جستجوی استاد راهنمای دانشگاهی

Haijun Xia is an Assistant Professor at the University of California, San Diego (UCSD), where he directs the Foundation Interface Lab and contributes to the Cognitive Science and Design Lab. His work focuses on Human-Computer Interaction (HCI) and Human-AI Collaboration , emphasizing the development of dynamic, malleable interfaces that integrate human cognition with intelligent systems to enhance thinking and working paradigms. Education: Bachelor's, Tsinghua University Master's and PhD, University of Toronto His research spans generative AI , interface design , and creative collaboration , with applications in data visualization, programming, and scholarly work. He advocates for treating information as a malleable material that can be flexibly manipulated by users and AI agents. Recent publications highlight advancements in: 2025 CHI Conference - Generative user interfaces, malleable overview-detail designs, and compositional structures for human-AI co-creation 2024 CHI Conference - Structured design space exploration with LLMs, ASCII diagram analysis, and adaptive presentation systems 2023 CHI/UIST Conferences - Data particle visualization, contextual logging tools, and metaphor generation for science communication Scientific recognitions include: Multiple Best Paper Awards and Honorable Mentions at CHI and UIST Hellman Fellowship and grants from NSF , Microsoft , Google , Meta , Apple , and Adobe He actively recruits undergraduate and graduate interns and has an upcoming postdoc position in early 2025 for researchers interested in foundational interface development.

• Human-Computer Interaction
• Human-AI Collaboration
• Interface Design
• +۲ مورد دیگر

Pratyush Mishra is an Assistant Professor at the University of Pennsylvania in the Department of Computer and Information Science, where he is affiliated with the Security and Privacy Laboratory. His research focuses on the intersection of cryptographic proof systems and computer security , particularly on efficient implementations of zero-knowledge proofs and secure computation protocols. Pratyush completed his PhD in Computer Science at UC Berkeley , advised by Alessandro Chiesa and Raluca Ada Popa , and holds a BSc in EECS from UC Berkeley, where he worked with David Wagner . His current research group includes PhD students Anubhav Baweja , Tushar Mopuri , Bharath Namboothiry , and Alireza Shirzad , along with Matan Shtepel , a former research assistant who moved to CMU for his PhD. His work spans advanced cryptographic techniques like zkSNARKs , accumulation schemes , and private delegation of provers , with applications in decentralized systems and secure inference. His recent publications focus on optimizing polynomial commitments , read-write streaming for SNARKs, and horizontally scalable proofs . Key awards include: ACM SIGSAC Doctoral Dissertation Award Runner-Up (2022) CSAW Applied Research Award (2016) for Hidden Voice Commands He teaches courses like CIS 5560: Cryptography and 7000-2: Theory and Practice of Succinct Zero Knowledge Proofs , covering topics such as symmetric cryptography, public-key encryption, digital signatures, zero-knowledge proofs, and secure multiparty computation. His lab contributes to the arkworks ecosystem for zero-knowledge proof libraries and co-founded the startup Aleo based on his research.

• Cryptography
• Computer Security
• Zero-Knowledge Proofs
• +۲ مورد دیگر

Erik Waingarten is an assistant professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on algorithms for massive datasets, including similarity search, streaming/sketching, property testing, and distribution testing. Former postdoctoral researcher at Stanford's CS Department under Moses Charikar PhD from Columbia University advised by Xi Chen and Rocco Servedio Key research areas: High-dimensional geometry Streaming algorithms Property testing Sketching techniques Clustering and metric optimization Recent article trends show expertise in: 2025 publications on monotonicity testing and metric property analysis 2024 work on Earth Mover's Distance and kernel evaluations 2023 papers on clustering, optimal transport, and MST algorithms 2022-2020 foundations in sublinear algorithms and entropy estimation Scientific recognition: NSF CAREER Award (2023) CCC Best Paper Award (2017) Invited to Journal of the ACM (2017) Academic advising includes PhD students: Ashwin Padaki Tian Zhang Nicolas Menand Krish Singal Junkai Song

• Algorithms for Massive Datasets
• High-dimensional Geometry
• Property Testing
• +۵ مورد دیگر

Aishwarya Ganesan is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. Her research focuses on distributed systems, storage systems, and fault tolerance mechanisms, with emphasis on high-performance computing and datacenter infrastructure. She leads projects addressing challenges in replicated storage, consensus protocols, and system resilience. Her work explores fault tolerance in disaggregated datacenters, log abstractions for low-latency applications, and novel replication strategies for modern storage systems. She has developed frameworks like LazyLog and IONIA to improve system efficiency and reliability. Her research also extends to automatic reliability testing for cluster management controllers and analyzing distributed storage vulnerabilities. Key contributions include demonstrating how redundancy alone does not guarantee fault tolerance, and proposing consistency-aware durability mechanisms for storage systems. She received the NSF CAREER Award in 2024 for her research on storage-aware fault tolerance. Her work spans 21 peer-reviewed publications, with notable contributions in conferences like SOSP, EuroSys, and FAST. Current projects investigate fault tolerance in emerging memory technologies and system recovery protocols for consensus-based storage.

• Distributed Systems
• High Performance Computing
• Fault Tolerance
• +۴ مورد دیگر

Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.

• Parallel Computing
• Algorithms
• Computer Architecture
• +۳ مورد دیگر

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +۴ مورد دیگر

Elette Boyle is an Associate Professor at Reichman University (IDC Herzliya) and a Senior Scientist at NTT Research . She holds a Ph.D. in Mathematics from MIT (advised by Shafi Goldwasser and Yael Tauman Kalai) and an undergraduate degree from Caltech . Education Ph.D. in Mathematics, MIT B.S. in Mathematics, Caltech Her research focuses on cryptographic solutions for secure data processing , particularly in secure multi-party computation , function/homomorphic secret sharing , and distributed point functions . Recent work explores topology-hiding communication , memory checking complexity , and sublinear-communication MPC . Key trends in her publications include: Advancements in Function Secret Sharing for branching programs and sparse vectors. Efficient Secure Multi-Party Computation protocols with preprocessing. Information-theoretic and computational Topology-Hiding Broadcast schemes. Optimized Oblivious Transfer with constant computational overhead. Scientific Awards European Research Council (ERC) Award Israeli Science Foundation (ISF) Grant United States Air Force Office of Scientific Research (AFOSR) Grant Google Research Scholar Award International Association for Cryptologic Research (IACR) Recognition As Director of the Foundations & Applications of Cryptography (FACT) Research Center , she leads collaborative work with institutions like Technion Israel , Cornell University , and NTT Research . Her students include Pierre Meyer (Ph.D.) , Matan Hamilis (Ph.D.) , and D'or Banon (MSc.) .

• Cryptography
• Computer Security
• Coding Theory
• +۷ مورد دیگر

Adam Marcus is an Associate Professor of Architecture at Tulane University School of Architecture and serves as Research Director of the Center on Climate Change and Urbanism. He directs Variable Projects, an award-winning design and research studio operating at the intersection of architecture, computation, and fabrication, and is a partner in Futures North, a public art collaborative dedicated to exploring data aesthetics. His educational background includes: Master of Architecture from Columbia University Bachelor of Arts from Brown University Adam's research focuses on critical approaches to design computation, digital fabrication, and robotics, exploring how these technologies enable new modes of ecological, material, and public engagement. His work particularly emphasizes climate adaptation, buoyant ecologies for sea level rise, computational representation, and multispecies architecture. He integrates these interests through practical design-build projects that address ecological challenges while advancing architectural representation and fabrication techniques. His recent publications demonstrate a strong emphasis on computational approaches to architectural representation and ecological design. The works consistently explore the intersection of digital technologies with ecological imperatives, particularly focusing on climate adaptation strategies, multispecies collaborations, and innovative drawing protocols that bridge analog and computational paradigms. His book 'Drawing Codes' represents a significant contribution to rethinking architectural drawing in the computational age. Notable awards include: Selected as one of the '30 Most Admired Educators' by Design Intelligence (2013) Recipient of the New Faculty Teaching Award from the Association of Collegiate Schools of Architecture (2016) Adam has served on ACADIA's Board of Directors from 2015 to 2021 and currently serves on the editorial board of the International Journal of Architectural Computing. His teaching at Tulane includes design studios and courses in computational design and digital fabrication with ecological applications, building on his decade of experience at California College of the Arts where he co-founded the Architectural Ecologies Lab. He leads research initiatives through Tulane's Center on Climate Change and Urbanism, developing practical design solutions for ecological challenges, particularly those related to climate change and sea level rise. His Buoyant Ecologies Float Lab project exemplifies this approach, creating 'optimized upside-down benthos for sea level rise adaptation' that has received attention from major media outlets including Smithsonian Magazine and Architectural Record.

• Architectural Design
• Computational Design
• Digital Fabrication
• +۵ مورد دیگر

Henry F. (Hank) Korth is a Professor of Computer Science and Engineering at Lehigh University, with a courtesy appointment in the Department of Decision and Technology Analytics in the College of Business. He serves as Director of the Blockchain Lab in the Center for Financial Services and Co-Director of the Computer Science and Business Program. Korth is a Fellow of the ACM and IEEE, and a recipient of the VLDB 10-Year Award and Bell Labs President's Silver Award for contributions to database technologies. PhD in Computer Science from Princeton University MA, MSE in Computer Science from Princeton University BA in Mathematics from Williams College Korth's research spans database systems, blockchain systems, distributed systems, and real-time systems. He has pioneered transaction management in parallel and distributed systems, query processing, and the impact of modern computing architectures on database performance. His recent work focuses on blockchain applications in enterprise databases, including acceleration of zero-knowledge proofs, benchmarking frameworks, central-bank digital currencies, and private-yet-provable accounting systems. His contributions are rooted in both theoretical advancements and practical implementations, such as the QTM™ aggregation engine and the DataBlitz™ main-memory storage manager. Scientific awards include: ACM Fellow IEEE Fellow 10-Year Award at the VLDB Conference Bell Labs President's Silver Award Korth actively supervises research within the Blockchain Lab and is affiliated with the Scalable Software Systems Research Group at Lehigh. His scholarly output reflects a deep engagement with blockchain benchmarking, concurrency control, verifiable databases, and the evolution of database systems in response to technological shifts.

• Database Systems
• Blockchain Systems
• Distributed Systems
• +۷ مورد دیگر

Steven Swanson is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He is the Director of the Non-Volatile Systems Laboratory (NVSL), where he leads cutting-edge research in non-volatile memory, storage systems, and hardware-software co-design. His work bridges computer architecture, systems, and software to develop efficient, reliable, and secure computing platforms. Ph.D., University of Washington, 2006 B.S., University of Puget Sound, 1999 Dr. Swanson's research centers on non-volatile and persistent memory systems , exploring how next-generation storage technologies can transform computing. His lab develops full-stack solutions including file systems like NOVA and Orion , programming models such as NV-Heaps , and hardware prototypes like Moneta and Onyx . The team also works on low-power co-processors (e.g., GreenDroid ) and tools for debugging and verifying persistent memory programs. Research spans system reliability, security, energy efficiency, and performance optimization. His recent publications reveal a strong focus on persistent memory safety , zero-copy I/O , RDMA-based distributed file systems , and real-world characterization of Intel Optane . These works appear in top venues including ASPLOS, FAST, MICRO, and USENIX ATC, demonstrating sustained innovation in storage and systems research. Scientific honors include: NSF CAREER Award Google Faculty Award Facebook Faculty Award NetApp Faculty Fellow Dr. Swanson has advised 15 PhD students and 4 postdocs , many now faculty or senior engineers at Google, Microsoft, Intel, and other leading tech firms. He has secured significant research funding and leads major community initiatives such as the annual Non-Volatile Memories Workshop and Persistent Programming In Real Life (PIRL) . His educational efforts include innovative courses on robotic system design, quadcopter building, and modern storage systems, emphasizing hands-on learning and real-world implementation. The Non-Volatile Systems Laboratory (NVSL) under his leadership fosters a collaborative, international research environment, hosting visitors and postdocs from around the world. The lab is recognized globally as a pioneer in storage systems research and a key contributor to the adoption of persistent memory technologies in industry.

• Non-Volatile Memory
• Persistent Memory Systems
• Storage Systems
• +۱۲ مورد دیگر

Alan Sussman is a Professor and Associate Chair of Undergraduate Education in the Computer Science department at the University of Maryland. His research focuses on databases, high-performance computing, parallel systems, and educational curriculum development for computing disciplines. He holds a Ph.D. from Carnegie Mellon University (1991) and a B.S.E. from Princeton University (1982). His educational contributions include integrating parallel and distributed computing concepts into early undergraduate courses, supported by NSF-funded initiatives like the CyberTraining program. He has advised students such as Harshit Soora (Master's) and Xiaolong Tian (PhD). His research spans compiler optimizations for parallel programs, distributed data management systems, and scientific workflow frameworks like DYFLOW. He collaborates with UMIACS and contributes to interdisciplinary projects like the TASCS center. Key innovations include VeloxDFS for distributed dataset streaming, compiler techniques for irregular memory access in PGAS programs, and NetCDFaster for geospatial data optimization. His work emphasizes productivity improvements for high-performance applications and curriculum modernization to address emerging computational challenges. Awards: No individual awards explicitly listed; however, collaborator Jik-Soo Kim received a best paper award in 2006. Grants: NSF CyberTraining, TCPP Curriculum Initiative, and Center for Technology for Advanced Scientific Component Software (TASCS). Labs/Teams: Active in UMIACS and interdisciplinary collaborations, including the TASCS center and InterComm framework development.

• Databases and Big Data
• High Performance Computing
• Parallel and Distributed Systems
• +۴ مورد دیگر

Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.

• System Infrastructure for Big Data Processing
• High-Performance Computing (HPC)
• Storage Systems
• +۵ مورد دیگر

Mehmet Koyutürk serves as the Andrew R. Jennings Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering, with additional affiliation as a Member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. His computational research bridges algorithm development with biological applications, focusing on network-structured data analysis to address complex biomedical challenges. Dr. Koyutürk earned his Ph.D. in Computer Science from Purdue University following B.S. and M.S. degrees in Electrical Engineering and Computer Engineering from Bilkent University. His primary research domains include high-throughput biological data analysis, systems/network biology methodologies, data mining algorithms, and scientific computing optimization, with particular emphasis on phosphorylation networks, genomic interactions, and multi-omics integration. Recent publication trends reveal expanding applications of his network science expertise into Alzheimer's disease phosphoproteomics, bipolar disorder biomarker discovery, and intimate partner violence analysis, while maintaining core contributions to graph neural networks and biological link prediction. His group actively develops open-source analytical tools like RokaiXplorer for phospho-proteomic data accessibility. Scientific Recognition Andrew R. Jennings Professorship Dr. Koyutürk leads multiple NIH-funded initiatives including R01-LM012980 for phosphoproteomics analysis, U01-CA198941 (BD2K program) for big network integration, and R01-LM011247 for GWAS enhancement, complemented by NSF CAREER Award CCF-0953195. He serves on the steering committee for CWRU's Systems Biology and Bioinformatics graduate programs and as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), with extensive collaboration through Mark Chance's Center for Proteomics and Bioinformatics. His laboratory specializes in developing scalable algorithms for biological network analysis, currently advancing projects on kinase-substrate association prediction, co-phosphorylation network characterization in cancer, and network-based approaches to intimate partner violence data mining, with strong emphasis on translating computational methods into biomedical insights through open-source software dissemination.

• Systems Biology
• Computational Genomics
• Network Biology
• +۴ مورد دیگر

Michelle Doas is an Associate Professor of Nursing at Chatham University 's College of Health Sciences , where she has been actively contributing since 2007. Her expertise spans clinical nursing, education, and interdisciplinary research applications. Ed,D., MSN, BSN, RN Research Interests Dr. Doas focuses on critical thinking development in nursing education , adult literacy in healthcare , and reminiscence therapy . Her work bridges clinical practice with pedagogical innovation, exploring how techniques from service industries (e.g., Disney's hospitality model) can enhance patient satisfaction and communication in healthcare settings. Article Trends Her publications and presentations demonstrate a consistent emphasis on improving clinical outcomes through structured interventions like mind mapping for education, relaxation therapy for post-operative care, and concept mapping for knowledge organization. Key themes include patient safety , workplace culture , and interdisciplinary applications in nursing. Scientific Awards She is a member of the prestigious Sigma Theta Tau International Honor Society , recognizing her contributions to nursing scholarship. Additional Contributions Dr. Doas has served on the Business Faculty Search Committee (2008) and presented on diverse topics such as food handling safety in environmental science contexts and nursing career advocacy at high school outreach events.

• Medical-surgical nursing
• Cardio-thoracic nursing
• Nursing in Long Term Care settings
• +۳ مورد دیگر

Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo, and a Faculty Member at the Vector Institute. She holds a Canada CIFAR AI Chair. Her research focuses on computational linguistics, natural language processing (NLP), and grounded language learning, with emphasis on multilingualism and spatial reasoning in vision-language systems. She earned her Ph.D. in Computer Science from the Toyota Technological Institute at Chicago (2024), advised by Karen Livescu and Kevin Gimpel, supported by a Google Ph.D. Fellowship. Her undergraduate degree is from Peking University (2018), with a minor in Sociology. Her academic career includes affiliations with the CompLING Lab at Waterloo and contributions to major conferences like ACL and NAACL. She has organized tutorials on NLP grounding and is actively involved in research on model robustness and cognitive insights. Awards include the Google Ph.D. Fellowship and Best Paper Nominations at ACL 2024 and EMNLP 2021, alongside her Thesis of Distinction. She teaches courses such as CS 784 (Computational Linguistics) and CS 486/686 (Artificial Intelligence), emphasizing both theoretical and applied aspects of NLP. Research trends in her articles highlight advancements in vision-language spatial reasoning, multilingualism, and model interpretability. Her work bridges cognitive science and computational methods, exploring how human language mechanisms inform the design of more trustworthy AI systems. Scientific Awards: Google Ph.D. Fellowship Best Paper Nominee (ACL 2024) Best Paper Nominee (EMNLP 2021) Thesis of Distinction (2024) Advising and Grants: As an advisor, she encourages prospective students to review her guidelines. Her grants include support from the Canada CIFAR AI Chair program and the Vector Institute. She collaborates in labs such as CompLING at Waterloo and co-organizes events at NAACL and ICLR. Labs/Teams: She leads the CompLING Lab at the University of Waterloo, affiliated with the Vector Institute. Her work integrates interdisciplinary teams focusing on grounded learning and multilingual NLP challenges.

• Computational Linguistics
• Natural Language Processing
• Multilingualism
• +۴ مورد دیگر