جستجوی استاد راهنمای دانشگاهی

• Resource Recovery from Wastewater
• Environmental Biotechnology
• Electrochemical Water Treatment
• +۲ مورد دیگر

Mohan Qin is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin–Madison. Her research focuses on developing novel approaches for resource recovery from waste streams and the concentration and detection of microplastics in the Great Lakes. Dr. Qin received her educational degrees as follows: Ph.D. in Civil Engineering from Virginia Tech (2017) M.S. in Environmental Engineering from Peking University (2013) B.S. in Environmental Engineering from Shandong University (2010) Her primary research areas include: Bioelectrochemical systems for resource recovery from wastewater Environmental biotechnology for sustainable wastewater treatment Electrochemical processes for desalination and water treatment Membrane-based technology for selective ion removal Her work particularly emphasizes ammonia recovery from manure and wastewater, and the detection of microplastics in freshwater systems, contributing to sustainable water management and resource conservation. Analysis of Dr. Qin's recent publications (2022-2025) reveals a strong focus on ammonia recovery using membrane and electrochemical systems, with increasing attention to microplastics detection in lake water. Her work spans from fundamental transport mechanisms to practical applications in dairy manure treatment and Great Lakes monitoring, often integrating novel sensor technologies and renewable energy sources. Dr. Qin has received numerous awards, including: 2024 IWA Membrane Technology Specialist Group (MTSG) Rising Star Award 2024 University of Wisconsin-Madison Hilldale Undergraduate/Faculty Research Fellowship 2023 UW-Madison Media Fellow and Sustainability Fellow 2022 UW-Madison Madison Teaching and Learning Excellence (MTLE) Fellow Multiple awards during her graduate studies at Virginia Tech Dr. Qin actively mentors students through thesis and independent study courses (CIV ENGR 890, 990, 699) and has been awarded the Hilldale Fellowship for undergraduate research collaboration. Her research is supported by several fellowships including the UW-Madison Sustainability Fellow and Media Fellow, which likely fund her innovative work in resource recovery and microplastics detection. She leads a research group at UW-Madison focused on environmental biotechnology and electrochemical systems, collaborating with institutions like Yale University (where she completed her postdoc) and contributing to journals as an associate editor for Desalination and Water Treatment and on the early career editorial board of ACS ES&T Engineering.

Bruno Castro da Silvaمشاهده پروفایل

استادیار

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Bruno Castro da Silva is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He holds a PhD in Computer Science from UMass Amherst (2014), and MSc and BSc degrees from the Federal University of Rio Grande do Sul (UFRGS), Brazil. Prior to UMass, he was an Associate Professor at UFRGS and a postdoctoral researcher at MIT's Aerospace Controls Laboratory. His research focuses on reinforcement learning (RL), robotics, and AI safety, aiming to develop algorithms that ensure safe and autonomous task decomposition while meeting user-specified safety criteria. Key areas include hierarchical policies, active learning, and biologically-plausible mechanisms. He has published in top venues like ICML, NeurIPS, and Science, and received awards such as the Best Paper at RLDM 2022 and Distinguished Reviewer distinctions. He teaches courses in reinforcement learning and machine learning at UMass, emphasizing accessibility and safety. His work also extends to fostering diversity in STEM education. He leads the Autonomous Learning Laboratory and collaborates with organizations like Adobe Research and the Laboratory of Computational Neuroscience in Rome.

Dan Sheldonمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Machine Learning
• +۵ مورد دیگر

Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.

• Malaria Research
• Infectious Diseases
• Antibiotic Resistance
• +۵ مورد دیگر

Martina Wade is a Researcher in the Department of Epidemiology of Microbial Diseases at Yale School of Public Health. Her work focuses on infectious diseases, particularly malaria and antibiotic resistance, with a global health perspective emphasizing sub-Saharan Africa. She collaborates on projects in Cameroon, Uganda, and Burkina Faso, addressing drug resistance mechanisms, diagnostic innovations, and public health interventions. Her research spans clinical trials (e.g., ivermectin mass drug administration protocols), microbiota analysis in livestock and human populations, and environmental transmission dynamics of pathogens. She has published extensively on malaria treatment efficacy, microbiome disruptions from antibiotic use, and novel diagnostic technologies like photoacoustic detection. Her work bridges laboratory science with field epidemiology, aiming to improve clinical outcomes and public health strategies. Key collaborations include Sunil Parikh, Justin Goodwin, and Fangyong Li, focusing on antimalarial resistance tracking and pediatric infection dynamics. Her laboratory is located at 60 College Street, Ste Room 711, New Haven, CT. Contact: martina.wade@yale.edu.

Simon Arthurمشاهده پروفایل

استاد

• Immunology
• Cell Signalling
• Inflammation Biology
• +۳ مورد دیگر

Simon Arthur is a Professor of Immune Signalling at the University of Dundee, School of Life Sciences, within the Department of Cell Signalling and Immunology. His research focuses on understanding inflammatory processes, particularly the role of innate immune cells in coordinating inflammation and resolving immune responses. He holds a PhD from the University of Oxford (1995) and a BSc from Durham University (1990). Arthur is a Fellow of the Royal Society of Biology (2015) and serves on the editorial board of the Journal of Biological Chemistry . His teaching includes courses on Genetics, Cell Signalling, Immunology, and advanced topics in immunology and cell signalling. He supervises PhD projects on microglial phenotypes in brain ageing and immunomodulatory factors in helminth-host interactions. Arthur leads research projects funded by the Medical Research Council and other agencies, including studies on liver fibrosis, bile acid diarrhoea, and pulmonary fibrosis. Key research themes include cytokine regulation, macrophage function, and the molecular mechanisms underlying chronic inflammation. His work spans from fundamental biology to translational research, aiming to develop therapies for autoimmune and inflammatory diseases. Arthur collaborates internationally and has over 180 publications in high-impact journals.

• Environmental Chemistry
• Microbiology
• Water Science and Engineering
• +۴ مورد دیگر

Professor Frederic Coulon holds the position of Professor of Environmental Chemistry and Microbiology at the Cranfield Water Science Institute, part of Cranfield University's School of Management. His work focuses on transdisciplinary environmental science, including soil and water chemistry, bioremediation, wastewater treatment, and risk assessment. He leads research on microbial interactions in engineered systems, biodegradation of petroleum hydrocarbons, and emerging contaminants. His professional interests span soil and water chemistry, fate of chemicals in environmental systems, hazardous waste site remediation, and policy-influencing research. Key collaborators include BBSRC, NERC, Defra, and the European Commission. He has advised on contaminated land management and developed solutions for pragmatic risk-based remediation strategies. Research activities include microbial adaptations in cold environments, environmental monitoring technologies, and anaerobic digestion optimization. Over 200 peer-reviewed articles highlight his contributions to environmental engineering and science, with a focus on bioremediation, bioaerosol risk assessment, and sustainable waste management. Current projects address challenges in low-carbon remediation, landfill mining, and antimicrobial resistance in agricultural environments. He supervises multiple PhD students and collaborates internationally on initiatives like the UK-India vWa project and the REGENERATIS program for metallurgical site regeneration.

Jochen Guckمشاهده پروفایل

استاد

• Biophysics
• Cell Biology
• Medical Diagnostics
• +۳ مورد دیگر

Prof. Jochen Guck is a Director at the Max Planck Institute for the Science of Light and holds the Chair of Biological Optomechanics at Friedrich-Alexander Universität Erlangen-Nürnberg. His research focuses on the physical properties of cells and tissues, combining photonics and biophysical tools to bridge gaps between cellular mechanics and medical applications. Education: PhD in Physics from the University of Texas at Austin (2001), followed by roles at Leipzig University, Cambridge University, and TU Dresden before his current positions. He leads the Biological Optomechanics Division, developing techniques like Deformability Cytometry (DC), Optical Diffraction Tomography (ODT), and Brillouin Microscopy to study cell mechanics and density distributions. Research emphasizes translating physical measurements into clinical diagnostics (e.g., cancer, spinal cord regeneration) and has produced over 145 publications and 6 patents. Awards include the Cozzarelli Award (2008), Paterson Medal (2011), and Humboldt Professorship (2012). Collaborations span medical and engineering fields, with projects like the Max-Planck-Zentrum für Physik und Medizin. His lab investigates cell density heterogeneity, spinal cord regeneration in zebrafish, and biomechanical markers for diseases like Covid-19 and depression. Key methods include ODT for 3D mass density mapping and RT-DC for high-throughput cell analysis.

Thomas Kunerمشاهده پروفایل

استاد

• Neuroscience
• Neurophysiology
• Pain Research
• +۳ مورد دیگر

Prof. Thomas Kuner is a Professor and Director of the Department of Functional Neuroanatomy at the University of Heidelberg's Medical Faculty. He holds a medical degree (MD) from Heidelberg (1998) and completed postdoctoral work at Duke University and the Marine Biological Laboratory. Since 2000, he has led a research group at the Max Planck Institute for Medical Research, followed by habilitation in Physiology (2003) and appointment as Professor of Anatomy and Cell Biology (2006). Research Focus: His work focuses on neuroanatomy, synaptic transmission mechanisms, and pain research. Key projects include investigations into the structural and functional properties of synapses (e.g., calyx of Held), the role of presynaptic proteins like Mover, and the molecular basis of pain signaling via the SFB 1158 consortium. His lab uses advanced imaging techniques (e.g., STED microscopy) and genetic models to study neuronal circuits and synaptic plasticity. Funding & Collaborations: Kuner's research is supported by grants from the DFG (e.g., SFB 1158), the Baden-Württemberg Foundation, and other national/international bodies. His interdisciplinary approach bridges cellular neuroscience, molecular biology, and clinical applications in pain management. Teaching & Leadership: He oversees the Institute of Anatomy and Cell Biology, contributing to graduate programs in medical education and anatomy. His team includes postdocs and technicians, with collaborations extending to imaging technology development and medical education innovation.

Danilo Bzdokمشاهده پروفایل

دانشیار

• Computational Biology
• Deep Learning
• Large Language Models (LLM)
• +۵ مورد دیگر

Danilo Bzdok is an Associate Professor in the Department of Biomedical Engineering at McGill University’s Faculty of Medicine and a Canada CIFAR AI Chair at Mila – Quebec Artificial Intelligence Institute. He holds dual expertise in systems neuroscience and machine learning, with two doctoral degrees: one in neuroscience from Forschungszentrum Jülich (Germany) and another in computer science (machine learning statistics) from INRIA–Saclay and Neurospin (France). His research bridges computational neuroscience and AI, focusing on understanding human intelligence through neuroimaging and biomedical data. Education: PhDs in Neuroscience (Jülich) and Computer Science (INRIA/Neurospin). Postdoctoral training at Harvard Medical School. Current affiliations include McGill University and Mila. Research interests span computational biology, deep learning, LLMs, and their applications in neuroimaging, precision medicine, and neurodegenerative diseases. Over 150+ peer-reviewed publications, with recent work on LLMs in autism diagnostics, brain network modeling, and social neuroscience. Key Awards: Canada CIFAR AI Chair. Lab focuses on interdisciplinary projects like AI4Science, neuroimaging analysis, and AI ethics. Supervises a dynamic team of PhD/Master’s students and postdocs. Collaborations include clinical institutions and industry partners through Mila’s Applied Research programs.

Peter Bearmanمشاهده پروفایل

استاد

• Social Network Analysis
• Historical Sociology
• Health and Population
• +۳ مورد دیگر

Peter Bearman serves as Director of the Incite Institute and holds the Jonathan R. Cole Professorship of Sociology at Columbia University, where he maintains an active research and teaching profile within the Department of Sociology. His office is located at Knox Hall on Columbia's Morningside Heights campus. Bearman specializes in social network analysis and historical sociology, with significant contributions to health/population studies and qualitative methodology. His research increasingly integrates computational approaches for text and narrative analysis, examining phenomena ranging from historical religious discourse to modern social conflict dynamics. This interdisciplinary work bridges classical sociological theory with contemporary methodological innovation. Recent publications reveal a trajectory toward computational social science applications, particularly in semantic network analysis of historical texts and neural correlates of social behavior. His work consistently connects micro-level interactions with macro-scale social structures across diverse contexts including religious history, workplace conflict, and collective violence. His distinguished scientific honors include: Kohli Prize for Sociology (2025) for contributions to movements, networks, health, and science Golden Goose Award (2016) for the National Longitudinal Study of Adolescent Health Election to the National Academy of Medicine (2019) Membership in the American Academy of Arts and Sciences Membership in the National Academy of Sciences Bearman co-designed the landmark National Longitudinal Study of Adolescent Health, demonstrating sustained leadership in major federally-funded research initiatives. As Director of the Incite Institute, he oversees interdisciplinary research teams and co-edits Columbia University Press's Middle Range and Oral History book series, actively shaping scholarly discourse through mentorship and publication curation. The Incite Institute functions as Bearman's primary research hub, facilitating collaborations that advance innovative methodologies in social science while connecting historical analysis with contemporary network theory applications.

Bruno TASSINمشاهده پروفایل

استاد

• Urban Hydrology
• Environmental Sciences
• Microplastics Contamination
• +۳ مورد دیگر

Bruno TASSIN is a Professor at École nationale des ponts et chaussées and serves as Research Director at the Ministry of Ecological and Inclusive Transition. His primary affiliation is with the Department of City Environment Transport Information. He leads research at the Water, Environment and Urban Systems (Leesu) laboratory and co-directs the Urban Futures Laboratory of Excellence (Labex). He also serves as deputy director of the EFLUVE Observatory of Universe Sciences. His work focuses on urban hydrology, water contamination dynamics, and microplastic pollution in urban watersheds. Since 2012, he has co-led a research program on plastics in urban waterways with Johnny Gasperi. He is a member of the ASTEE research committee and vice-president of the scientific council of the Seine Normandy water agency. Research interests include microplastic dispersion mechanisms, estuarine contamination, and policy applications for pollution mitigation. His projects integrate interdisciplinary approaches to address environmental challenges in urban and aquatic systems, emphasizing real-world policy support. Publications highlight advancements in understanding microplastic transport, estuary dynamics, and the impact of urban runoff. Key contributions include studies on the Seine River Basin and tropical river-estuarine systems, contributing to global plastic pollution discourse.

• Electron Microscopy
• Materials Science
• Nanotechnology
• +۳ مورد دیگر

Martin Saunders is an Associate Professor and leader of the Physical Science Electron Microscopy Platform at the University of Western Australia's Centre for Microscopy, Characterisation & Analysis (CMCA). He holds leadership roles in national microscopy consortia, including Microscopy Australia and the National Imaging Facility. His academic career spans over 20 years, with roles as Deputy Director and Acting Director of CMCA, and President of the Australian Microscopy and Microanalysis Society (AMMS). Saunders earned a PhD in Physics from the University of Bath (UK) and postdoctoral experience at institutions including the University of Bristol and the US Naval Postgraduate School. His research focuses on advanced electron microscopy techniques, including TEM, STEM, EELS, and tomography, applied across physical, biological, and geo sciences. Education: PhD in Physics (University of Bath, 1994), BSc in Applied Physics (University of Bath, 1990). Research interests include structural and chemical analysis of nanomaterials, biominerals, and geological samples. He collaborates globally, contributing to high-impact journals like Nature and Advanced Materials . Saunders has secured over $25M in grants from ARC, NHMRC, and NCRIS, funding cutting-edge microscopy infrastructure. Awards: Inaugural AMMS Fellow (2025), Life Membership (AMMS), Fellow of the UK Institute of Physics (2012). Teaching: Coordinates materials characterization courses for biomedical engineering and nanotechnology programs. Provides training in electron microscopy for researchers and postgraduates. Labs/Infrastructure: Manages state-of-the-art facilities including FEI Titan G2 80-200 TEM/STEM and DualBeam FIB-SEM systems at CMCA.

Ivan Canayمشاهده پروفایل

استاد

• Econometric Theory
• Partially Identified Models
• Moment Inequalities
• +۵ مورد دیگر

Ivan Canay is a Professor of Economics and Director of the Mathematical Methods in the Social Sciences Program at Northwestern University’s Weinberg College of Arts & Sciences. He holds a PhD from the University of Wisconsin, Madison (2008). His research focuses on econometric theory, particularly developing statistical methods for assessing partially identified models, including tests for moment inequalities and randomization-based inference techniques. Recent work addresses challenges in clustered data analysis, covariate-adaptive randomization, and regression discontinuity designs. Canay’s academic contributions include advancing methodologies for handling non-ignorable cluster sizes and improving the robustness of inference in settings with limited data. He serves as an associate editor for the Journal of Econometrics , Journal of Business and Economic Statistics , and Econometrics Journal . His work bridges theoretical econometrics with practical applications in policy evaluation and causal inference. Key research themes include: Partially identified models and moment inequality frameworks Bootstrap methods for clustered data Covariate-adaptive randomization in clinical trials Statistical software development (e.g., Stata modules) His publications emphasize methodological rigor while addressing real-world complexities in economic data. Current projects likely expand his work on inference under structural constraints and improving accessibility of econometric tools for applied researchers.

Jian Zhangمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Data Analytics
• +۳ مورد دیگر

Prof. Jian Zhang is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), specializing in computer vision, pattern recognition, and multimedia signal processing. He leads the Multimedia Data Analytics Lab at the Global Big Data Technologies Centre, focusing on agri-food sector applications such as livestock monitoring and AI-driven solutions for agricultural efficiency. Education : PhD, School of Information Technology and Electrical Engineering, University of New South Wales, 1999 MSc, The Flinders University of South Australia, 1994 BSc, East China Normal University, 1982 Research Interests : His work spans 2D/3D computer vision, large-scale image/video analytics, and cross-disciplinary projects in agriculture and remote sensing. He has pioneered AI systems for livestock counting, poultry welfare monitoring, and fish quality assessment, funded by organizations like Meat & Livestock Australia and Australian Eggs. Grants & Projects : Current projects include AI-based hen health monitoring ($5M+ funding since 2011) Collaborations with industry partners like Sydney Fish Market and Fremantle Port Students & Academic Leadership : Supervised 19 PhD graduates and 5 research fellows Recruiting new PhD candidates in computer vision and data analytics Labs & Teams : Director of the Multimedia Data Analytics Lab, collaborating with global experts through UTS's Distinguished Visiting Scholars program.

Kejun Huangمشاهده پروفایل

استادیار

• Machine Learning
• Signal Processing
• Optimization
• +۲ مورد دیگر

Kejun Huang is an Assistant Professor in the Department of Computer and Information Science and Engineering at the University of Florida's Herbert Wertheim College of Engineering. His primary research area is Machine Learning, with additional interests in algorithms, computer vision, and data science. He received his Ph.D. in Electrical Engineering from the University of Minnesota in 2016. His research focuses on machine learning, signal processing, optimization, and statistics. Recent work tackles unsupervised learning challenges and AI-powered medical research through NIH-funded projects. Dr. Huang's publications demonstrate consistent focus on optimization techniques for tensor decomposition, dictionary learning identifiability, and nonnegative matrix factorization. Key themes include algorithmic efficiency and theoretical guarantees in machine learning models.