جست‌وجوی استاد و پژوهشگر

• Soft Robotics
• Artificial Muscles
• Robotics
• +۳ مورد دیگر

Jiefeng Sun serves as Assistant Professor in the Department of Aerospace and Mechanical Engineering within Arizona State University's School for Engineering of Matter, Transport and Energy. His research program centers on designing artificial-muscle-driven robots that replicate biological adaptivity through advanced modeling and control systems. His academic credentials include: Ph.D. in Robotics and Control from Colorado State University (2022) M.S. in Mechanical Engineering from Dalian University of Technology (2017) B.S. in Mechanical Engineering from Lanzhou University of Technology (2014) Dr. Sun's research integrates soft robotics, artificial muscles, and adaptive control to create morphologically intelligent systems. His work spans aerial robotics, wearable exoskeletons, and biomimetic locomotion, with emphasis on shape-changing mechanisms and energy-efficient actuation that enables robots to operate in unstructured environments. Analysis of his recent publications reveals dominant themes in twisted-and-coiled actuators, tensegrity structures, and physics-informed control methods. Key trends include variable-stiffness systems for wearable devices, data-efficient simulation techniques using Koopman operators, and bistable mechanisms for aerial grasping applications. His research excellence has been recognized through: Finalist for Best Student Paper Award at IEEE/RSJ IROS 2018 Reviewer of the Year 2021 for Smart Materials and Structures Journal 2022 DARPA Riser designation Dr. Sun actively recruits graduate students for robotics research and has secured significant funding including DARPA support. He teaches core courses including System Dynamics and Control I (MAE 318) while supervising thesis research and applied projects through MAE 599 and MAE 792. He directs the Sun Robotics Lab (https://sunroboticslab.github.io), which collaborates across biomechanics, materials science, and control theory to develop next-generation adaptive robotic systems with applications in healthcare, exploration, and human augmentation.

Kenneth M. Andersonمشاهده پروفایل

استاد

• Software Engineering
• Crisis Informatics
• Human-Computer Interaction
• +۳ مورد دیگر

Professor Kenneth M. Anderson is Chair of the Department of Computer Science and holds the Palmer Endowed Chair at the University of Colorado Boulder's College of Engineering & Applied Science. He co-directs Project EPIC, a $4M NSF-funded initiative on social media use during mass emergencies, and serves as Co-Director of the Center for Software and Society. Ph.D. in Computer Science, University of California, Irvine (1997) Joined CU Boulder in 1998; tenured in 2005 Former Associate Dean for Education (2016-2019) Former Associate Chair (2010-2013) His research bridges software engineering, crisis informatics, and human-computer interaction. Current projects focus on large-scale social media analytics, crisis response systems, and software architecture for data-intensive environments. Recent publications explore multilingual social media analysis (ML-EPIC), bug fix patterns in software development (FIXR), and collaborative big data platforms. Themes include disaster risk communication, data modeling challenges, and asynchronous analysis tools. ATLAS Fellow (2010) As department chair, he spearheaded initiatives for inclusivity, including creating a Bachelor of Arts in Computer Science and establishing the NSF Broadening Participation in Computing plan. He oversees major academic reforms and departmental operations.

Ramana Vinjamuriمشاهده پروفایل

دانشیار

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +۴ مورد دیگر

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.

Srinivas Narayanaمشاهده پروفایل

استادیار

• Computer Networks
• Formal Verification
• Compilers
• +۴ مورد دیگر

Srinivas Narayana is an Assistant Professor in the Department of Computer Science at Rutgers University, specializing in programmable networking, formal verification, and systems research. He holds a PhD from Princeton University and a B.Tech from IIT Madras, with postdoctoral work at MIT. His research focuses on building safe, high-performance networks through optimizing compilers, verified programming, and distributed system monitoring. He has received NSF grants, the CGO 2022 Distinguished Paper Award, and the 2017 SIGCOMM Best Paper Award. Education: PhD and MA in Computer Science, Princeton University (2016) B.Tech in Computer Science, IIT Madras (2010) Postdoctoral Research, MIT (2018) Research Interests: His work bridges networking and systems with a focus on compilers, formal methods, and programmable hardware. Notable projects include K2 compiler for eBPF, the eBPF verifier soundness work, and congestion control mechanisms like CCP. He explores parallel packet processing, privacy-preserving analytics, and load balancing strategies. Grants & Awards: NSF Awards #2422076, #1910796, #2019302 eBPF Foundation Grant Facebook Networking Research Award Network Programming Initiative (NPI) Funding Lab & Teams: Leads the NetSys group at Rutgers, collaborating with teams on projects like the eBPF verifier, verified packet processing, and network monitoring tools like Marple. His lab emphasizes open-source contributions and industry collaboration.

• Water resources engineering
• Surface water hydrology
• Data science
• +۷ مورد دیگر

Jeff Sadler is an Assistant Professor in the Department of Biosystems & Agricultural Engineering at Oklahoma State University, where he also serves as an Extension Specialist for Water Resources with OSU Extension. He leads the WaDE (Water Data and Education) Lab, focusing on data science and machine learning applications in water resources. Education: PhD in Civil and Environmental Engineering, University of Virginia (2019) MS in Civil Engineering, Brigham Young University (2015) BS in Civil Engineering, Brigham Young University (2013) Research Interests: Jeff’s research lies at the intersection of data science and water resources. He specializes in machine learning, particularly physics-guided and process-aware deep learning, for modeling stream temperature, water quality, flood dynamics, and hydrological forecasting. His work emphasizes real-time decision support, reproducible modeling, and integrating domain knowledge into data-driven systems. Recent Research Trends: His recent publications demonstrate a strong focus on advanced deep learning architectures (e.g., graph neural networks, recurrent models), data assimilation, multi-task learning, and surrogate modeling for environmental systems. Applications center on the Delaware River Basin and coastal Virginia, with implications for climate change adaptation and infrastructure resilience. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Jeff mentors graduate students and supervises master's and doctoral research. He is actively funded through multiple grants from the USDA, NOAA, and USGS, supporting projects in water quality monitoring, rural health, evapotranspiration forecasting, and integrated hydrological modeling. Labs and Teams: He leads the WaDE Lab, which develops data-driven tools for water resource education and management. He has collaborated extensively with researchers from the U.S. Geological Survey, University of Virginia, and other institutions on cyberinfrastructure, reproducible modeling, and environmental machine learning.

Marta Halinaمشاهده پروفایل

دانشیار

• Philosophy of Cognitive Science
• Nonhuman Animal Cognition
• Mechanistic Explanation
• +۷ مورد دیگر

Marta Halina is a University Associate Professor in the Philosophy of Cognitive Science at the University of Cambridge, affiliated with the Department of History and Philosophy of Science. She serves as a Senior Research Fellow at the Leverhulme Centre for the Future of Intelligence and is a Fellow of Selwyn College. Her academic journey began with a PhD in Philosophy and Science Studies from the University of California, San Diego in 2013, followed by a McDonnell Postdoctoral Fellowship in the Philosophy-Neuroscience-Psychology Program at Washington University in St. Louis before joining Cambridge in 2014. Halina's educational background includes a PhD from UC San Diego (2013) and postdoctoral training at Washington University in St. Louis. Her academic trajectory reflects a strong interdisciplinary foundation bridging philosophy, cognitive science, and neuroscience. Her research focuses on nonhuman animal cognition, mechanistic explanation, and artificial intelligence, with particular emphasis on comparative cognition and the philosophical foundations of cognitive science. Halina investigates how researchers design studies to address complex questions about animal minds, arguing that current methods in comparative cognition often face challenges with hypothesis underdetermination by empirical evidence. She advocates for additional behavioral constraints on theorizing, known as 'signature testing,' while emphasizing the need to incorporate neuroscience and biology more substantially into animal cognition research. Her work on major transitions in cognitive evolution proposes treating the evolution of cognition as a series of major evolutionary transitions to better comprehend cognitive complexity across species. Analysis of Halina's recent publications reveals a clear trajectory toward computational comparative cognition. Her work increasingly integrates AI and machine learning techniques with traditional comparative cognition approaches, exemplified by her development of the Animal-AI Testbed. This platform allows for direct comparison between AI systems, humans, and animals on cognitive tasks, revealing that while AI and children perform similarly on basic navigational tasks, children outperform AI on more complex cognitive tests requiring object permanence. Her research demonstrates how computational modeling can generate novel hypotheses about animal behavior that generate precise, testable predictions beyond what traditional experimental methods alone can achieve. McDonnell Postdoctoral Fellowship Halina directs research initiatives at the Leverhulme Centre for the Future of Intelligence, particularly focusing on the intersection of AI and animal cognition. Her work on the Animal-AI Environment has received significant funding and collaborative support, enabling interdisciplinary research that bridges computer science, cognitive science, and biology. She actively collaborates with researchers across multiple institutions to develop computational frameworks for understanding nonhuman animal cognition. Halina leads significant research initiatives through the Leverhulme Centre for the Future of Intelligence, where she develops the Animal-AI Environment—a research platform for conducting cognitive experiments with artificial agents, humans, and nonhuman animals in directly comparable, ecologically valid contexts. This environment facilitates interdisciplinary collaboration between computer scientists, engineers, biologists, and cognitive scientists, reducing the 'language barrier' between these fields and enabling cross-pollination of ideas and methodologies.

Jessica Hullmanمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-Computer Interaction
• Uncertainty Quantification
• +۴ مورد دیگر

Jessica Hullman is the Ginni Rometty Professor of Computer Science at Northwestern University's McCormick School of Engineering and a Faculty Fellow at the Institute for Policy Research. Her research develops theoretical frameworks and interfaces for human-AI collaboration, focusing on uncertainty quantification, statistical modeling, and decision-making in domains like scientific research and AI-assisted analysis. Education: PhD in Information (Visualization), University of Michigan (2013) MS in Information Analysis, University of Michigan (2008) BA in Comparative Studies, Ohio State University (2003) Tableau Postdoctoral Fellowship, UC Berkeley (2015) Research Focus: Hullman's work bridges formal models of rational inference (e.g., Bayesian decision theory) with real-world applications. Key areas include: human-AI complementarity in decision-making, visualization of uncertainty, statistical reform, and LLM applications in behavioral science. Her research consistently addresses the alignment of data-driven interfaces with human cognitive capabilities. Publication Trends: Recent work demonstrates a strong emphasis on human-AI collaboration frameworks, decision-theoretic evaluation of visualizations, and methodological rigor in machine learning and social science. Key themes include uncertainty quantification (conformal prediction, privacy tradeoffs), behavioral experiments in AI-assisted tasks, and critical analyses of scientific practices. Awards & Honors: Microsoft Faculty Fellow (2019) Google Faculty Award NSF CAREER, Medium, and Small Awards Multiple best paper/honorable mention awards at top HCI/visualization venues (CHI, VIS) Funding & Labs: Principal Investigator for NSF-funded projects including HCC: Medium on visualization tools. Previously affiliated with University of Washington's Interactive Data Lab and DataLab. Current research includes NSF-supported work on improving data visualization for reasoning about analytical assumptions.

Kathryn Stoleeمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Software Engineering Education
• +۷ مورد دیگر

Kathryn Stolee is an Associate Professor in the Department of Computer Science at North Carolina State University. She received her Ph.D. in Computer Science from the University of Nebraska-Lincoln under Sebastian Elbaum after graduating from the Jeffrey S. Raikes School of Computer Science and Management. Her research spans multiple perspectives in software engineering: technical (program analysis), human (human aspects of software engineering, software product management), and educational (comparative comprehension of algorithms). Notable contributions include work on regular expression refactoring for improved comprehension, constraint solvers for code reuse identification, and cross-language code-to-code search to aid developers learning new languages. Dr. Stolee has secured over $2,000,000 in federal grants, including an NSF CAREER award. Her research combines analysis techniques (refactoring, semantic code search, code-to-code search) with human factors (comprehension, reuse, learning). Her scholarly contributions have been recognized with a National Science Foundation Faculty Early CAREER Award (2018) and a Best Paper Award at the International Symposium on Empirical Software Engineering and Measurement (ESEM, 2011). Actively engaged in the software engineering research community, Dr. Stolee serves as an author, reviewer, and organizer at top conferences. She is committed to mentoring the next generation of computer scientists and has developed educational interventions focused on software testing and code comprehension.

• Database Systems
• Distributed Computing
• Cloud Computing
• +۲ مورد دیگر

Bettina Kemme is a faculty member at McGill University in Montreal, Canada. Her research focuses on database systems , distributed computing , and cloud data management . She has made significant contributions to database replication, consistency models, and middleware frameworks for scalable applications. Research Themes : Database replication, distributed systems, cloud computing, and software engineering. Notable Collaborations : Jörg Kienzle, Joseph Vinish D'silva, Yunjia Zheng, and Marta Patiño-Martínez. Publications span critical areas such as graph database view management, transactional recovery in key-value stores, and latency-aware publish/subscribe systems. Her work is published in venues like VLDB , ICDE , Middleware , and SRDS .

Grant Bollmerمشاهده پروفایل

عضو هیئت علمی

• Digital Cultures
• Critical Theory
• Cultural Studies
• +۵ مورد دیگر

Grant Bollmer is an Associate Research Professor in the Department of Communication at the University of Maryland, College Park. His research focuses on digital cultures, critical theory, cultural studies, and the history of science. He holds a Ph.D. in Communication Studies from the University of North Carolina at Chapel Hill. His work examines topics such as influencer culture, emotion measurement, media materialism, and the philosophical implications of technology. Key publications include The Influencer Factory (2024), The Affect Lab (2023), and Materialist Media Theory (2019). His research has been supported by grants from the National Endowment for the Humanities and a residency at the Media Archaeology Lab. Bollmer’s recent scholarship explores AI ethics, absurd temporalities, and neo-feudal economic structures. He has received awards including the NC State CHASS Outstanding Junior Faculty Award and contributed to a magazine issue honored by the Canadian National Magazine Awards. His current projects include studies on 'bad' video games, generative AI in brain decoding, and the intersection of capitalism and billionaire behavior. Bollmer’s interdisciplinary approach synthesizes Marxist theory, media archaeology, and critical cultural analysis.

Genya Ishigakiمشاهده پروفایل

استادیار

• 5G and Beyond Networking
• Network Slicing
• Reinforcement Learning
• +۵ مورد دیگر

Genya Ishigaki is an Assistant Professor in the Department of Computer Science at San José State University's College of Science. His research focuses on network slicing, combinatorial optimization, and reinforcement learning, addressing resource allocation challenges in next-generation telecommunications networks. Ph.D. in Computer Science, The University of Texas at Dallas, 2021 M.S. in Computer Science, The University of Texas at Dallas, 2021 M.S. in Engineering, Soka University, Japan, 2016 B.S. in Engineering, Soka University, Japan, 2014 Dr. Ishigaki's work explores adaptive network control through machine learning and combinatorial optimization, including elastic network slices , explainable AI , and federated learning . His research addresses critical tradeoffs in resource utilization versus capacity reservation for future demands. Recent publications demonstrate his focus on network automation (2025), information diffusion (2025), federated learning platforms (2024), and DDoS attack detection (2024). Articles span network security , AI-driven optimization , and social network dynamics . NSF Student Travel Grant (2019) Shigeta Education Foundation Ph.D. Scholarship (2019-2021) Outstanding TA Award (2019) JASSO Ph.D. Scholarship (2016-2019) NEC C&C Foundation Travel Grant (2015) He leads the Interconnect Lab, which investigates accountability in autonomous network operations and edge computing-oriented federated learning. His grants include SJSU's RSCA Seed Grant (2022-2023) and University Grant Academy Award (2022).

Janet F. Werkerمشاهده پروفایل

استاد

• Developmental Psychology
• Language Acquisition
• Speech Perception
• +۷ مورد دیگر

Dr. Janet F. Werker is a University Killam Professor and Canada Research Chair at the University of British Columbia , affiliated with the Faculty of Arts and Department of Psychology . She directs the Infant Studies Centre and co-founded the UBC Language Sciences initiative. Her research focuses on perceptual foundations of language acquisition , studying speech perception mechanisms in monolingual and bilingual infants, critical periods of development, and neural correlates using behavioral, ERP, and NIRS methods. Recent work extends to literacy development in multicultural contexts through an SSHRC Partnership Grant. Key themes in 15 most recent articles: Infant brain development, statistical learning, speech perception, bilingualism, musical sophistication, microbiome-brain links, sensorimotor integration, voice recognition, and cross-linguistic generalization. Studies employ EEG, NIRS, and behavioral paradigms to explore early language, social understanding, and neurodevelopmental pathways. Scientific accolades include election to the National Academy of Sciences (2020) , Benjamin Franklin Institute Medal (2024) , and multiple fellowships (AAAS, Royal Society of Canada). Her work is funded by NSERC, SSHRC, NIH, and CIFAR. Dr. Werker supervises graduate students and leads international collaborations examining language acquisition, bilingualism, and developmental disabilities. She is actively involved in pandemic-era research adaptations, emphasizing online methodologies.

Fangwei Siمشاهده پروفایل

استادیار

• Biophysics
• Bacterial Physiology
• Cellular Adaptation
• +۷ مورد دیگر

Fangwei Si is the Cooper-Siegel Assistant Professor of Physics at Carnegie Mellon University's Department of Physics, with courtesy appointments in Biomedical Engineering. His research focuses on uncovering biological laws through quantitative biophysics , integrating microfluidics , imaging , and physical modeling . He previously held postdoctoral positions at The Scripps Research Institute and University of California, San Diego, and earned his Ph.D. in Mechanical Engineering from Johns Hopkins University. Ph.D.: Johns Hopkins University (2015) B.S.: Peking University (2009) His research bridges cell surface biophysics , cellular adaptation , and bacteria-phage interactions , emphasizing how cells optimize fitness through precise membrane organization and component redundancy . Current projects explore mechanical compression effects , quantitative adaptation principles , and phage-host coevolution . The lab's articles reveal trends in cell size control (2017-2019), mechanosensation (2018), and stochastic modeling (2020-2021), extending to machine learning approaches (2025) and high-throughput imaging (2024). Key methods include microfluidics , genetic modulation , and physical modeling . At CMU, Si leads the Experimental Cell Biophysics Lab, mentoring Ph.D. students like Mo Zhou and Christopher Aldrich , alongside postdocs and undergraduates. His lab received NIH and NSF grants in 2023 to advance research on microbial systems.

Gaetano Montelioneمشاهده پروفایل

استاد

• Protein Structure and Dynamics
• NMR Spectroscopy
• Structural Bioinformatics
• +۶ مورد دیگر

Gaetano Montelione is a Professor and Constellation Endowed Chair in the Department of Chemistry and Chemical Biology at Rensselaer Polytechnic Institute (RPI), directing the Center for Biotechnology and Interdisciplinary Studies (CBIS). His laboratory pioneers NMR methodology development for protein structure and dynamics analysis, with extensive expertise in high-throughput structural genomics from leading the NIGMS-funded Northeast Structural Genomics Consortium (NESG) for 16 years. His research spans Protein Structure and Dynamics , NMR Spectroscopy , and Structural Bioinformatics , with critical applications in Membrane Proteins , Virology , and Drug Design . The lab integrates X-ray crystallography, SAXS, and computational modeling to tackle challenging targets like integral membrane proteins and viral systems, emphasizing hybrid approaches where sparse experimental data guides AI-driven structure prediction. Analysis of 2024-2025 publications reveals dominant trends in AI-structural biology integration, particularly AlphaFold2 applications for membrane proteins and protein complexes. Virology research focuses intensely on SARS-CoV-2 protease inhibitors and host-pathogen interactions, while de novo protein design and structural validation methods drive innovation in therapeutic development and fundamental biophysics. The Montelione Laboratory maintains a global collaborative network with experts in evolutionary coupling (Sander, Marks), Rosetta modeling (Baker), and biophysical methods (Luchinat, Tainer). As a central node in structural biology consortia like CASP and wwPDB, the lab develops rigorous quality assessment frameworks while advancing biomedical projects on influenza, DNA repair, and cancer biology through partnerships with Rutgers, Mt. Sinai, and international institutions.

Ulrich Schroedersمشاهده پروفایل

استاد

• Psychological Diagnostics
• Technology-based Competency Diagnostics
• Measurement of Cognitive Abilities
• +۵ مورد دیگر

Ulrich Schroeders is a Professor of Psychological Diagnostics at the University of Kassel, where he has been employed since October 2017. His work focuses on developing and validating psychological assessment tools, with particular expertise in cognitive diagnostics and educational measurement. He teaches various programs for approximately 500 students annually and serves as a supervisor for teacher training students preparing for their oral state examinations in Pedagogy/Psychology. Dr. Schroeders earned his PhD from Humboldt University of Berlin in 2010 with a dissertation titled "Measurement of Cognitive Abilities Using Modern Technologies: Artifacts, Equivalence, and New Constructs." Prior to that, he completed his Diploma in Psychology at Julius-Maximilians-University Würzburg in 2004 with a thesis on diagnosing dyscalculia in first-grade students. His research spans several key areas in psychological assessment. He specializes in technology-based competency diagnostics, developing innovative methods for measuring cognitive abilities and school competencies. A significant portion of his work involves applying Machine Learning and metaheuristics to psychometric problems, particularly in structural equation modeling. His methodological expertise includes advancing techniques in Local Structural Equation Modeling (LSEM) and Meta-Analytic Structural Equation Modeling (MASEM), with applications across educational and clinical psychology contexts. Analysis of Dr. Schroeders' recent publications reveals a strong focus on computational approaches to psychological assessment. His work frequently employs optimization algorithms like Ant Colony Optimization and Bee Swarm Optimization to address challenges in test construction and validation. There's a clear trajectory toward game-based and technology-enhanced assessment methods, as seen in studies using Mastermind and Wordle as assessment tools. His research also demonstrates growing interest in applying machine learning to predict behavioral outcomes, including juvenile delinquency, suicide risk, and psychotherapy outcomes. Dr. Schroeders has secured significant research funding, including projects funded by the German Research Foundation (DFG) and the Hector Foundation. His current projects include "Facing the Replication Crisis in Machine Learning Modeling" (2025-2027) and "PINGUIN: Potenzialidentifikation IN der GrUndschule" (2024-2027), which focuses on identifying elementary students' initial competencies. He leads the development of the BEFKI assessment system (Berliner Test zur Erfassung fluider und kristalliner Intelligenz), which includes versions for different age groups (5-7, 8-10, and 11+). His methodological toolbox includes specialized approaches for test construction and validation, particularly focusing on optimization algorithms applied to psychological measurement problems.