جست‌وجوی استاد و پژوهشگر

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Steven I-Jy Chienمشاهده پروفایل

استاد

• Transportation Engineering
• Urban Rail Transit
• Sustainable Transportation Systems
• +۴ مورد دیگر

Steven I-Jy Chien serves as Professor and Director of the Transportation Program within the Department of Civil and Environmental Engineering at the New Jersey Institute of Technology (NJIT). His career spans academia, government, and industry with significant contributions to transportation engineering research and practice. His educational credentials include: Ph.D. in Civil and Environmental Engineering from University of Maryland-College Park (1995) M.S. in Civil and Environmental Engineering from University of Maryland-College Park (1991) B.S. in Civil Engineering from Tamkang University (1983) Professor Chien's research focuses on transportation system resilience, sustainable transit operations, and advanced traffic modeling. His work addresses critical challenges in urban rail networks, demand-responsive transit services, and energy-efficient transportation systems. He employs sophisticated computational methods including genetic algorithms, machine learning, and Bayesian approaches to solve complex transportation problems involving network vulnerability, passenger behavior, and infrastructure optimization under uncertainty. Analysis of his recent publications reveals strong emphasis on transportation resilience against cascading failures, sustainable operations for electric transit systems, and pandemic-responsive aviation scheduling. His research spans diverse geographic contexts including major Chinese metropolitan areas and U.S. transportation networks, demonstrating global applicability of his methodologies. His professional recognition includes: 2024 Keynote Speaker at Union-Tech Lecture, National Taiwan Ocean University 2022 Keynote Speaker at International Conference of Chinese Institute of Transportation 2022 Best Poster Award from New Jersey Department of Transportation Professor Chien has secured research funding from federal agencies including the Federal Highway Administration (FHWA) where he contributed to CORSIM traffic simulation software development. His industry experience with China Engineering Consultants Inc. and Information Dynamic Inc. informs his practical approach to transportation challenges. As Transportation Program Director, he oversees curriculum development and industry partnerships that prepare students for transportation sector careers. He maintains active collaboration with transportation agencies globally, particularly through his leadership in the Transportation Program at NJIT which serves as a hub for innovation in urban mobility solutions.

Andrew Headمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Programming
• Reading
• +۱۳ مورد دیگر

Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.

Patrick McDanielمشاهده پروفایل

استاد

• Security of Machine Learning/AI Systems
• Adversarial Machine Learning
• Systems Security
• +۴ مورد دیگر

Patrick McDaniel is a Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. He is a Fellow of IEEE, ACM, and AAAS, and leads the MadS&P (UW-Madison Security and Privacy) group. His research spans computer and network security, adversarial machine learning, and technical public policy. Academic Affiliation : Tsun-Ming Shih Professor of Computer Sciences Key Research Areas : Mobile/IoT security, election systems security, AI policy Research Trends : His recent publications and funded projects focus on adversarial machine learning, SDN security, and robustness of AI systems. Keywords include cybersecurity, machine learning, network security, and privacy. Grants and Leadership : He has secured over $5M in NSF funding for end-to-end trustworthiness of ML systems and leads collaborative projects with Army Research and UW-Madison. Awards : Multiple best/most influential paper awards (ICSE 2015, PLDI 2014, ACSAC 2024, EthiCS 2023) Advising : Mentored 13+ PhD students and postdocs now at top institutions like Google, Purdue, and University of Toronto

Alexandra Boldyrevaمشاهده پروفایل

استاد

• Cryptography
• Information Security
• Searchable Encryption
• +۳ مورد دیگر

Alexandra Boldyreva is a Professor at the Georgia Institute of Technology, holding joint appointments in the School of Cybersecurity and Privacy and the School of Computer Science. She serves as Associate Chair for Graduate Studies in the School of Cybersecurity and Privacy and coordinates the Information Security Master’s program in the College of Computing. Her affiliations include the Institute for Information Security & Privacy (IISP), the Algorithms, Combinatorics and Optimization (ACO) program, and the Algorithms and Randomness Center (ARC). She earned her Ph.D. in Computer Science from the University of California, San Diego, and holds bachelor’s and master’s degrees in applied mathematics from St. Petersburg State Technical University, Russia. Her research focuses on cryptography and information security, with notable contributions to encryption methods, authentication protocols, and privacy-preserving systems. Boldyreva’s work emphasizes provable security analysis of protocols like FIDO2 and TLS 1.3, as well as searchable encryption and data privacy techniques. Her recent studies include secure communication channel establishment, leakage quantification in encryption systems, and applications of fuzzy search in encrypted databases. She has received Test of Time Awards for foundational contributions to cryptography. Boldyreva leads initiatives in cybersecurity education and has secured grants for research in secure communication protocols and human-computing approaches to key exchange. Her interdisciplinary collaborations span computer science, mathematics, and privacy engineering.

Emily Wallمشاهده پروفایل

استادیار

• Data Visualization
• Visual Analytics
• Cognitive Psychology
• +۳ مورد دیگر

Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.

Mona Merlingمشاهده پروفایل

دانشیار

• Algebraic K-Theory
• Equivariant Stable Homotopy Theory
• Manifold Theory
• +۳ مورد دیگر

Mona Merling is an Associate Professor in the Department of Mathematics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. She holds a Ph.D. from the University of Chicago (2014) under Peter May. Prior to Penn, she was a J.J. Sylvester Assistant Professor at Johns Hopkins University. Her research focuses on algebraic K-theory, its applications to number theory and manifold theory, and equivariant stable homotopy theory. She has held research positions at institutions like the Mathematical Sciences Research Institute (MSRI) and the Max Planck Institute for Mathematics. Merling teaches advanced courses in algebraic topology, homotopy theory, and calculus, including innovative programs like the Penn Directed Reading Program and collaborations with the Prison Teaching Initiative to provide education in incarcerated settings. She organizes conferences and workshops, such as the Algebraic Topology Bridge Summer Workshop, fostering accessibility in topology education. Her research explores cutting-edge topics like scissors congruence K-theory, equivariant infinite loop space theory, and parametrized cobordism categories. Recent work includes collaborations on derived scissors congruence and multiplicative equivariant K-theory, with applications to geometric and algebraic structures. Her grants include funding for social justice initiatives in education.

Hamed Zamaniمشاهده پروفایل

دانشیار

• Information Retrieval
• Neural Models
• Conversational Search
• +۳ مورد دیگر

Hamed Zamani is an Associate Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he also serves as Associate Director of the Center for Intelligent Information Retrieval (CIIR). He joined UMass Amherst in 2020 after working as a researcher at Microsoft. His research focuses on designing and evaluating statistical and machine learning models for information access systems, including search engines, recommender systems, and question answering. Education: PhD in Computer Science, University of Massachusetts Amherst MS in Computer Engineering, University of Tehran BS in Computer Engineering, University of Tehran Zamani's current research explores neural information retrieval, conversational search, and retrieval-enhanced machine learning. He develops efficient neural models for core IR tasks and emerging areas like conversational information seeking. His work bridges information retrieval with large language models to enhance capabilities in understanding complex queries and generating relevant responses. His recent publications demonstrate a strong focus on retrieval-augmented generation, personalized information access, and efficient neural ranking models. There's a clear trend toward integrating large language models with information retrieval systems, optimizing multi-agent frameworks, and developing evaluation metrics for generative AI applications in search contexts. Scientific Awards: NSF CAREER Award ACM SIGIR Early Career Excellence in Research & Community Engagement Awards (2023) UMass CICS Outstanding Dissertation Award Paper awards at SIGIR (2022, 2023, 2024), CIKM (2020), ICTIR (2019) Microsoft Research Award (AI and New Future of Work program) Amazon Research Award (Optimization of Retrieval-Enhanced ML Models) Zamani actively advises PhD students and postdoctoral researchers, with his students receiving prestigious awards including NSF Graduate Research Fellowships and SIGIR Best Paper awards. He leads the CIIR Talk Series, hosting IR researchers to share recent findings. His Alexa Prize TaskBot Challenge team was selected for two consecutive years, advancing task-oriented dialogue systems. He directs research at the Center for Intelligent Information Retrieval (CIIR), where he oversees projects in neural retrieval models, conversational AI, and retrieval-augmented generation. The center serves as a hub for developing next-generation information access systems with industry and academic collaborators.

• Tangible computing
• Human-Computer Interaction (HCI)
• Accessible interfaces
• +۳ مورد دیگر

Andrés Cuervo-Rubio is a Part-time Lecturer at Parsons School of Design, The New School. They are an interdisciplinary artist, programmer, and HCI researcher based in Brooklyn. As a co-founder of Folk Computer, a research consultancy focused on tangible computing hardware/software and programming languages, they explore accessible interfaces and paper-based interaction systems. They advise the W3C Immersive Web Working Group (WebAR/VR/XR APIs) and have spoken at global JavaScript conferences since 2017. Education: BA in Computer Science, Oberlin College (2017) BA in Creative Writing, Oberlin College (2017) Research Interests: Focuses on material-based interaction design, bridging physical/digital systems through accessible technologies. Current work emphasizes tangible computing interfaces and inclusive design practices. Active in developing educational frameworks for dynamic content creation in design curricula. Teaching: Recently taught Core 1: Interaction (PUCD 2035) and Core 2: Interaction Studio (PUCD 2125). Upcoming courses include CD Foundations: Interaction (PSAM 1028) in Fall 2025 and CD Studio: Dynamic Content (PSAM 2090) in Spring 2026. Professional Engagement: Serves as technical advisor for immersive web standards and collaborates with industry on accessible XR solutions. Co-leads workshops on physical computing pedagogy.

James Foleyمشاهده پروفایل

استاد

• Computer Graphics
• Human-Computer Interaction (HCI)
• Visualization
• +۳ مورد دیگر

Dr. James D. Foley is a Professor in the College of Computing and the School of Electrical and Computer Engineering at Georgia Institute of Technology. He holds the Stephen Fleming Chair in Telecommunications. He earned his Ph.D. in Computer Information and Control Engineering from the University of Michigan and a BSEE from Lehigh University, where he was inducted into Phi Beta Kappa, Tau Beta Pi, and Eta Kappa Nu. Founded the Graphics, Visualization & Usability (GVU) Center at Georgia Tech, ranked #1 in graphics and user interaction. Directed Mitsubishi Electric Research Lab and later served as CEO of Mitsubishi Electric ITA. Executive Director of Yamacraw, Georgia's broadband development initiative. His research focuses on computer graphics, human-computer interaction (HCI), visualization, and user interface design. He co-authored foundational textbooks like Interactive Computer Graphics , translated into multiple languages. Professional leadership includes Chair of the Computing Research Association (CRA), Fellowships with ACM and IEEE, and the ACM/SIGGRAPH Stephen Coons Award. He advised over 30 graduate students and led numerous conferences, panels, and editorial roles in top journals. Contributions span academia and industry, including roles in NSF workshops, JTEC studies on HCI in Japan, and advisory boards for institutions like MIT and Lehigh University.

Reed Maxwellمشاهده پروفایل

استاد

• Hydrology
• Water Resources
• Groundwater Modeling
• +۸ مورد دیگر

Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science in the Department of Civil and Environmental Engineering and the High Meadows Environmental Institute at Princeton University. He serves as Director of the Integrated Groundwater Modeling Center (IGWMC) and leads a research group comprising graduate students, postdoctoral researchers, and staff. His academic appointments include concurrent roles in both the School of Engineering and Applied Science and the High Meadows Environmental Institute. Maxwell's research focuses on understanding connections within the hydrologic cycle and how they relate to water quantity and quality under anthropogenic stresses. His work centers on hard problems in hydrology including groundwater, evapotranspiration and snow. His research group uses integrated hydrologic modeling, field observations, and remote sensing products to study terrestrial freshwater systems. Key research areas include surface water and the terrestrial hydrologic cycle; interactions of the land-surface, surface water and groundwater; and human health risk assessment. Maxwell has authored more than 185 peer-reviewed journal articles with an H-Index of 66 and over 19,000 citations. His recent work emphasizes machine learning applications in hydrology, continental-scale modeling, and physically rigorous scenario generation through projects like HydroFrame and HydroGEN. He teaches courses including CEE 306/ENV 318 Hydrology: Water and Climate and CEE 586/ENV 586 Physical Hydrology. 2020 Distinguished Henry Darcy Lecturer American Geophysical Union Fellow (2019) 2018 Boussinesq Lecturer Belle van Zuylen Chair (visiting), University of Utrecht 2017 School of Mines Research Award recipient Maxwell has mentored 17 PhD students and 20 MS thesis students throughout his career. His current research group includes multiple postdocs, research software engineers, and graduate students working on projects spanning continental-scale hydrologic modeling, groundwater-stream interactions, and machine learning applications in hydrology. The IGWMC maintains an active education and outreach program including STEM fairs, school visits, and digital educational tools like the HydroFrame Education Team's virtual sandtank aquifer model.

Kristofer S.J. Pisterمشاهده پروفایل

استاد

• Micro/Nano Electro Mechanical Systems (MEMS)
• Control Systems
• Robotics
• +۶ مورد دیگر

Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.

• Security and Privacy
• Cryptography
• Blockchain
• +۴ مورد دیگر

Andrew Miller is an Associate Professor in the Electrical and Computer Engineering department at the University of Illinois, specializing in Programming Languages, Formal Methods, Software Engineering, Security and Privacy, and Systems and Networking. His research focuses on blockchain technologies, cryptography, and secure systems. Professor Miller's research spans multiple critical areas in modern computer security. His work primarily focuses on blockchain technologies , where he has made significant contributions to understanding and improving the security, privacy, and performance of systems like Bitcoin and Ethereum. He has conducted empirical analyses of privacy in the Lightning Network and developed protocols for confidential smart contracts. His work in cryptography includes research on multiparty computation, zero-knowledge proofs, and formal methods for cryptographic protocol design. Miller also investigates security vulnerabilities in proof-of-stake systems and resource exhaustion attacks, contributing to the robustness of decentralized systems. His applied security research extends to privacy-preserving health applications, as evidenced by his work on the Safer Illinois platform for COVID-19 contact tracing. Miller's publication record demonstrates consistent contributions to top security and systems venues including IEEE Security & Privacy, ACM CCS, Financial Cryptography, and USENIX Security. His research shows a clear trajectory from foundational work in blockchain security to more applied systems addressing real-world privacy and security challenges. Recent work focuses on making multiparty computation services publicly auditable and developing decentralized identity solutions that maintain compatibility with existing systems. Distinguished Reviewer Award, IEEE Security & Privacy 2018 Professor Miller has advised students including Vivek Nair, who joined the prestigious Hertz Fellows program in 2022. He has taught various courses including Introduction to Algorithms & Models of Computation, Advanced Computer Security, Cryptography, Applied Cryptography, and Ideal Functionality in Cryptography. His research has received support through grants including the SaTC: CORE: Medium project on "Automated Support for Writing High-Assurance Smart Contracts" in 2018. Miller is actively involved in research groups focusing on blockchain security, cryptographic protocols, and privacy-preserving systems. His lab appears to collaborate extensively with researchers across multiple institutions, as evidenced by the diverse author lists on his publications. Current work seems to be focused on making decentralized systems more secure, privacy-preserving, and accessible for real-world applications.

Tian Liمشاهده پروفایل

استادیار

• Federated Learning
• Distributed Optimization
• Trustworthy Machine Learning
• +۴ مورد دیگر

Tian Li is an Assistant Professor of Computer Science at the University of Chicago. She holds a Ph.D. in Computer Science from Carnegie Mellon University and undergraduate degrees in Computer Science and Economics from Peking University. Her research focuses on distributed optimization, federated learning, and trustworthy machine learning, emphasizing algorithm design that addresses accuracy, scalability, and privacy concerns in practical systems. Key areas of expertise include federated learning systems, privacy-preserving technologies, and scalable distributed algorithms. She has contributed to foundational work on tilted empirical risk minimization and decentralized knowledge propagation. Notable achievements include winning the Best Paper Award at the ICLR Workshop on Secure Machine Learning Systems and First Place in the U.S. Privacy-Enhancing Technologies Pandemic Challenge (2023). Her academic trajectory includes recognition as a Rising Star in Machine Learning/Data Science and participation in prestigious workshops like the EECS Rising Stars Program. Her work bridges theoretical advancements with practical applications, aiming to enhance both the robustness and accessibility of machine learning systems.

Fred B. Schneiderمشاهده پروفایل

استاد

• Trustworthy Systems
• Fault-Tolerant Distributed Systems
• System Security
• +۶ مورد دیگر

Fred B. Schneider is the Samuel B. Eckert Professor of Computer Science at Cornell University , where he has been a faculty member since 1978. His career spans foundational work in trustworthy systems , fault-tolerant distributed systems , and system security . He served as department chair from 2014–2018 and previously earned a B.S. in Engineering from Cornell (1975) and a Ph.D. in Computer Science from Stony Brook University (1978).