Find Academic Supervisors

Gauri JoshiView profile

Associate Professor

• Distributed Machine Learning
• Federated Learning
• Stochastic Modeling
• +7 more

Gauri Joshi is an Associate Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with affiliate appointments in the Machine Learning Department and Robotics Institute. Her work focuses on system-aware algorithms for distributed machine learning, combining optimization, probability, and coding theory to address communication and computational constraints in edge networks. MIT Ph.D. in EECS (2016) IIT Bombay B.Tech/M.Tech in Electrical Engineering (2010) Research themes include federated learning with communication efficiency, erasure coding for non-linear computations, and reinforcement learning for heterogeneous queueing systems. She leads the Optimization, Probability and Learning (OPAL) lab , affiliated with the Parallel Data Lab (PDL), CyLab, and FLAME Center. Recent publications highlight advances in: federated fine-tuning with low-rank adaptation, robust PCA for model aggregation, privacy-preserving prediction mechanisms, and adaptive reinforcement learning for job dispatching systems. Her group has received 15+ paper awards across SIGMETRICS, MobiHoc, and NeurIPS workshops. Scientific recognition includes: IEEE Goldsmith Lecturer (2025), MIT Technology Review '35 Innovators Under 35' (2022), ONR Young Investigator Award (2023), NSF CAREER (2021), and ACM SIGMETRICS Best Paper (2020). She has advised 20+ graduate students, many now at tech giants like Google, Apple, and Meta. Service contributions span program co-chair roles (MLSys 2025), associate editorships (IEEE/ACM Transactions on Networking), and workshop organization (ICML, NeurIPS). Her NSF AI-EDGE Institute leadership (2021-present) drives next-generation intelligent edge networks for robotics and aerospace applications.

Rashmi VinayakView profile

Associate Professor

• Computer Systems
• Networked Systems
• Information Theory
• +5 more

Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.

Alfred O. Hero, IIIView profile

Professor

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +7 more

Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).

• Anthro-political linguistics
• Sociolinguistics
• U.S. Latin@ language varieties
• +7 more

Ana Celia Zentella is a distinguished Professor in the Department of Ethnic Studies at the University of California, San Diego, renowned for pioneering "anthro-political linguistics" that examines language through sociopolitical and justice-oriented frameworks. Her work centers on U.S. Latin@ language communities, challenging linguistic discrimination and advocating for language rights. Education: Ph.D. in Educational Linguistics, University of Pennsylvania M.A. in Romance Languages and Literatures, Pennsylvania State University B.A. in Spanish, Hunter College (Bronx) Zentella's research critically analyzes U.S. Latin@ varieties of Spanish and English, Spanglish, and language socialization in families, with particular focus on Puerto Rican communities. She exposes how English-only laws and anti-bilingual education policies facilitate linguistic profiling that marginalizes communities. Her scholarship consistently bridges academic rigor and community advocacy, emphasizing "el habla del pueblo" (the people's speech) as resistance against top-down language classifications. Her publications reveal enduring themes: language as a site of political struggle (2018-2023), border linguistics examining Tijuana-San Diego dynamics (2013-2016), and historical analyses of Nuyorican language (2004). She challenges harmful frameworks like the U.S. Census Bureau's "linguistically isolated" category while documenting language revitalization efforts in multilingual urban centers. Scientific Awards: British Association of Applied Linguistics Book Prize (1997) Association of Latina and Latino Anthropologists Book Award (1997) Manhattan's "Doctor Ana Celia Zentella Day" declaration (1997) LASA Public Intellectual of the Year (2014) Society for Linguistic Anthropology Public Outreach Award (2016) American Academy of Arts and Sciences induction (2023) Choice Outstanding Academic Title (2018) Zentella actively mentors through community-engaged scholarship, co-authoring Multilingual San Diego (2008) and Multilingual Philadelphia (2010) with undergraduates. Her public outreach includes challenging linguistic erasure and advising policymakers on language-inclusive practices, demonstrating consistent commitment to translating academic work into tangible community impact.

Aleksander KubicaView profile

Assistant Professor

• Quantum Information Science
• Quantum Error Correction
• Fault Tolerance
• +3 more

Aleksander Kubica is an Assistant Professor of Applied Physics at Yale University, specializing in quantum information science with a focus on quantum error correction and fault tolerance. His research bridges quantum many-body physics and topological codes, particularly exploring applications in superconducting circuits and quantum architectures. He holds a Ph.D. from the California Institute of Technology and a B.S. from the University of Warsaw. Dr. Kubica's work addresses foundational challenges in scalable quantum computing, including optimizing error correction protocols, developing fault-tolerant architectures, and analyzing the intersection of quantum metrology with error mitigation. Recent contributions include advancements in erasure qubits, correlated noise decoding, and topological code adaptations. His research often involves interdisciplinary approaches, combining theoretical physics with algorithm design and hardware-efficient solutions. Key themes in his publications include improving error correction thresholds, designing low-overhead quantum architectures, and exploring novel decoding strategies for topological codes. While no specific awards are listed, his active research trajectory and contributions to quantum computing indicate significant scholarly engagement in the field.

• Cloud Computing
• Operating Systems
• File/Storage Systems
• +3 more

Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.

Henry D. PfisterView profile

Professor

• Information Theory
• Error-Correcting Codes
• Quantum Computing
• +3 more

Henry D. Pfister is the Addy Family Professor of Electrical and Computer Engineering at Duke University, with a secondary appointment in Mathematics. He holds affiliations with the Pratt School of Engineering and the Duke Quantum Center. His research focuses on information theory, error-correcting codes, quantum computing, and machine learning applications in communications. Pfister earned his Ph.D. from UC San Diego and has held prior roles at Texas A&M University, École Polytechnique Fédérale de Lausanne, and Qualcomm. Education: Ph.D. in Electrical Engineering, UC San Diego (2003); M.S. degrees in Public Policy and Environmental Management from Duke University; J.D. and additional degrees from UNC Chapel Hill. Research interests include Reed-Muller codes, quantum error correction, neural decoders for DNA storage, and capacity-achieving coding schemes. Recent work highlights include proving Reed-Muller codes achieve capacity on binary-erasure channels and developing quantum-enhanced classical communication protocols. Publications span topics like polar codes for quantum channels, belief-propagation algorithms, and neural network-based decoding. Notable grants include NSF funding for DNA storage coding and quantum simulation projects. Pfister has advised over 20 graduate students and is a recipient of the STOC Best Paper Award and NSF CAREER Award.

Kaiyang LiuView profile

Assistant Professor

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +3 more

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.

• Networked Control Systems
• Cyber-Physical Systems
• Cybersecurity
• +4 more

Professor Daniel Quevedo is a leading academic in Electrical and Computer Engineering at The University of Sydney. Previously, he held positions at Queensland University of Technology and Paderborn University, Germany, where he founded the Chair in Automatic Control. He earned his PhD from the University of Newcastle (Australia) and MSc/Ing. degrees from Universidad Técnica Federico Santa María (Chile). His research focuses on networked control systems, cyber-physical systems, and cybersecurity, with contributions to state estimation, control of power converters, and human-in-the-loop systems. He has pioneered work integrating machine learning, behavioral economics, and advanced mathematics to address challenges in interconnected digital-physical environments. Quevedo serves as Associate Editor for IEEE Transactions on Control of Networked Systems and IEEE Control Systems. He chairs the Committee of Experts for Germany’s Excellence Strategy on Digital Methods and has held leadership roles in IEEE technical committees. Notable awards include the IEEE Axelby Outstanding Paper Award (2018) and multiple fellowships. Teaching includes advanced control systems courses like Reinforcement Learning and Optimal Control. He is a Fellow of the IEEE and has published over 200 peer-reviewed articles, with recent work emphasizing privacy-preserving state estimation, resilient control systems, and energy-efficient wireless control. His research labs explore topics such as human-machine collaboration, cybersecurity in Industry 5.0, and data-driven control strategies. Current projects include secure remote state estimation frameworks and adaptive control under adversarial conditions.

Krishna JagannathanView profile

Professor

• Stochastic modeling
• Communication networks
• Information theory
• +3 more

Krishna Jagannathan is a full-time Professor in the Department of Electrical Engineering at the Indian Institute of Technology Madras (IIT Madras), India. He specializes in stochastic modeling, communication networks, information theory, and queuing theory. He obtained his B.Tech from IIT Madras in 2004, followed by S.M. and Ph.D. degrees from MIT in 2006 and 2010, respectively. After post-doctoral positions at Caltech and MIT, he joined IIT Madras in 2011. Education: B.Tech in Electrical Engineering, IIT Madras (2004) S.M. in Electrical Engineering and Computer Science, MIT (2006) Ph.D. in Electrical Engineering and Computer Science, MIT (2010) Research Interests: His research focuses on stochastic modeling and analysis of communication networks , information theory , and queuing theory . He has made significant contributions to understanding network performance, resource allocation, and risk-aware decision-making in complex systems. He leads the Networks and Stochastic Systems lab at IIT Madras, mentoring a large cohort of Ph.D. and M.S. students working on cutting-edge problems in networking, optimization, and stochastic systems. Scientific Awards: Best Paper Award at WiOpt 2013, Tsukuba, Japan Young Faculty Recognition Award for Excellence in Teaching and Research, IIT Madras (2014) Teaching & Mentorship: He has taught a wide range of courses including Probability Foundations , Stochastic Modeling and Queuing Theory , Convex Optimization , and Signals & Systems , consistently receiving high teaching evaluations. He has supervised over 15 Ph.D. and M.S. students to completion and continues to guide several active researchers.

Jossy SayirView profile

Lecturer

• Information Theory
• Data Compression
• Error Correction Coding
• +5 more

Jossy Sayir is an Affiliated Lecturer and Senior Research Associate in the Department of Engineering at the University of Cambridge . Holding a Dipl. El.-Ing. ETH and Dr. Techn.-Wiss. from ETH Zurich, Sayir’s work bridges Information Theory and Bioinformatics , focusing on DNA-based data storage and error correction systems. They serve as Director of Studies in Engineering at Newnham College and coordinate Engineering Admissions. Interdisciplinary collaboration with the European Bioinformatics Institute Research on DNA data storage efficiency and cost reduction Expertise in channel coding, source coding, and 5G algorithms Teaching spans mathematics and information engineering modules in Part I Engineering Tripos, with Part II contributions on information theory, error control coding, and cryptography. Sayir also oversees data compression labs and serves as Wine Committee Chair, reflecting diverse interests in food, coffee, wine, music , and jazz . Best Lecturer Award, 2017-18 Research Fellowships in coding theory Key research trends include DNA storage encoding , LDPC decoders , polar code optimization , and Sudoku-inspired constraint coding . Sayir’s work addresses both theoretical and practical challenges in high-density data storage and next-generation communication protocols .

Vipul GoyalView profile

Associate Professor

• Cryptography
• Quantum Cryptography
• Security & Privacy
• +1 more

Vipul Goyal is an Associate Professor at the Computer Science Department of Carnegie Mellon University and a Senior Scientist at NTT Research in California. He is on leave from CMU since joining NTT Research in June 2020 after a 7-year tenure at Microsoft Research. His research focuses on Cryptography , Quantum Cryptography , Security & Privacy , and Theoretical Computer Science , supported by grants from NSF, DARPA, Department of Energy NETL, JP Morgan, Cisco, PNC, Ripple, and CyLab initiatives.

Markus MüllerView profile

Professor

• Quantum Information Processing
• Quantum Simulation
• Quantum Error Correction
• +5 more

Markus Müller is a Professor in Theoretical Quantum Technology at RWTH Aachen University and the Forschungszentrum Jülich's Peter Grünberg Institute. He leads a research group focused on quantum information processing, quantum simulation, and topological quantum computing/ error correction using atomic, molecular, and optical systems. His work bridges theory and practice, with collaborations on trapped-ion and neutral-atom quantum processors. Education: PhD in Quantum Simulation (2011, University of Innsbruck). Postdoctoral positions at Complutense University Madrid. Faculty roles at Swansea University (2015–2019) before moving to Aachen. Research Interests: Quantum error correction, fault-tolerant protocols, topological phases, quantum neural networks, and scalable quantum hardware. His group explores applications in trapped ions, Rydberg atoms, and superconducting qubits, with projects funded by ERC and EU Quantum Flagship grants. Grants/Projects: ERC Starting Grant (Quantum Neural Networks), EU AQTION (Trapped Ion Quantum Computing), VEQTOR (Fault-Tolerance Validation), and Munich Quantum Valley (Neutral-Atom Processors). Key Publications: Includes experimental demonstrations of fault-tolerant gates, surface-code error correction, and quantum neural networks. Over 120 peer-reviewed articles in Nature Physics , Physical Review X , and Quantum .

• Big Data management
• Cloud computing
• Distributed systems
• +3 more

Shadi Ibrahim is a Researcher at the Inria Centre at Rennes University , specializing in Big Data management since 2013. His work focuses on optimizing data flow processing in cloud environments, particularly through the KerStream project (2017–2022), which addressed limitations in platforms like Spark and Flink by developing methods for task lag detection and energy-efficient allocation. Key research areas: Big Data, Cloud computing, Erasure codes, HPC convergence Notable collaborations include industry partnerships with OVH and startup Hive (2021–2022) to reduce data duplication via erasure codes. He received the ACM Distinguished Member honor (2022) and the IEEE TCSC Excellence Prize (2020). His work also intersects with exascale computing initiatives via partnerships with Argonne National Laboratory and Lawrence Berkeley National Laboratory .

• Cloud Computing
• Data Center Optimization
• Resource Allocation
• +4 more

Bhuvan Urgaonkar is a Professor in the Department of Computer Science and Engineering at Penn State University's College of Engineering. His research centers on optimizing cloud computing systems through innovative approaches to resource allocation, cost efficiency, and energy management. Current research focuses on Burstable Instance Scaling Serverless Computing Optimization Distributed Storage Systems Multi-resource Fair Allocation Cloud Economics Recent publications highlight advancements in autoscaling techniques, serverless architecture design, and trace modeling for high-load scenarios. These works emphasize practical solutions for cost-effective resource utilization in public cloud environments. Scientific Awards: CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud (NSF, 2022-2025) CNS Core: Small: Principled Methodologies for Automated Cost-Effective Service Blending (NSF, 2021-2024) PPoSS: Cross-Layer Design for HPC in the Cloud (NSF, 2020-2022) CSR: Burstable Instances for Cost-Efficacy (NSF, 2017-2020) CSR: Student Travel Support for SIGMETRICS (NSF, 2016-2017)