Find Academic Supervisors

Walid G. ArefView profile

Professor

• Databases
• Data Mining
• Spatial Databases
• +6 more

Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.

Ryan P. HuangView profile

Associate Professor

• Computer Systems
• Operating Systems
• Distributed Systems
• +7 more

Ryan P. Huang is an Associate Professor in the Computer Science & Engineering department at the University of Michigan, College of Engineering, where he leads the Order Lab. Previously, he was an Assistant Professor at Johns Hopkins CS department from 2017 to 2022. His research focuses on computer systems, particularly operating systems and distributed systems, with emphasis on reliability, efficiency, and defensibility across cloud data centers and mobile devices. Dr. Huang's research interests center on pushing the boundaries of cloud systems availability and observability. His work addresses critical challenges such as gray failures and partial failures in distributed systems, developing principled techniques for failure detection and localization. His research spans multiple thrusts including Panorama for enhanced observability, Watchdog for runtime checking, OmegaGen for partial failure localization, and Narya for predictive failure mitigation. He also investigates energy-efficient mobile systems and system misconfiguration prevention. His recent publications demonstrate a strong trend toward addressing silent failures in distributed systems, with multiple papers accepted to top-tier conferences including SOSP and OSDI in 2025. His work bridges theoretical principles with practical system implementations, focusing on real-world challenges in cloud infrastructure and distributed computing environments. NSF CAREER award recipient Multiple Best Paper Awards (OmegaGen, Argus, LeaseOS) CRA Outstanding Undergraduate Researcher Award honorable mentions for advisees Dennis Ritchie doctoral dissertation award honorable mention Dr. Huang actively mentors PhD students including Yuzhuo Jing, Wanning He, Yuxuan Jiang, and others. His lab has produced graduates who have gone on to faculty positions at institutions like University of Virginia and Boston University. He serves on program committees for major systems conferences including SOSP, OSDI, and NSDI, contributing significantly to the academic community. The Order Lab maintains active research collaborations and regularly publishes in top-tier venues, with multiple papers accepted to SOSP and OSDI in 2025.

Sainyam GalhotraView profile

Assistant Professor

• Databases
• Data Management
• Responsible Data Science
• +7 more

Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for effective and responsible analytics, leveraging techniques from causal inference, data management, theoretical computer science, machine learning, and human-computer interaction to address challenges in trustworthy system design including robustness, explainability, and fairness. Education: Postdoc: University of Chicago PhD: University of Massachusetts Amherst (supervised by Barna Saha) BTech: Indian Institute of Technology Delhi (IIT Delhi) (supervised by Prof. Amitabha Bagchi) Research Interests: Dr. Galhotra's research spans several interconnected areas in data science and artificial intelligence. His work primarily focuses on Responsible Data Science , where he develops methods to ensure that data-driven systems operate fairly and transparently. Within this broad area, his specific interests include: Causal Inference techniques for understanding cause-effect relationships in complex data Algorithmic Fairness approaches to mitigate bias in machine learning systems Explainable AI methods that make black-box models more interpretable Data Management systems for efficient and reliable data processing Entity Resolution techniques for integrating data from multiple sources Trustworthy System Design that addresses robustness, explainability, and fairness His recent publications demonstrate a clear trend toward developing frameworks that combine causal reasoning with practical data management systems, particularly focusing on how to make data-driven decisions more transparent and equitable. The intersection of database systems with fairness considerations appears to be a particularly active area of his research. Scientific Awards: Rising Star in Data Science at the Data Science Institute, UChicago (Oct 2021) Computing Innovation Fellowship Award Recipient (by CRA, CCC and NSF) (Apr 2021) DAAD AInet Fellow (Feb 2021) ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist (May 2020) Most reproducible paper award in SIGMOD 2018 and 2019 (Jun 2019) First recipient of Krithi Ramamritham Computer Science Scholarship (Jun 2019) Best paper award in SIGSOFT FSE 2017 (May 2017) Dr. Galhotra is actively seeking students to collaborate with on his research projects. His work has been supported by various fellowships and awards, including the prestigious Computing Innovation Fellowship. He has mentored several students through his research projects, with a focus on developing the next generation of data scientists who can build responsible and trustworthy systems. His research group appears to focus on the intersection of database systems and responsible AI, developing tools like HypeR for causal reasoning, Ver for view discovery, and Nexus for correlation discovery in spatio-temporal data. This work suggests a cohesive research agenda centered around making data systems more transparent, fair, and user-friendly.

• Digital Media Art
• Electronic Music
• Hip-Hop
• +5 more

Paul D. Miller, known professionally as DJ Spooky, That Subliminal Kid, is a Professor of Music and Mediated Art at The European Graduate School (EGS). Based in New York City, he is an experimental electronic and hip-hop musician, conceptual artist, and writer whose work spans music production, film, multimedia installations, and critical theory. He has released over a dozen albums pioneering 'illbient' and trip hop genres, authored influential books including Rhythm Science and Sound Unbound , and exhibited internationally at venues like the Venice Biennale and Andy Warhol Museum. Education: Bowdoin College: Studied philosophy and French literature; thesis on Richard Wagner's Gesamtkunstwerk as precursor to new media. Research Interests: Miller's work centers on remix culture as a postmodern methodology, exploring intersections of sound, image, and technology. His theoretical framework positions the DJ as a 'post-subjective auteur' who creates meaning through layered connections across time, culture, and media. Key projects include Rebirth of a Nation (reworking D.W. Griffith's film to address racial history) and Terra Nova (Antarctica sound recordings addressing climate change), demonstrating his focus on historical reinterpretation and environmental crisis through digital media. Publications: His 14 scholarly articles (2001-2009) analyze remix as cultural practice across domains from album art history ( The Inner Sleeve ) to Baudrillard's philosophy ( Baudrillard: A Remembrance ). The corpus reveals consistent investigation into how sampling, digital layering, and media archaeology reshape narrative, memory, and cultural identity in the information age. Awards: No scientific awards or fellowships were documented in the source text. Advising and Grants: The source text provides no information regarding students supervised, academic advising, or research grants administered by Miller. Labs and Teams: Miller operates through project-based collaborations rather than fixed labs, forming temporary collectives with institutions like the American Museum of Natural History (for Terra Nova ) and artists including Yoko Ono, Ryuichi Sakamoto, and choreographer Francesca Harper. His 'Unfinished Stories' project with Pulitzer-winning critic Margo Jefferson exemplifies his interdisciplinary approach bridging dance, music, and social commentary.

Timothy HarrisView profile

Lecturer

• Distributed Systems
• Operating Systems
• Machine Learning
• +5 more

Timothy Harris is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology, where he jointly teaches courses on multicore semantics and programming. Currently, he works at OpenAI, focusing on performance optimization for GPU inference of large language models, including the Azure OpenAI Service. Previously, he held roles at Microsoft, AWS, Oracle Labs, and was a faculty member at the University of Cambridge (2000–2004). His research spans distributed systems, runtime systems, operating systems, and high-performance computing, with an emphasis on scalability and performance. He contributed to projects like the Xen hypervisor and the Barrelfish research OS. Key research interests include distributed training of PyTorch models in the ONNX runtime, large-scale storage performance with Amazon S3, and runtime systems for in-memory graph analytics. His work often bridges 'big data' and high-performance computing techniques. Notable contributions include the book Transactional Memory (2010) and the Barrelfish OS, alongside numerous publications in top-tier conferences like SOSP, ASPLOS, and EuroSys. He has served as PC chair for ISMM 2025, VEE 2017, and EuroSys 2015, reflecting his leadership in the systems research community. His awards include a Best Paper Award at PACT 2010. Beyond academia, Harris is an avid hiker, aiming to complete the UK coastline, and maintains a photography portfolio at tlhphotography.uk .

Desmond ElliottView profile

Associate Professor

• Vision-Language Models
• Multimodal Machine Learning
• Natural Language Processing
• +5 more

Desmond Elliott is an Associate Professor and Villum Young Investigator at the Department of Computer Science, University of Copenhagen. His research focuses on vision-language models, multilingual and multimodal processing, with particular emphasis on tokenization-free language modeling approaches. He leads a research group actively working on pixel language models and cross-lingual multimodal understanding. University of Copenhagen, Department of Computer Science Villum Young Investigator Associate Editor for JAIR (2025-2028) Senior Area Chair for ACL 2025 Elliott's research spans vision-language integration, multilingual NLP, and multimodal machine learning. His work explores how language models can operate directly on visual pixels without traditional tokenization, enabling more seamless integration of vision and language processing. He investigates compositional generalization in multimodal systems, retrieval-augmented image captioning, and cross-lingual transfer in vision-language tasks. His group develops methods for low-resource language processing and creates benchmarks for evaluating multimodal systems across diverse cultural contexts. His recent publications demonstrate strong trends in pixel-based language modeling, synthetic dataset generation through retrieval augmentation, and multilingual vision-language processing. The work spans theoretical advances in model architectures and practical applications in areas like medical text analysis, food culture understanding, and social media content moderation. His research often bridges computer vision and natural language processing with a focus on making these technologies accessible across diverse languages and cultures. Best Paper Honorable Mention at CVPR Visual Concepts Workshop 2025 Best Long Paper Award at EMNLP 2021 Area Chair Favourite paper at COLING 2018 Elliott actively supervises student projects in BSc and MSc programs related to his research interests. His research has received substantial funding from Google (2024-2025), Facebook (2022-2024), Villum Foundation (2021-2026), Novo Nordisk Foundation (2019-2024), and European Union (2023-2026). He regularly recruits postdocs for projects including the Danish Foundation Models project and the Responsible AI for the People Project. His group holds regular meetings on Tuesdays from 13:00-14:00 in IF G.03, with an active mailing list for announcements. The research environment appears collaborative, with frequent co-authorship across institutions and regular participation in major NLP and computer vision conferences.

• Information Systems
• Model-Driven Engineering
• Requirements Engineering
• +2 more

Alberto Rodrigues da Silva is a Professor at the Institute Superior Técnico , part of the University of Lisbon . He teaches Fundamentals of Information Systems , primarily during the 1st Semester of the 2025/2026 academic year. His scientific interests revolve around Information Systems , Model-Driven Engineering (MDE), Requirements Engineering (RE), Social Computing , and Software Engineering . He has extensively contributed to the development of rigorous requirements specification languages like RSL (Requirements Specification Language) and its extensions (e.g., RSL-IL4Privacy for privacy policies). His collaborative work spans automated acceptance testing, GDPR compliance, and domain-specific languages (DSLs) for applications such as mobile development , digital twins , and legal contexts (e.g., LegalLanguage ). His research trends focus on integrating model-driven engineering with privacy policies , IoT applications , and low-code platforms . He has also explored tools like Maestro for data classification and usability testing, and RiverCure for flood simulation. Email: alberto.silva@tecnico.ulisboa.pt .

Mark BattyView profile

Professor

• Concurrent Systems
• Formal Verification
• Programming Languages
• +3 more

Mark Batty is a Professor in the School of Computing at the University of Kent, specializing in formal methods for concurrent systems. His work bridges hardware-software interfaces, focusing on memory models for C/C++, OpenCL, and architectures including x86, ARM, POWER, and GPUs. As a member of the Programming Languages and Systems Research Group, he develops mathematical specifications and verification tools for real-world concurrency challenges. His research centers on empirical testing of hardware/compiler behavior, formal modeling of system components, and verification of fine-grained concurrent algorithms. Key contributions address relaxed memory semantics, transactional memory, and compositional reasoning for concurrent data structures. His work combines theoretical rigor with practical tool development to ensure correctness in complex concurrent environments. Analysis of his 2015-2025 publications reveals consistent focus on memory consistency models, formal verification of weak memory concurrency, and compiler optimizations. Dominant themes include C/C++11 standards, GPU concurrency semantics, and mechanized verification techniques. His research demonstrates strong industry relevance through collaborations with hardware vendors and contributions to language standards. Mark Batty has received significant recognition: John C. Reynolds Doctoral Dissertation Award (2015) from ACM SIGPLAN CPHC and BCS Distinguished Dissertation Award (2015) Lloyds Register Foundation and Royal Academy of Engineering Research Fellowship (2016) He actively leads major research initiatives and mentors next-generation researchers: Current Funding: EPSRC Standard Grant 'Verifiably Correct transactional memory' (2018), VeTTS Grant 'Specification and verification of C++ data structure libraries' (2018), EPSRC First Grant 'Compositional, dependency-aware C++ concurrency' (2018) PhD Recruitment: Actively seeking candidates for UKRI-funded studentship in Verified Trustworthy Software Systems Batty drives community engagement through Kent Concurrency Workshop (2016) and South of England Programming Language Seminars, fostering national collaboration in programming languages research. His leadership in organizing Royal Society discussions underscores his influence in trustworthy systems verification.

Dimitris SamarasView profile

Professor

• Computer Vision
• Machine Learning
• Medical Imaging
• +5 more

Dimitris Samaras is a SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He leads the Computer Vision Lab and holds adjunct roles in Biomedical Informatics and Radiology. His research focuses on computer vision, machine learning, medical imaging, and computational behavioral sciences, with interdisciplinary collaborations in psychology and neuroscience. Education: Ph.D. in Computer Science (University of Pennsylvania, 2001), M.S. in Computer Science (Northeastern University, 1994), Diploma in Computer Engineering (University of Patras, Greece, 1992). Research Interests: Modeling 3D shape and illumination interactions, facial expression analysis, medical image analysis, and applying machine learning to brain imaging. Current funded projects include NIH/NIDA grants, NSF initiatives, and collaborations with institutions like Brookhaven National Lab and Adobe. Publications: Over 150 peer-reviewed papers in top venues like ICCV, CVPR, and MICCAI, with impactful work on shadow removal, face relighting, and digital pathology. Recent trends emphasize medical AI, generative models, and multimodal interactions. Awards: SUNY Chancellor’s Award (2018), Dean’s Millionaire’s Club (2016), and multiple NIH/NSF grants. Recognized for contributions to scholarship and creative activities in academia. Grants & Teams: Leads over $10M in active grants, including projects on AI for penguin population tracking, histopathology image analysis, and robotic assistance. Collaborates with interdisciplinary teams in medicine, engineering, and cognitive science. Labs & Initiatives: Directs the Computer Vision Lab, contributes to the ColdSteel/NSF CVDI-NY SPIR consortium, and co-leads the Sensor and Transportation Security Center with Farmingdale State College.

• Programming Language Design
• Program Verification
• Type Theory
• +3 more

Neelakantan R. Krishnaswami is a Professor of Computer Science at the University of Cambridge's Computer Laboratory , and a Fellow of Trinity College . His research focuses on the intersection of program verification, programming language design, and foundational topics like type theory and semantics. His work spans areas such as refinement types, parser design, separation logic for systems software, and the semantics of reactive programming. Notable contributions include the Datafun language for higher-order Datalog and the λert type theory for explicit refinement types. He has also developed foundational frameworks for verifying imperative programs using advanced type systems and logical relations. Key publications include 'Explicit Refinement Types' (ICFP 2023), 'flap: A Deterministic Parser with Fused Lexing' (PLDI 2023), and 'CN: Verifying Systems C Code' (POPL 2023). His work frequently addresses challenges in efficiency, correctness, and modularity for both functional and imperative systems. His awards include Distinguished Paper Awards at PLDI 2019 and POPL 2020. His research integrates theoretical rigor with practical tooling, exemplified by contributions to languages like Coq, Lean, and Haskell.

• Robotics
• Control Systems
• Multi-Agent Systems
• +6 more

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

Stefano GogiosoView profile

Academic

• Quantum Computing
• Quantum Theory
• Quantum Software
• +3 more

Stefano Gogioso is a Departmental Lecturer at the University of Oxford , specializing in quantum theory and quantum software. He holds a DPhil in Computer Science from Oxford (2013–2017) and advanced degrees from Cambridge (MASt, BA) and the University of Genova (MSc, BSc). As a Fellow at Kellogg College and co-founder of Hashberg Ltd , he develops quantum programming tools and focuses on quantum causal structures, quantum field theory, and natural language processing applications. His research bridges foundational quantum theory with practical applications, including near-term quantum computing and educational outreach through visual methods like Quantum in Pictures . Research Interests: Quantum foundations, quantum software, categorical quantum mechanics, quantum field theory, and quantum causality. His work emphasizes pictorial formalisms and compositional methods, with contributions to indefinite causality, quantum cellular automata, and quantum natural language processing (QNLP). Key Contributions: Published over 25 papers, including works on causal polytopes, categorical Feynman diagrams, and QNLP pipelines. Co-developed Hashberg 's quantum programming tools and serves as a mentor for AI initiatives at CDL-Oxford. His thesis introduced dynamics in categorical quantum mechanics, addressing symmetry and quantum clocks. Teaching: Teaches quantum computing courses for MSc/MFoCS students, professionals, and continued education. Courses include Quantum Software , Quantum Computing for Software Engineers , and bespoke corporate training. Labs/Teams: Part of the Oxford Quantum Group and involved in collaborative projects with industry and academia. Advising: Supervised students like Nicola Pinzani (causal orders) and Maria Stasinou (quantum field theory). Grants/Awards: Not explicitly listed, but recognized for contributions to quantum foundations and education.

Julien DutantView profile

Lecturer

• Epistemology
• Formal Epistemology
• Philosophy of Language
• +2 more

Julien Dutant is a Lecturer in Philosophy at King's College London, affiliated with the Department of Philosophy within the Faculty of Arts & Humanities. His research examines epistemological frameworks, including the principles of knowledge, rational belief, and epistemic value. He holds a Ph.D. from the University of Geneva (2010) and has held research positions at the University of Oxford and University of Michigan. Dutant's primary research explores foundational questions in epistemology, formal models of knowledge, and the semantics of propositional attitudes. His work integrates philosophical analysis with formal methods to address epistemic norms and contextual variations in knowledge attribution. His publications demonstrate sustained focus on refining theories of justification, rationality, and epistemic norms. Recent research trends include investigations into practical rationality, epistemic utility, and the interplay between evidence and ignorance. Dr. Dutant teaches undergraduate and postgraduate courses including Introduction to Philosophy, Methodology, Intermediate Logic, and advanced topics in epistemology. He maintains active engagement with philosophical communities through invited talks and editorial roles.

• Artificial Intelligence
• Healthcare Informatics
• Data Science
• +5 more

Luís B. Elvas is an Assistant Professor at ISCTE-University Institute of Lisbon's Department of Social and Business Sciences (SINTRA) and a Research Assistant at ISTAR-Iscte Research Center. He holds qualifications including a Technical Specialization in TensorFlow for AI (Coursera, 2021) and certifications in IoT/Blockchain from ISCTE and cybersecurity from Palo Alto Networks. His research spans artificial intelligence, healthcare informatics, smart cities, and blockchain, with applied work in medical imaging, data sharing, and urban analytics. Research interests include: Healthcare AI : Developing deep learning models for cardiac diagnostics, medical imaging analysis, and blockchain-based health data systems Smart Cities : Implementing IoT solutions for urban mobility optimization, disaster management, and sustainable transportation Data Science : Creating predictive analytics frameworks for clinical decision support and urban planning His publications demonstrate a strong focus on AI-driven healthcare solutions (67% of recent works) and smart city technologies (33%), with emerging interests in blockchain and NLP. Research consistently targets real-world applications in clinical settings and urban environments. Awards: Award for best internship, Order of Engineers (2022) Distinction for best internship, Order of Engineers (2021) He leads/contributes to multiple EU research consortia including AMR-EDUCare (antimicrobial resistance education), NEEM (e-health in Nepal), and Blockchain.PT. Coordinates the IEEE Computational Intelligence Society Student Branch Chapter at ISCTE and developed the ManagiDiTH master's program in digital health transformation.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +12 more

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols