Find Academic Supervisors

• Artificial Intelligence
• Computational Biology
• Graph Neural Networks
• +5 more

Pietro Liò is a Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he is also a member of the Artificial Intelligence group. His work bridges computer science and biomedical applications, with a strong focus on advancing AI methods for healthcare. Research Interests: His research is centered on developing Artificial Intelligence and Computational Biology models to unravel the complexity of diseases and support personalized and precision medicine. A current emphasis is on Graph Neural Network modeling, leveraging topological data structures to represent biological and medical systems. His interdisciplinary background enables innovative approaches at the intersection of machine learning and life sciences. The trends in his research, though no specific articles are listed, indicate a strong focus on AI-driven biomedical discovery, particularly using deep learning on structured data for health applications. This includes modeling biological networks, disease mechanisms, and patient-specific conditions through advanced neural architectures. Scientific Awards: As a principal investigator and academic leader, Pietro Liò likely supervises PhD and postdoctoral researchers and secures research grants in AI for health, though specific advisees and funding details are not mentioned in the text. His dual PhD background and affiliation with a leading AI group suggest a robust research program with significant grant involvement. He is associated with AI research activities at Cambridge, including seminars and software development, possibly contributing to or leading a research lab or initiative focused on AI applications in biology and medicine, though no formal lab name is provided beyond the general AI group membership.

• 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.

• Knowledge Representation
• Semantic Web
• Ontology Engineering
• +7 more

Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC

Xiaoning QianView profile

Professor

• machine learning
• Bayesian methods
• optimal experimental design
• +5 more

Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.

Thierry BadardView profile

Associate Professor

• Geospatial Big Data
• GeoAI
• GeoNLP
• +9 more

Thierry Badard is an Associate Professor at the Department of Geomatics Sciences , Université Laval, where he also serves as Director of the Center for Research in Geospatial Data and Intelligence (CRDIG) . With over 28 years of experience in geospatial science, he leads research initiatives at the intersection of GeoAI , LiDAR processing , and smart city technologies . Director, CRDIG (2016-2022) Steering Committee Member, Big Data Research Centre (CRDM) Researcher, Institute for Intelligence and Data (IID) Research Expertise spans geospatial big data, GeoNLP, and IoT applications for digital twins. His work addresses flood risk modeling , 3D urban analytics , and environmental monitoring through AI-driven solutions. Recent publications focus on contrastive learning for LiDAR segmentation and geospatial ontologies for early warning systems. Grant Leadership includes collaborative projects on smart insurance analytics (2018-2025), Arctic bioaerosol research (2019-2025), and Quebec-Morocco digital twin partnerships (2022-2023). He has advised 15+ graduate students in geomatics and related fields.

Tim ConradView profile

Researcher

• Bioinformatics
• Computational Biology
• Machine Learning
• +5 more

Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.

Lisa WillsView profile

Assistant Professor

• Computer Architecture
• Hardware Acceleration
• Hardware-Software Co-Design
• +5 more

Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.

David Zhigang PanView profile

Professor

• Electronic Design Automation
• Machine Learning
• Hardware Acceleration
• +5 more

David Zhigang Pan is a Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin. He also holds the Silicon Laboratories Endowed Chair. Prior to joining UT Austin, he was a Research Staff Member at IBM T. J. Watson Research Center from 2000 to 2003. His academic journey began with a B.S. from Peking University, followed by M.S. and Ph.D. degrees from UCLA. Research Areas: Electronic Design Automation (EDA), Machine Learning Hardware, FPGA Prototyping, Optical Computing, Hardware Security, and CAD for Emerging Technologies Academic Timeline: Assistant Professor (2003-2008), Associate Professor (2008-2013), Full Professor (2013-present) His research focuses on design automation for mixed-signal circuits , GPU-accelerated EDA tools , and hardware-software co-design for AI . Recent work explores FFT-based optical neural networks and deobfuscation techniques for integrated circuits , reflecting his interdisciplinary approach at the intersection of machine learning , computer architecture , and semiconductor manufacturing . Key publication trends reveal expertise in: VLSI design , lithography optimization , and deep learning applications for EDA tools. His work has been recognized with multiple Best Paper Awards at top conferences including DAC , ASP-DAC , and HOST . Awards: IEEE Fellow (2014), SPIE Fellow (2017), ACM SRC Graduate Category Honors for students Patents: 8 U.S. Patents in electronic design and hardware optimization Prof. Pan has mentored 40 PhDs and postdocs who now hold key positions in academia and industry. He leads research initiatives involving GPU acceleration frameworks and optical computing architectures . His lab focuses on vertical integration of architecture, CAD tools, and fabrication technologies for next-generation hardware solutions.

Tao HouView profile

Assistant Professor

• Topological Data Analysis
• Computational Topology
• Topological Machine Learning
• +5 more

Tao Hou is an Assistant Professor in the Department of Computer Science at the University of Oregon, where he conducts research at the intersection of computational topology and machine learning. His academic journey includes a Ph.D. in Computer Science from Purdue University, a M.E. in Software Engineering from Tsinghua University, and a B.E. in Software Engineering from Beijing Institute of Technology. His research focuses on improving computational methods for topological data analysis, particularly through efficient algorithms for zigzag persistence and its applications across domains like neuroscience and materials science. Interdisciplinary applications in neuroscience (MICCAI 2024) and computational materials science (Comp. Mat. Sci. 2022) Developed open-source Python software packages for persistent cycle computation Contributed to advancements in zigzag persistence computational complexity Current research explores topological machine learning through projects like FastZigzag and LvlsetPersCyc . He teaches graduate courses on topological data analysis and algorithms theory, and actively seeks PhD students interested in combining mathematics with computer science.

Robert PatroView profile

Associate Professor

• Computational Biology
• Genomics
• Algorithms
• +8 more

Rob Patro is an Associate Professor in the Department of Computer Science at the University of Maryland, with an appointment at the University of Maryland Institute for Advanced Computer Studies. His work bridges computational biology and computer science, focusing on algorithm design and data structures for genomics applications. Education: Ph.D. in Computer Science, University of Maryland, College Park (2012) B.S. in Computer Science, University of Maryland, College Park (2006) with academic and departmental honors Patro’s research centers on developing computational methods for analyzing high-throughput genomics data, combining algorithmic innovation with statistical inference. His work extends to programming languages, parallel computing, and machine learning applications in biology. His publications at ISMB, RECOMB, and in journals like Nature Methods and Cell Systems highlight advancements in RNA-seq quantification, metagenomic analysis, and compressed genomic representations. These contributions emphasize efficiency and scalability in genomic data processing.

Yinzhi CaoView profile

Associate Professor

• Security and privacy of machine learning
• Web security and privacy
• Mobile security and privacy
• +1 more

Yinzhi Cao is an Associate Professor at the Johns Hopkins University Department of Computer Science . He serves as Technical Director of the Johns Hopkins Information Security Institute and is affiliated with the Data Science and Artificial Intelligence Institute and the Institute for Assured Autonomy . Cao joined JHU in 2018 from Lehigh University, where he was an Assistant Professor. Doctor of Philosophy (PhD) in Computer Science, Northwestern University (2014) Bachelor of Engineering (BE) in Electronic Engineering, Tsinghua University (2008) Research Interests focus on security and privacy of web, mobile, and machine learning systems . Key projects include Vulnerability Analysis of Web Applications and Security, Privacy, and Fairness Analysis of ML Systems . His work addresses prototype pollution in JavaScript, node.js vulnerabilities, browser fingerprinting, federated learning privacy, and automated exploit generation. Scientific Recognition includes the NSF CAREER Award (2021) DARPA Young Faculty Award (2022) & Director's Fellowship (2024) Amazon Research Awards (2022, 2017) IEEE Security & Privacy Test of Time Award (2025) Distinguished Paper Awards at IEEE S&P 2025, CCS 2023, USENIX Security 2022 Advising & Grants highlight mentorship of 20+ PhD and Master’s students across institutions. Major grants include $1.2M collaborative CICI TCR grant (2024-2026) with Dr. John Aucott $750K DARPA YFA grant (2022-2025) $500K NSF SaTC grant (2022-2025) NSF EAGER grant (2016-2017) Labs & Teams : Affiliated with Johns Hopkins Information Security Institute , Data Science AI Institute , and Institute for Assured Autonomy . Collaborates with institutions like Columbia, UC Santa Barbara, and SRI International. His group investigates real-world vulnerabilities in over 2,500 websites and NPM packages, uncovering 80+ zero-day issues.

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +5 more

Adjunct Professor Evgeny Osipov is affiliated with La Trobe University's Business Analytics department. His research spans artificial intelligence, hyperdimensional computing, and neural network architectures. Academic Rank: Adjunct Professor Department: Business Analytics Email: E.Osipov@latrobe.edu.au Research interests include: Hyperdimensional computing and vector symbolic architectures Spiking neural networks and reservoir computing Hardware-efficient AI implementations Causal reasoning in large language models Self-organizing maps and spatiotemporal sequence learning Applications in smart cities and robotic navigation Recent research outputs demonstrate expertise in: Developing unsupervised learning frameworks using hypervectors Optimizing reservoir computing with cellular automata Creating memory-efficient neural network models Advancing hyperdimensional classification techniques Exploring causal graph integration in language models

Sean RenView profile

Associate Professor

• Natural Language Processing
• Commonsense Reasoning
• Graph Neural Networks
• +3 more

Sean Ren is an Associate Professor in Computer Science at the University of Southern California, where he holds the Andrew and Erna Viterbi Early Career Chair. He directs the INK Research Lab and serves as Research Team Leader at USC's Information Sciences Institute. Affiliated with the USC NLP Group and Machine Learning Center, his research focuses on developing robust NLP systems through knowledge-aware architectures and data-efficient learning. His research interests include: Evaluation methods exposing NLP limitations in reasoning tasks Augmenting models with commonsense/knowledge via novel algorithms Graph neural networks for relational inference Model robustness verification and enhancement Neural-symbolic integration for interpretable AI Recent publications demonstrate strong emphases on language model reasoning, knowledge distillation, and compositional generalization. His group's ACL/NeurIPS papers frequently address robustness gaps in state-of-the-art models. Honors include: ACL Outstanding Paper (2023) MIT TR Innovator 35 Asia Pacific (2023) NSF CAREER Award (2021) Forbes 30 Under 30 (2019) ACM SIGKDD Dissertation Award (2018) Research is supported by NSF, DARPA, IARPA, and industry partners (Google, Amazon, Meta). He leads the INK Lab with focuses on label-efficient learning and knowledge-guided NLP, while actively recruiting PhD students for projects bridging symbolic and neural paradigms.

Lisa Wu WillsView profile

Assistant Professor

• Computer Architecture
• Hardware-Software Co-design
• Hardware Accelerators
• +6 more

Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.

Alessandro AliakbargolkarView profile

Professor

• Federated Satellite Systems
• CubeSat Constellations
• Spacecraft Systems Architecture
• +3 more

Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994