جست‌وجوی استاد و پژوهشگر

• Hydroinformatics
• Watershed Hydrology
• Surface Water Quality
• +۸ مورد دیگر

Jeffery Horsburgh is a Professor in the Department of Civil and Environmental Engineering and the Utah Water Research Laboratory at Utah State University, where he conducts research and teaches in hydroinformatics, GIS, and environmental data systems. He is affiliated with the College of Engineering and actively contributes to advancing cyberinfrastructure for hydrologic science. His educational background includes a PhD (2009), MS (2001), and BS (1999), all in Civil and Environmental Engineering from Utah State University. Horsburgh's research focuses on watershed hydrology, surface water quality, environmental sensor networks, data models, and cyberinfrastructure for environmental observations. He leverages GIS for data analysis and develops modeling techniques for hydrologic and water quality prediction. His work emphasizes open, reproducible science and data interoperability. His recent publications highlight a strong trend in hydroinformatics, with emphasis on open data platforms (e.g., HydroShare), sensor data integration, metadata standards, and reproducible modeling. The articles span topics from edge computing in smart water meters to national-scale hydrologic modeling frameworks. Outstanding Researcher, USU CEE, 2024 Reproducibility Author Award, 2023 Outstanding Teacher, USU CEE, 2019 Early Career Excellence Award, iEMSs, 2014 Outstanding Reviewer Award, Environmental Modelling & Software, 2014 Horsburgh has mentored over a dozen graduate students in civil and environmental engineering, focusing on hydroinformatics and water systems. He leads multiple research projects funded by federal and state agencies, including HydroShare, I-GUIDE, and Logan River Observatory. He is also involved in developing open-source software for environmental data management and has contributed to national standards for hydrologic data sharing. He leads or participates in key research initiatives such as the Logan River Observatory, HydroShare, and the Critical Zone Collaborative Network, emphasizing interdisciplinary collaboration, real-time data integration, and next-generation water infrastructure.

Greg Bowmanمشاهده پروفایل

استاد

• Protein Dynamics
• Biophysics
• Computational Biology
• +۱۷ مورد دیگر

Greg Bowman is a Professor in the Department of Biochemistry and Biophysics at the University of Pennsylvania's School of Engineering and Applied Science. He leads the Bowman Lab, which focuses on understanding protein dynamics to advance therapeutic development and interpret genetic variation. His research integrates biophysical experiments, machine learning, physics-based simulations, and the Folding@home distributed computing project, one of the world’s largest volunteer computing systems. His research interests lie at the intersection of computational biophysics and biomedical innovation. He develops and applies advanced methods such as Markov state models (MSMs), adaptive sampling algorithms, and deep learning to map the conformational landscapes of proteins. His work targets critical global health issues, including Alzheimer’s disease and emerging viral threats like SARS-CoV-2 and Ebola. A major focus is uncovering cryptic pockets and allosteric mechanisms to expand the druggable proteome, particularly for proteins considered 'undruggable' due to lack of traditional binding sites. Although no articles are listed in the provided text, his research output centers on protein dynamics, simulation methodology, and structure-based drug design, with applications in neurodegeneration and virology. His lab has developed key open-source software tools including MSMBuilder, Enspara, PocketMiner, and FAST, which are widely used in the computational biophysics community. Scientific honors include being named the Louis Heyman University Professor, a distinguished title at the University of Pennsylvania. His work is supported by large-scale collaborative science and public engagement through Folding@home, which involves over 200,000 citizen scientists worldwide. He actively mentors a diverse team of postdoctoral scholars, graduate students, and undergraduate researchers. His mentoring philosophy emphasizes unlocking each trainee’s potential and promoting inclusivity in STEM, informed by his personal experience as a legally blind scientist. He encourages public engagement and supports outreach initiatives within his lab. The Bowman Lab operates at the forefront of integrative biophysics, combining cutting-edge computational methods with experimental validation. The lab collaborates extensively and maintains a strong open-science ethos, sharing code via GitHub and involving the global community through Folding@home. Their adaptive sampling and deep learning approaches enable unprecedented insights into protein behavior and disease mechanisms.

• Exoplanetary Atmospheres
• Astrophysics
• Planetary Science
• +۳ مورد دیگر

Eva-Maria Ahrer is a postdoctoral researcher in the Atmospheric Physics of Exoplanets department, focusing on exoplanetary atmospheres and data analysis techniques for space-based telescopes like JWST and HST. Her work bridges observational astronomy with computational methods to advance planetary science. Her research spans exoplanet atmospheric characterization , planetary formation models , and JWST spectroscopic data analysis . Key projects include the BOWIE-ALIGN comparative survey and the KRONOS initiative, emphasizing the link between migration history and atmospheric composition. Recent publications highlight her contributions to understanding hot Jupiter alignment , metallicity , and chemical signatures using JWST’s NIRSpec and NIRISS instruments. She co-developed the open-source Eureka! pipeline for time-series observations and advocates for equity in STEM through the Equitea forum.

Marika Apostolova Trpkovskaمشاهده پروفایل

دانشیار

• Cybersecurity
• Blockchain Technology
• Industrial Internet of Things (IIoT)
• +۴ مورد دیگر

Marika Apostolova Trpkovska is an Associate Professor at the Faculty of Contemporary Sciences and Technologies, South East European University (Tetovo, Macedonia). Her academic work spans cybersecurity, blockchain technology, and educational technology with a focus on Industrial IoT security, semantic web applications, and digital learning systems. PhD in Computer Science (Specialty: e-Medical Services based on Semantic Web) MSc in Software Development and Applications BSc in Computer Sciences Her research covers cybersecurity frameworks for Industrial IoT, blockchain applications in public administration, and educational technology innovations including VR/AR integration and gamification. She has published extensively in international conferences and journals on these topics, with recent works focusing on machine learning-enhanced security protocols and decentralized real estate management systems. Current publications (2023-2024) highlight her expertise in IIoT security standards, smart contract development using Ethereum/IPFS, and cybersecurity threat modeling. Earlier works examine e-learning systems implementation, gender disparities in CS research, and real-time operating system validation. She serves as part-time academic staff member while maintaining technical roles in learning management system administration (LIBRI, ANGEL) and teaching responsibilities across software engineering, algorithms, and programming disciplines.

Mark Zylkaمشاهده پروفایل

استاد

• Autism Research
• Neurodevelopmental Disorders
• Pain Mechanisms
• +۳ مورد دیگر

Mark Zylka serves as the W.R. Kenan, Jr. Distinguished Professor in the Department of Cell Biology and Physiology at the UNC School of Medicine, University of North Carolina at Chapel Hill. He directs a research laboratory focused on neurodevelopmental disorders and pain mechanisms, with affiliations at the UNC Neuroscience Center and support from multiple NIH institutes. His research spans autism etiology , Angelman syndrome , chronic pain pathways , and environmental neurotoxicology . The lab employs CRISPR/Cas gene editing, AAV vectors, single-cell RNA-seq, and behavioral analysis to investigate molecular mechanisms underlying neurodevelopmental disorders and pain sensation. Recent work emphasizes environmental chemical impacts on neurodevelopment and gene therapy approaches for autism-related conditions. Publications from 2023-2025 reveal strong trends in CRISPR-based therapeutics for Angelman syndrome, environmental chemical screening for neurodevelopmental risks, and computational pain assessment tools . Key interdisciplinary fields include neurogenetics, molecular toxicology, and translational neuroscience with emphasis on Wnt signaling pathways and epigenetic regulation. Scientific Awards: Rita Allen Foundation Award Alfred P. Sloan Foundation Fellowship Searles Scholars Program Whitehall Foundation Grant Ester A. & Joseph Klingenstein Fund Award Brain & Behavior Research Foundation Grant Laboratory funding demonstrates exceptional grant acquisition across federal and private sources. Major support comes from NIH/NIEHS, NIH/NIMH, NIH/NINDS, Rett Syndrome Research Trust, Angelman Syndrome Foundation, Autism Speaks, and Simons Foundation. The lab maintains active collaborations through the UNC Neuroscience Center and NC TraCS institute, with recent focus on developing PainFace software for standardized pain assessment and CRISPR-based therapies for neurogenetic disorders.

Junfeng Zhaoمشاهده پروفایل

استادیار

• Connected and Automated Vehicles
• CAV System Integration and Safety Assessment
• Motion Planning and Controls
• +۵ مورد دیگر

Dr. Junfeng Zhao is an Assistant Professor at Arizona State University's Polytechnic School within the Ira A. Fulton Schools of Engineering. He serves as the Principal Investigator of the Battery Electric & Intelligent Vehicle (BELIV) Lab and holds graduate faculty appointments in both the Robotics & Autonomous Systems (RAS) and Clean Energy Systems (CES) programs. Dr. Zhao's educational background includes: Ph.D. in Mechanical Engineering from The Ohio State University (2015), recipient of OSU Presidential Fellowship M.S. in Mechanical Engineering from University of British Columbia (2009) B.S. in Electrical Engineering from Tsinghua University (2007) Dr. Zhao's research focuses on connected and automated vehicles (CAV), with expertise spanning system integration, safety assessment, motion planning and controls, cooperative perception, AI/ML applications in automotive systems, and electrified propulsion control. His BELIV Lab develops advanced systems for battery electric and intelligent vehicles, targeting safer, cleaner, and more energy-efficient transportation solutions. With industry experience at General Motors R&D and Cummins Technical Center, his work bridges theoretical research with practical automotive applications. Analysis of Dr. Zhao's recent publications reveals a strong emphasis on cooperative perception frameworks, safety assessment methodologies for automated driving systems, integration of satellite positioning for vehicle localization, and novel testing approaches using digital twin and augmented reality technologies. His research consistently addresses critical challenges in autonomous vehicle deployment. Professional recognitions include: OSU Presidential Fellowship during doctoral studies 15 patents in automotive and vehicle systems Dr. Zhao actively mentors 9 current graduate students across PhD and Master's programs, with research areas spanning autonomous driving, battery management systems, and intelligent transportation. His former students have secured positions at major companies including Caterpillar Inc., BYD North America, and TSMC. The BELIV Lab has established itself as an Autoware Center of Excellence, received funding from industry partners like Cox, and has been featured in media coverage including The State Press and documentaries on self-driving technology. BELIV Lab maintains active industry collaborations and participates in outreach activities such as the 10th Annual Hands-on STEM Fair, demonstrating Dr. Zhao's commitment to inspiring future engineers and advancing autonomous vehicle technology through both academic research and practical implementation.

• Database Management Systems
• Query Processing and Optimization
• Approximate Query Processing
• +۶ مورد دیگر

Dr. Feng (George) Yu is an Associate Professor of Computer Science and Information Systems at Youngstown State University in Youngstown, Ohio. He serves as the Campus Champion of NSF Extreme Science and Engineering Discovery Environment (XSEDE) at YSU and has been collaborating with XSEDE and Pittsburgh Supercomputing Center since 2014 to bring national workshop series on High-Performance Computing to YSU. Ph.D. in Computer Science, Southern Illinois University (2013) M.S. in Pure Mathematics, Shandong University (2008) B.S. in Information and Computation Science, Northeastern University (2005) Dr. Yu's primary research focuses on database management systems, particularly Approximate Query Processing (AQP) for big data analytics. His work spans multiple areas including Cloud Computing , Blockchain , NoSQL Databases , and Bioinformatics . His recent research has centered on error assessment for AQP using bootstrap sampling techniques, as evidenced by his 2024 publications AQPrius and Error Assessment for Multi-Join AQP . He has also made significant contributions to plant genomics through alternative splicing analysis in various crops. His publication trends show a consistent focus on query processing and optimization, with a recent shift toward more sophisticated error estimation techniques in approximate query processing. His work bridges theoretical database research with practical applications in bioinformatics and educational technology, as seen in his 2024 paper on computing curriculum accessibility for students with ASD. Best of QUEST 2024 Finalist (Faculty Advisor) Research Professorship (2023, 2022, 2019, 2018) Distinguished Professor in Scholarship (2020) Best Paper Award at International Conference on Software Engineering and Data Engineering (2019) Faculty Membership in The Honor Society of Phi Kappa Phi (2022) Dr. Yu actively mentors undergraduate researchers, having advised students including Govardhan Gula for the BEST of QUEST project on accelerating bootstrap resampling, and led a CREU-funded project on recommender systems with undergraduate researchers Alyssa Adams, Olivia Bindas, Maddie Cope, and Elizabeth Durflinger. His research has been supported by external funding sources including Amazon Inc. and the Computer Research Association. He directs the YSU Data Lab , which focuses on data-oriented sciences and operates multiple high-performance research clouds including Sarah Cloud and YSU STEM Cloud. The lab conducts cutting-edge research in approximate query processing, blockchain, and heterogeneous cloud infrastructure.

Akos Ledecziمشاهده پروفایل

استاد

• Computer Science Education
• Wireless Sensor Networks
• Model Integrated Computing
• +۳ مورد دیگر

Akos Ledeczi is a Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on computer science education and wireless sensor networks (WSN) , with notable contributions to visual programming tools like NetsBlox and anti-poaching systems like WIPER. He holds a Ph.D. from Vanderbilt University and a Diploma from the Technical University of Budapest. His education initiatives include the NetsBlox platform for K-12 STEM education and a popular Coursera MOOC on introductory programming. In WSN, his team developed a countersniper system with real-time shooter localization and a wearable system for military applications. He is also a leader in Model Integrated Computing , creating tools like the Web-based Generic Modeling Environment. Recent work includes NSF-funded projects on cybersecurity education in middle schools and animal-borne acoustic monitoring. His awards include the ACM SenSys Test of Time Award (2014) and the Best Showpiece Award at IEEE VL/HCC (2021). Key grants and collaborations span NSF projects, Vodafone innovation programs, and the Cyber Makerspace initiative. His lab develops tools like DeepForge and RoboScape Online , emphasizing open-source and collaborative platforms. Students supervised include Devin Jean (K-12 tools), Gordon Stein (robotics simulations), and Brian Broll (NetsBlox extensions).

Annette Veeمشاهده پروفایل

دانشیار

• Computation and Writing
• Digital Humanities
• Coding Literacy
• +۶ مورد دیگر

Annette Vee is an Associate Professor of English and Director of the Composition Program at the University of Pittsburgh, housed in the Kenneth P. Dietrich School of Arts & Sciences. She specializes in the intersection of computation and writing, with affiliations to the DSAM Certificate, Forbes Corridor Digital Initiative, Humanities Engage, Pitt Cyber, and STEM Coding Lab. Her research explores coding literacy, blockchain, AI-based text generation, and automated writing systems, with a focus on their implications for education, law, and rhetoric. Key publications include Coding Literacy: How Computer Programming is Changing Writing (MIT Press, 2017) and co-edited TextGenEd: Teaching with Text Generation Technologies (2023). Current projects include a monograph on the historical pursuit of automating writing and a co-curated digital exhibit on the 1966 Dartmouth Seminar. She teaches courses like Digital Humanity and Computational Media , emphasizing digital composition and critical engagement with technology. Vee’s work bridges disciplines, examining how computational systems reshape literacy, pedagogy, and societal trust. She has led a Mellon-funded Sawyer Seminar on information ecosystems and collaborates across computer science and humanities departments to design interdisciplinary programs like the Digital Narrative and Interactive Design major. Her research also addresses ethical concerns in AI, automated writing’s impact on political discourse, and the legal frameworks governing code. Vee advocates for open-access publishing and has published widely in journals like Computational Culture , Interfaces , and Literacy in Composition Studies .

Anya Helene Baggeمشاهده پروفایل

دانشیار

• Programming Language Design and Implementation
• Software Language Engineering
• Semantics of Programming Languages
• +۸ مورد دیگر

Anya Helene Bagge is an Associate Professor in the Department of Informatics at the University of Bergen, where she is a researcher at the Bergen Language Design Laboratory. Her work focuses on programming languages and software language engineering, with strong ties to education and tool development. Research Interests: Programming language design and implementation Software language engineering Semantics of programming languages Domain-specific languages and language extensions Program transformation using Rascal, MPL, and Stratego/XT Aspect-oriented programming, especially Domain-Specific Aspect Languages Her recent publications reflect a consistent focus on software language engineering, refactoring, microservice security, and programming education. Themes across her work include language design, program analysis, and the development of educational tools and methodologies. Scientific Awards: The Realist Committee's Teaching Award 2016/17 Lecturer of the Year in Computer Science (Spring 2015) Anya Bagge actively supervises students, including PhD and Master’s candidates such as Tero Hasu, Kristoffer Haugsbakk, and Nina Andersen. She has been involved in numerous academic activities, including organizing the OOPSLE workshop and serving on program committees for conferences like SLE, HILT, and WCRE. She has also taught core courses including INF101, INF225, and INF328, demonstrating a strong commitment to both research and teaching. Labs and Teams: She is affiliated with the Bergen Language Design Laboratory , collaborating with researchers such as Magne Haveraaen, Eva Burrows, and Tero Hasu.

Srishti Yadavمشاهده پروفایل

پژوهشگر ارشد

• AI and Society
• Machine Learning
• Computer Science
• +۳ مورد دیگر

Srishti Yadav is a Research Fellow at the University of Copenhagen and University of Amsterdam , affiliated with the Pioneer Centre for AI and ILLC respectively. She is advised by Dr. Serge Belongie and Dr. Ekaterina Shutova . Education: M.Sc. (Research-Track) in Computing Science, Simon Fraser University , Canada Research Interests: AI and Society Cross-Cultural Competency in Multimodal Models AI Safety and Evaluation Frameworks Model Interpretability and Dataset Creation Scientific Awards: ELLIS PhD Fellowship Advising & Community: Board Member, Women in Computer Vision (WiCV) Advisor for WiCV@ICCV2023 and WiCV@CVPR 2021 Chaired workshops at CVPR 2024, CVPR 2023, CVPR 2020 Labs & Teams: Belongie Lab (University of Copenhagen) Shutova Lab (University of Amsterdam) Collaborator at MILA Biodiversity Monitoring Project

Gizem Ates Venåsمشاهده پروفایل

دانشیار

• Human-Robot Interaction (HRI)
• Inertial Measurement Units (IMUs)
• Gamified User Training
• +۴ مورد دیگر

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

Mariana Silvaمشاهده پروفایل

عضو هیئت علمی

• Educational Technology
• Computer Science Education
• Large Language Models (LLMs) in Assessment
• +۳ مورد دیگر

Mariana Silva is a Teaching Associate Professor at the Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign, and CEO of PrairieLearn Inc. She holds a Ph.D. in Theoretical and Applied Mechanics from UIUC (2009). Her research focuses on leveraging educational technologies, such as Large Language Models (LLMs), to enhance computer-based assessments and scalable teaching practices. Silva has taught over 11 courses to 9,000+ students, emphasizing innovations in STEM education. She has pioneered adaptive testing tools and randomized question generators to improve equity and accessibility in large-scale courses. Education : Ph.D., Theoretical and Applied Mechanics, UIUC (2009) M.S., Mechanical Engineering, Federal University of Rio de Janeiro (2003) B.S., Mechanical Engineering, Federal University of Rio de Janeiro (2001) Research Interests : Silva’s work centers on technology-driven education , including automated grading using LLMs, design of adaptive assessment systems, and fostering collaborative learning. She has developed tools like PrairieLearn to streamline teaching workflows while maintaining educational rigor. Awards & Recognition : Scott H. Fisher Computer Science Teaching Award (2022) Rose Award for Teaching Excellence (2022) Multiple “List of Teachers Ranked as Excellent” (2009–2017) Engineering Council Outstanding Advising Award (2014–2019) Labs & Teams : Co-founder of PrairieLearn Inc., which provides open-source platforms for STEM education. She collaborates with interdisciplinary teams to advance educational technologies and testing infrastructure.

Timothy Drysdaleمشاهده پروفایل

استاد

• Remote Laboratories
• Engineering Education
• Electromagnetics
• +۲ مورد دیگر

Timothy Drysdale is Professor and Chair in Technology Enhanced Science Education at the University of Edinburgh, where he directs the Remote Laboratories group and serves as Director of Strategic Digital Education. His practable.io platform revolutionizes laboratory education through aesthetically-designed remote experiments installed in campus foyers, saving physical space while enhancing accessibility. Research develops novel remote labs based on experiments likened to "science museum exhibits," integrating real-time equipment access via web interfaces. Additional work explores orbital angular momentum in radio communications. His £3M openEngineering Laboratory project received multiple honors including the Queen's Anniversary Prize and THE Digital Innovation Award. Global Online Laboratories Consortium Award (2024, 2018) Queen's Anniversary Prize (2024) Jisc Digital Transformation Award (2023) Guardian Teaching Excellence Award (2018) Teaches across undergraduate engineering programs and supervises doctoral projects in digital education innovation. Advocates for assessment transformation and generative AI integration in education.

Stephen Ramseyمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .