جست‌وجوی استاد و پژوهشگر

Liang Jiangمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Communication
• Quantum Sensing
• +۳ مورد دیگر

Professor Liang Jiang is a faculty member at the University of Chicago's Pritzker School of Molecular Engineering, specializing in theoretical quantum systems. He leads the Jiang Group, focusing on quantum communication, computing, sensing, and error correction. His work emphasizes protecting quantum information via advanced control techniques and error correction to enable robust quantum processing. Education: BS from Caltech (2004), PhD from Harvard (2009), Sherman Fairchild Postdoc at Caltech. Previously held roles at Yale University before joining UChicago in 2019. Awards include Alfred P. Sloan and Packard Fellowships. Research spans modular quantum computation, global networks, and nano-scale sensors. Key areas of study include quantum control protocols, error correction mechanisms, and applications in sensing/simulation. Group members include graduate/postdoc researchers like Gideon Lee, Senrui Chen, and Junyu Liu. Active in patent development (e.g., quantum error correction patents from 2016–2020). Labs/Teams: Jiang Group actively recruits students/postdocs, emphasizing interdisciplinary research at the intersection of quantum theory and applied physics. Collaborates on hardware-software integration for scalable quantum systems.

• Machine Learning
• Theoretical Statistics
• Health Sciences
• +۷ مورد دیگر

Barnabas Poczos is an Associate Professor in the Machine Learning Department at the School of Computer Science, Carnegie Mellon University. He is a member of the Auton Lab and has established himself as a leading researcher in theoretical machine learning with applications spanning numerous scientific domains. Carnegie Mellon University, School of Computer Science Machine Learning Department Auton Lab member Dr. Poczos earned his M.Sc. in applied mathematics from Eotvos Lorand University in Budapest, Hungary in 2001, followed by a Ph.D. in computer science from the same institution in 2007. He completed postdoctoral training at the University of Alberta (2007-2010) in the RLAI group and at Carnegie Mellon University (2010-2012) in the Auton Lab. His research focuses on theoretical questions of statistics and their applications to machine learning. Dr. Poczos develops machine learning methods for advancing automated discovery and efficient data processing across diverse scientific fields including health-sciences, neuroscience, bioinformatics, cosmology, agriculture, robotics, civil engineering, and material sciences. His work bridges theoretical foundations with practical applications, making significant contributions to both machine learning methodology and domain-specific scientific problems. Analysis of his recent publications reveals a strong emphasis on diffusion models and generative AI techniques applied to scientific discovery. His work spans drug design, genomics, cosmology, and materials science, demonstrating a consistent pattern of developing novel machine learning methodologies that address specific challenges in scientific domains. The interdisciplinary nature of his research is particularly evident in the application of advanced ML techniques to solve complex problems in biology, physics, and engineering. Yahoo! ACE award Dr. Poczos has served as PI or co-Investigator on 15+ federal and non-federal grants, supporting his research in theoretical machine learning and its scientific applications. His teaching portfolio at CMU includes advanced courses in optimization, convex optimization, and machine learning with large datasets. While specific students aren't mentioned in the provided information, as an Associate Professor at CMU, he undoubtedly mentors graduate students in the Machine Learning Department. As a core member of the Auton Lab at Carnegie Mellon University, Dr. Poczos contributes to a research environment focused on developing machine learning methods for real-world applications, particularly in healthcare and scientific discovery. The lab's work emphasizes both theoretical foundations and practical implementations of machine learning systems.

• Water Science and Engineering
• Anaerobic Digestion
• Wastewater Treatment
• +۳ مورد دیگر

Dr. Yadira Bajón Fernández is a Senior Lecturer in Bioresources Science and Engineering at Cranfield University, part of the Cranfield Water Science Institute. She holds a PhD in Applied Sciences from Cranfield University and specializes in water science and engineering, particularly focusing on bioresources treatment and wastewater management. Her research emphasizes anaerobic digestion of waste materials, including municipal sludge, energy crops, and municipal solid waste, alongside thermal and biological pre-treatment technologies. Current research activities include optimizing anaerobic digestion processes, studying gas-to-liquid mass transfer in wastewater treatment, and developing low-energy treatment methods for rural areas. She leads the UK Wastewater Network, a collaborative initiative involving 11 water utilities and 3 universities. Dr. Fernández’s work spans interdisciplinary topics such as microbial chemotaxis in xenobiotic degradation, artificial neural networks for corrosion prediction in wastewater infrastructure, and UAV-based monitoring of greenhouse gas emissions. Her publications highlight advancements in biogas production optimization, phosphorus removal technologies, and wastewater-based epidemiology. She advises students like Nasreen Nasar and contributes to projects like intelligent flood response strategies and constructed wetland innovations. Collaborative efforts include the MiDAS 5 study on anaerobic digester microbiota diversity.

• Water Science and Engineering
• Membrane Technology
• Environmental Engineering
• +۳ مورد دیگر

Professor Ewan McAdam is a leading academic in Membrane Science and Technology at Cranfield University’s Water Science Institute. He specializes in developing hybrid membrane technologies for water treatment, focusing on selective separations, chemical transformations, and resource recovery. His work addresses global challenges in sustainable water management, energy efficiency, and environmental protection. He leads high-impact projects like the Nanomembrane Toilet (winner of the 2018 IWA Innovation Award) and holds a prestigious ERC Fellowship for membrane-assisted crystallization research. With over £10M in research funding, he collaborates with global entities such as the Bill & Melinda Gates Foundation, NGOs, and industrial partners like Anglian Water and Severn Trent PLC. Educational Background: Dr. McAdam earned his doctorate in Membrane Technology from Cranfield University in 2008 after a career in industrial analytical chemistry and environmental consulting. His research integrates fundamental science with practical solutions, emphasizing scalable innovations for water sectors worldwide. Research Interests: His work spans membrane crystallization, CO2 capture, ammonia recovery, and decentralized sanitation systems. He explores low-energy processes, process intensification, and the integration of membrane technologies with renewable energy systems. Recent advances include CRISPR-enabled sensors for environmental contaminants and novel methods to control nucleation and crystal growth in membrane systems. Scientific Contributions: With over 140 peer-reviewed articles and five authored books, his publications emphasize membrane technology applications in water treatment, energy recovery, and resource efficiency. He holds six patents related to membrane innovations and is Editor-in-Chief of the Water and Environment Journal . Grants & Collaborations: Secured funding from EPSRC, the Gates Foundation, and industry partners. His projects include UKCRIC infrastructure resilience initiatives and international networks for wastewater innovation. He supervises early-career researchers and mentors students in interdisciplinary teams. Labs & Facilities: Utilizes Cranfield’s advanced facilities for membrane testing, water infrastructure resilience, and real-world prototyping. Key platforms include the UKCRIC National Research Facility for Water and Wastewater Treatment, enabling full-scale trials of novel technologies.

Mert D. Peséمشاهده پروفایل

استادیار

• Autonomous Vehicle Security
• Adversarial Machine Learning
• Generative AI Applications in Security
• +۳ مورد دیگر

Mert D. Pesé is an Assistant Professor of Computer Science and Founding Director of the TigerSec Laboratory at Clemson University's School of Computing. His research focuses on autonomous vehicle security, adversarial machine learning, generative AI applications in security, and automotive data privacy. He holds a PhD in Computer Science and Engineering from the University of Michigan (2022), an MSc in Electrical Engineering from Technische Universität München (2016), and dual BSc degrees in Electrical Engineering and Computer Science (2015). His work involves collaboration with automotive companies like BMW, General Motors, Ford, Audi, DENSO, and Harman, supported by grants from the US Army GVSC and NSA. Recent projects include DENSO-funded research with Purdue University and leadership in the TigerSec Lab, which has onboarded PhD students Alkim Domeke and David Fernandez and Master’s student Jan de Voor. Key research trends in his publications emphasize securing automotive networks (e.g., CAN Bus vulnerabilities), adversarial attacks on AI-driven systems, and privacy-preserving techniques for vehicular data. His frameworks like FuzzSense and AutoWatch showcase innovations in automotive software testing and driver behavior analysis. Grants and collaborations highlight applied security solutions for modern vehicles, while his TigerSec Lab serves as a hub for exploring cutting-edge automotive cybersecurity challenges.

Alessio Orsinoمشاهده پروفایل

پژوهشگر ارشد

• Big Data Analysis
• Distributed Computing
• Edge-Cloud Computing
• +۵ مورد دیگر

Alessio Orsino is a Research Fellow at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES) at the University of Calabria. His academic journey includes a Ph.D. in Information and Communication Technologies (ICT) awarded in January 2025, a Master’s in Computer Engineering (Big Data Analytics and Data Science) from 2020, and a Bachelor’s in Computer Engineering from 2018, all from the University of Calabria. He also served as a visiting researcher at the University of Cambridge’s Department of Computer Science and Technology (July–December 2023). His research focuses on big data analysis, distributed computing, machine learning, and edge-cloud integration. He contributes to projects like ASPIDE (Exascale programming models) and INSIDER (cloud-edge IoT deployment optimization), funded by EU Horizon 2020 and Italy’s MIUR. He is a contract professor at DIMES and a member of the Scalable Computing and Cloud Laboratory (SCALab). Orsino’s work bridges theoretical and applied computing, emphasizing efficient machine learning on resource-constrained systems and real-time adaptive models for edge environments. His publications address edge-cloud continuum challenges, adversarial robustness in AI, and scalable big data frameworks. Key grants include Horizon 2020 (ASPIDE) and PRIN 2022 (INSIDER), supporting his interdisciplinary research in hybrid cloud-edge infrastructures and data-intensive applications. He has authored/co-authored a book on big data programming and collaborates internationally through visiting positions and lab engagements.

Jovita Lukasikمشاهده پروفایل

پژوهشگر ارشد

• Neural Architecture Search
• Model Robustness
• Deep Learning
• +۳ مورد دیگر

Jovita Lukasik is a Research Fellow in the Institute for Vision and Graphics at the University of Siegen. Her research focuses on neural architecture search (NAS), model robustness, and efficient deep learning methods, with applications in computer vision and performance prediction. Key Research Areas: Developing zero-cost proxies for efficient neural architecture evaluation Improving CNN robustness via frequency-based regularization Creating benchmark datasets for architecture design and robustness analysis Exploring generative approaches for efficient NAS Her work has been published in TMLR, GCPR, ECCV, and ICLR. She contributed to the organization of the NAS workshop at ICLR 2021 and co-leads the AutoML seminar series. Recent work investigates transferable surrogates for expressive search spaces and texture/shape biases in vision-language models.

Mubarak Shahمشاهده پروفایل

استاد

• Computer Vision
• Video Surveillance
• Visual Tracking
• +۷ مورد دیگر

Dr. Mubarak Shah is the Trustee Chair Professor of Computer Science and Founding Director of the Center for Research in Computer Vision (CRCV) at the University of Central Florida. His research focuses on computer vision, including video surveillance, visual tracking, human activity recognition, and UAV video analysis. He has held prestigious fellowships from ACM, NAI, AAAS, IAPR, and IEEE. Roles: Trustee Chair Professor, Director of CRCV, Graduate Faculty Member Affiliations: University of Central Florida, College of Engineering and Computer Science Research interests span visual analysis of crowded scenes, video registration, and privacy-aware diffusion models. He has pioneered geolocalization techniques and multimodal learning frameworks. His work impacts security, autonomous systems, and medical imaging. Recent publications emphasize diffusion models, 3D object detection, and adversarial learning. His work has been presented at CVPR, ECCV, and NeurIPS. Awards: Pegasus Professor (2006) ACM SIGMM Technical Award (2019) Multiple fellowships and recognitions from IEEE, AAAS, and others Advising and mentorship include over 50 graduate students and NSF REU programs. He leads CRCV, a hub for interdisciplinary vision research, and contributes to high-impact datasets like MAVREC. Labs/Teams: CRCV hosts cutting-edge projects in geolocalization, action recognition, and medical vision. Collaborative efforts include global partnerships in AI and robotics.

Wenbin Zhangمشاهده پروفایل

استادیار

• Societal Aspects of AI
• Generative AI
• Health Informatics
• +۵ مورد دیگر

Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing & Information Sciences at Florida International University and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. His research focuses on the theoretical foundations of machine learning with societal impact, including fairness, generative AI, health informatics, and interdisciplinary applications in healthcare, digital forensics, and energy. He has received awards such as the NSF CRII Award and recognition in the AAAI’24 New Faculty Highlights. Zhang serves in organizing committees for major conferences like AAAI, WSDM, and AIES, contributing to academic leadership. Ph.D., University of Maryland, Baltimore County (2020) Research interests span societal aspects of AI, generative models, and fairness-aware machine learning. His work bridges theory and practice, addressing ethical challenges in AI deployment across domains like healthcare and cybersecurity. Key contributions include frameworks for fair graph learning, bias mitigation in LLMs, and adaptive pruning techniques for large models. Publications emphasize fairness in ML systems, digital forensics, and interdisciplinary applications. Over 50 papers span venues like FAccT, ICDM, and AAAI, with multiple best-paper recognitions. His NSF-funded research highlights innovation in fair AI and robust model adaptation. Scientific Awards: NSF CRII Award, FAccT’23 Best Paper Candidate, ICDM’23 Best Paper Academic service includes roles as Travel Award Chair (AAAI’24), Volunteer Chair (WSDM’24), and Student Program Chair (AIES’23). Teaching and mentorship are integral to his mission, fostering next-generation AI researchers through rigorous training in ethics-driven innovation. Labs/Teams: Active collaborations with the Te Ipu o te Mahara Institute, focusing on AI ethics and societal impact.

Majid Mahzoonمشاهده پروفایل

استادیار

• Network Economics
• Bayesian Persuasion
• Information Economics
• +۲ مورد دیگر

Dr. Majid Mahzoon is an Assistant Professor in the Department of Economics at the University of Central Florida's College of Business. His research bridges economics, information theory, and distributed systems. Research interests include: Network economics and information flow Bayesian persuasion in hierarchical organizations Performance indicator selection in incentive systems Energy-efficient distributed algorithms Quantum cryptography and information limits Recent publications examine pay-for-performance metric selection and vice presidents' role in Bayesian persuasion models. Earlier foundational work developed distributed learning algorithms and quantum key distribution bounds.

• Plant Breeding
• Genetic Resources Management
• Hemp Genetics
• +۳ مورد دیگر

Jos Mieog is a Research Fellow at Southern Cross University's Faculty of Science and Engineering. Previously, he spent seven years at CSIRO in Canberra specializing in plant breeding for cereal crops using both genetic modification and conventional methods. His PhD research at the University of Sydney investigated coral bleaching mechanisms, focusing on symbiotic relationships between corals and algae, particularly thermal tolerance adaptation. Currently, his work centers on establishing international-standard genetic resources for hemp, pre-breeding characterization of hemp germplasm, and leading a tea tree essential oil breeding program. His research interests span plant breeding methodologies, genetic resource management, cannabis metabolism regulation, and symbiotic relationships in marine ecosystems. He has developed high-throughput genotyping platforms (HASCH) and advanced tissue culture techniques for cannabis propagation. His work integrates proteomics, metabolomics, and spectral analysis to optimize crop yields and quality. Notable research trends include exploring light spectra effects on cannabinoid production, developing non-destructive analytical tools for essential oil composition, and investigating symbiotic flexibility in corals under environmental stress. His contributions bridge laboratory innovations with commercial agricultural applications, emphasizing both hemp and tea tree oil industries. Advising and grants: Mieog leads interdisciplinary projects involving genetic resource curation and industry partnerships. His work aligns with Southern Cross University's focus on applied ecological and agricultural research. Lab facilities include state-of-the-art genetic resource facilities and analytical laboratories for plant metabolite analysis.

Nils Tiltonمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Hydrodynamic Stability
• Flow Through Porous Media
• +۵ مورد دیگر

Nils Tilton is an Associate Professor of Mechanical Engineering at the Colorado School of Mines. His research focuses on theoretical and computational fluid mechanics, with applications in membrane filtration, CO2 sequestration, and flow control for drag reduction. He holds a Ph.D. from McGill University (2009) and has held postdoctoral positions at the University of Aix-Marseille and the University of Maryland. Tilton's work integrates spectral methods, fractional step methods, and multi-domain computational techniques to model complex fluid flows in porous media and membrane systems. His research interests include hydrodynamic stability, flow through porous media, and the water-energy-climate nexus. He has contributed to advancements in modeling transient boundary layers, optimal perturbations in porous media, and the development of pseudospectral algorithms for fluid dynamics simulations. His numerical work emphasizes accuracy and efficiency, particularly in systems involving permeable walls and multiphase flows. Tilton's publications span over two decades, with recent focus on membrane distillation, reactive transport modeling, and buoyancy-driven instabilities. His research bridges fundamental fluid mechanics with practical applications in environmental engineering, energy systems, and materials science. He leads the Computational Fluid Dynamics Group at Mines and has developed innovative numerical tools for simulating heat and mass transport in membrane-based systems. His work addresses challenges in desalination, carbon sequestration, and fluid dynamics in complex geometries.

Shane Garlandمشاهده پروفایل

پژوهشگر

• Waste Heat Utilization
• Hybrid Energy Systems
• Thermal Energy Storage
• +۳ مورد دیگر

Shane Garland is a Researcher in the Department of Mechanical Engineering at Colorado State University, affiliated with the Walter Scott, Jr. College of Engineering. He serves as a Research Associate in the REACH CoLab led by Dr. Todd Bandhauer. His work focuses on cross-disciplinary energy systems, including hybrid solid oxide fuel cell (SOFC)-internal combustion engine systems, waste heat utilization, and membrane desalination technologies. Education: M.S. 2018, Colorado State University – Mechanical Engineering (Thesis: Waste Heat Driven Turbo-Compression Cooling) B.S. 2015, Bradley University – Mechanical Engineering Research interests emphasize energy efficiency, carbon capture, and sustainable technologies. Key collaborations include the ARPA-e INTEGRATE Program with Colorado School of Mines and industry partners, and a waste heat-powered desalination project with Dr. Tiezheng Tong (Civil Engineering). Garland advises on Turbo-Compression Cooling Technologies for manufacturing and naval applications, supported by DOE EERE and DOD grants. Labs/Teams: Active in the REACH CoLab and collaborates with the Colorado School of Mines and industry partners (e.g., Kohler Power Systems, Air Squared, Inc.). Current projects target 70% energy efficiency in hybrid systems and scalable wastewater treatment solutions using waste heat.

David Montgomeryمشاهده پروفایل

دانشیار مدعو

• Engine Research & Development
• Alternative Fuels
• Internal Combustion Engines
• +۳ مورد دیگر

Dr. David Montgomery is an Adjunct Associate Professor in the Department of Mechanical Engineering at Colorado State University (CSU), holding this position since August 2018. With over 20 years of industry experience at Johnson/Evinrude and Caterpillar, his work focuses on engine research and development. He manages Caterpillar’s Natural Gas and Alternative Fuels Research group and oversees the Caterpillar/CSU Cat Lab at the Engine and Energy Conversion Laboratory (EECL), involving 6 graduate and undergraduate students. Dr. Montgomery also teaches an advanced course on Industrial Natural Gas and Dual-Fuel Engines at CSU. His educational background includes a Ph.D. (2000) and M.S. (1996) in Mechanical Engineering from the University of Wisconsin-Madison, and a B.S. (1993) in Mechanical Engineering from Colorado State University. Dr. Montgomery’s research interests span alternative fuels, engine combustion optimization, emissions reduction, and fuel system design. His work has led to over 17 peer-reviewed publications and 23 patents, emphasizing innovations in engine efficiency and environmental sustainability. His recent studies focus on natural gas engines, dual-fuel systems, and cryogenic technologies. Through his industry-academia collaborations, he bridges cutting-edge research with practical applications, particularly in reducing greenhouse gas emissions and improving fuel efficiency in heavy-duty engines. His lab’s projects highlight advanced fuel injection systems, exhaust gas recirculation strategies, and cryogenic seal technologies.

Haopeng Zhangمشاهده پروفایل

استادیار

• Natural Language Processing
• Generative AI
• AI4Science (Healthcare, Linguistics, Sensing)
• +۳ مورد دیگر

Haopeng Zhang is an Assistant Professor in the Department of Information and Computer Sciences at the University of Hawaii at Mānoa, leading the ALOHA Lab. He holds a Ph.D. from the University of California, Davis (advised by Dr. Jiawei Zhang), dual M.S. degrees from Georgia Tech (Electrical Engineering and Computational Science), and a B.S. from UIUC (Electrical Engineering). His research focuses on NLP, Generative AI, AI4Science, and Graph Mining, emphasizing biomedical applications, multimodal reasoning, and workflow generation. Recent achievements include organizing workshops at EMNLP and ACL, receiving grants from NSF and OpenAI, and developing tools like StrucSum and MermaidFlow. Education: Ph.D., Computer Science, UC Davis (2024) M.S., Electrical Engineering & Computational Science, Georgia Tech B.S., Electrical Engineering, UIUC Research interests span structural summarization, LLM applications in healthcare and linguistics, and graph-based NLP systems. His work bridges theoretical advancements with practical tools, including the ALOHA Lab's contributions to reliable summarization systems and multimodal benchmarks. Notable contributions include the DomainSum benchmark and Wi-Chat wireless sensing framework. Key awards include UH travel grants, OpenAI credits, and NSF computing allocations. He actively serves on conference committees (ACL, KDD) and reviews for top journals (ACM TKDD, IEEE transactions). Teaching responsibilities include Advanced AI and Machine Learning courses at UH Mānoa. Lab activities focus on AI-driven solutions for scientific domains, with ongoing projects in biomedical event extraction and low-resource language support.