جستجوی استاد راهنمای دانشگاهی

Monica Soمشاهده پروفایل

دانشیار

• Materials Science
• Nanotechnology
• Sustainable Energy
• +۶ مورد دیگر

Monica So is an Associate Professor of Chemistry and Biochemistry and Lantis University Chair at California State University, Chico, within the College of Natural Sciences. Her research integrates chemical intuition with physics to address sustainability challenges through nanomaterials engineering. Education : B.S. in Organic Materials Chemistry from UCLA; Ph.D. in Inorganic Solid-State Chemistry from Northwestern University Her research focuses on three core areas: energy transfer pathways, charge transport mechanisms, and interfacial phenomena in metal-organic frameworks (MOFs). These programmable nanosponges enable applications in water decontamination, solar energy conversion, and petroleum separation. Her lab develops conductive MOFs for batteries, adsorbent materials for removing heavy metals from water, and semi-transparent agrivoltaic systems that co-locate solar panels with crop production. Analysis of her 15 most recent publications reveals dominant themes in sustainable materials design, with 73% focused on MOF applications for environmental remediation and energy systems. Key trends include the development of tunable nanosponges for water purification (27% of works), charge transport optimization in electronic materials (33%), and agrivoltaic integration for land productivity (13%). Lantis University Chair Gates Millennium Scholar National Defense Science & Engineering Graduate (NDSEG) Fellow CSU STEM-NET Faculty Fellow (3 terms) As a mentor, she has trained over 1,000 undergraduates since 2015, with 63% of research alumni entering Ph.D. or M.D. programs. Her $3.3 million in active funding includes major grants from NSF, DOE, and ACS Petroleum Research Fund supporting projects like Raman spectrometer acquisition ($225k), agrivoltaics crop studies ($250k), and the $2.25M BATTERIES energy storage initiative. She co-founded Women in STEM at Chico State, creating mentorship programs and coding workshops for Girl Scouts. The So Lab operates as a collaborative hub for chemists, physicists, and materials scientists developing nanostructured solutions for sustainability. Current projects include the DOE-funded BATTERIES initiative for emerging energy researchers and NSF-supported agrivoltaics research optimizing light transmission for crop growth beneath solar panels.

Jan Daciukمشاهده پروفایل

دانشیار

• Natural Language Processing
• Finite-State Automata
• Speech Recognition
• +۳ مورد دیگر

Dr. Jan Daciuk serves as Associate Professor and Head of the Department of Intelligent Interactive Systems at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His academic position combines leadership responsibilities with active research in computational linguistics and formal language theory. His research focuses on Finite-State Automata , Natural Language Processing , and Speech Recognition with particular expertise in automata construction, optimization, and application to language processing tasks. His work bridges theoretical computer science with practical implementations in language technology systems. Dr. Daciuk's publication history shows a consistent trajectory in automata theory with applications spanning from dictionary implementation to speech recognition systems. His recent work demonstrates increasing sophistication in handling complex language modeling tasks while maintaining theoretical rigor in automata optimization. As Project Manager for the Computer Mastery Center (CMI), funded by the Operational Program Poland Digital under agreement PCPC.03.02.00-00-0002/18-00, he leads initiatives that translate theoretical research into practical educational technology applications. The project, realized at his faculty, commenced on April 25, 2018.

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

• Remote Sensing
• Geoinformatics
• Landscape Planning
• +۵ مورد دیگر

Professor Damian Bargiel serves as Head of Institute and holds the Professorship for Applied Geoinformatics in Landscape Planning within the Department of Landscape Planning & Nature Conservation at Geisenheim University. His work bridges advanced geospatial technologies with practical applications in agricultural monitoring and landscape planning. Professor Bargiel's research focuses on applied geoinformatics with particular expertise in remote sensing technologies , especially Synthetic Aperture Radar (SAR) satellite data. His work spans crop classification, agricultural land monitoring, habitat structure detection, and the integration of time series analysis with crop phenology information. He has developed innovative classification methods that significantly improve accuracy over conventional approaches by leveraging the temporal dimension of radar imagery. His publication record demonstrates consistent contributions to the field since 2011, with recent work expanding into satellite-based disease detection (2025) and small satellite Earth observation systems (2024). The research shows a clear progression from foundational work on TerraSAR-X applications to more integrated approaches combining multiple data sources and advanced machine learning techniques. Professor Bargiel is currently leading significant research initiatives including the AGRECO4CAST project (2025-2028), which aims to develop a transdisciplinary assessment framework for agroecological transitions in European agroforestry landscapes. This EU and German government-funded project reflects his expanding research scope into sustainable agricultural systems and policy-relevant applications of geospatial technologies. His work provides valuable methodological advances for agricultural monitoring systems and contributes to practical applications in precision agriculture, landscape planning, and environmental conservation. The integration of technical remote sensing expertise with real-world agricultural and landscape planning challenges represents the distinctive contribution of Professor Bargiel's research program.

Ali Muqadas Jaffriمشاهده پروفایل

استادیار

• Financial Econometrics
• Machine Learning in Finance
• Risk Management
• +۳ مورد دیگر

Dr. Ali Muqadas Jaffri serves as an Assistant Professor of Practice in Finance within the College of Business at North Dakota State University (NDSU). Holding a Ph.D. in Economics from Texas Tech University with a specialization in Financial Economics and being a Chartered Financial Analyst (CFA) charterholder, he combines academic rigor with extensive industry experience from roles at Allied Bank and MCB Bank Limited in risk management. His teaching portfolio includes advanced courses such as Analysis of Fixed Income Securities and Advanced Data Analytics in Finance, designed to equip students with analytical and practical financial skills. Educational Background Ph.D. in Economics (Financial Economics) from Texas Tech University Research Focus Dr. Jaffri's research lies at the intersection of financial econometrics, machine learning, and risk management. He investigates portfolio optimization under semi-heavy-tailed distributions and structural breaks, develops early warning systems for flash crashes using subordinated Lévy processes, and constructs global indices for geopolitical and climate risk. His methodological approach integrates advanced statistical modeling, affine GARCH, and transformer-based NLP to address complex challenges in asset pricing, systemic risk, and international finance. Publication Insights His recent publications demonstrate a strong focus on applying quantitative methods to real-world financial problems. The 2025 paper on Pakistan-exposed ETFs explores portfolio risk and performance in emerging markets, while the 2023 cryptocurrency volatility study examines asymmetric effects under structural breaks. Collectively, his work emphasizes robust portfolio construction, volatility forecasting, and the integration of non-traditional risk factors such as geopolitical events and climate policy. Scientific Awards No scientific awards are listed in the provided information. Advising and Grants The provided text does not specify any student advisees or research grants. Professional Experience Before joining NDSU, Dr. Jaffri held leadership positions in the financial industry, including Associate Manager of Market Risk at Allied Bank and Manager of Financial Institutions Risk Management at MCB Bank Limited. His responsibilities encompassed developing comprehensive risk frameworks and automating regulatory reporting systems, which directly inform his applied research and teaching in finance.

• Data analysis
• Chemometrics
• Statistics
• +۷ مورد دیگر

Franciscus Winfred J van der Berg serves as an Associate Professor in the Department of Food Science at the University of Copenhagen's Faculty of Science, specializing in Ingredient and Dairy Technology. His academic appointment focuses on Advanced Chemometrics in Food Technology. Dr. van der Berg's educational background includes a PhD in Process Analysis and Chemometrics from the University of Amsterdam, a graduate program in Analytical Chemistry at the same institution, and a Civil Engineering degree in Laboratory Information and Automation from The Netherlands. His research interests span Data analysis , Chemometrics , Statistics , Mathematics , and Process Analytical Chemistry , with particular emphasis on vibrational spectroscopy for process monitoring and control. His recent publications demonstrate a strong focus on food structure analysis, particularly in dairy products like mozzarella cheese and milk powders. His work integrates advanced spectroscopic techniques with statistical modeling to understand food properties and processing challenges. Dr. van der Berg has published extensively with 152 research outputs including journal articles, book chapters, and conference presentations. Dr. van der Berg teaches courses in Design of Experiments and Optimization, Food Process Equipment, and Process Analytical Chemistry and Technology. His research addresses practical challenges in food processing through advanced data analysis techniques. His professional network shows collaborations across multiple countries, indicating international research partnerships focused on food science and technology applications.

Kyra Ganمشاهده پروفایل

استادیار

• Operations Research
• Machine Learning
• Healthcare Analytics
• +۴ مورد دیگر

Kyra Gan is an Assistant Professor at Cornell University, affiliated with the School of Operations Research and Information Engineering and Cornell Tech. She holds field memberships in ORIE, the Center for Applied Mathematics (CAM), Statistics, ECE, and CS, and is affiliated with the Weill Cornell Medicine (WCM) Institute of AI for Digital Health. Prior to Cornell, she was a postdoctoral fellow at Harvard University's Statistical Reinforcement Lab, and earned her Ph.D. in Operations Research from Carnegie Mellon University (2022) and B.A. degrees in Mathematics and Economics from Smith College (2017). Her research focuses on adaptive/online algorithms for personalized healthcare interventions, robust causal discovery in medical data, and fairness in healthcare systems. Key areas include micro-randomized trials, N-of-1 trials, kernel-based statistical inference, and scalable causal discovery methods. Her work has been supported by AWS Credit Grants via Cornell’s Center for Data Science for Enterprise and Society. Notable awards include the 2021 INFORMS Pierskalla Best Paper Award and the 2021 CHOW Best Student Paper Award. She actively collaborates with students and researchers on topics ranging from algorithmic fairness to digital health technologies. Education: Ph.D. in Operations Research, Carnegie Mellon University (2022) B.A. in Mathematics & Economics, Smith College (2017) Grants & Funding: AWS Credit Grants (Cornell Center for Data Science) Labs/Teams: WCM Institute of AI for Digital Health

Lara Sousaمشاهده پروفایل

پژوهشگر

• Behavioral Ecology
• Spatial Ecology
• Marine Biology
• +۵ مورد دیگر

Dr. Lara Sousa is a Research Staff member at the Wildlife Conservation Research Unit (WildCRU), University of Oxford. Her research focuses on behavioral and spatial ecology of wild vertebrates, particularly marine species and African carnivores. She holds a BSc in Biology from the University of Évora, an MRes in Marine Biology from the University of Aveiro, and a PhD from the University of Southampton. Her doctoral work examined the behavior, predator-prey interactions, and fisheries impacts on ocean sunfish in the North Atlantic. At WildCRU, she leads the Trans-Kalahari Predator Project, investigating spatial ecology of lions, leopards, and hyaenas in Zimbabwe to understand population densities, habitat selection, and intraguild interactions. She employs satellite tagging and camera trap surveys for species distribution studies. Her work integrates environmental drivers and anthropogenic threats to guide conservation strategies. Key research areas include spatial ecology of large carnivores, marine megafauna tracking, and the impact of human activities on wildlife. Collaborations span marine species (e.g., blue sharks, hammerhead sharks) and terrestrial systems (e.g., African wild dogs, cheetahs). She has published extensively on topics ranging from climate-driven species redistribution to conservation policy implications of human-carnivore conflict. Her interdisciplinary approach bridges ecology, conservation, and technology to address global biodiversity challenges.

Predrag Radivojacمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Biomedical Informatics
• +۶ مورد دیگر

Predrag Radivojac is a Professor and former Associate Dean of Research at the Khoury College of Computer Sciences, Northeastern University. His academic roles include tenure-track faculty positions and courtesy appointments at the Department of Chemistry and Chemical Biology and the Roux Institute. He leads research in computational biology, machine learning, and biomedical informatics, focusing on protein function prediction, genome interpretation, and semi-supervised learning. Education details are limited in the text, but his career trajectory reflects advanced academic training in computer science and computational biology. His research interests include protein structure/function relationships, machine learning from biased data, and precision medicine applications. He collaborates widely, contributing to consortia like the IGVF and CAGI. Radivojac has authored over 200 publications, with recent focus on variant interpretation (e.g., MutPred2), fairness in ML, and genomic analysis tools. Awards include 2025 ISCB Fellowship and 2007 NSF CAREER Award. He advises numerous PhD students and oversees grants in AI, genomics, and pregnancy outcomes. His lab develops methods for variant prioritization, functional annotation, and proteomics analysis. Key Projects: CAGI challenges, ClinGen collaborations, Hoosier Moms Cohort study Grants: NIH-funded studies on pregnancy genetics, NSF CAREER Award, and institutional research initiatives He serves as ISCB President and editorial board member for Bioinformatics and Human Genetics. His lab is affiliated with Northeastern’s Experiential AI Institute and the Barnett Institute.

Tor Anders Nygaardمشاهده پروفایل

استاد

• Offshore Wind Energy
• Aerodynamics
• Structural Engineering
• +۳ مورد دیگر

Tor Anders Nygaard is a Professor at the Department of Mechanics, University of Oslo. His research focuses on aerodynamics, structural mechanics, and offshore wind energy systems. He specializes in the design and analysis of floating offshore wind turbines, including rotor dynamics, mooring systems, and hydrodynamic loading. Nygaard leads projects like OC6 (aerodynamic validation) and REDWIN (cost reduction in offshore wind), and collaborates internationally on initiatives such as the Offshore Code Comparison Collaboration (OC4). His work emphasizes experimental validation, computational fluid dynamics (CFD), and the integration of structural and geotechnical design for sustainable energy solutions. Key projects include the OC6 project (aerodynamic loading validation), REDWIN (cost reduction strategies), and DIMSELO (sea load analysis). He has contributed to advancements in floating platform design, mooring line dynamics, and the application of advanced numerical methods like VOF (Volume of Fluid) simulations for wave modeling. Nygaard’s research bridges theoretical modeling with practical field data, ensuring robust solutions for offshore renewable energy challenges. His publications span journals like Wind Energy Science , Renewable Energy , and Journal of Physics , with a focus on interdisciplinary topics such as fatigue analysis, fluid-structure interaction, and the optimization of offshore structures. Nygaard’s contributions have been pivotal in advancing the technical and economic feasibility of offshore wind energy systems.

Miguel Anjosمشاهده پروفایل

استاد

• Operational Research
• Optimization
• Power Systems
• +۴ مورد دیگر

Professor Miguel Anjos is Chair and Professor of Operational Research at the University of Edinburgh's School of Mathematics, with prior academic roles in Canadian engineering departments. His research focuses on optimization applications in power systems, smart grids, and predictive maintenance. He emphasizes bridging theoretical optimization with practical engineering challenges, advocating for operational research as a versatile tool for real-world problem-solving. As former President of the INFORMS Energy Section (2018–2020), he drives interdisciplinary collaboration in energy systems. His work spans mathematical abstraction and industrial applications, including electric vehicle charging infrastructure, hydropower scheduling, and facility layout optimization. Education: Holds a PhD in optimization, with foundational work in mathematical programming. His theoretical contributions, such as semi-definite optimization, have been applied to practical domains like power flow analysis. Collaborations with engineers highlight his cross-disciplinary approach. Research interests include optimization algorithms for energy networks, maintenance strategies under uncertainty, and decision-focused data acquisition. Recent projects address challenges in smart grid design, water supply systems, and transportation infrastructure. He advocates for the integration of machine learning and stochastic modeling in operational research. Leadership and Awards: Recognized for leadership in energy-related operational research through his INFORMS presidency. His work has influenced policy and industry practices in Canada and beyond. Grants and Labs: Engages in applied research funded by energy and infrastructure sectors. Collaborates with industry partners on projects like EV charging station optimization and grid reliability improvements.

Greg Moriمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Video Analysis
• +۴ مورد دیگر

Greg Mori is a Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. from UC Berkeley and an Hon. B.Sc. from the University of Toronto. His research focuses on computer vision and machine learning, particularly human pose estimation and activity recognition. He has served as Director of the School of Computing Science (2015–2018) and is now Senior Research Director at Borealis AI. Mori has contributed to methodologies like hidden Conditional Random Fields (hCRF), LDA variants, and deep networks. His work emphasizes features capturing visual intuition operationalized via machine learning. Education: Ph.D. in Computer Science, University of California, Berkeley (2004) Hon. B.Sc. in Computer Science and Mathematics, University of Toronto (1999) Research Interests: Computer vision, machine learning, video analysis, human activity recognition, human pose estimation, pedestrian detection and tracking, object recognition. Awards: CIPPRS Research Excellence Award (2008) NSERC Discovery Accelerator Supplement (2008, 2015) ICCV Helmholtz Prize (2017) Outstanding Reviewer Awards (ICCV 2009, CVPR 2010) Grants & Service: Program Chair for CVPR 2020 and General Chair for CVPR 2023 Editorial roles for IJCV and T-PAMI Organizing committee member for CVPR, ICCV, ECCV, NeurIPS, and ICLR Labs/Teams: Vision and Media Lab at Simon Fraser University, leading research in computer vision and machine learning applications.

• Statistics
• Econometrics
• Biostatistics
• +۷ مورد دیگر

Professor Alessio Farcomeni is a distinguished statistician at the University of Rome Tor Vergata, affiliated with the Department of Economics and Finance. His work spans statistical methodology, biostatistics, and applied econometrics. He teaches advanced courses such as Statistical Learning and Big Data Analysis for Economics and Finance , reflecting his expertise in modern data science. His research focuses on latent Markov models, quantile regression, and population size estimation, with applications in healthcare, epidemiology, and policy analysis. Farcomeni has contributed significantly to statistical methodology through publications in high-impact journals. Notable projects include modeling the Italian response to the Covid-19 pandemic, analyzing atrial fibrillation risk prediction, and developing Bayesian approaches for complex datasets. His work often bridges theoretical statistics with real-world challenges, such as optimizing treatment protocols for cancer patients and evaluating macroprudential policies. He maintains active collaborations with institutions like the Italian National Health Service and has advised on international studies, including the CALIPSO transplant infection project. His research has addressed critical issues like material deprivation measurement in Europe and the prognosis of metastatic prostate cancer. Farcomeni is recognized for pioneering methods in latent variable modeling and robust statistical inference. His teaching and mentorship are complemented by software development contributions, such as the Qtools package for quantile regression analysis. Despite prolific output, he emphasizes interdisciplinary collaboration, evidenced by his work in cardiology, oncology, and public health.

• Landscape Ecology
• Climate Change Ecology
• Ecosystem Carbon & Water Cycling
• +۴ مورد دیگر

Dr. Anne Griebel is a Lecturer in the School of Life Sciences at the University of Technology Sydney (UTS), specializing in landscape ecology with a focus on land-atmosphere interactions, carbon-water cycling, and ecosystem responses to climate change and disturbances. She leads the Atmospheric Ecosystem Research & Observation (AERO) Lab, leveraging eddy covariance flux measurements, remote sensing, and data-model integration to study climate change impacts. Her work includes managing the NSW Hub of TERN Ecosystem Processes and leading flux tower research in central Australia. She holds a PhD in Physical Geography from the University of Melbourne (2016) and a BSc from the University of Bonn (2011). Before joining UTS in 2023, she held postdoctoral positions at Western Sydney University, focusing on flux ecology, fire dynamics, and remote sensing. Her interdisciplinary collaborations span government agencies (NSW Health, Rural Fire Service) and research networks like TERN. Her research emphasizes sustainable climate adaptation strategies for forests, semi-arid, and urban ecosystems. She teaches terrestrial ecology at UTS and contributes to academic governance roles, including HDR mentoring and editorial work at Critical Insights in Plant Sciences . Her >40 publications address topics from live fuel moisture modeling to ecosystem resilience in fire-prone environments.

• Computational Fluid Dynamics (CFD)
• Transition and Turbulence
• Jet Engine Noise
• +۳ مورد دیگر

Gregory A. Blaisdell is a Professor at Purdue University's School of Aeronautics and Astronautics since 1991. His research focuses on computational fluid mechanics, transition and turbulence phenomena, and advanced numerical methods for aeroacoustics and high-speed flow simulations. He has held significant roles in academic and applied research, contributing to the understanding of turbulent flows and their impact on engineering systems. Blaisdell earned his B.S. and M.S. in Applied Mathematics from the California Institute of Technology (1980, 1982), followed by a Ph.D. in Mechanical Engineering from Stanford University (1991). His work integrates parallel computing with large-eddy simulation (LES) and direct numerical simulation (DNS) to model complex fluid dynamics, such as shock wave-boundary layer interactions and jet engine noise reduction. He frequently collaborates with colleagues like A. S. Lyrintzis, advancing LES-based methodologies for high-fidelity flow analysis. Key research interests include turbulence modeling, aerodynamic heating in supersonic flows, and the development of communication-efficient algorithms for petascale simulations. He has pioneered techniques like wall-modeled sharp immersed boundary methods and high-order shock capturing schemes to enhance LES accuracy. His studies also address fluid injection strategies for mitigating jet noise and improving propulsion efficiency. Blaisdell has received the W. A. Gustafson Teaching Award (1997 and 2014) and a NASA-ASEE Summer Faculty Fellowship (1995-1996). His research has been presented at major conferences like AIAA/CEAS Aeroacoustics and the AIAA Aerospace Sciences Meeting, with a focus on interdisciplinary challenges in fluid dynamics and aeroacoustics. In advising and grants, while no formal advisees are listed, Blaisdell collaborates extensively with research teams on projects funded by NASA and other institutions. His contributions include developing semi-empirical noise models and validating computational methods against experimental data. He has also led efforts in petascale computing for jet noise simulations, emphasizing scalable algorithms and high-performance computing integration. Labs and teams: Blaisdell's work is primarily conducted through the School of Aeronautics and Astronautics at Purdue, leveraging advanced computational resources for LES and DNS. He collaborates with the Engineering Computer Network (ECN) for computational infrastructure support, as evidenced by the contact details provided on his profile page.