جست‌وجوی استاد و پژوهشگر

• Control Systems
• Optimization
• Market Design
• +۴ مورد دیگر

Junjie Qin is an Assistant Professor of Electrical and Computer Engineering at Purdue University’s Elmore Family School of Electrical and Computer Engineering. His research focuses on control systems, optimization, market design, and data analytics applied to power systems and the energy-transportation nexus. He explores challenges in distributed energy resource management, smart grid technologies, and the integration of renewable energy sources. His work addresses issues such as scheduling under limited observability, neural risk-limiting dispatch, and joint optimization of transportation-energy systems through electric vehicle charging strategies. Key research areas include power system stability, inverter-dominated grid dynamics, and machine learning applications in energy systems. He investigates topics like real-time charging control for electric roadways, loss function selection in learning-based optimal power flow, and pricing mechanisms for workplace EV charging. His contributions span theoretical frameworks and practical algorithms, emphasizing data-driven solutions and system-level optimization. While no awards or grants are explicitly listed, his publications reflect a strong focus on advancing smart grid technologies and sustainable energy systems. His advising activities are not detailed here, but his research group likely engages in cutting-edge projects at the intersection of control theory and energy infrastructure.

• Biomedical Engineering Education
• Biomaterials
• Microfluidics
• +۳ مورد دیگر

Ali Ansari is a Teaching Assistant Professor in the Department of Bioengineering at the University of Illinois Urbana-Champaign (UIUC), part of the Grainger College of Engineering. He previously served as a Visiting Assistant Professor at Bucknell University’s Bioengineering/Electrical and Computer Engineering departments (2021–2023). Ansari holds a B.S. in Electrical Engineering from Southern Methodist University (2012) and a Ph.D. and M.S. in Bioengineering from UIUC (2018 and 2016, respectively). Education: Bachelor of Science in Electrical Engineering, Southern Methodist University, 2012 Master of Science in Bioengineering, University of Illinois Urbana-Champaign, 2016 Doctor of Philosophy in Bioengineering, University of Illinois Urbana-Champaign, 2018 His primary research focus is in Biomedical Engineering Education, with scholarly contributions spanning biomaterials, microfluidics, and cell isolation technologies. Recent work includes studies on extracellular matrix microparticles for heart regeneration, microfluidic systems for cell co-culture, and surface functionalization techniques. Ansari’s research trajectory reflects advancements in translational biomedical engineering, balancing foundational studies with applied innovations. Ansari’s articles highlight trends in biomaterials for regenerative medicine, microfluidic device design, and cell isolation methodologies. His work emphasizes integrating engineering principles with biological systems to address challenges in tissue repair and biomedical device development. No scientific awards are listed in the provided information. Professional affiliations include membership in the American Society for Engineering Education (ASEE), Biomedical Engineering Society (BMES), and the Biomedical Engineering Education Council (BEEC). His academic roles suggest involvement in teaching, student mentorship, and educational program development. Details about grants or lab affiliations are not explicitly detailed, though his research interests align with UIUC’s bioengineering research ecosystem.

Zhiyu Quanمشاهده پروفایل

استادیار

• Actuarial Science
• Data Science
• Computational Statistics
• +۳ مورد دیگر

Zhiyu (Frank) Quan is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding positions in the Department of Mathematics, Department of Statistics, and National Center for Supercomputing Applications (NCSA). He is also an affiliate faculty member at Discovery Partners Institute as InsurTech Lead and serves as an ORMI Faculty Fellow in Finance. His research focuses on data science applications in actuarial science, including tree-based models, natural language processing, and deep learning for insurance risk modeling, predictive analytics, and InsurTech innovation. Education: Ph.D. in Actuarial Science (University of Connecticut, 2019), MS in Applied Statistics (Michigan State University, 2014), and BS in Mathematics and Applied Mathematics (Xiamen University, 2012). Research interests include computational statistics, insurance analytics, and leveraging machine learning for actuarial challenges such as claim prediction, rate-making, and cyber risk modeling. He leads the Illinois Risk Lab, bridging academic research with industry needs, and has pioneered hybrid tree-based models to address imbalanced data in insurance. Notable achievements include the Arnold O. Beckman Research Award and Society of Actuaries Research Institute recognition. He advises two doctoral students and teaches advanced predictive analytics courses, emphasizing practical applications in actuarial science and data ethics. Key collaborations involve InsurTech companies and NCSA, focusing on NLP-driven academic paper repositories (CyLit) and federated learning for privacy-preserving insurance data sharing. His work addresses real-world challenges in cyber insurance and automated machine learning systems.

Michelle Ellefsonمشاهده پروفایل

استاد

• Cognitive Science
• Educational Neuroscience
• Child Development
• +۶ مورد دیگر

Professor Michelle Ellefson is a Professor of Cognitive Science at the University of Cambridge's Faculty of Education, where she serves as Director of CAM-DTP and as Undergraduate Tutor for Gonville and Caius College. She convenes the INSTRUCT Research Group (Implementing New Student Thinking Resources Using Cognitive Theory) and is affiliated with multiple interdisciplinary initiatives including Cambridge Neuroscience, Cambridge Big Data, and Cambridge Language Sciences. Her educational background includes a PhD and MA in Brain and Cognitive Sciences from Southern Illinois University and a BA in Psychology, summa cum laude, from the University of Minnesota. She is a member of several professional organizations including the Psychonomic Society, Cognitive Science Society, Women in Cognitive Science, and SPARK Society. Professor Ellefson's research integrates cognition, neuroscience, child development, and education into a multi-disciplinary program aimed at improving math and science education. Her work focuses on executive functions in school achievement, children's causal reasoning about scientific phenomena, and applying cognitive principles like simplicity and desirable difficulties to classroom learning. Her research spans laboratory-based studies paired with classroom applications to understand cognitive development mechanisms and improve educational practice. Her recent publications reveal strong trends in executive function research across cultural contexts, particularly examining East-West contrasts and the relationship between executive functions and academic achievement. Her work increasingly focuses on developing and validating assessment tools like the Zoo Task for metacognitive problem-solving, and she is committed to open science practices including registered reports and pre-registration of studies. Psychonomic Society Cognitive Science Society (CogSci) Women in Cognitive Science (WiCS) SPARK Society Professor Ellefson actively supervises PhD students through the Psychology, Education & Learning Studies program and teaches cognitive psychology and educational neuroscience in the PGCE program. She emphasizes quantitative methods and R programming for data analysis, requiring doctoral students to develop strong statistical skills. Her lab follows Cambridge's inquiry-based learning model and maintains a commitment to diversity and inclusion, welcoming researchers from varied socioeconomic, national, and cultural backgrounds. The INSTRUCT Lab also focuses on open science practices, sharing data and materials through OSF and publishing preprints in Psych-Archive.

Juewen Liuمشاهده پروفایل

استاد

• Chemistry
• Nanotechnology
• Biosensors
• +۱ مورد دیگر

Juewen Liu is a Professor and Canada Research Chair in Biosensors and Bionanotechnology in the Department of Chemistry at the University of Waterloo, affiliated with the Faculty of Science. His research program employs principles of chemistry, physics, and biology to develop innovative nanoscale materials and systems with applications in sensing, diagnostics, and therapeutics. Research encompasses: DNA aptamer development and applications Nanoparticle engineering for biomedical use Biointerface chemistry innovations Nanozyme design and catalytic applications Liu maintains an extensive publication record in high-impact journals, with recent work emphasizing advanced biosensing platforms, targeted drug delivery systems, and nanomaterial-based solutions for environmental and health challenges. His research integrates fundamental science with practical applications in diagnostics and therapy. Teaching responsibilities include courses on nano-biomaterials and nano-instrumentation. Professor Liu collaborates extensively through the Waterloo Institute for Nanotechnology, Water Institute, and Centre for Bioengineering and Biotechnology.

Amit Sachdevaمشاهده پروفایل

دانشیار

• Chemical Biology
• Synthetic Biology
• Protein Engineering
• +۴ مورد دیگر

Amit Sachdeva is an Associate Professor of Bio-Organic Chemistry at the University of East Anglia (UEA), where he leads the School of Chemistry, Pharmacy and Pharmacology's Department of Chemistry. He serves as Director of Postgraduate Research in Chemistry, overseeing doctoral training and academic strategy. His research focuses on Chemical Biology and Synthetic Biology, with a specific emphasis on expanding genetic code capabilities to engineer novel proteins for biomedical applications. Dr. Sachdeva completed his PhD at the University of Illinois at Urbana-Champaign, investigating DNA-based enzymes, followed by postdoctoral work at the MRC Laboratory of Molecular Biology in Cambridge. His current work includes developing light-responsive antibodies for targeted therapies and ultrafast viral diagnostics. Notable achievements include pioneering photoactive antibody fragments and securing patents (e.g., WO-2020193981-A1) for light-controlled antigen binding. Key Projects: Designing cancer biotherapeutics (Leverhulme Trust), fluorescent switches for SARS-CoV-2 detection (Royal Society of Chemistry), and biomolecular wire development (Engineering and Physical Sciences Research Council). Grants: Over £5M from institutions like The Big C Appeal and Wellcome Trust. Research Interests: Genetic code expansion, protein engineering, non-natural amino acids, and their applications in diagnostics/therapeutics. His work contributes to UN Sustainable Development Goals, particularly in health and innovation. Publications: Over 25 peer-reviewed articles in top journals like Nature Chemical Biology , Angewandte Chemie , and Nature Reviews Chemistry , with several highly cited contributions (e.g., 99th percentile in 2023).

Laura Burrusمشاهده پروفایل

استاد

• Developmental biology
• Cancer biology
• Climate change
• +۴ مورد دیگر

Laura (Kai) Burrus is a Professor in the Department of Biology within the College of Science and Engineering at San Francisco State University, where she has been a faculty member since 1997. Her research has evolved from studying Wnt signaling in developmental biology to addressing climate change resilience through microbial ecology. Education: BS in Chemistry - College of William and Mary (1986) PhD in Biochemistry - University of Wisconsin-Madison (1991) Post-doc in Developmental Biology - Harvard University (1996) Dr. Burrus' research has historically focused on the biochemical and cellular mechanisms underlying Wnt gradient formation in chick embryos, with relevance to birth defects and cancer. Her work has been funded by NIH, NSF, CSUPERB, and MDA over the last 20 years. She is currently transitioning her research to analyze the role of microbes in climate change resilience, motivated by her recognition that 'climate change is inherently racist.' She is actively involved with Lifting Black Voices in STEM and is leading an effort to launch an interdisciplinary Certificate in Climate Change Causes, Impacts, and Solutions. As one of five LGBTQIA biology faculty at SFSU, she brings unique perspectives to her work on inclusive classroom practices. Her publication record spans from 1994 to 2021, with recent work focusing on Wnt signaling mechanisms, educational approaches in STEM, and the transition to climate change research. Her research shows a clear evolution from fundamental developmental biology to applied environmental science, while maintaining a strong foundation in molecular and cellular mechanisms. Scientific Contributions: Pioneering work on Wnt protein trafficking and modification Research connecting Wnt signaling to cancer biology Leadership in developing inclusive STEM education practices Transition to climate change resilience research with focus on microbial ecology Dr. Burrus has mentored numerous undergraduate and graduate students throughout her career, with many alumni going on to pursue advanced degrees and careers in science. She has been actively involved in pedagogical innovation and is pioneering new approaches to effectively communicate research to both scientific and lay communities. Her lab has received funding from multiple sources including NIH, NSF, CSUPERB, and MDA. She is currently involved in oyster reef restoration research in the San Francisco Bay, investigating microbial communities as indicators of climate resilience. This work represents an aspirational shift from her previous focus, leveraging molecular expertise to address pressing climate challenges while mentoring students from diverse backgrounds.

Amit Moreyمشاهده پروفایل

دانشیار

• Food Safety
• Poultry Science
• Meat Quality Analysis
• +۵ مورد دیگر

Amit Morey is an Associate Professor in the Department of Poultry Science at Auburn University's College of Agriculture. His research focuses on food safety, poultry meat quality, and advanced sensing technologies. He leads projects involving biosensor development, microbial pathogen detection, and spoilage prediction using machine learning and spectral imaging. His work bridges laboratory innovations with industry applications, addressing challenges in poultry processing, packaging, and supply chain management. Education details are not explicitly listed, but his extensive publications suggest advanced training in food science, microbiology, and engineering. Research interests include antimicrobial biopolymer films, texture analysis of catfish and chicken fillets, and the application of functional ice in seafood preservation. He has pioneered methods for rapid Salmonella detection using microfluidics and fiber optics-based SERS sensors. Notable contributions include developing predictive models for spoilage using near-infrared spectroscopy and exploring cyclic temperature abuse impacts on poultry safety. His interdisciplinary approach integrates artificial intelligence with traditional food science techniques to enhance food safety and reduce waste. While no specific grants or awards are listed, his active publication record (over 100 papers from 2005–2025) indicates sustained research funding. He collaborates on projects addressing global food safety inequities, such as sensor-enabled decision support systems (SENS-D) for vulnerable communities. Lab activities include the Auburn Poultry Science Lab, focusing on meat quality assessment, microbial interventions, and smart packaging solutions. His work has direct industry impact, with applications in poultry processing plants and retail cold chain management.

Amanda N. Laubmeierمشاهده پروفایل

استادیار

• Mathematical Ecology
• Theoretical Ecology
• Applied Mathematics
• +۳ مورد دیگر

Amanda N. Laubmeier is an Assistant Professor in the Department of Mathematics & Statistics at Texas Tech University. Her research integrates mathematical modeling with ecological systems, focusing on predator-prey dynamics, pest suppression, and biodiversity mechanisms. She holds a Ph.D. in Applied Mathematics from North Carolina State University (advised by H. T. Banks) and a B.S. in Mathematics from the University of Arizona. Her postdoctoral work at the University of Nebraska-Lincoln (under Richard Rebarber and Brigitte Tenhumberg) further developed her expertise in ecological modeling. Her research interests emphasize theoretical exploration and data-driven validation of ecological processes, particularly in agricultural and climate-sensitive contexts. Key areas include predator community dynamics, temperature effects on ecosystems, and the compatibility of biological control with pesticides. She actively engages in scientific outreach to promote inclusivity in academia and supports underserved communities in STEM. Her recent publications explore topics such as trap crop efficacy, predator-prey models under climate change, and parameter estimation in ecological systems. These studies highlight interdisciplinary approaches combining mathematical theory with empirical validation. She also contributes to educational initiatives like the Science Meets Popular Culture Speaker Series, bridging academic research with public engagement. Laubmeier advises students through her research group, which focuses on ecological modeling projects. While no named advisees are listed, her group’s work is detailed on her website. Her grants and funding history are not explicitly mentioned, but her CV (dated Jan. 2025) likely provides further details. She advocates for inclusive academic practices and integrates outreach into her professional activities.

Douglas Nychkaمشاهده پروفایل

استاد

• Spatial Statistics
• Nonparametric Regression
• Computational Statistics
• +۵ مورد دیگر

Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.

• Data Integration
• Big Data Management and Analytics
• Database Systems and Applications
• +۳ مورد دیگر

Dr. En Cheng is an Associate Professor in the Department of Computer Science at The University of Akron, affiliated with the College of Engineering and Polymer Science. She joined the university in 2012 and specializes in research areas such as Data Integration, Big Data Management, Database Systems, Mobile Applications, Business Intelligence, and Bioinformatics. Her work bridges theoretical computer science with practical applications, including educational mobile games and business productivity tools. Dr. Cheng’s education includes a Ph.D. from Case Western Reserve University and advanced degrees from Huazhong University of Science and Technology, China. Her research emphasizes innovative solutions in database systems and interdisciplinary applications like bioinformatics. Notable publications include studies on mobile game integration, web content extraction libraries, and business intelligence optimization. Her recent work explores supramolecular assembly, material science, and nanostructure engineering, reflecting a shift toward interdisciplinary collaboration. Over 30 publications since 2014 highlight her contributions to both computer science and materials research. Dr. Cheng teaches courses such as Data Integration, Database Management, and NoSQL systems. She maintains an active research lab and collaborates on projects funded by institutional grants. Her office is located in CAS 229, and she can be reached via echeng@uakron.edu or her website.

Richard W. Grossمشاهده پروفایل

استاد

• Analytical Chemistry
• Biological Membranes
• Lipidomics
• +۴ مورد دیگر

Dr. Richard W. Gross is Professor of Medicine and Developmental Biology at Washington University School of Medicine and Professor of Chemistry. He holds a PhD from Washington University, MD from New York University Medical School, and BA from Columbia College. His research focuses on the chemical biology of membranes in health and disease, specializing in lipidomics, metabolomics, and membrane signaling. He pioneered shotgun lipidomics technology for direct identification and quantification of lipid molecular species in biological systems. Developed novel shotgun lipidomics technology combining intrasource separation, multidimensional MS, and array analysis Investigates phospholipase regulation and membrane signaling complexes Studies altered lipid metabolism in obesity, diabetes and cardiovascular disease

Akshay Narayanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• AI Planning
• Reinforcement Learning
• +۶ مورد دیگر

Akshay Narayan is a Senior Lecturer (Educator Track) at the School of Computing, National University of Singapore (NUS), where he teaches senior undergraduate and graduate-level courses in AI Planning and Decision Making, as well as introductory and intermediate-level Software Engineering courses. Education: Ph.D. in Computer Science from National University of Singapore (completed in 2020) M.Tech. in Information Technology from International Institute of Information Technology Bangalore, India B.E. in Computer Science & Engineering from Visveswaraya Technological University, India Research Interests: Dr. Narayan's research spans multiple domains within computer science with a primary focus on artificial intelligence and its applications. His current research centers on transfer learning in reinforcement learning, multi-agent decision making, and AI planning. He has also made significant contributions to cloud computing research, particularly in areas such as smart metering, chargeback systems, power-aware cloud metering, and workload analysis for virtual machine sizing. His work bridges theoretical foundations with practical applications, addressing real-world challenges in computing systems. He has recently expanded his research to include technology in education, exploring how AI can be integrated into teaching and learning processes. Publication Trends: Dr. Narayan's publication record demonstrates a clear evolution from foundational work in cloud computing to more recent explorations in reinforcement learning and AI education. His early work focused on practical applications in cloud systems, including smart metering and QoS monitoring. More recently, his research has shifted toward AI planning, decision making, and the educational applications of AI. This progression shows his ability to adapt to emerging fields while maintaining a strong foundation in systems research. Awards and Recognition: Teaching and Mentoring: Dr. Narayan teaches a variety of courses at NUS including CS2113 Software Engineering & Object-Oriented Programming, CS3219 Software Engineering Principles and Patterns, CS3268 Responsible AI: From Algorithms to Impact, and IT5100F Industry Readiness: Data Analytics and AI in Practice. He has also taught CS4246/CS5446 AI Planning and Decision Making. His teaching approach integrates his research expertise with practical applications, providing students with both theoretical foundations and hands-on experience. He has taught these courses across multiple academic years from AY-2013/14 through AY-2020/21. Research Groups and Collaborations: Dr. Narayan has collaborated with researchers across multiple institutions, including work with Prof. Tze Yun Leong at NUS (his PhD advisor), Shrisha Rao, Zhuoru Li, and others. His research has often involved interdisciplinary collaborations that bridge theoretical computer science with practical system implementations.

Low Jun Siongمشاهده پروفایل

استادیار

• Immunology
• Cancer Biology
• Infectious Diseases
• +۴ مورد دیگر

Assistant Professor Low Jun Siong is affiliated with the Department of Microbiology and Immunology at the National University of Singapore (NUS), under the Yong Loo Lin School of Medicine. His research focuses on understanding T and B cell biology in the context of infection, cancer, and autoimmunity. He collaborates with clinical partners to characterize immune cell responses in patient cohorts and explores strategies to manipulate these cells for therapeutic purposes. Key areas of interest include antigen specificity, immune cell dysfunction, and immune-based disease interventions. Education: Holds a BSc and PhD (specific disciplines unspecified). Affiliated with the Cancer Science Institute (CSI) and A*STAR Infectious Diseases Labs. His work spans translational immunology, virology, and cancer immunotherapy. Recent projects include studies on SARS-CoV-2 immune responses, tumor microenvironment interactions, and tropical sponge microbiome evolution. He employs high-throughput approaches and machine learning for immune profiling. Research highlights include: Characterizing T/B cell responses against pathogens and cancers Engineering immune cells for enhanced functionality Investigating antibody mechanisms against coronaviruses Dissecting metabolic influences on T cell efficacy in tumors Exploring symbiotic microbiome evolution in marine environments No specific grants or advising roles are detailed in the provided text. He contributes to collaborative initiatives like the Department Safety and Health Programme (DSHP) and the Department Microbial Culture Collection (DMCC).

Linan Chenمشاهده پروفایل

دانشیار

• Probability Theory
• Analysis
• Geometry
• +۴ مورد دیگر

Linan Chen is an Associate Professor in the Department of Mathematics and Statistics at McGill University since 2014, following a postdoctoral position at the same institution (2011–2014). He holds a Ph.D. from MIT (2011, supervised by Daniel Stroock) and a B.Sc. from Tsinghua University (2006). His research focuses on probability theory and its intersections with analysis and geometry, including partial differential equations, functional analysis, Gaussian measures, and random geometry. He is affiliated with the Probability Lab of the Centre de Recherches Mathématiques (CRM) and the CNRS-Unite Mixte Internationale (CNRS-UMI) since 2014. Chen teaches advanced probability courses such as Honours Probability (Math 356) and Advanced Probability Theory I/II (Math 587/589), alongside special topics courses like Topics in Geometry and Topology (Math 599). He has advised students including Leila Sloman, Reinhold Willcox, Ulysse Blau, and Olivier Nadeau-Chamard through independent study programs. His research explores cutting-edge topics in probability, including Gaussian free fields, degenerate diffusion equations, and asymptotic properties of geometric stochastic structures. Recent work addresses high-dimensional phenomena, stochastic processes in geometry, and applications in mathematical physics. Chen’s contributions span theoretical advancements and methodological innovations, with publications in journals such as the Journal of Theoretical Probability, Annales Henri Poincaré, and SIAM Journal on Mathematical Analysis.