جستجوی استاد راهنمای دانشگاهی

Abhishek Jain is an Associate Professor in the Department of Computer Science at Johns Hopkins University and a Senior Scientist at NTT Research. He is affiliated with the Data Science and AI Institute, the Information Security Institute, and the Algorithms and Complexity group. University: Johns Hopkins University Department: Computer Science Academic Rank: Associate Professor Institutional Roles: Co-director of the Advanced Research in Cryptography group, member of the Theory Group, and co-leader of the Cryptography Group Education: Ph.D. in Computer Science from the University of California Los Angeles (2012), advised by Amit Sahai and Rafail Ostrovsky. Recipient of the Symantec Outstanding Graduate Student Award during his Ph.D. Research Interests Cryptography Secure computation Proof systems Program obfuscation Privacy Blockchain Theoretical computer science Research Trends: His recent publications emphasize cryptographic protocols for homomorphic encryption, zero-knowledge proofs, and secure multi-party computation, alongside applications in blockchain and privacy-preserving systems. Key themes include scalability, verifiable evaluation, and adapting cryptographic techniques to dynamic and distributed environments. Scientific Awards 2020 NSF CAREER Award Best Paper Awards at Eurocrypt and the International Conference on the Theory and Applications of Cryptographic Techniques Advising & Grants: Mentors Ph.D. students and interns/postdocs at NTT Research. Research funded by NSF, DARPA, JP Morgan, Ethereum Foundation, Stellar, Cisco, Samsung, and JHU Catalyst awards. Labs & Teams: Co-leads the Cryptography Group at Johns Hopkins, participates in the DC Area Crypto Day, and organizes the weekly Theory Seminar. Collaborates with institutions including MIT CSAIL, BUSEC, and Microsoft Research New England.

• Cryptography
• Computer Security
• Privacy
• +۵ مورد دیگر

Jianguo Wang is an Assistant Professor in the Department of Computer Science at Purdue University, joining in Spring 2021. His research focuses on database systems for the cloud and large language models, including disaggregated databases and vector databases. He holds a PhD from the University of California, San Diego, and has worked at Zilliz (Milvus) and Amazon Web Services (AWS). Education: PhD in Computer Science (UC San Diego, 2019), MPhil (Hong Kong Polytechnic University), BSc (Zhengzhou University) Research interests include Disaggregated Databases, Vector Databases for Large Language Models, and cloud-native systems. Notable work includes OpenAurora (an open-source Amazon Aurora prototype) and contributions to Milvus. He has received grants like the NSF CAREER Award and honors such as the IEEE TCDE Rising Star Award. Advising a team of students in database systems and teaching courses like CS592 (Disaggregated Database Systems) and CS440 (Large-scale Data Analytics). Serves on program committees for SIGMOD, VLDB, and ICDE.

• Database Systems
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.

• Computational Linguistics
• Natural Language Processing
• Semantics
• +۵ مورد دیگر

Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses

• Computer Architecture
• Machine Learning
• FPGA Acceleration
• +۴ مورد دیگر

Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.

• Data Management Systems
• Database Systems
• Distributed Systems
• +۵ مورد دیگر

Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .

• Microbial Evolutionary Genomics
• Antibiotic Resistance
• Computational Biology
• +۷ مورد دیگر

Aaron J. Elmore is an Associate Professor in the Department of Computer Science and the College of the University of Chicago. His research focuses on cloud computing, databases, and distributed systems, with an emphasis on resource-efficient database execution and collaborative analytics. PhD in Computer Science from University of California, Santa Barbara MS in Computer Science from University of Chicago Research interests include: Elastic databases and multitenancy (Database-as-a-Service) Resource-efficient systems (CrocodileDB, DenseStore, EdgeTSD) Database versioning (Datahub, Decible, OrpheusDB) Data discovery (DataSwamp, Relic) Recent publications highlight advancements in cloud-native query execution, dynamic compression frameworks, and time-series anomaly detection. His work often bridges systems design with practical data science applications. Scientific awards include: NSF CAREER Award (2021) Multiple Google and Intel research grants ACM SIGMOD Best Demo Honorable Mention Aaron has advised multiple PhD students including Jun Hyuk Chang and Riki Otaki, with former advisees now at institutions like MIT, Harvard, and UC Berkeley. He leads the ChiDATA research group and collaborates with Systems Group and CERES Center.

• Cloud Computing
• Databases
• Distributed Systems
• +۳ مورد دیگر

Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.

• Database Systems
• Query Processing
• Query Language Design
• +۷ مورد دیگر

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.

• Large Scale Machine Learning
• Randomized Algorithms for Big Data
• Graph Mining
• +۳ مورد دیگر

Ion Stoica is a Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley, where he holds the Xu Bao Chancellor Chair. He serves as Director of the Sky Computing Lab and is Executive Chairman of both Databricks and Anyscale. His research spans distributed systems, cloud computing, and AI systems, with significant contributions to large-scale data processing frameworks. Stoica's research interests focus on the intersection of AI and systems, with emphasis on developing practical implementations that bridge theoretical foundations with real-world deployability. His work addresses fundamental challenges in distributed computing, resource management, and large-scale machine learning systems. Current projects include Ray (a distributed execution framework), vLLM (a high-throughput inference engine for LLMs), Chatbot Arena (an open platform for human preference evaluations), and SkyPilot (a framework for running AI workloads across clouds). His research output demonstrates a consistent trajectory toward more efficient, scalable systems for modern AI workloads, particularly focusing on optimizing inference performance, resource utilization, and cross-cloud deployment. Recent publications reflect growing interest in large language model serving, video generation optimization, and agent-based systems. ACM Fellow SIGOPS Hall of Fame Award (2015) SIGCOMM Test of Time Award (2011) ACM Doctoral Dissertation Award (2001) Member of National Academy of Engineering Honorary Member of the Romanian Academy Stoica has advised an extensive number of doctoral students who have gone on to prominent positions in academia and industry, including assistant professorships at Stanford, MIT, Carnegie Mellon, and other top institutions. He has received significant research funding through his lab activities and startup ventures. His research group has been particularly successful in translating academic research into widely adopted open-source technologies and commercial products. Stoica leads the Sky Computing Lab at UC Berkeley, which focuses on developing systems for AI workloads across multiple clouds. His research group has produced numerous influential open-source projects including Apache Spark, Apache Mesos, and Alluxio, which have become industry standards for large-scale data processing. The lab maintains strong industry partnerships while pursuing fundamental research in distributed systems and AI infrastructure.

• Distributed Systems
• Cloud Computing
• AI Systems
• +۳ مورد دیگر

Ragib Hasan is a Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. His research focuses on cybersecurity, with specialties in cloud security, IoT systems, digital forensics, and biomedical device security. He leads the Secure and Trustworthy Computing Lab (SECRETLab) and contributes to the UAB Center for Cyber Security and NIST Cloud Forensics Working Group. Education: M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, followed by a postdoctoral fellowship at Johns Hopkins University. Affiliations: NIST Cloud Forensics Working Group, UAB Center for Cyber Security. His research addresses threats in smart cities, autonomous vehicles, and healthcare technologies. Key interests include securing IoT networks, mitigating cyberattacks on critical infrastructure, and advancing forensic methodologies in cloud environments. Recent work emphasizes threat modeling for connected vehicles, medical devices, and AI-driven systems. Dr. Hasan’s funding comes from the Department of Homeland Security, NSF, ONR, and industry partners like Facebook, Google, and Amazon. His awards include the NSF CAREER Award (2014), Google RISE Award (2013), and Deutsche-Welle Best of Blogs (2014) for his BanglaBraille initiative. Grants & Projects: Supported by DHS, NSF, and corporate collaborations. Outreach: Founded Wikimedia Bangladesh, Shikkhok.com (STEM education platform), and contributed to Bangla and English Wikipedia. His lab develops frameworks like StreetBit for pedestrian safety and InSight for emergency alert systems, integrating Bluetooth beacon technology to enhance urban security and sustainability.

• Cybersecurity
• Cloud Security
• Internet of Things (IoT) Security
• +۸ مورد دیگر

Phil Bernstein is a Distinguished Scientist in the Data Systems Group at Microsoft Research Redmond and an Affiliate Professor at the University of Washington where he occasionally teaches CSEP 545 Transaction Processing. With over four decades of pioneering work in database systems, he has made significant contributions across transaction processing, data integration, and distributed systems. His research interests focus on database systems, transaction processing, and data integration, with recent work on approximate nearest neighbor search over vector databases, improving database servers using disaggregated cloud resources, and the Orleans distributed systems programming framework. Bernstein's work on Orleans (2012-2019) resulted in an open-source framework widely used inside and outside Microsoft, with components addressing indexing, geo-distribution, and transactions. Bernstein has received numerous prestigious awards including being named a Fellow of the ACM and AAAS, receiving the SIGMOD Edgar F. Codd Innovations Award, and election to the National Academy of Engineering and Washington State Academy of Sciences. Fellow of the ACM Fellow of the AAAS SIGMOD Edgar F. Codd Innovations Award Member of the National Academy of Engineering Member of the Washington State Academy of Sciences As an active researcher and academic, Bernstein serves on numerous conference program committees including SIGMOD 2024 (keynotes), VLDB 2024 (Industry), and has held editorial positions for Information Systems and Springer Data-Centric Systems and Applications. His influential books, Principles of Transaction Processing (2009) and Concurrency Control and Recovery in Database Systems, remain foundational texts in the field.

• Database Systems
• Transaction Processing
• Data Integration
• +۳ مورد دیگر

Maneesh Agrawala is the Forest Baskett Professor of Computer Science at Stanford University and Director of the Brown Institute for Media Innovation. His research spans computer graphics, human-computer interaction, and information visualization. Agrawala's lab develops computational tools for visual communication, investigating how design principles improve media effectiveness. Current projects include diffusion models for image and video generation, sketch-based interfaces, and visualization tools for scriptwriting. His team creates systems that enable new forms of content creation and analysis. His publications demonstrate consistent innovation in visual computing, with recent advances in controllable generative models, video understanding, and visualization design. Agrawala has received numerous honors including the MacArthur Fellowship and ACM Fellowship for his contributions to visual computing.

• Computer Graphics
• Human-Computer Interaction
• Information Visualization
• +۲ مورد دیگر

Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.

• Computer Architecture
• Hardware-Software Co-design
• Hardware Accelerators
• +۶ مورد دیگر