Knowledge Graph Embeddings Professors and Supervisors | ApplySharif

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Madelon HulsebosView profile

Researcher

Technical University of Berlin (TU Berlin)Germany

Madelon Hulsebos is a Researcher at CWI in Amsterdam, where she leads the Table Representation Learning (TRL) Lab and contributes to the Database Architectures group. She is also a faculty member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Amsterdam unit. Her career bridges academia and industry, including a postdoctoral fellowship at UC Berkeley and prior industry experience in automating data analysis pipelines with ML. Education : PhD in Computer Science (University of Amsterdam, 2023), with research at Sigma Computing and MIT; Postdoctoral Fellow (UC Berkeley, 2024). Her research focuses on establishing tabular data as a key AI modality through Table Representation Learning , generative models for relational data, and robust systems for data analysis. Key interests include: Relational Table Embeddings LLMs for QA/text2SQL and data wrangling Retrieval over Data Lakes and Databases Agentic Systems for Data Science Democratizing insights from structured data Recent work highlights trends in benchmarking table retrieval (TARGET), semantic column detection (AdaTyper, Sherlock), and large-scale tabular data curation (GitTables, SchemaPile). These projects address challenges in metadata utilization, data lake search, and end-to-end systems for structured data. She has secured significant funding, including the NWO AiNed Fellowship Grant ($1M) for her 5-year DataLibra project. Madelon organizes workshops at NeurIPS , SIGMOD , and ACL , and reviews for top venues like VLDB and NeurIPS. Scientific Awards : NWO AiNed Fellowship Grant ($1M) She actively mentors students and collaborates on European AI initiatives, including monthly TRL seminars and workshops. Her lab's tools (GitTables, TARGET) are widely adopted for training foundation models on tabular data.

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Arman CohanView profile

Assistant Professor

Yale UniversityUnited States

Arman Cohan is an Assistant Professor of Computer Science at Yale University, affiliated with the School of Engineering & Applied Science. His research focuses on the intersection of Machine Learning and Natural Language Processing (NLP), particularly in language modeling, representation learning, retrieval systems, and applications in specialized domains such as scientific text processing. He earned his Ph.D. in Computer Science from Georgetown University and has received notable awards, including the Dr. Harold N. Glassman Distinguished Doctoral Dissertation Award (2019) and the EMNLP 2017 Best Long Paper Award. His work emphasizes ethical AI, robustness of LLMs, and interdisciplinary applications in healthcare, science, and education. Cohan's research group, the Yale NLP Lab, develops advanced techniques for multi-document summarization, adversarial fact-checking, and LLM-driven tools for scientific discovery. Recent projects include frameworks like SciBERT, Longformer, and ChemAgent, which enhance domain-specific reasoning and safety in AI systems. His publications address challenges in table reasoning, uncertainty expression, and multimodal reasoning, with applications in medical decision-making and educational problem-solving. He collaborates on initiatives like the Roberts Innovation Fund to advance AI in healthcare and environmental technology.

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Sirui LiView profile

Lecturer

Murdoch UniversityAustralia

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

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Jiaqi GongView profile

Associate Professor

University of AlabamaUnited States

Dr. Jiaqi Gong serves as Associate Professor in Computer Science and Adjunct Associate Professor in Mechanical Engineering at The University of Alabama's College of Engineering, while directing the Alabama Center for the Advancement of Artificial Intelligence. His academic foundation includes: B.S. in Engineering, China University of Geoscience (2004) Ph.D. in Engineering, Huazhong University of Science and Technology (2010) Dr. Gong's research pioneers human-AI convergence through cyber-physical systems and smart health technologies, developing mobile/wearable platforms to enhance human perceptual, cognitive, and physical capabilities. His work spans artificial intelligence, machine learning, computer vision, and IoT with applications in healthcare, environmental monitoring, and education. The Sensor-Accelerated Intelligent Learning (SAIL) laboratory he founded drives innovation in behavior change interventions, human movement modeling, and educational data mining. Recent publications reveal strong interdisciplinary trends: healthcare AI dominates with medication adherence prediction and surgical classification systems, while environmental applications feature flood-risk communication and drought analysis. His work increasingly integrates generative AI and LLMs across domains, demonstrating methodological innovation in federated learning, knowledge graphs, and explainable storytelling frameworks. Notable recognitions include: Best Student Paper Award, IEEE/ACM Connected Health Conference (2022) Best Student Paper Award, Body Sensor Networks Conference (2019) Data Challenge Win, IEEE Biomedical Health Informatics (2018) Best Paper Award, Body Area Networks Conference (2014) Best Demonstration Award, IEEE Wireless Health Conference (2014) Dr. Gong leads significant funded projects including a $2M CDC/NIOSH grant for first responder safety and $3M NSF funding for hydrologic research. As SAIL laboratory director, he mentors students in developing clinically deployed technologies for multiple sclerosis, dementia, and mental health. Future work focuses on scaling AI applications in chronic disease management and climate resilience through the Alabama AI Center. The SAIL laboratory (founded 2017) operates as a multidisciplinary hub developing wearable/mobile systems for health applications, with active collaborations across medical clinics and engineering departments for real-world deployment of behavior change interventions and movement analysis tools.

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Jundong LiView profile

Assistant Professor

University of VirginiaUnited States

Jundong Li is an Assistant Professor at the University of Virginia with primary appointment in the Department of Electrical and Computer Engineering and secondary appointments in Computer Science and the School of Data Science. He is affiliated with the School of Engineering and Applied Science and conducts research at the intersection of machine learning, data mining, and artificial intelligence. Education: Ph.D. in Computer Science, Arizona State University, 2019 M.Sc. in Computer Science, University of Alberta, 2014 B.Eng. in Software Engineering, Zhejiang University, 2012 His research focuses on graph machine learning , trustworthy and fair AI , and large language models . He investigates how to make deep learning models more interpretable, robust, and equitable, especially in graph-structured data and NLP applications. His work combines causal inference, feature selection, and model explanation techniques to build reliable AI systems. His recent publications (2024–2022) reveal a strong trend toward large language models , with topics including in-context learning, knowledge editing, and collaborative reasoning. Simultaneously, he continues pioneering research on fairness and interpretability in graph neural networks , addressing structural bias, adversarial attacks, and node attribution. His work is highly interdisciplinary, spanning computer science, data science, and social impact. Scientific Awards: SIGKDD Rising Star Award (2024) PAKDD Best Paper Award (2024) NSF CAREER Award (2022) SIGKDD Best Research Paper Award (2022) JP Morgan Faculty Research Award (2021, 2022) Cisco Faculty Research Award (2021) Stanford/Elsevier Top 2% Scientist (2024) Jundong Li actively advises graduate students, as seen in his co-authored papers with researchers like Song Wang, Yushun Dong, and Binchi Zhang. His research is generously funded by the National Science Foundation (NSF) through multiple programs including CAREER, III, SaTC, SAI, and S&CC, as well as by the Department of Energy (DOE) , Office of Naval Research (ONR) , Jefferson Lab , and industry partners including JP Morgan, Cisco, Netflix, and Snap . He leads a dynamic research group focused on advancing the frontiers of graph learning and trustworthy AI, with projects on causal inference, model unlearning, and explainable systems. His lab contributes to both theoretical foundations and real-world applications in public health, transportation, and network security.

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Vibhav GogateView profile

Professor

University of Texas at DallasUnited States

Vibhav Gogate is a Professor and Associate Head of Research at University of Texas at Dallas, specializing in machine learning and artificial intelligence. His research focuses on probabilistic graphical models, statistical relational learning, and integrating deep learning with graphical models. Professor Gogate has received numerous awards including the NSF CAREER Award (2017), Outstanding Researcher Award (2022, 2017), and Best Paper awards at top AI conferences. His research funding includes projects from NSF and DARPA. Education: PhD, University of California, Irvine MS, University of Maine BS, University of Mumbai Recent Publications: His research spans probabilistic inference, tractable models, and neural network approaches for efficient reasoning, with publications in NeurIPS, AAAI, UAI, and AISTATS. Recent work focuses on scalable inference methods and explainable AI systems for complex domains. Research Funding: Secured over $8M in grants from DARPA and NSF for projects in explainable AI and probabilistic reasoning. Teaching: Regularly teaches graduate and undergraduate courses in Machine Learning, Artificial Intelligence, and Advanced Statistical Methods.

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Jianming LiangView profile

Professor

Arizona State UniversityUnited States

Jianming Liang is a full professor at Arizona State University's College of Health Solutions, specializing in biomedical informatics, data science, and computer vision. His research focuses on self-supervised learning, foundation models, and improving transfer learning techniques for medical imaging applications. National Academy of Inventors Fellow (2021) ASU Faculty Innovation Award (2019) ASU Distinguished Faculty Award (2023) NIH R01 grant recipient Led lab producing FDA-approved medical imaging products His lab has developed multiple open-source frameworks like Ark , Foundation_X , and ModelsGenesis for medical image analysis. Team has received over 70 student research awards including NCWIT Collegiate and AMIA Ph.D. Dissertation honors. Key research contributions include: Anatomically consistent foundation models Domain-adaptive pretraining strategies Annotation-efficient deep learning Integrated classification/localization/segmentation frameworks 40+ US patents (50+ pending) Major publications demonstrate leadership in self-supervised learning for chest radiography, pulmonary embolism detection, and medical AI explainability.

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Ian HorrocksView profile

Professor

Graz University of TechnologyAustria

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA

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Stefania DumbravaView profile

Associate Professor

Télécom SudParisFrance

Stefania Dumbrava is an Associate Professor of Computer Science at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise) and a permanent member of the ACMES team in the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. She is also actively involved in the Property Graph Schema Working Group and the European Research Network on Formal Proofs (EuroProofNet). Education PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Interests Dumbrava's research lies at the intersection of formal methods and data management . She designs and verifies algorithms and systems for graph databases , with emphasis on property graphs , schema discovery , query optimization , and distributed graph processing . Recently, her work focuses on certifying large-scale distributed graph systems under the ANR JCJC VERDI project (2025–2029). Awards & Honors SIGMOD Best Paper Award 2023 – “PG-Schema: Schemas for Property Graphs” SIGMOD Research Highlight Award 2023 – “Threshold Queries” VLDB 2022 Best Regular Research Paper Runner-Up – “Threshold Queries in Theory and in the Wild” SIGMOD 2025 Distinguished Reviewer Award ICDE 2025 Best Program Committee Member Award EASST Best Software Science Paper Award, ICGT 2025 Students & Grants Dumbrava has supervised numerous research interns and is actively recruiting PhD students for her ANR VERDI project on verified foundations of large-scale distributed graph systems. She has also served on six PhD thesis committees as examiner since 2021. Labs & Teams She leads the ACMES research group within the SAMOVAR laboratory (Télécom SudParis, Institut Polytechnique de Paris), where her team develops formally verified graph-database engines and tools such as GRASP, VerDILog, and DatalogCert.

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Rex YingView profile

Assistant Professor

Stanford UniversityUnited States

Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.

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Debjit PalView profile

Research Fellow

Cornell UniversityUnited States

Debjit Pal is a Post-Doctoral Associate at the School of Electrical and Computer Engineering, Cornell University, and a member of the Computer Systems Laboratory. His research focuses on machine learning techniques for hardware verification, SoC validation, and FPGA optimization. Education: Ph.D. in Computer Engineering (University of Illinois at Urbana-Champaign, 2019) M.S. in Computer Science (IIT Kharagpur, 2012) B.E. in Electronics Engineering (Jadavpur University, 2008) Research Interests: Machine Learning for Electronic Design Automation (EDA) System-on-Chip (SoC) Verification Edge Intelligence as a Service Compiler Optimizations for Reconfigurable and High-Performance Computing Scientific Awards: IEEE CEDA Student Research Award (2016) Best Paper Nomination (ICCAD 2015, DAC 2018, ASP-DAC 2019) E. J. McCluskey Best Doctoral Thesis Competition Semi-Finalist (2020) Travel Grants for ICCAD/DAC/ASPDAC (2018-2019) Professional Roles: Technical Program Committee Member (DAC, VLSID), Reviewer (IEEE TVLSI, DATE, ICCAD). Collaborates with researchers like Zhiru Zhang and Shobha Vasudevan.

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Vasant HonavarView profile

Professor

Pennsylvania State UniversityUnited States

Dr. Vasant Honavar is a Professor of Computer Science and Informatics at Pennsylvania State University, holding the Edward Frymoyer Endowed Chair. He serves as Director of the Center for Artificial Intelligence Foundations and Scientific Applications and Associate Director of the Institute for Computational and Data Sciences. His expertise spans artificial intelligence, machine learning, causal inference, and bioinformatics. Honavar has led over $60M in research grants and mentored 36 PhD students, 30 MS students, and numerous undergraduates. He is a Fellow of the AAAS and recipient of NSF Director’s Awards. Education: Ph.D., Computer Science and Cognitive Science, University of Wisconsin–Madison (1990) M.S., Computer Science, University of Wisconsin–Madison (1989) M.S., Electrical and Computer Engineering, Drexel University (1984) B.E., Electronics Engineering, Bangalore University (1982) Research Interests: His work focuses on machine learning, causal inference, knowledge representation, health informatics, and algorithmic fairness. Notable contributions include scalable algorithms for big data analytics and predictive modeling, as well as computational infrastructure for interdisciplinary science. Awards: Fellow, AAAS (2018) ACM Distinguished Member NSF Director’s Award for Superior Accomplishment (2013) Edward Frymoyer Endowed Chair (2013) Leadership: Honavar co-founded the Penn State Center for Artificial Intelligence and led the NIH-funded Biomedical Data Sciences Ph.D. program. He is a Co-PI of the North East Big Data Innovation Hub and serves on editorial boards of journals like IEEE/ACM Transactions on Computational Biology and Bioinformatics. Lab & Teams: Directs the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics, fostering collaborations across computer science, life sciences, and health sciences.

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Georgia ChalvatzakiView profile

Professor

Technical University of DarmstadtGermany

Georgia Chalvatzaki is a Professor in the Department of Computer Science at TU Darmstadt, leading the PEARL-Lab (Interactive Robot Perception and Learning Lab) with a team of 13 PhD candidates and postdocs. Her research focuses on human-centered robotics, integrating perception, planning, and action to develop robots that adapt to dynamic environments through structured knowledge embedding. Key research areas: Robotics, Artificial Intelligence, Machine Learning, and Human-Robot Interaction Applications: Healthcare (assistive systems), logistics automation, and sustainable agriculture Notable innovation: SE(3)-Diffusionsmodelle for 3D spatial learning in robots Her work combines model-based robotics with modern learning techniques like reinforcement learning and graph-based neural networks, enabling robots to transfer knowledge across scenarios and adjust behavior contextually. Georgia has received significant accolades, including: ERC Starting Grant (2024) Alfried Krupp Prize (2025, €1.1 million) Ellis Scholar recognition in the European Lab for Learning and Intelligent Systems She actively promotes open science, diversity, and early-career researcher development, serving as a keynote speaker at major conferences like IROS and CoRL.

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Anil MadhavapeddyView profile

Professor

University of CambridgeUnited Kingdom

Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.

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Robert DickView profile

Professor

University of Michigan-Ann ArborUnited States

Robert Dick is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, part of the College of Engineering. He previously held roles as Associate Professor at Northwestern University and Visiting Professor at Tsinghua University. He earned his Ph.D. from Princeton University and a Bachelor's degree from Clarkson University. His research focuses on Embedded Systems, Learning Dynamics, Efficient Machine Learning, Privacy, and Censorship Resistance. Key themes include defining problems with correct costs/constraints, broadening access to information technology, mitigating negative tech impacts, and solving inference problems with limited resources. He leads the Embedded Systems Graduate Program and is a member of the Michigan Integrated Circuits Laboratory (MICL). His work spans thermal management, energy-efficient computing, and low-power wireless networks. Notable contributions include innovations in embedded system design, machine vision frameworks, and sensor networks. Courses taught include EECS 507 (Embedded Systems Research), EECS 373 (Embedded System Design), and ENGR 100 (Autonomous Systems). His recent publications emphasize AI model analysis, energy-efficient networks, and environmental sensing. Projects include MemX (attention-aware wearable tech) and LoRa-based LPWAN protocols. Dick co-founded Stryd, a company commercializing embedded systems innovations.

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