Find Academic Supervisors

• Computer Systems
• Operating Systems
• Distributed Systems
• +2 more

Russ Ross is a Professor of Computer Science in the Department of Computing at Utah Tech University, teaching core systems courses including Computer Organization, Operating Systems, and Distributed Systems. His academic background includes: Graduation as valedictorian and National Merit Scholar from Pine View High School (1995) Bachelor of Science in Computer Science magna cum laude from Harvard University (2001) PhD in Computer Science from the University of Cambridge (2007) Research focuses on computer systems infrastructure, with teaching directly reflecting expertise in operating systems, distributed architectures, and low-level programming. Current courses demonstrate consistent engagement with hardware-software interfaces and system design principles across multiple semesters. Active mentoring occurs through CS 4800r (Undergraduate Research), where students pursue independent projects under his supervision. Office hours are maintained Monday-Thursday 3:00-4:00 PM in North Burns 226.

• Internet of Things
• Human Activity Recognition
• Machine Learning
• +5 more

Chiara Contoli is a Researcher (Tenure Track Assistant Professor) at the University of Urbino Carlo Bo, working within the Department of Pure and Applied Sciences (DiSPeA) under a fixed-term contract per Law 240/10. She specializes in Information Processing Systems (IINF-05/A 09/IINF-05) with a focus on intelligent systems for constrained devices. Her research centers on applying machine and deep learning techniques to Internet of Things applications, with emphasis on creating accurate and energy-efficient network models for low-power devices. She works across multiple domains including human activity recognition, power management optimization, and networked systems such as Software-Defined Networks. Dr. Contoli actively pursues interdisciplinary research that bridges technical computing with practical real-world applications. At the university, she teaches core computer science courses including Data Bases, Machine Learning and Deep Learning principles, and Web Technologies for Land Management across Informatics, Applied Informatics, and Economics programs. Her teaching spans both undergraduate and graduate levels with courses offered in multiple languages.

• Compiler Design
• GPU Programming
• Static Analysis
• +2 more

Fugen Tang is a researcher at the University of Science and Technology of China, specializing in programming languages and compiler design with significant contributions to the field of software engineering. His research interests focus on: Compiler Design and Optimization GPU Programming and CUDA Architecture Static Program Analysis Applications of Large Language Models in Software Engineering Programming Language Implementation Dr. Tang's recent work demonstrates a sophisticated integration of traditional compiler techniques with modern AI approaches. His publications reveal expertise spanning low-level GPU programming (PTX, CUDA) and high-level language analysis (Golang), with particular emphasis on optimization and decompilation tasks. The trajectory of his research shows increasing sophistication in applying machine learning to traditionally difficult compiler problems. Dr. Tang has established himself through publications at premier software engineering venues including ASE and SPLASH, where he has contributed innovative approaches to long-standing challenges in program analysis and compiler design.

Ben L. TitzerView profile

Researcher

• WebAssembly
• Programming Languages
• Virtual Machines
• +3 more

Ben L. Titzer is a researcher affiliated with Carnegie Mellon University specializing in programming language implementation and virtual machine technology. His work focuses on WebAssembly tooling, compiler design, and JavaScript engine optimization, with consistent contributions to major conferences including PLDI, SPLASH, and ICFP since 2015. His research interests center on WebAssembly infrastructure , where he develops tools for debugging, testing, and benchmarking WebAssembly modules. He has pioneered techniques in record-replay systems (Wasm-R3), program reduction, and diversified execution for uncovering elusive concurrency bugs. His work bridges theoretical compiler design with practical constraints of real-world deployment in browser and non-browser environments. Trends across his publications reveal a consistent focus on making WebAssembly more practical for developers through improved tooling and reliability. His recent work emphasizes standalone WebAssembly execution environments beyond browsers, addressing debugging challenges and performance bottlenecks in compilation pipelines. Earlier work examined JavaScript engine evolution (particularly V8) and memory management optimizations. Titzer actively contributes to the programming languages community through program committee service across multiple conferences including PLDI, POPL, SPLASH, and ISMM. He has chaired workshops and sessions on WebAssembly and virtual machine technologies, and delivered keynotes on WebAssembly's potential as a universal compilation target. As an organizer, he has served as Rebase Co-chair for SPLASH and co-chaired JENSFEST. His tutorial development work helps disseminate WebAssembly research tools to broader audiences. His research has practical impact through integration with major browser engines and WebAssembly toolchains.

• Political Science
• Development Economics
• Political Violence
• +4 more

Macartan Humphreys is the director of the Institutions and Political Inequality group at the WZB Berlin Social Science Center and holds the position of honorary professor of social sciences at both Trinity College Dublin and Humboldt University Berlin. His academic work spans political science, development economics, and methodological innovation, with a particular focus on causal inference, field experiments, and mixed-methods research. Humphreys' research interests encompass political violence, development economics, causal inference methodologies, and the political economy of natural resources. His work often examines how information technologies affect political engagement, the relationship between traditional and state authorities, and the impact of development assistance on collective action capacity. A significant portion of his recent work has focused on methodological innovations, particularly in integrating qualitative and quantitative approaches through causal modeling and Bayesian updating. Humphreys' publication record demonstrates a clear evolution from early work on civil wars and resource conflicts toward increasingly sophisticated methodological contributions. His most recent publications center on research design frameworks (MIDA), causal modeling for mixed-methods research, and applications of these approaches to pressing policy questions like vaccine acceptance during the pandemic. His work consistently bridges theoretical innovation with practical applications across diverse contexts, particularly in developing countries. Humphreys has authored or co-authored numerous influential publications in top journals including American Political Science Review, Journal of Conflict Resolution, and Science. His methodological contributions, particularly the DeclareDesign framework and the Integrated Inferences approach, have gained significant traction in political methodology circles. He has also contributed to important policy-relevant research on topics including democratic practices, gender quotas, and the political economy of development assistance. As director of the Institutions and Political Inequality group at WZB Berlin, Humphreys leads research examining how institutional arrangements affect political inequality. His work often involves large-scale field experiments conducted across multiple countries, demonstrating a commitment to rigorous empirical testing of theoretical propositions in real-world settings. His research has been supported by major funding agencies and has influenced both academic debates and policy discussions in international development contexts.

Qi XinView profile

Associate Professor

• Software Engineering
• Automated Program Repair
• Software Debugging
• +4 more

Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.

Arthur LeskView profile

Professor

• Genomics
• Structural Biology
• Protein Structure and Evolution
• +3 more

Arthur Lesk is a Professor of Biology and Biochemistry and Molecular Biology at Pennsylvania State University, affiliated with the BMMB Graduate Program and Bioinformatics initiatives. His research focuses on genomics, protein structure evolution, and biological network analysis, with notable contributions to mammoth genome sequencing and alternative splicing studies. Collaborations include work with Profs. Miller and Schuster on comparative genomics of mammoths and elephants. Lesk has authored influential textbooks like Introduction to Protein Science and pioneered methods for protein folding pattern classification. His lab explores structural bioinformatics, regulatory network motifs, and systems-level biological organization, bridging computational and experimental approaches. Research Interests: Genomics: Comparative analysis of protein-coding genes, alternative splicing consequences, and evolutionary pressures Structural Biology: Protein folding patterns, evolutionary conservation, and functional implications of structural changes Biological Networks: Regulatory network motif connectivity, transcription pathways, and hierarchical network assembly principles Key Projects: Decoding mammoth/elephant genome differences to study protein evolution Developing MUSTANG structural alignment algorithms Mapping network motif combinations in yeast regulatory systems Advising & Grants: While no specific students are listed, Lesk’s work has been supported by grants enabling large-scale genomic projects and computational infrastructure development. His lab contributes to the UCSC Genome Browser and structural bioinformatics tools. Labs/Teams: Active in cross-disciplinary collaborations within PSU’s Biochemistry department and international genome sequencing consortia.

Saeed AnwarView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Saeed Anwar is a Senior Lecturer at The University of Western Australia's School of Physics, Maths and Computing within the Department of Computer Science & Software Engineering. He maintains honorary affiliations as Visiting Scientist at CSIRO Data61 (since December 2024) and Honorary Faculty at the Australian National University (since July 2019). His research spans Computer Vision , Machine Learning , and AI with specialized expertise in Deep Learning, 3D Vision, Low-Level Vision, and Generative AI. Applications target Medical Devices, Agriculture, Transport, Disability support systems, and Government sectors through data-centric computer vision approaches. Dr. Anwar received Best Paper Awards at ICPR 2024 and RS 2022 with nominations at CVPR 2020 and PR 2019. He serves as Associate Editor for IEEE Journal of Oceanic Engineering and previously held editorial roles at Neurocomputing, contributing guest editorials for IEEE and MDPI journals. His recent work focuses on vehicle re-identification using synergistic deep learning architectures, demonstrating consistent contributions to computer vision pattern recognition. He actively supervises PhD and Master's students to completion while leading commercial industry projects as team lead.

Sam H. NohView profile

Professor

• Systems
• Storage Systems
• Cloud Computing
• +7 more

Sam H. Noh is a Professor in the Department of Computer Science at Virginia Tech’s College of Engineering. His research focuses on systems software for storage, cloud computing, distributed systems, and embedded/mobile systems, with a strong emphasis on optimizing storage efficiency, data management, and hardware-software co-design. He earned his Ph.D. in Computer Science from the University of Maryland, College Park (1993) and a B.S. in Computer Engineering from Seoul National University (1986). His work spans innovations in SSD optimization, non-volatile memory architectures, and fault-tolerant storage systems. Education: Ph.D., Computer Science, University of Maryland, College Park (1993) B.S., Computer Engineering, Seoul National University (1986) Research Interests: Storage Systems (SSD optimization, LSM trees, indexing) Non-Volatile Memory (NVM, CXL interconnects, persistent memory) Cloud and Distributed Computing (data management, performance scaling) Embedded Systems (real-time data handling, mobile systems) Security in Storage (atomic writes, fault tolerance) Recent Trends in Publications: His recent work emphasizes reducing write amplification in storage systems, optimizing compaction in log-structured systems, and exploring emerging memory technologies like CXL and ZNS SSDs. His research bridges low-level hardware architectures with high-level system software to enhance efficiency and reliability. Labs/Teams: While specific lab names are not mentioned, his contributions align with Virginia Tech’s research in storage systems, embedded computing, and distributed storage architectures.

Steven FlammiaView profile

Professor

• Quantum Information Theory
• Quantum Error Correction
• Quantum Computing
• +3 more

Steven Flammia is a Professor of Computer Science at Virginia Tech’s College of Engineering and Director of the Center for Quantum Architecture and Software Development at the Virginia Tech Innovation Campus. His research focuses on quantum error correction, quantum fault tolerance, and learning quantum noise dynamics. He has held prior roles at Amazon Web Services, University of Sydney, and University of Washington, with over 80 peer-reviewed publications and 9,000+ citations. Education & Experience: Postdoctoral Researcher, California Institute of Technology & Perimeter Institute (2000s) Professor at University of Sydney (2010s) Principal Research Scientist at Amazon Web Services (2020s) Research Interests: His work bridges theoretical foundations (e.g., quantum coding theory, representation theory) with practical implementations (e.g., noise modeling, quantum tomography). Key areas include developing fault-tolerant architectures, optimizing quantum error correction codes, and applying statistical learning to quantum systems. Recent Contributions: Recent papers explore Hamiltonian learning with Heisenberg limits, Zauner’s conjecture via number theory, and fiber bundle fault tolerance of GKP codes. His work emphasizes scalable quantum computing solutions under realistic noise conditions. Grants & Leadership: Leads the Center for Quantum Architecture, overseeing faculty hiring and strategic direction for quantum software/hardware integration. Collaborates on projects funded by industry and academic partnerships.

Borivoje NikolicView profile

Professor

• Integrated Circuits (VLSI)
• Computer Architecture
• Signal Processing
• +3 more

Borivoje Nikolic is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He holds a Ph.D. from UC Davis (1999) and academic degrees from the University of Belgrade (Dipl.Ing. 1992, M.Sc. 1994). His research focuses on integrated circuits, VLSI design, and energy-efficient electronics, with contributions to analog/digital circuit design, signal processing algorithms, and wireless communication systems. Notable roles include Technical Program Chair for the 2022 Symposium on VLSI Technology and Circuits, and General Chair for the 2024 IEEE Symposium on VLSI Technology and Circuits. He was an IEEE Distinguished Lecturer (2014–2015). His work has led to over 100 publications in top-tier venues like ISSCC, IEEE Journal of Solid-State Circuits, and IEEE Transactions on Circuits and Systems. Research interests span: VLSI Design Automation and Generators Low-Power and Energy-Efficient Circuits Wireless Communication Systems and MIMO Architectures Robotics and Heterogeneous SoC Design His publications highlight innovations in clock generators, data converters, and hardware-software co-design for AI/ML acceleration. Awards include the NSF CAREER Award (2003), IEEE Fellow (2017), and the 2024 SSCS Innovative Education Award.

Amit LevyView profile

Assistant Professor

• Systems & Networking
• Security & Privacy
• Embedded Systems
• +8 more

Amit Levy is an Assistant Professor in the Department of Computer Science at Princeton University. He leads the Praxis lab and co-leads the SNS group, focusing on systems and security research. His work bridges practical system design with rigorous security guarantees, often leveraging programming language tools. Education: BSc in Computer Science and Economics, University of Washington (2009) PhD in Computer Science, Stanford University (2018) Research Interests: Secure system design for embedded and distributed environments Memory and type safety in low-level systems Trade-offs between security and performance Language-driven hardware-software co-design End-to-end data privacy Project Leadership: Principal developer of Tock OS , an embedded operating system written in Rust Co-founder of the Hails framework for web application data privacy Creator of Beetle for Bluetooth Low Energy access control Designer of Stickler for CDN integrity verification Architect of LARPs (Leak-Avoidant Resource Provisioners) for side-channel mitigation Scientific Awards: National Science Foundation CAREER award (2025) PhD Advisees: Natalie Popescu Anja Kalaba Shai Caspin Christopher Branner-Augmon Jingyuan (Leo) Chen Gongqi Huang Leon Schuermann Alumni: David Liu (PhD 2022), Ryan Torok (Masters 2022), Yue Tan (PhD 2024)

Klaus HofmannView profile

Professor

• Analog and Digital Integrated Circuits
• High-Voltage Generation Systems
• Sensor Integration in Robust Environments
• +5 more

Prof. Klaus Hofmann is a Professor at the Technical University of Darmstadt, leading the Department of Electrical Engineering and Information Technology. His research focuses on integrated circuits, high-voltage systems, and sensor-integrating machine elements, with applications in Industry 4.0 and robust environments. Previously, he held senior engineering roles at Siemens HL, Infineon Technologies AG, and Qimonda AG, specializing in semiconductor product development and advanced technology. Education: Holds a doctorate in engineering from TH Darmstadt (1997), with earlier studies at Ruhr University Bochum (Germany), Purdue University (USA), and the University of Cooperative Education Stuttgart. His academic journey includes training in electrical engineering and communications systems. Research Interests: Dr. Hofmann's work spans analog/digital circuit design, reconfigurable electronics, and sensor integration in mechanical systems. He explores emerging technologies like memristive devices and oscillator-based computing for optimization problems. His projects emphasize practical applications, such as energy-efficient sensor platforms and sustainable microelectronics education. Recent Work: Key contributions include studies on Germanium FETs, adaptive differential amplifiers, and sensor-integrating bolts for multi-axial force measurement. He collaborates on initiatives like the SENSOTERIC project to advance smart sensors and promotes STEM education through innovative teaching methods. Labs & Teams: Leads the Integrated Electronic Systems (IES) Group at TU Darmstadt, focusing on interdisciplinary research in circuits, systems, and emerging technologies.

Paula ButteryView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Language Cognition
• +5 more

Dr. Paula Buttery is a Professor at the University of Cambridge , affiliated with the Faculty of Modern and Medieval Languages and Linguistics and the Department of Theoretical and Applied Linguistics . She serves as the Director of the Automated Language Teaching and Assessment (ALTA) Institute , focusing on computational approaches to language learning and endangered language revitalization. Her research spans Natural Language Processing (NLP) , Computational Psycholinguistics , and Language Acquisition , with particular emphasis on non-canonical language forms (e.g., spoken learner language, aphasics) and low-resource language processing . She has lead projects funded by the Cambridge English Language Assessment , EPSRC , and the Wellcome Trust . Dr. Buttery’s recent publications explore topics such as disfluency detection , lexical diversity , subcategorization frames , and spoken learner language modeling . Her work bridges machine learning and cognitive science to develop tools for language education and endangered language preservation. Scientific Awards : Cambridge University Pilkington Prize for teaching (2015) She has contributed to interdisciplinary collaborations, including the Cambridge Cookie-Theft Corpus for speech analysis in brain-damaged patients and the UKPMC project for biomedical text mining. Her teaching centers on Computational Linguistics and Language Learning .

• Software Engineering
• Human-Computer Interaction
• End-User Development
• +2 more

Dongmei Gao is a Doctoral Researcher in the Department of Computer Science at Aalto University, working under Professor Fabian Fagerholm's supervision. Her research sits at the intersection of software engineering and human-computer interaction, with a specialized focus on low-code development platforms and end-user development methodologies. Her core research interests include: Software Engineering Human-Computer Interaction End-User Development Low-Code Development Platforms Developer Experience Measurement Gao's recent publications analyze episodic experiences of developers using low-code platforms through empirical studies, examining how these tools impact workflow efficiency and user satisfaction for non-professional developers. This work contributes to optimizing platform design for diverse user skill levels. As a doctoral researcher, she is advised by Professor Fabian Fagerholm and does not currently supervise students or hold independent research grants. She actively participates in Professor Fagerholm's research group at Aalto University, contributing to ongoing projects in human-centered software engineering.