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Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.

• Database Management Systems
• Self-Driving Database Architectures
• Transaction Processing Systems
• +4 more

Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.

• Data Systems
• Machine Learning
• Database Systems
• +2 more

Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.

• Self-Driving Database Management Systems
• Machine Learning for Databases
• GPU-Accelerated Data Analytics
• +4 more

Justine Sherry is the A. Nico Habermann Associate Professor of Computer Science at Carnegie Mellon University, affiliated with the College of Engineering. She holds a PhD (2016) and MS (2012) from UC Berkeley and a BS/BA (2010) from the University of Washington. Her research focuses on networked systems, including middleboxes, cloud computing, congestion control, and hardware acceleration (e.g., SmartNICs/FPGAs). Notable projects include Pigasus (open-source 100Gbps IDS), APLOMB (cloud-based middlebox scaling), and BlindBox (encrypted traffic scanning). Her academic roles include serving on the SIGCOMM CARES Committee, DARPA ISAT Study Group, and ACM CoNEXT Steering Committee. Awards include the Alfred P. Sloan Fellowship, VMware Systems Award, and IETF Applied Networking Prize. She advises over 15 students and collaborates with industry partners like Intel and VMware. Research highlights include radical shifts in datacenter architectures (SmartNIC compute control), fairness in congestion algorithms (BBR analysis), and database-proxy innovations (Tigger with eBPF). Her teaching emphasizes systems as science labs, integrating experimental design and hypothesis testing into projects. Education: PhD UC Berkeley (2016), MS UC Berkeley (2012), BS/BA University of Washington (2010) Labs/Teams: CyLab, SNAP Research Group, CMU Portugal Collaboration Grants: NSF, Intel, Google Faculty Awards

• Computer Networking
• Networked Systems
• Middleboxes
• +5 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.

• Data Management
• Database Systems
• Machine Learning
• +3 more

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.

• Parallel Computing
• Distributed Systems
• Databases
• +4 more

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.

• Machine Learning
• Artificial Intelligence
• Explainability of AI
• +3 more

Bruce MacDowell Maggs is a Professor in the Department of Computer Science at Duke University and serves as Vice President of Research at Akamai Technologies. His career bridges academic research and industrial innovation in computer science, particularly in distributed systems and networking. Research Interests: His work spans computer networks , distributed systems , parallel algorithms , content delivery , and fault-tolerant computing . He has made significant contributions to network routing, load balancing, scalability of web applications, and energy efficiency in large-scale systems. His research often combines theoretical rigor with practical system design. The recent publications highlight a consistent focus on scalability , network performance , and security in internet-scale applications. Key themes include query caching , traffic modeling , resilient routing , and energy optimization , reflecting his deep involvement in the infrastructure of modern web services. No scientific awards are mentioned in the provided text. Advising and Teaching: He has advised numerous Ph.D. students, many of whom are now faculty or researchers at top institutions. His former students include Ramesh Sitaraman, Anja Feldmann, and Andrea Richa. He currently advises Anat Talmy at Duke. He has taught a wide range of courses at Duke, Carnegie Mellon, and MIT, including Computer Networks, Operating Systems, Algorithms, and Discrete Mathematics. Labs and Teams: While not explicitly named, his research is closely tied to systems and networking groups at Duke and his industrial work at Akamai, a leader in content delivery networks. His collaborations with Tom Leighton and others at Akamai suggest leadership in research teams developing foundational internet technologies.

• Computer Networks
• Distributed Systems
• Algorithms
• +7 more

Michael E. McHenry is a Professor of Materials Science and Engineering at Carnegie Mellon University's College of Engineering. He holds appointments with multiple research centers including the Data Storage Systems Center, Engineering Research Accelerator, Materials Research Science and Engineering Center, and Wilton E. Scott Institute for Energy Innovation. Dr. McHenry received his BS in Metallurgical Engineering and Materials Science from Case Western Reserve University in 1980, his PhD in Materials Science and Engineering from MIT in 1988, and completed a postdoctoral fellowship at Los Alamos National Laboratory. His research focuses on soft magnetic nano-composites for power and energy applications, with particular expertise in metal amorphous nanocomposites (MANCs) for high-efficiency electric motors and power systems. His work spans advanced materials processing, magnetic properties under various conditions, and rare earth materials criticality. His research portfolio demonstrates a clear progression toward practical applications of magnetic materials, particularly in high-power density, high-efficiency motors that can operate at high rotational speeds with minimal energy loss. His publications reveal a strong focus on translating fundamental materials science into engineering solutions for energy conversion, with significant emphasis on rare earth-free alternatives and high-frequency applications. IEEE Distinguished Lecturer (2013) TMS Awardee for Research Excellence (2014) Subject of TMS Symposium in Honor of M. E. McHenry (2016) NATO Series Lecturer on Rare Earth Criticality (2016/17) Dr. McHenry has co-founded CorePower Magnetics Inc. with Paul Ohodnicki and Samuel Kernion, commercializing soft magnetic technologies with applications in grid modernization and electric vehicles. His extensive publication record and leadership in major research initiatives including a MURI on high-temperature magnetic materials and an ARPA-E program demonstrate significant impact in both academic and industrial contexts. He has served in various leadership roles for Magnetism and Magnetic Materials and Intermag Conferences, and continues to advise on rare earth scarcity issues for organizations like NATO.

• Magnetic Materials
• Soft Magnetic Composites
• Nanomaterials
• +5 more

Feras Saad is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the Principles of Programming and Artificial Intelligence groups. He received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022, where his dissertations on probabilistic programming systems earned him the George M. Sprowls PhD Thesis Award and Charles & Jennifer Johnson MEng Thesis Award. His research focuses on developing scalable computing systems for probabilistic modeling and inference, integrating ideas from programming languages and probabilistic AI. Key research themes include probabilistic programming languages, automated probabilistic model discovery, statistical estimation and testing, random sampling algorithms, and applications in science and engineering. His lab explores new techniques to improve reasoning systems through automation, accuracy, and scale. Dr. Saad has published extensively in top venues including PLDI, POPL, ICML, and Nature Communications. His work spans foundational computational questions to practical software systems for probabilistic inference. Research trends show consistent focus on bridging theoretical computer science with practical applications in probabilistic modeling, with recent advances in random sampling algorithms and probabilistic programming systems. Awards and honors include: George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making (2023) Charles & Jennifer Johnson MEng Thesis Award in Computer Science (2017) Editor's Highlight for Nature Communications paper (2024) He currently advises graduate students Gaurav Arya and Thomas Draper in the Probabilistic Computing Systems Lab. His research is supported by software libraries including GenSQL, BayesNF, and SPPL that enable practical applications across scientific domains.

• Probabilistic Programming
• Artificial Intelligence
• Programming Languages
• +4 more

Russell Schwartz is Professor of Biological Sciences and Computational Biology at Carnegie Mellon University, with courtesy appointments in Computer Science and Machine Learning. He serves as Head of the Ray and Stephanie Lane Computational Biology Department and Co-Director of the Joint CMU-University of Pittsburgh PhD Program in Computational Biology. His research develops computational models for complex biological systems, focusing on cancer genomics and macromolecular assembly. Key areas include reconstructing tumor clonal evolution through integration of bulk and single-cell sequencing data, developing algorithms for stochastic simulation of self-assembly systems, and creating optimization methods for fitting models to experimental data. Recent publications demonstrate advancements in single-cell lineage tracing, circulating tumor DNA analysis, and generative AI for biological databases. His team's BrM-Phylo software enables phylogeny reconstruction from matched transcriptome data in breast cancer brain metastases. NSF CAREER Award Alfred P. Sloan Research Fellowship Schwartz leads multiple NIH-funded projects on tumor evolution modeling and mentors students in computational cancer research. His laboratory develops open-source tools including FISHtrees for tumor phylogenetics and TUSV for integrating structural variants in clonal reconstruction.

• Cancer Genomics
• Tumor Evolution
• Computational Phylogenetics
• +3 more

Dr. Raja Sooriamurthi is a Teaching Professor and Program Director of the Decision Analytics and Systems minor at the Information Systems Program of Carnegie Mellon University's Heinz College. His teaching and research focus on artificial intelligence, cognitive science, and educational pedagogy. Teaching Interests: Data science, database systems, big data, puzzle-based learning, system development lifecycle Research Interests: Case-based reasoning, knowledge management, distributed reasoning, machine learning, software development pedagogy Dr. Sooriamurthi leads curriculum innovation in information systems education, particularly through the IS2020 competency model . His work bridges AI applications with educational technologies, emphasizing authentic learning and generative AI tools for skill development. Key publication themes include: SQL instruction using AI-driven assessment Information systems curriculum design Puzzle-based learning for critical thinking Service-learning in leadership development Integration of NoSQL databases in education

• Artificial Intelligence
• Case-Based Reasoning
• Knowledge Management
• +14 more

Jignesh Patel is a Professor in the Computer Science Department at Carnegie Mellon University, specializing in database systems and data-intensive computing. His research focuses on hardware-software synergy for high-performance databases and democratizing data analytics through no-code interfaces. He co-founded DataChat, a startup focused on intuitive data analytics platforms. He holds fellowships from AAAS, ACM, and IEEE, along with teaching awards. His work emphasizes building systems that leverage novel hardware and user-friendly interfaces. Research interests include scalable data platforms, LLM-based query interfaces, and optimizing database performance through hardware collaboration. Notable projects include the Quickstep data platform and the Ava conversational interface. Awards: Fellow of AAAS, ACM, IEEE; Multiple Teaching Awards Labs/Teams: CRISP (Intelligent Storage and Processing), DataChat startup

• Systems
• Data-Intensive and Cloud Computing
• Databases
• +3 more

Andrew Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University , part of the School of Computer Science . His research focuses on database systems, particularly self-driving architectures, transaction processing, and large-scale analytics. He is a member of the CMU Database Group and Parallel Data Laboratory. His awards include the NSF CAREER (2019), Sloan Fellowship (2018), and ACM SIGMOD Jim Gray Dissertation Award (2014). He co-founded OtterTune, a database tuning startup, though it later ceased operations. Current research interests emphasize autonomous database systems, query optimization, and distributed computing. Recent publications (2024) highlight work on self-driving DBMS, null representation in columnar formats, and UDF optimization techniques. Awards: NSF CAREER Award (2019) Sloan Fellowship (2018) ACM SIGMOD Jim Gray Dissertation Award (2014) Advising: Mentors students in database systems, including Sam Arch, Wan Shen Lim, and William Zhang. Labs/Teams: Leads the Database Group and collaborates with the Parallel Data Laboratory.

• Databases
• Distributed Systems
• Data-Intensive Computing
• +3 more

Xiaoying Tu serves as an Assistant Teaching Professor in the Information Systems department at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. Her work bridges technology, business strategy, and entertainment industry dynamics. Her research focuses on: How disruptive technologies have transformed entertainment industry ecosystems Evolution of revenue chains in film and music industries Impact of digitized content, high-speed internet, and social media on traditional business models Data-driven content promotion strategies employed by companies like Netflix Dr. Tu teaches foundational courses in databases, data mining, and machine learning, emphasizing practical applications of working with real-world data that she describes as "very messy." She integrates her research insights into classroom instruction, helping students understand data structuring, cleaning, and management processes. Originally joining CMU in 2012 as a master's student with plans to return to business IT, Dr. Tu shifted toward academia after being inspired by an interactive marketing course examining disruptive technology in entertainment. Her teaching philosophy reflects her parents' influence as university faculty members, emphasizing student engagement and clear conceptual explanation.

• Information Systems
• Technology Disruption
• Entertainment Industry
• +5 more