جست‌وجوی استاد و پژوهشگر

Amanda Kay Montoyaمشاهده پروفایل

دانشیار

• Mediation Analysis
• Moderation Analysis
• Conditional Process Models
• +۸ مورد دیگر

Amanda Kay Montoya is an Associate Professor in the Quantitative Psychology area of the Department of Psychology at UCLA. She received her PhD in Quantitative Psychology from The Ohio State University in 2018 under the supervision of Dr. Andrew Hayes. Her research focuses on improving statistical methodology in psychological science, with particular expertise in mediation, moderation, and conditional process analysis. Dr. Montoya's educational background includes: B.S. in Psychology with minor in Mathematics from University of Washington (2013) M.A. in Psychology and M.S. in Statistics from Ohio State University (2016) Ph.D. in Quantitative Psychology from Ohio State University (2018) Her research program centers on developing and improving statistical methods for psychological research, with particular focus on mediation and moderation analysis in various experimental designs. She has made significant contributions to understanding how to properly conduct and interpret mediation analyses, especially in within-subject designs. Beyond methodological development, she is also interested in meta-science issues including research practices that affect replicability and addressing inequities in STEM education for women. Dr. Montoya's publications demonstrate a clear trajectory of advancing methodological approaches in psychological science, with recent work spanning from foundational methodological papers on mediation analysis to applied studies examining weight stigma. Her research has been funded by prestigious organizations including NSF and NIH. Her scientific awards include: NSF Graduate Research Fellow Dr. Montoya actively mentors graduate students in the Quantitative Psychology program at UCLA and teaches several statistics courses including Psychological Statistics (Psych 100A), Advanced Psychological Statistics - Regression Analysis (Psych 250C), and Mediation, Moderation, and Conditional Process Analysis (Psych 254B). Her research has been supported by NSF, NIH, and internal UCLA grants. She runs the Mechanisms and Contingencies Lab (MAC lab) which focuses on developing and applying advanced statistical methods to psychological research questions. She is an active presenter at major conferences, with upcoming workshops scheduled in 2024 on mediation and moderation analysis for both between-subjects and within-subject designs.

Frederik Anseelمشاهده پروفایل

استاد

• Organizational Behavior
• Work Motivation
• Innovation Management
• +۲ مورد دیگر

Professor Frederik Anseel serves as Dean of UNSW Business School and Professor of Management. An elected Fellow of the Academy of Social Sciences in Australia, his research examines organizational behavior with emphasis on feedback systems, motivation mechanisms, and innovation in workplace settings. Previously held leadership roles include Vice Dean positions at King's College London. Research pillars: Feedback-seeking behaviors across organizational hierarchies Algorithmic HR systems and employee wellbeing Behavioral interventions for ethical decision-making Nonverbal communication analytics in assessments Publications (2021-2025) demonstrate methodological diversity including: Global field experiments on climate behavior Machine learning analysis of interview dynamics Meta-analyses of hiring discrimination Stepped-wedge trials in healthcare ethics Awards and recognition include Fellowship of the Academy of Social Sciences Australia for contributions to organizational psychology.

• Biophysics
• DNA Replication
• Single-Molecule Techniques
• +۴ مورد دیگر

Professor Nynke Dekker is a Professor of Biophysics at the University of Oxford and a Professorial Fellow at St Peter's College. Her research focuses on understanding biophysical mechanisms underlying DNA replication, chromatin dynamics, and viral RNA synthesis. She develops advanced microscopy techniques, including super-resolution imaging and single-molecule assays, to study molecular processes at high resolution. Key areas of expertise include nucleosome dynamics, replication fork progression, and the interplay between chromatin structure and enzymatic activity. Her work bridges fundamental biology with engineering advancements in microscopy instrumentation. Education details are not explicitly stated in the provided text, but her academic positions suggest a strong background in physics and biophysics. Research grants and lab affiliations are inferred from her active role in developing biophysical tools and studying replication processes. Her research interests span viral RNA polymerase mechanisms, DNA replication barriers, and the application of machine learning in automated cell analysis. She contributes to both theoretical models (e.g., Ising models for DNA compaction) and experimental innovations like Zernike polynomial-based distortion correction in microscopy. No formal awards or student advisees are listed in the provided text, though her extensive publication record indicates active collaboration and mentorship in the field. Her lab likely focuses on single-molecule studies using magnetic tweezers, optical torque wrenches, and high-throughput experimental setups.

Jun Jinمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Jun Jin is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Alberta and a Fellow of the Alberta Machine Intelligence Institute (Amii). His research focuses on robotic reinforcement learning, embodied artificial intelligence, and human-centered autonomy. He holds a PhD and has eight years of industry experience in autonomous systems, including autonomous driving and construction automation. He teaches ECE 720 B01 Robot Learning: Principles and Advances and leads the Human-Centered Autonomy (HCA) Lab, which hosts a weekly reading club. His research interests include developing scalable learning architectures for robots to adapt to dynamic environments, ensuring safe human-robot interaction, and advancing open-ended learning agents. Notable achievements include the ICRA 2022 Outstanding Student Paper Award and the KUKA Innovation Award 2018. He emphasizes applying robotics to construction (e.g., infrastructure maintenance robotics) and healthcare. Dr. Jin actively mentors students through competitive research assistant positions and collaborates with Amii for computational and industrial resources. His lab fosters a community-driven approach to advancing robotics through interdisciplinary research and public engagement.

Elad Michael Schillerمشاهده پروفایل

دانشیار

• Distributed Computing
• Fault-Tolerant Systems
• Self-Stabilizing Algorithms
• +۴ مورد دیگر

Elad Michael Schiller is an Associate Professor at the Department of Computer Science and Engineering, Chalmers University of Technology, within the Division of Networks and Distributed Systems. His affiliation includes membership in the Distributed Computing and Systems research group. He has held significant roles in organizing conferences, including co-chair positions at ApPLIED 2019/2018 and general chair at PODC 2017. His research focuses on fault-tolerant distributed systems, self-stabilizing algorithms, vehicular networks, and secure communication protocols. Dr. Schiller has led projects such as KARYON (predictable coordination in vehicular systems) and Chameleon-MAC (adaptive media access control for MANETs). He has contributed to testbeds like Gulliver for vehicular systems and Renaissance for SDN control planes. His work emphasizes practical applications of distributed systems in safety-critical domains like autonomous vehicles and intelligent transportation systems (ITS). He has served on technical program committees for events including SSS, ICDCS, and DISC. His research outputs span self-stabilizing consensus algorithms, Byzantine fault tolerance, and vehicular coordination mechanisms. Key themes in his publications include fault recovery, secure distributed messaging, and resilient network architectures.

Peter Wägemannمشاهده پروفایل

پژوهشگر

• Real-Time Systems
• Embedded Systems
• Worst-Case Analysis
• +۵ مورد دیگر

Peter Wägemann is a postdoctoral researcher leading the Embedded Systems Software group at the Chair of Computer Science 4 (System Software) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His research focuses on real-time systems, energy-constrained systems, worst-case analysis, and fault tolerance in embedded systems. He has received multiple awards, including the Best Paper Award at ECRTS '24 and Outstanding Paper Awards at ECRTS '18 and RTAS '17. His work spans topics like clock reconfiguration, energy-neutral systems, and Byzantine fault tolerance. Wägemann's teaching includes courses such as System-Level Programming, Real-Time Systems, and Dependable Real-Time Systems. He has organized conferences like WCET and RTSS and serves on program committees for top venues. His funded projects include DFG grants for Byzantine Fault Tolerance (BFTeam), whole-system analysis (Watwa), and power-constrained systems (ResPECT). He also collaborates with industry partners like Schaeffler AG on safety-critical AI systems. Key contributions include the Platin WCET analysis tool, TinyBFT for resource-constrained systems, and energy-efficient frameworks like FusionClock. His work bridges theoretical analysis with practical implementations in embedded systems, emphasizing sustainability and real-time guarantees.

Hong Wanمشاهده پروفایل

دانشیار

• Complex Simulation Modeling
• Blockchain Analysis
• Data Science
• +۵ مورد دیگر

Hong Wan is an Associate Professor in the Department of Industrial and Systems Engineering (ISE) at North Carolina State University (NC State). She holds a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2004), with earlier degrees in Chemistry (Peking University, 1998), Materials Science (Northwestern, 2001), and Industrial Engineering (Northwestern, 2002). Before joining NC State, she was an Associate Professor at Purdue University’s School of Industrial Engineering, where she directed the Purdue Blockchain Lab and co-directed the Smart Design Lab. She is also affiliated with the SEED Center for Data Farming at the Naval Postgraduate School. Her research focuses on blockchain analysis, complex simulation modeling, healthcare systems simulation, and game theory applications in quality management. She leads NC State’s ISE Blockchain Lab, studying blockchain as a complex system using simulation, optimization, and statistical methods. Notable contributions include modeling Bitcoin network dynamics, developing sequential elimination procedures for optimization, and integrating machine learning with blockchain systems. Education: B.S. in Chemistry (Peking University, 1998) M.S. in Materials Science (Northwestern University, 2001) M.S. in Industrial Engineering & Management Sciences (Northwestern University, 2002) Ph.D. in Industrial Engineering & Management Sciences (Northwestern University, 2004) Awards: 2017 Featured Paper in IIE Transactions 2014 Best Student Paper Finalist (QSR) 2013 Military Application Society Koopman Prize (INFORMS) Labs/Teams: ISE Blockchain Lab (NC State) Purdue Blockchain Lab (former) SEED Center for Data Farming (Naval Postgraduate School) Her work bridges operations research and emerging technologies, emphasizing strategic decision-making in decentralized systems and healthcare applications. Recent projects explore generative AI for simulation and blockchain’s role in supply chain transparency.

• Agricultural Economics
• Consumer Behavior
• Food Policy
• +۳ مورد دیگر

Dr. Carola Grebitus is the Dean's Council Distinguished Professor and Professor at the Morrison School of Agribusiness, Arizona State University. Her research focuses on agricultural economics, consumer behavior, and sustainability, with an emphasis on food policy, marketing strategies, and environmental impact. She has conducted extensive studies on consumer preferences for sustainably produced foods, country-of-origin labeling, and the role of information in decision-making. Her work spans topics such as beef demand in global markets, plant-based alternatives, antibiotic usage in pork, and the effects of labeling on consumer choices. She also explores innovative methods like experimental auctions and behavioral experiments, leveraging tools such as eye-tracking and social media analytics. Dr. Grebitus has been recognized with the Dean's Council Distinguished Professor title, highlighting her academic excellence and contributions to the field. Her research frequently addresses policy implications, including agricultural trade dynamics, open science practices, and the adoption of sustainable technologies. She has collaborated on projects analyzing crisis communication in livestock industries and the role of urban agriculture in food systems. Her interdisciplinary approach bridges economics, marketing, and behavioral science to address contemporary challenges in agribusiness.

Yi Zhangمشاهده پروفایل

استادیار

• Atmosphere/Ocean Science
• Climate Dynamics
• Atmospheric Thermodynamics
• +۳ مورد دیگر

Dr. Yi Zhang is an Assistant Professor at the Courant Institute of Mathematical Sciences, New York University, affiliated with the Center for Atmosphere Ocean Science. He holds a B.S. in Physics from Peking University (2016) and a Ph.D. in Atmospheric and Oceanic Sciences from Princeton University (2021). His research focuses on climate dynamics, atmospheric thermodynamics, and radiative transfer, with an emphasis on reducing complex climate phenomena to foundational physical laws. He previously served as a Miller Postdoctoral Fellow at UC Berkeley (2021–2023). Key research includes predicting tropical extreme wet-bulb temperatures using El Niño indices, with notable success in 2024 predictions. His work bridges climate modeling and practical applications, such as forecasting heatwave impacts and advancing climate adaptation strategies. He develops physics-based statistical models to predict climate extremes and explores the interplay between atmospheric dynamics and thermodynamics. Recent articles highlight advancements in machine learning for molecular interactions, drug repurposing, and protein-ligand docking. These span computational chemistry, drug discovery, and bioinformatics, reflecting interdisciplinary collaboration. No scientific awards are explicitly mentioned, though his work has been highlighted in news outlets like Forecast . Dr. Zhang’s advising and grants focus on climate science and theoretical frameworks, with notable collaborations like his postdoc mentorship under William Boos. He maintains an active lab within the Courant Institute, contributing to both academic and applied climate research.

• Security
• Privacy
• Cryptography
• +۷ مورد دیگر

Cédric Fournet serves as Senior Principal Research Manager at Microsoft Research in Cambridge, UK, where he leads the Constructive Security research team. He also directs the Secure Distributed Computations project at the MSR-INRIA Joint Center in collaboration with INRIA Paris's Prosecco team. With over two decades of experience since joining Microsoft Research in 1998, Fournet maintains active collaborations across academic and industrial research communities. His educational background includes graduation from École Polytechnique in 1992, followed by a year working on deductive databases at BULL. He obtained a second engineering degree from École Nationale des Ponts et Chaussées in 1995 and completed his PhD in computer science at INRIA Rocquencourt, where he applied concurrency theory to distributed programming through a variant of the pi calculus. Fournet's research spans multiple interconnected domains in computer security and programming languages. His primary focus involves building, verifying, and deploying secure components for critical infrastructure, most notably through the Everest project which aims to create verified replacements for HTTPS components like miTLS. His work integrates formal methods with practical security concerns, addressing challenges in cryptographically-verifiable computing, TLS security, secure cloud outsourcing using trusted hardware (SGX, TPM), side-channel resistance, and information-flow security. A unifying thread throughout his career is the application of dependent types, particularly through the F* programming language, to create provably secure systems. Analysis of his recent publications reveals a consistent trajectory toward verified security implementations across diverse platforms and protocols. His work has evolved from theoretical foundations in cryptographic protocol verification to practical, production-ready systems like EverCrypt and EverParse. The research demonstrates increasing sophistication in handling real-world constraints while maintaining formal guarantees, with recent work addressing microarchitectural security, confidential computing in AI accelerators, and media provenance for combating synthetic media. CCS Distinguished Paper Award (2024) for Principled Microarchitectural Isolation on Cloud CPUs Best Paper Award Nominee (2023) for Confidential Consortium Framework Distinguished Paper Award (2015) for A Messy State of the Union: Taming the Composite State Machines of TLS As leader of the Constructive Security team at Microsoft Research, Fournet oversees multiple significant research initiatives with substantial funding from both internal Microsoft resources and external collaborations. His team's work on Everest has received sustained support for developing verified cryptographic implementations, while the MSR-INRIA Joint Center provides a framework for international collaboration on secure distributed systems. The team maintains strong industry partnerships that facilitate the transition of research prototypes into deployed systems. The Constructive Security research team operates at the intersection of theory and practice, maintaining close connections with both the INRIA Prosecco team in Paris and various Microsoft product groups. Their work environment emphasizes formal verification techniques applied to real-world security challenges, with particular focus on creating tools and frameworks (like F*) that enable building systems with strong security guarantees. The team's approach combines deep theoretical insights with practical engineering considerations to produce solutions that are both formally verified and deployable in production environments.

Jaydeb Sarkerمشاهده پروفایل

استادیار

• Human Factors Software Engineering
• Empirical Software Engineering
• Natural Language Processing
• +۳ مورد دیگر

Dr. Jaydeb Sarker (he/him/his) serves as a Tenure Track Assistant Professor in the Department of Computer Science at the University of Nebraska at Omaha since Fall 2024. He previously completed his Ph.D. (2024) and MSc (2022) in Computer Science at Wayne State University, with a BS (2016) from Rajshahi University of Engineering and Technology. Research Focus : Human Factors Software Engineering, Empirical Software Engineering, Natural Language Processing, Human-Computer Interaction Current Work : Antisocial behavior analysis in software engineering platforms His publications appear in top-tier venues including FSE, TOSEM, ASE, and ESEM. He has received multiple travel grants (SIGSOFT CAPS, ACM SIGSOFT, NSF) and fellowships (Thomas C. Rumble). As a reviewer for journals like TSE and TOSEM, and committee member for conferences like Mining Software Repositories, he actively contributes to the academic community.

Steven Ullmanمشاهده پروفایل

استادیار

• Cybersecurity Analytics
• Vulnerability Management
• Open-Source Software Security
• +۶ مورد دیگر

Dr. Steven Ullman is an Assistant Professor in the Department of Information Systems and Cyber Security at the Alvarez College of Business, University of Texas at San Antonio (UTSA). He holds a Ph.D. in Management Information Systems from the University of Arizona, alongside an M.S. from the same institution and an M.B.A. and B.S. from Colorado State University-Pueblo. His research focuses on AI-driven cybersecurity solutions for vulnerability detection, remediation, and management in open-source software and IT infrastructure. Key areas include deep learning, self-supervised learning, graph representation, and computational design science. His work has been published in journals like the Journal of Management Information Systems and conferences such as IEEE Intelligence and Security Informatics. Dr. Ullman’s research is funded by NSF grants under the SaTC and CICI programs. He has presented at INFORMS Annual Meetings, ODSC, and HICSS. He is affiliated with professional organizations like IEEE, ACM, and INFORMS.

Wyatt Lloydمشاهده پروفایل

استاد

• Distributed Systems
• Networking
• Cloud Computing
• +۷ مورد دیگر

Wyatt Lloyd is a Professor in the Computer Science department at Princeton University, leading the S* Network Systems (SNS) group with Mike Freedman, Amit Levy, and Mae Milano. His research focuses on distributed systems, networking, and cloud computing, emphasizing consistency models, geo-replication, and fault tolerance. He holds a PhD from Princeton (2013), followed by a postdoc at Facebook and an assistant professorship at the University of Southern California before returning to Princeton in 2017. Education : PhD, Computer Science, Princeton University, 2013 Postdoctoral Researcher, Facebook, 2013–2014 Assistant Professor, University of Southern California, 2014–2017 Research Interests : Lloyd’s work addresses tradeoffs between consistency guarantees and system performance in large-scale systems. Notable projects include TurboDB (NSDI ’24) for skewed workloads, Gryff (NSDI ’20) for consensus-unified storage, and Tectonic (FAST ’21), Facebook’s exabyte-scale filesystem. His group explores causal consistency, read-optimized transactions, and geo-distributed storage solutions. Publications : Recent work spans concurrency control (Natural Concurrency Control, OSDI ’23), caching optimizations (Learning Relaxed Belady, NSDI ’20), and fault-tolerant systems (Speculative Recovery, ATC ’22). These contributions balance strong consistency with high performance. Grants & Recognition : Supported by NSF grants (e.g., CNS Core, CISE), his work addresses modern distributed system challenges. Over 30 students have been advised, contributing to influential projects like Eiger, COPS, and Janus. Labs/Teams : The SNS group at Princeton focuses on systems research, collaborating on projects like the S* Network Systems initiative. Tools developed include Eiger (Apache Cassandra fork) and frameworks for causal consistency and distributed transactions.

Kewei Shaمشاهده پروفایل

دانشیار

• Data Analytics
• Edge Computing
• Security and Privacy
• +۳ مورد دیگر

Dr. Kewei Sha is an Associate Professor at the University of North Texas (UNT) in the College of Information. Previously, he held positions as Associate Professor and Department Chair at Oklahoma City University (OCU) and the University of Houston-Clear Lake (UHCL). He earned a Ph.D. and M.S. from Wayne State University and a B.S. from East China University of Science and Technology. His research focuses on Data Analytics, Edge Computing, Security and Privacy, Blockchain, and IoT, with over $5M secured in research grants from NSF, NASA, and other institutions. He has published over 70 peer-reviewed papers and serves as an Associate Editor for IEEE IoT Journal, Smart Health, and Computing. Dr. Sha has received awards including the UHCL President’s Outstanding Research Award and IEEE Outstanding Leadership Award, and holds senior membership in ACM and IEEE. He chairs conferences like IEEE MOST 2024 and ACM/IEEE SEC 2023. His recent work includes securing NSF funding for edge-based multi-robot systems and NSF Noyce Scholarships for STEM teacher training. Key projects involve EdgeBrain for edge video analytics and ElasticEdge for live video processing. He has pioneered frameworks for data governance, secure cloud storage, and disaster rescue UAV systems. Dr. Sha also develops educational tools like AR therapy systems for autistic children and network forensics labs for teaching institutions.

Panagiotis Toulisمشاهده پروفایل

دانشیار

• Causal Inference
• Experimental Design
• Randomization Methods
• +۴ مورد دیگر

Panagiotis Toulis (Panos) serves as Associate Professor of Econometrics and Statistics and John E. Jeuck Faculty Fellow at the University of Chicago Booth School of Business, where his research focuses on model-agnostic causal inference methods for complex networked environments using randomization techniques that provide robustness beyond classical statistical approaches. His academic credentials include: Ph.D. in Statistics from Harvard University (2016) under Edo Airoldi, David Parkes, and Don Rubin Master’s in Computer Science from Harvard University (2011) under David Parkes M.Eng. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece (2006) Toulis pioneers randomization and permutation tests for causal inference under network interference, developing methods applicable to experimental design in complex domains where traditional model-based approaches fail. His work bridges statistics , optimization , and economics , with significant contributions to invariance-based inference for observational studies and machine learning integration through stochastic gradient descent frameworks. This research has practical applications in business analytics, vaccine distribution, and economic policy. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in extending randomization tests to handle network spillover effects, two-sided market experiments, and temporal dynamics with treatment habituation. A growing emphasis on machine learning assistance for complex treatment effect detection demonstrates his innovative fusion of statistical rigor with computational scalability, consistently providing finite-sample theoretical guarantees uncommon in asymptotic-focused literature. Toulis has earned recognition through prestigious awards: Arthur P. Dempster Award from Harvard University’s Department of Statistics LinkedIn Economic Graph Challenge award 2012 Google United States/Canada PhD Fellowship in statistics He teaches core Booth courses including Applied Regression Analysis (41100), Causal Inference for Business Applications (41207), and the Econometrics and Statistics Colloquium (41600), while maintaining active industry connections through prior software engineering roles at Google and Greek startups. His research appears in top journals including Journal of the Royal Statistical Society and Annals of Statistics, addressing critical methodological challenges in modern data science. No dedicated laboratory structure is documented, but his work inherently involves collaborative research networks across statistics, economics, and computer science disciplines through the Booth School’s academic infrastructure.