Find Academic Supervisors

Dongyoon LeeView profile

Associate Professor

• Computer Systems
• Software Reliability
• Software Security
• +9 more

Dongyoon Lee is an Associate Professor in the Department of Computer Science at Stony Brook University. His research focuses on building reliable, secure, and efficient software systems, spanning compiler design, operating systems, security, and hardware architecture. He holds a Ph.D. (2013) from the University of Michigan, Ann Arbor, and has held positions at Virginia Tech (2014-2019) and internships at Microsoft Research, NEC, and IBM. Education: Ph.D. in Computer Science & Engineering, University of Michigan, Ann Arbor (2013) M.S. in Computer Science & Engineering, University of Michigan, Ann Arbor (2009) B.S. in Electrical and Computer Engineering, Seoul National University (2004) Research Interests: Lee's work addresses challenges in software reliability (e.g., concurrency bugs, transient faults), security (e.g., memory safety, kernel permissions), and runtime systems (e.g., stream processing, distributed storage). He leads the COSY Lab (Compilers, Operating Systems, and Security), emphasizing cross-stack solutions. Recent Article Trends: Recent publications focus on homomorphic encryption compilers (e.g., DaCapo , HECATE ), energy-efficient systems ( Write-Light Cache ), and security tools like DevFuzz for device driver testing. Themes include optimizing performance-security trade-offs and addressing vulnerabilities in edge computing and embedded systems. Awards: Distinguished Paper Awards (ESEC/FSE 2018, ASE 2019), ICTAS Junior Faculty Award (2017), Google Research Award (2015), and ProQuest Distinguished Dissertation Award (2013). Advising & Grants: Supervises students in Ph.D., MS, and undergraduate programs. Secured grants including NSF SaTC funding for homomorphic encryption compilers and projects with the South Korean Agency for Defense Development (ADD). Leads research on ARM TrustZone security and energy harvesting systems. Labs/Teams: COSY Lab (Compilers, Operating Systems, and Security) at Stony Brook, collaborating on cross-disciplinary projects with industry and government partners.

Adam BelayView profile

Associate Professor

• Computer Architecture
• Distributed Systems
• Operating Systems
• +5 more

Adam Belay is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, affiliated with the Computer Science & Artificial Intelligence Laboratory (CSAIL). He holds the Jamieson Career Development Chair and focuses on operating systems, runtime systems, and distributed systems, with a particular emphasis on optimizing datacenter efficiency, security, and resource utilization. His work bridges theoretical systems research with practical implementations in cloud environments. Before joining MIT, Belay completed his Ph.D. at Stanford University and worked at Google on datacenter networking. His research addresses challenges such as latency reduction, resource fungibility, and carbon-aware software design. He leads the Parallel and Distributed Operating Systems (PDOS) group and contributes to the Systems Community of Research (CoR) at MIT. His recent publications highlight innovations like Quicksand (resource-efficient datacenter systems), Junction (kernel-bypass networking), and Hermit (low-latency remote memory systems). These projects emphasize real-world scalability and sustainability, reflecting his commitment to advancing both academic and industrial systems. Awarded the 2014 OSDI Best Paper for IX (a protected dataplane OS), Belay has advised numerous students and postdocs, many of whom now hold prominent roles at top universities and companies like Google, Microsoft, and UCSD. He actively serves on program committees for top systems conferences (e.g., OSDI, NSDI, SOSP) and teaches graduate-level OS seminars and undergraduate systems courses.

Emery D. BergerView profile

Professor

• Programming Languages
• Runtime Systems
• Operating Systems
• +3 more

Emery D. Berger is a Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He holds a Ph.D. in Computer Science from the University of Texas at Austin (2002). His research focuses on programming languages, runtime systems, and operating systems, emphasizing reliability, security, and performance. Key contributions include influential software systems like Hoard , DieHard , and DieHarder , which have influenced industry standards such as the Windows 7 Fault-Tolerant Heap and Mac OS X memory manager. Professor Berger's honors include an ACM Fellowship (2019), NSF CAREER Award, Lilly Teaching Fellowship, and multiple best paper awards. He has served as Program Chair for PLDI 2016 and co-chair for ASPLOS 2021, and is a co-director of the PLASMA research lab. His work spans academic contributions and practical tools like CSRankings.org , a widely used academic ranking site. Research interests include systems resilience, automated debugging (e.g., Coz ), and secure memory management. His group explores deterministic multithreading ( Grace ), spreadsheet error detection ( CheckCell ), and browser-based systems ( Browsix ). Collaborations with industry include Microsoft Research and Barcelona Supercomputing Center.

Mike FaganView profile

Lecturer

• Programming Languages
• Automatic Differentiation
• High-Level Languages
• +3 more

Mike Fagan is a Lecturer in Computer Science and Research Scientist at Rice University. He holds a B.A. in Mathematics, Mathematical Sciences, and Electrical Engineering, an M.S. in Electrical Engineering, and a Ph.D. in Computer Science, all from Rice University. His research focuses on programming language design, automatic differentiation, and high-level language implementation. He has contributed to tools like HPCToolkit for performance analysis of parallel programs and explored inexact computation strategies to improve energy efficiency in supercomputing. Dr. Fagan has taught courses such as Introduction to Computer Systems, Cyber-Physical Systems, and Advanced Algorithms. His pedagogical approach emphasizes problem-solving and personalized instruction through class-wide collaborative sessions. He has collaborated on projects addressing energy-precision tradeoffs in computing systems and received a Distinguished paper award in 2009 for work on performance analysis of petascale applications. Education: B.A./M.S./Ph.D. in Mathematics/EE/CS (Rice University) Research: Programming Languages, Automatic Differentiation, Parallel Computing Tools: HPCToolkit, OpenAD/F Awards: Distinguished Paper Award (2009) His career has been entirely at Rice University, balancing research and teaching since 1991. He has explored innovative approaches to overcoming hardware limitations through inexact computing and contributed to advancing methodologies for scientific programming and libraries.

• Human-AI Interaction
• Edge Computing
• Workplace Wellness Technology
• +3 more

Oihane Gomez Carmona is a dedicated researcher at the Faculty of Engineering, University of Deusto, specializing in human-AI interaction systems and edge computing applications. Her work bridges computer science with practical implementations in workplace wellness, smart cities, and behavioral change technologies. Affiliated with multiple European research consortia, she actively contributes to Horizon 2020/Europe projects driving digital transformation. Her research focuses on privacy-preserving IoT architectures , adaptive machine learning systems , and community-centric digital solutions . Key themes include optimizing computational resources at the edge, designing ethical behavioral nudges, and developing micro-volunteering frameworks for environmental monitoring. Her work consistently addresses the human factor in technology adoption, particularly in workplace wellness programs and rural community engagement. Gomez Carmona's publication trends reveal strong emphasis on Human-in-the-loop machine learning (2022-2024) Energy-efficient behavioral systems (2023-2024) Rural digital transformation (2022-2024) Privacy-by-design architectures (2020-2021) with increasing focus on EU-funded large-scale deployments since 2021. Her scientific contributions are supported by significant European funding: AURORAL (2021-2025): €7M EU project for rural digital ecosystems REACH (2020-2024): €11M data value chain incubator WOSOCIEBE (2021-2024): €5M city observation project DEUSTEK (2022-2025): Basque government research grant She actively collaborates with the Deustek research group and participates in cross-institutional teams developing citizen science platforms, workplace wellness systems, and smart city furniture. Current projects include Food Assistant (consumer behavior change) and EngageMate (classroom interaction analytics), demonstrating her commitment to socially impactful technology design.

Akshitha SriramanView profile

Assistant Professor

• Computer Architecture
• Software Systems
• Sustainable Computing
• +4 more

Akshitha Sriraman is an Assistant Professor at Carnegie Mellon University with joint appointments in Electrical and Computer Engineering and Computer Science. Her research bridges computer architecture and software systems to enable sustainable, equitable, and efficient hyperscale web infrastructures. She holds a PhD in Computer Science from the University of Michigan (2021) and an M.S. in Embedded Systems from the University of Pennsylvania (2015). Research Focus: Dr. Sriraman redesigns computing stacks to achieve high-performance web systems while addressing critical ethical implications like sustainability (e.g., reducing hardware carbon footprint) and equity (e.g., eliminating demographic bias in web scheduling). Her solutions span from analytical models to production deployments at hyperscale, influencing industry hardware designs including Intel's CPUs and Infrastructure Processing Units. Publications: Her 15 most recent publications demonstrate consistent focus on optimizing data center systems through architectural innovations. Key themes include carbon-efficient cloud servers, hardware-accelerated metadata management, and profile-guided microarchitectural optimizations. Work appears in premier venues like ISCA, MICRO, ASPLOS, and IEEE Micro, with two papers receiving IEEE Micro Top Picks distinctions. Awards & Honors: Google ML and Systems Junior Faculty Award (2025) NSF CAREER Award (2023) Intel Rising Star Award (2023) George Tallman Ladd Research Award (2024) IEEE Micro Top Picks (2020, 2024) ACM SIGARCH/TCCA Outstanding Dissertation Award HM (2022) Meta Systems Research Award Facebook Fellowship Students & Funding: Advises 10+ PhD/Master's students working on sustainable systems, equitable computing, and hyperscale architectures. Student achievements include NSF Graduate Fellowships and K&L Gates Presidential Fellowships. Research supported by NSF, Google, Meta, Intel, VMWare, and AWS grants. Lab & Service: Leads the Ethical Computer Systems Lab focusing on hardware/software co-design for societal impact. Founded workshops like HotEthics and initiated DEI activities recognized by CMU's Service Award. Regularly serves on program committees for OSDI, ISCA, and ASPLOS.

Kirill LevchenkoView profile

Associate Professor

• Cyber-physical system security
• Network security
• e-crime
• +4 more

Kirill Levchenko is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign, previously at UC San Diego. He leads research in cyber-physical system security, network security, and e-crime analysis. His work focuses on evidence-based studies of cybersecurity threats, including aviation cybersecurity and embedded system vulnerabilities. Research Interests: Cyber-physical systems, network security, e-crime, Internet service abuse, and aviation cybersecurity. His major projects include the PacketLab network measurement system and aviation cybersecurity analysis. Key contributions span malware ecosystems, ransomware tracking, and healthcare privacy breaches. Teaching includes courses like Computer Security I (ECE 422/CS 461), Computer Systems Engineering (ECE 391), and introductory computing courses. He advises Ph.D. and M.S. students in security-related research. Labs/Teams: Based in the Coordinated Science Lab (CSL 458), he collaborates on projects like the Triton avionics testbed and PacketLab measurement tools. His work integrates hardware-software co-design for embedded security and network programmability.

Abdullah Al-MamunView profile

Assistant Professor

• High-Performance Computing
• Distributed Systems
• Blockchain Technology
• +5 more

Abdullah Al-Mamun is an Assistant Professor and Director of the High Performance & Secure Data Computing (HPSDC) Lab at Augusta University's School of Computer and Cyber Sciences. His research focuses on resource-efficient distributed systems, high-performance computing, and blockchain-based solutions for secure data management. He holds a Ph.D. from the University of Nevada, Reno, and a double M.Sc. from RWTH Aachen University and the University of Trento under the Erasmus Mundus program. His work has been supported by grants including an NSF CRII award ($165,000) and a Google Cloud Research Grant ($10,000). Education: Ph.D., Computer Science & Engineering (2022), University of Nevada, Reno M.Sc., Computer Science (2013), RWTH Aachen University / University of Trento (Double Degree) B.Sc., Computer Science & Engineering (Summa Cum Laude, 2009), American International University-Bangladesh Research Interests: High-Performance Blockchain Resource-Efficient HPC Extreme-Scale Data Management Secure Data Provenance Edge Computing Selected Awards: NSF CRII Award (2024–2026) Best Paper Award (Cloud'19) Outstanding Graduate Student Scholarship (UNR, 2020–2021) Service & Teaching: ACM Programming SIG Club Advisor (2023–present) Reviewer for IEEE IoT Journal, IEEE Transactions on Consumer Electronics, and others Teaches courses in Low-Level Programming, Computer Architecture, and High-Performance Computing Labs & Teams: Directs the HPSDC Lab, publishing in top-tier venues like SC, ICDE, and IPDPS.

Michael UelschenView profile

Professor

• Software Engineering
• Embedded Systems
• Multicore/Manycore Computing
• +5 more

Prof. Dr.-Ing. Michael Uelschen is a Professor of Software Engineering for Technical Systems at Osnabrück University of Applied Sciences, part of the Faculty of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and has over a decade of industry experience in automotive software development. His research focuses on energy-efficient embedded systems, model-driven software engineering, multicore systems, and medical technology applications such as motion analysis for rehabilitation. He teaches courses in software engineering, embedded systems, and parallel algorithms at both bachelor's and master's levels. Research Interests : Software Engineering for Automotive Embedded Systems, Power Optimization, Energy-Aware Design Patterns, Medical Motion Analysis (gait stability), IoT Sustainability, and Process Improvement (CMMI). His work often bridges theoretical models with practical implementation in real-time systems. Key Projects : Developed a contactless monitoring system for elderly/patient care using commodity electronics, supported by Bayer Healthcare Grants4Apps. Also contributed to frameworks for energy-efficient embedded systems design patterns and algorithms for kinematic analysis in clinical settings. Teaching : Leads courses like 'Software Engineering for Parallel Systems', 'Embedded Systems', and 'Object-Oriented Design'. Supervises student projects and theses in automotive and medical software domains.

José Orlando PereiraView profile

Associate Professor

• Dependable Distributed Systems
• Data Management
• Database Replication
• +7 more

José Orlando Pereira is an Associate Professor at the Department of Informatics, University of Minho, and Research Coordinator at INESC TEC (INESC Technology and Science). His primary research interests lie in dependable distributed systems, with a focus on data management, group communication protocols, and tools for distributed system evaluation. He leads the High-Assurance Software Laboratory (HASLab) , part of INESC TEC and the University of Minho. Roles: Academic supervisor, conference committee member (e.g., SRDS 2025 General Co-Chair), and grant recipient (e.g., ADAPQO funding from CMU Portugal 2024). Key Projects: Includes CYBERACTIONING (cybersecurity education), EUMaster4HPC (European HPC master's program), and AIDA (adaptive assurance platform). Research Themes: Database replication, georeplicated systems, consensus protocols, edge computing, and fault injection tools like LAZYFS. His work emphasizes practical applications, such as improving transactional performance in hybrid workloads (TiQuE) and designing scalable NoSQL middleware (CloudMdsQL). He has authored over 200 publications, including seminal contributions to gossip protocols (HyParView) and consensus algorithms. Awards: Honorable Mention at ACM SIGMOD 2025 for CRDV research. Active in organizing conferences such as SRDS, DSN, and EuroSys. Teaching: Offers advanced courses on distributed systems, database administration, and large-scale data replication at both undergraduate and graduate levels.

Matthew TancretiView profile

Academic

• Computer Systems
• Software Engineering
• Internet of Things (IoT)
• +3 more

Matthew Tancreti is an Assistant Teaching Professor in the Department of Computer Science at Iowa State University. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Purdue University, and dual B.S. degrees in Computer Science and Electrical Engineering from the University of Iowa. His research focuses on computer systems, software engineering, and Internet of Things (IoT) technologies. He has contributed to projects in wireless sensor networks, system-level debugging (e.g., TARDIS framework), and embedded systems profiling (e.g., Aveksha tool). His work is supported by grants from the National Science Foundation, Air Force, Missile Defense Agency, and industry partners. Tancreti co-founded a startup focused on IoT product development, demonstrating his industry-academia collaboration. Recent publications highlight advancements in IoT security, control-flow analysis, and resource-constrained system debugging. He actively participates in interdepartmental initiatives like the LAS Outcomes Committee, emphasizing educational quality and outcomes.

Lukasz GolabView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +6 more

Lukasz Golab is a Professor in the Department of Management Science and Engineering at the University of Waterloo, cross-appointed to the David R. Cheriton School of Computer Science. He holds a BSc from the University of Toronto (2001) and a PhD from the University of Waterloo (2006, Alumni Gold Medal recipient). Previously, he was a Senior Member of Research Staff at AT&T Labs (2006–2011) and held the Canada Research Chair (2014–2024). His research focuses on data-centric systems with societal impact, including explainable AI, blockchain systems, and applications in sustainability and education. Key research areas include: Explainable AI and model transparency High-speed data systems (blockchains, stream processing) Hate speech detection via multi-modal analysis Education data mining (co-op programs, mental health) Sustainability analytics (climate policy, energy systems) Notable achievements include Best Demo Award at EDBT 2025 and Best Short Paper at SSDBM 2023. His work spans 180+ publications in top venues like VLDB, ICDE, and AAAI. Current projects emphasize societal impact through data-driven solutions.

Ivana DusparicView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +6 more

Ivana Dusparic is an Ussher Associate Professor in Future Cities and the Internet of Things at the School of Computer Science and Statistics , Trinity College Dublin . She is also a Funded Investigator at the CONNECT SFI Research Centre and co-director of the Center for Doctoral Training in Artificial Intelligence . Previously, she held positions as Assistant Professor at University College Dublin (2015-2016) and Research Fellow at Trinity (2010-2015). Education: PhD in Computer Science, Trinity College Dublin (2010) Research Interests: Her research centers on leveraging Artificial Intelligence —particularly machine learning , intelligent agents , and multi-agent systems —to autonomously optimize large-scale heterogeneous infrastructures. A primary focus is on smart cities applications, including sustainable urban mobility, intelligent transport systems, autonomous car sharing, and smart energy grids. Research Projects & Labs: Surpass : Investigates how shared autonomous cars will transform cities, using Dublin as a case study. LAMP : Applies agent learning to smart grid management for renewable energy optimization. REALT : Real-time adaptive learning-based traffic control using multi-agent reinforcement learning. ClearWay : Optimizes travel-time reliability in mixed traffic using deep reinforcement learning and swarm intelligence. SATORI : Focuses on reinforcement learning in smart cities and moving networks. Grants & Funding: She has secured multiple high-profile grants, including Science Foundation Ireland (SFI) funding for CONNECT , ENABLE , and ClearWay . She has also launched initiatives like the IBM-TCD Pre-doc Fellowship in AI and the CRT in AI, offering 30+ funded PhD positions across Irish universities. Teaching & Supervision: She has taught courses such as Artificial Intelligence , Advanced Data Structures and Algorithms , and Computer Architecture at both Trinity and UCD. She actively supervises PhD students, postdocs, and interns across multiple projects.

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +7 more

Dr. Satish Mahade Van Srinivasan is an Associate Professor of Information Sciences within the College of Engineering at Penn State University's Great Valley campus. With an h-index of 10 and over 285 Scopus citations, his work bridges computer science, data analytics, and interdisciplinary applications in healthcare, cybersecurity, and sustainability. Active research in AI/ML for disease detection Contributions to software testing and cybersecurity Focus on emotion classification and social data analysis Recent work connecting IoT and circular economy Publications span 2005-2025 with increased output in recent years, particularly in AI safety (2024: 6 papers) and healthcare diagnostics. Research aligns with UN Sustainable Development Goals through biomedical informatics and sustainable systems applications. Current collaborations include work with Dr. Y. F. Paat (social sciences) and Dr. M. L. Lin (medical fields), though no specific advising relationships appear publicly listed. No formal awards are mentioned in current profiles, but his fingerprint shows 100% concentration in AI applications, disease diagnosis, and data-driven approaches.

Norbert SiegmundView profile

Professor

• Software Engineering
• Machine Learning Systems
• Performance Analysis
• +3 more

Norbert Siegmund is Professor and Chair of Software Systems at Leipzig University's Institute of Computer Science in Germany. His research bridges software engineering and artificial intelligence, focusing on configurable software systems, performance analysis, and energy consumption optimization. He has established himself as a leading researcher in the field of configurable systems through extensive publications in top-tier software engineering venues. His research interests span multiple interconnected domains: Configurable, complex systems that evolve over time Non-functional properties, particularly performance and energy consumption Sampling, learning, and optimization methods from AI applied to software engineering Contemporary empirical methods in software engineering MLOps and DevOps practices Social aspects of AI-production teams His publication record reveals a consistent focus on creating accurate performance models for configurable software systems, with increasing attention to energy consumption aspects in recent years. The research trajectory shows evolution from basic performance modeling to incorporating machine learning techniques, transfer learning across environments, and addressing energy efficiency concerns. His scientific achievements have been recognized through several prestigious awards: ACM SIGSOFT Distinguished Paper Award at ICSE 2015 SPLC Best Paper Award in 2012 Multiple keynote invitations including at QAVS 2021 Open-Source Award from the state of Thuringia (2020) Best PhD award from University of Magdeburg (2013) Prof. Siegmund has secured significant research funding including DFG projects like 'Green Configuration' (€590,000) and 'Pervolution' (€290,000), as well as BMBF projects related to AI and software engineering. He has mentored numerous students through research projects and has served on program committees for major conferences including ICSE, ASE, ESEC/FSE, and SPLC. His teaching portfolio includes courses on Software Engineering for AI-Enabled Systems, Configurable Software Systems, and Automated Software Engineering.