluorescence phenotyping to reduce environmental noise and spectral feature extraction for accelerated trait assessment. Analysis of his 2021-2025 publications reveals consistent leadership in genotype-environment interaction studies and computational phenotyping , with dominant themes including transcription factor binding site variation explaining heritability, nonphotochemical quenching kinetics in stress responses, and scalable satellite-based yield prediction methods. His work frequently employs the Genomes to Fields Initiative infrastructure for multi-state field validation. Charles O. Gardner Professorship Dr. Schnable directs an active research program involving advanced sensor networks, genomic selection pipelines, and multi-institutional field trials. His team develops computational frameworks like PlantSegNet for 3D plant reconstruction and SPARC-LoRa for agricultural IoT applications. While specific grant details aren't provided in source materials, his extensive publication record in high-impact journals indicates sustained funding from major agricultural research programs. The laboratory maintains cutting-edge phenotyping infrastructure including UAV fleets, hyperspectral imaging systems, and gas sensor networks for early stress detection. Current projects focus on nitrogen-responsive growth trajectories, chilling tolerance mechanisms in panicoid grasses, and gut microbiome interactions with grain composition, reflecting an integrative approach from molecular mechanisms to field performance.

Zhiqiang TaoView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Computational Imaging
• +7 more

Zhiqiang Tao is an Assistant Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). Prior to RIT, he served as an Assistant Professor at Santa Clara University (2020–2022). He holds a Ph.D. from Northeastern University (ECE Department) and B.E. and M.E. degrees from Tianjin University. Research Focus: His research group develops smart, scalable, and secure machine learning algorithms for healthcare, AI education, computational imaging, and data science. Key areas include large vision-language models (LVLMs), calibrated uncertainties in low-level vision, ranking fairness, and multimodal understanding. Recent works explore LLM fairness, medical question-answering systems, and diffusion models for text-video retrieval. Awards & Honors: RIT GCCIS Emerging Scholar Award (2025) Teaching: IDAI-780: Capstone Project ISTE-780: Data-Driven Knowledge Discovery Advising & Team: Actively seeking motivated graduate/undergraduate students to join his research group. His team has produced impactful work in NeurIPS, CVPR, ECCV, and IEEE TPAMI/TKDE. Labs & Collaboration: Collaborates across disciplines to advance AI applications in healthcare and education, leveraging computational imaging and fair ML principles.

Nicole AbaidView profile

Associate Professor

• Dynamical Systems
• Control Theory
• Multi-Agent Systems
• +5 more

Nicole Abaid is an Associate Professor in the Department of Mathematics at Virginia Tech, affiliated with the College of Science. Her research integrates dynamical systems, control theory, and robotics to study collective behavior in biological and engineered systems. She explores biologically-inspired models, such as bat swarms and brain networks, to develop applications in robotics and environmental monitoring. Her work combines agent-based modeling, network analysis, and data-driven approaches to understand interactions in animal groups and robotic systems. Notable projects include analyzing bat echolocation for acoustic sensing in robotics and studying stress effects in human crowd dynamics during emergencies. She has also contributed to piezoelectric energy harvesting and ex vivo muscle stimulation via Functional Electrical Stimulation (FES). Abaid’s research has been supported by grants like the NSF CAREER Award for collective behavior studies. She collaborates on interdisciplinary projects, including outreach programs in biomimetics and robotics for K-12 education. Her publications span journals and conferences, emphasizing applications in ecology, robotics, and control systems.

Didier TheilliolView profile

Professor

• Fault Tolerant Control
• Control Engineering
• Aerospace Systems
• +3 more

Didier Theilliol is a Professor of Control Engineering at the University of Lorraine, France, since 2004. He holds a Ph.D. in Control Engineering from Nancy-University (1993). He was awarded the CAS Visiting Professorship by the Chinese Academy of Sciences (CAS) in 2012, during which he collaborated with the Shenyang Institute of Automation (SIA) on flight control and fault-tolerant systems. His research focuses on model-based fault diagnosis (FDI), fault-tolerant control (FTC) for complex systems, and reliability analysis, with applications in aerospace, industrial automation, and robotics. Education: Ph.D. in Control Engineering (Nancy-University, 1993). Research interests include advanced control strategies for linear and nonlinear systems, multi-agent coordination, and safety-critical applications. His work integrates theoretical advancements with practical implementations across industries such as steel production, wastewater treatment, and aerospace. Notable contributions include methodologies for degradation management, distributed observer design, and health-aware control. Recent article trends emphasize fault-tolerant control in multi-agent systems, reinforcement learning for safety-critical tasks, and integration of physics-informed neural networks for system modeling. His publications span topics from model-based diagnostics to real-world applications in UAVs and propulsion systems. Awards: CAS Visiting Professorships for Senior International Scientists (2012). Collaborations include co-working with SIA’s rotorcraft UAV project team and leading European R&D initiatives. He serves as Associate Editor for ISA Transactions and Unmanned Systems , and chairs conferences on fault-tolerant control systems. His research also extends to Bayesian networks for system reliability and particle filter-based prognostics in industrial settings. Labs/Teams: Active in the State Key Laboratory of Robotics (visited during his CAS tenure) and coordinates projects within the German-French Institute for Automation and Robotics.

• Fuzzy Logic
• Artificial Intelligence
• Neural Networks
• +6 more

Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.

Emily JensenView profile

Assistant Professor

• Control Systems
• Power Systems
• Robotics
• +3 more

Emily Jensen is an Assistant Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado, Boulder. She specializes in the analysis and control of spatially-distributed systems with applications in power grids, satellite constellations, and bio-inspired robotics. Her work focuses on developing theoretical frameworks to optimize system performance under constraints such as communication limitations and nonlinear dynamics. Education: B.S. in Engineering Mathematics & Statistics, UC Berkeley, 2015 M.S./Ph.D. in Electrical & Computer Engineering, UC Santa Barbara, 2020 Professional Appointments: Postdoctoral Researcher at UC Berkeley (2022-2023) Postdoctoral Researcher at Northeastern University (2021) Her research interests center on optimal distributed control, system-level synthesis, and the interplay between system structure and controller design. Key projects include multi-timescale power network control, nonlinear wave dynamics in engineering systems, and distributed parameter systems with limited communication. Dr. Jensen has delivered invited talks at high-profile venues such as the International Symposium on Mathematical Theory of Networks & Systems (2024) and the System Level Synthesis Workshop (2022). She currently advises a team of graduate and undergraduate researchers, including Addie McCurdy, Andy Gusty, and Matt Baughman (co-advised with Prof. Hodge). Awards: UC Regents’ Graduate Fellowship (2016) Zonta Amelia Earhart Fellowship (2019) IFAC Conference Young Author Award Honorable Mention (2022) She teaches ECEN 3300 (Linear Systems) and ECEN 5738 (Nonlinear Control Systems) at CU Boulder. Her work has been published in IEEE journals and conferences, emphasizing theoretical contributions with practical engineering relevance.

Sangtae HaView profile

Associate Professor

• Machine Learning
• Deep Learning Systems
• Networks
• +6 more

Sangtae Ha is an Associate Professor in the Computer Science Department at the University of Colorado Boulder. His research focuses on building practical computer systems spanning multiple disciplines, including machine learning/deep learning systems, networks and distributed systems, internet protocols, wireless networks, video streaming, storage systems, and security. He specializes in creating efficient and scalable solutions for real-world computing challenges. His work emphasizes interdisciplinary approaches, bridging theoretical computer science with practical system design. Key areas of exploration include optimizing neural network execution for edge computing, developing adaptive streaming protocols, and enhancing wireless network performance through novel signal processing and spectrum management techniques. Recent projects include frameworks for semantic offloading in neural networks and reinforcement learning-based cloud scheduling. No scientific awards or notable grants are explicitly mentioned in the provided materials. While no formal advisees are listed, his research team likely involves graduate students and collaborators. No dedicated labs or teams are named, though his work aligns with broader university initiatives in computer systems and networking.

Gizem Ates VenåsView profile

Associate Professor

• Human-Robot Interaction (HRI)
• Inertial Measurement Units (IMUs)
• Gamified User Training
• +4 more

Gizem Ates Venås is an Associate Professor in the Department of Computer Science, Electrical Engineering and Mathematical Sciences at Western Norway University of Applied Sciences. She leads research in Human-Robot Interaction (HRI), focusing on intuitive human-robot cooperation through inertial measurement units (IMUs), gamified training systems, and teleoperation. Her work emphasizes safety and natural interaction in collaborative robotics applications. Education: Bachelor's in Electronics and Communication Engineering (2010-2015), Izmir Institute of Technology Master's in Mechanical Engineering (2015-2018), Izmir Institute of Technology PhD in Computer Science (Software Engineering, Sensor Networks) (2018-2023), Western Norway University of Applied Sciences Research interests include: IMU-based motion estimation for cooperative manipulation Design of gamified training systems to improve HRI proficiency Medical robotics applications in teleoperated surgical systems Sensor fusion and real-time data processing for collaborative robots Teaching focuses on robotics fundamentals: ROS programming, embedded systems (Arduino), and robot control Courses: ELE306 Robotics, DAT160 Intelligent Robots, ADA526 Applied Robot Prototyping Labs/Teams: Core member of HVL Robotics Lab, developing open-source robotics education materials and simulation tools.

• Condition Monitoring
• Fault Analysis
• Renewable Energy Systems
• +5 more

Dr Nur Sarma is an Assistant Professor in the Department of Engineering at Durham University. He specializes in renewable energy systems, electrical power systems, and advanced control strategies. His research focuses on condition monitoring, fault analysis, and fault mitigation strategies for electrical machines and drive systems. He currently serves as the EDI and Wellbeing Director of the Durham University Engineering Department and holds an EPSRC EDI+ Fellowship. He is actively involved in promoting equity, diversity, and inclusion through roles such as Co-chair of the Durham University International Staff Network, member of the Women in Engineering Society (WES Durham), and Mentoring Coordinator for IEEE UK & Ireland Women in Power Group. He is also an EPSRC-WES Ambassador and a Programme Committee Member for the IEEE Women in Engineering International Leadership Conference (WiE ILC 2025). His research interests include electrical machine modeling, grid integration of renewable systems, and sensor-based condition monitoring. Key contributions include work on DFIG control strategies, fault detection using controller signals, and fiber optic sensing for machine diagnostics. Dr Sarma has authored over 20 publications in top conferences and journals, including IEEE Transactions on Sustainable Energy and International Journal of Electrical Power & Energy Systems. His work has been recognized with awards such as the Best Paper Prize at ICRERA 2016 and the TÜBİTAK International Postdoctoral Fellowship (2019). He currently supervises two PhD students at the Electrical Power Node and has taught modules like ENGI4467 on electrical energy conversion since 2022.

Bradley DenbyView profile

Assistant Professor

• Cyber-physical systems
• Computational nanosatellite constellations
• Machine learning for autonomy
• +3 more

Bradley Denby is an Assistant Professor in the Department of Aerospace & Ocean Engineering at Virginia Tech, holding the Marty and Anna Irvine AOE Faculty Fellow position. He specializes in cyber-physical systems, computational nanosatellite constellations, and machine learning for autonomy, with a focus on orbital edge computing. Denby leads the Starbelt Lab, focusing on advanced air mobility and satellite systems research. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (20XX) MS in Computer Engineering, Air Force Institute of Technology (AFIT) BS in Physics, Southern Illinois University Research Interests: Denby’s work bridges computer science and aerospace engineering, addressing challenges in computational satellite systems, edge computing in space, and autonomous decision-making. His projects include batteryless satellites (Tartan Artibeus) and high-coverage nanosatellite constellations (EagleEye). He explores orbital edge computing frameworks to enable real-time data processing directly on satellites. Recent Research Trends: His publications emphasize scalable nanosatellite constellations, overcoming computational bottlenecks in space, and integrating machine learning for on-orbit inference. Themes include energy-efficient systems, inter-satellite communication, and mission-critical autonomy. Awards: Marty and Anna Irvine AOE Faculty Fellow (Virginia Tech) Teaching & Labs: Instructs courses on space engineering, satellite design, and advanced air mobility. Runs the Starbelt Lab (website/GitHub linked).

Mayank ChadhaView profile

Adjunct Assistant Professor

• Structural Health Monitoring
• Applied Mechanics
• Machine Learning
• +7 more

Mayank Chadha is an Assistant Adjunct Professor in the Department of Structural Engineering at the University of California, San Diego. He holds a Ph.D. and M.S. in Structural Engineering from UC San Diego and a B.E. in Civil Engineering from PSG College of Technology, Anna University. His research focuses on integrating applied mechanics, machine learning, and Bayesian methodologies to address challenges in structural health monitoring (SHM), risk analysis, and optimal sensor design. He also explores the application of physics-constrained machine learning in hydrology and battery degradation modeling. Education: 2019: Ph.D. in Structural Engineering, UC San Diego 2023: Postdoctoral Fellowship, UC San Diego 2014: B.E. in Civil Engineering, PSG College of Technology Dr. Chadha’s research interests include structural health monitoring, applied mechanics, uncertainty quantification, and the development of decision-making frameworks for engineering systems. He has pioneered work on Bayesian model optimization, sensor placement strategies, and value-of-information analysis. His interdisciplinary approach bridges theoretical mechanics with practical data-driven solutions for modern engineering problems. His recent work emphasizes hybrid physics-based machine learning models for streamflow forecasting and battery RUL prediction, demonstrating the fusion of computational efficiency and physical principles. He has authored numerous articles on optimal sensor design, VoI metrics, and risk-informed decision-making in SHM systems. Scientific Awards: Gold medal for CBSE 12th exams (2010) Central Government Fellowship (2014) Gold medal for Civil Engineering proficiency (2014) In addition to academia, Dr. Chadha co-founded LogSpi LP, a quantitative investment hedge fund. He teaches courses on mechanics and signal processing, emphasizing foundational engineering principles and their real-world applications. His research also extends to the development of higher-order geometrically exact beam theories, with applications in computer graphics and shape sensing. He actively collaborates with the U.S. Army Corps of Engineers on projects involving inland waterway infrastructure and risk-based sensor design.