Find Academic Supervisors

Dahlia MalkhiView profile

Professor

• Distributed Systems
• Blockchain Technology
• Financial Technology
• +3 more

Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.

• Homomorphic Encryption
• Big Data Analysis
• Database Systems
• +6 more

Hayato Yamana serves as Professor at Waseda University's Faculty of Science and Engineering and concurrently holds the position of Vice President for IT Promotion and Chief Information Officer since October 2020. His academic journey began with a Dr. Eng. degree from Waseda University in 1993, followed by positions at the Electrotechnical Laboratory of MITI, before joining Waseda University as Associate Professor in 2000 and becoming full Professor in 2005. He has held significant leadership roles including Director of the Database Society of Japan and the Information Processing Society of Japan, as well as Vice Chair of IEICE's Information and Communication Society. Waseda University, Faculty of Science and Engineering (2005-Present) National Institute of Informatics, Visiting Professor (2005-Present) Waseda University, Vice President for IT Promotion (2020-Present) Deputy Chief Information Officer (2015-2020) His research spans homomorphic encryption, big data analysis, and computer architecture, with notable contributions in privacy-preserving computation, recommender systems, and secure data processing. His work bridges theoretical cryptography with practical applications in smart cities, healthcare, and e-commerce security. Recent publications demonstrate strong focus on accelerating homomorphic encryption operations, improving recommendation system diversity, and developing novel authentication mechanisms. Analysis of his 15 most recent publications reveals consistent emphasis on privacy-preserving technologies (particularly homomorphic encryption applications), innovative recommender system architectures, and biometric security solutions. His research group produces highly cited work at the intersection of cryptography, machine learning, and systems security, with practical implementations in real-world scenarios including smart grids, e-commerce, and healthcare. Fellow, Information Processing Society of Japan (IPSJ), 2020 Golden Core Award, IEEE Computer Society, 2018 Fellow, Institute of Electronics, Information and Communication Engineers (IEICE), 2018 IBM Faculty Award, 2009 Multiple Best Paper Awards from IEICE, IPSJ, and ITE Yamana has secured substantial research funding for projects in homomorphic encryption, smart city infrastructure, and privacy-preserving systems. His leadership extends to advising numerous doctoral students and directing major research initiatives including the Smart Systems and Services Innovative Professional Education Program. He maintains active collaborations with industry partners through projects involving secure computation and data analytics. His research group operates at the forefront of secure computing, with specialized laboratories focused on homomorphic encryption acceleration, privacy-preserving machine learning, and secure mobile authentication. The team actively develops practical implementations of cryptographic protocols for real-world applications in healthcare, finance, and smart city infrastructure, bridging theoretical cryptography with deployable security solutions.

Maria FällmanView profile

Professor

• Molecular microbiology
• Bacterial pathogenesis
• Infection biology
• +7 more

Maria Fällman is a Professor at the Department of Molecular Biology at Umeå University, where she also serves as Deputy Head of Department. She is affiliated with Molecular Infection Medicine Sweden (MIMS), a leading research center for molecular infection medicine in Sweden. Dr. Fällman's research focuses on understanding the molecular mechanisms behind bacterial adaptation to different environments, with particular emphasis on Yersinia pseudotuberculosis and Salmonella enterica Typhimurium. Her group investigates gene regulation critical for establishing and maintaining infections, bacterial stress responses, and the molecular mechanisms of the Type Three Secretion System (T3SS). The lab has developed advanced methods for RNA extraction from complex tissue samples and performs in vivo gene expression analyses. Her publication record shows consistent contributions to understanding bacterial pathogenesis, with recent articles in high-impact journals including Nature Communications, Science, and PLOS Pathogens. Her work spans from fundamental molecular mechanisms of bacterial virulence to computational approaches for analyzing pathogen stress responses. A significant contribution is the PATHOgenex database (http://www.pathogenex.org), containing gene expression data of over 30 human pathogens exposed to different stress conditions. Dr. Fällman leads the Maria Fällman Lab, which has made important discoveries including the finding that sub-lethal doses of Yersinia result in persistent infection in mice with reprogramming of bacterial gene expression. Current projects focus on stress response modeling and deciphering heterogeneous populations of infecting bacteria using single-cell RNA-seq.

Jamie MorgensternView profile

Associate Professor

• Machine Learning
• Ethics & Fairness
• Human-Centered AI
• +3 more

Jamie Morgenstern is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington . She was previously an assistant professor at Georgia Tech and a Warren Center Fellow at University of Pennsylvania . Expertise: Ethics & Fairness, Human-Centered AI, Machine Learning Education: PhD in Computer Science from Carnegie Mellon University (2015) Her research examines the social impact of machine learning and ensuring ML models do not exacerbate societal inequalities. She investigates robustness to human-generated training data, fairness in clustering and active learning, and algorithmic equity in recommendation systems. Recent publications focus on interactive ML systems , fairness constraints , and privacy-preserving methods across conferences like NeurIPS, ICML, and AIES. Key subfields include multimodal learning , membership inference attacks , and data equity . Scientific Awards: NSF Career award for "Strategic and Equity Considerations in ML" Simons collaboration project Simons Award for Graduate Students in Theoretical Computer Science (2014-2016) NSF GFRP fellowship Microsoft Research Graduate Women's Scholarship Spotlight presentation at NeurIPS 2015 Mentoring: She advises current PhD students Rachel Hong , Jie (Claire) Zhang , and Yuanyuan (Chloe) Yang . Former advisees include Daniel Jiang (MS), Bhuvesh Kumar (PhD), and Angel (Alex) Cabrera (BS). Grants: Funded by NSF Career award and Simons collaboration projects. Previously supported by Simons, NSF, and Microsoft Research fellowships. Labs & Collaborations: Collaborates with researchers like Michael Kearns , Aaron Roth , and Avrim Blum . Affiliated with the Allen School's Artificial Intelligence research group.

Ioachim PupezaView profile

Professor

• Optical Spectroscopy
• Field-Resolved Spectroscopy
• Precision Optical Measurements
• +6 more

Prof. Dr. Ioachim Pupeza serves as Group Leader in the Department of Spectroscopy/Imaging at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany. His research focuses on advanced optical measurement techniques, particularly in the field of field-resolved spectroscopy and precision optical measurements. Dr. Pupeza's research interests center around optical spectroscopy with a particular emphasis on field-resolved techniques that capture the complete electric field waveform of light-matter interactions. His work spans infrared spectroscopy , molecular fingerprinting , ultrafast laser technology , and precision optical measurements . He has made significant contributions to electro-optic sampling techniques, which enable characterization of electric-field waveforms across the terahertz to visible spectral range. His research also extends to mid-infrared light generation , terahertz spintronic emitters , and cavity-enhanced spectroscopy , with applications ranging from fundamental physics to medical diagnostics. Analysis of Dr. Pupeza's recent publications reveals a strong trend toward increasingly sophisticated field-resolved spectroscopy techniques with applications in both fundamental science and practical diagnostics. His work has evolved from basic measurement techniques to applications in cancer detection through molecular fingerprinting of biofluids. A consistent theme across his publications is the pursuit of higher precision, broader bandwidth, and improved sensitivity in optical measurements, often achieving attosecond-level precision. His research bridges physics, engineering, and medical applications, demonstrating how fundamental optical advances can translate to real-world diagnostic tools. Dr. Pupeza leads the research group "Field-Resolved Optical Precision Measurement Methods" at Leibniz-IPHT, which appears to collaborate extensively with other research institutions and groups. His work involves sophisticated laser systems including high-power Yb:YAG thin-disk oscillators, femtosecond enhancement cavities, and dual-oscillator systems for precision measurements. The group's research has implications for molecular spectroscopy, medical diagnostics, and fundamental studies of light-matter interactions at the most fundamental time scales.

Benjamin RicaudView profile

Associate Professor

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +3 more

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

Sofya RaskhodnikovaView profile

Professor

• Sublinear-time algorithms
• Data Privacy
• Approximation Algorithms
• +3 more

Sofya Raskhodnikova is a Professor in the Department of Computer Science at Boston University, part of the College of Arts and Sciences. She holds a Ph.D. from MIT and has held positions at Penn State University and postdoctoral fellowships at the Hebrew University of Jerusalem and the Weizmann Institute of Science. Her research focuses on sublinear-time algorithms, data privacy, approximation algorithms, and complexity theory. She is a recipient of the NSF CAREER Award and has contributed significantly to the theoretical foundations of privacy-preserving computation and algorithm design. Education: Ph.D. in Computer Science from MIT (2003), postdoctoral research at Hebrew University of Jerusalem and Weizmann Institute of Science (2003–2006). Visiting positions at UCLA, Harvard University, and the Simons Institute for the Theory of Computing. Research Interests: Sofya’s work bridges theoretical computer science and practical applications, emphasizing algorithms that operate efficiently on large datasets. Key areas include property testing (e.g., monotonicity, sortedness), differential privacy, and sublinear-time algorithms. She explores how algorithms can analyze data while preserving privacy guarantees and minimizing computational resources. Publications: Over 50 peer-reviewed articles in top venues such as STOC, FOCS, and SODA, with recent contributions focusing on dynamic graph algorithms under privacy constraints and robust property testing against adversarial noise. Professional Activities: Editor for ACM Transactions on Computation Theory and Algorithmica ; program committee chair for WOLA 2021 and CSR 2022; active in mentoring initiatives like Sigma Camp and Artemis. Advising & Students: Current advisees include Ephraim Linder and Debanuj Nayak. Notable alumni include Iden Kalemaj (Meta Research) and Nithin Varma (Max Planck Institute). She has supervised over 15 Ph.D. students and postdocs, fostering a collaborative research environment.

Aaron PuriView profile

Assistant Professor

• Chemical Ecology
• Drug Discovery
• Microbiome
• +7 more

Aaron Puri is an Assistant Professor of Chemistry at the University of Utah, specializing in chemical ecology and natural product discovery. His research focuses on bacterial interactions in methane-oxidizing communities and the biosynthesis of secondary metabolites. Education: B.S. from University of Chicago, Ph.D. from Stanford University School of Medicine Dr. Puri's work bridges microbiology and chemistry, with projects targeting: Chemical Ecology: Decoding interspecies signaling in methane-oxidizing bacteria Natural Products: Discovering therapeutics from underexplored bacterial genomes Biosynthesis: Activating cryptic gene clusters for novel compound production Recent publications highlight advancements in quorum sensing mechanisms (2025), inverse stable isotopic labeling techniques (2024), and methanotroph community dynamics. His research also explores spatially resolved model ecosystems for studying microbial phenotypes (2023-2024). Key methods include GNPS Dashboard for mass spectrometry analysis (2021-2022). Dr. Puri leads the CAREER-funded project on quorum sensing in methanotrophs (2024) and has developed genetic tools for industrial methanotrophs (2015). His lab maintains a strong focus on environmental microbiology and biotechnological applications.

Steven E. BrennerView profile

Professor

• molecular biology
• computational biology
• evolutionary biology
• +6 more

Steven E. Brenner is a Professor at the University of California, Berkeley, affiliated with the Department of Bioengineering, Department of Molecular & Cell Biology, and Department of Plant and Microbial Biology. His research bridges computational and experimental genomics, focusing on gene regulation, protein function prediction, metagenomics, and structural genomics. The Brenner lab investigates nonsense-mediated mRNA decay (NMD) through its role in gene regulation (RUST), computational methods like SIFTER for protein function prediction, medical/environmental metagenomics (e.g., Crohn’s disease microbiota), and structural genomics (SCOP/ASTRAL databases, protein complex analysis). Collaborations include Don Rio (NMD studies), Michael Jordan (statistical models), Jack Kirsch (experimental validation), and institutions like the Venter Institute and Tata Consultancy Services. Recent projects include a grant-funded initiative to assess newborn genetic screening and a Tata Consultancy partnership to develop software for personal genome interpretation. His lab offers research opportunities for postdocs, graduate, and undergraduate students, contributing to large-scale genomic efforts such as modENCODE and the Sorcerer II Global Ocean Sampling project.

Rodrigo FreitasView profile

Professor

• Materials Science
• Computational Materials Design
• Machine Learning in Materials
• +5 more

Professor Rodrigo Freitas holds the TDK Professorship in Materials Science and Engineering at MIT. His research focuses on computational materials design, bridging atomistic simulations with mesoscale microstructural analysis. He leads the Freitas Research Group, specializing in machine learning-driven modeling of materials kinetics and solidification processes. Education: B.S. and M.S. in Physics, University of Campinas, Brazil M.S. and Ph.D. in Materials Science & Engineering, UC Berkeley Research Interests: Professor Freitas investigates microstructural evolution in metals and alloys using advanced computational methods. Key areas include solidification mechanisms, interstitial atom behavior in superalloys, and machine learning applications for materials discovery. His work emphasizes bridging atomistic and mesoscale phenomena to guide industrial applications like semiconductor manufacturing and battery design. Publications Trend: Recent work emphasizes machine learning potentials for alloy modeling, short-range order analysis in high-entropy alloys, and kinetic modeling of complex chemical systems. Themes include alloy phase stability, defect dynamics, and data-driven materials discovery. Labs/Teams: Leads the Freitas Research Group at MIT, which develops novel computational tools for materials engineering.

Keith ObadikeView profile

Professor

• Sound Art
• Digital Media
• Interdisciplinary Art
• +12 more

Keith Obadike is a Professor in the Department of Art at Cornell University's College of Architecture, Art, and Planning (AAP), where he also serves as Director of Undergraduate Studies. He is an acclaimed interdisciplinary artist and composer, known for his collaborative work with Mendi Obadike, which spans sound art, digital media, public installations, performance, and internet-based projects. His artistic and academic work critically engages with technology, race, history, and identity. Obadike earned a B.A. in Visual Art from North Carolina Central University and an M.F.A. in Sound Design from Yale University. Prior to joining Cornell, he taught at William Paterson University and held visiting positions at Princeton University, Columbia University, Northwestern University, and the Art Institute of Chicago. His research and creative practice focus on sound art, digital media, public art, performance, and collaborative practice , with strong interdisciplinary connections to Afrofuturism, data sonification, and social justice. He explores how sound and technology mediate racial identity, memory, and liberation, notably through the concept of acousmatic blackness , which he and Mendi developed to describe the racial perception of sound when its source is unseen. Obadike’s selected works include large-scale public sound installations such as Blues Speaker (for James Baldwin) , Free/Phase , Compass Song (Times Square Arts), and GuideStar (Space Needle, Seattle). His projects often use data sonification, archival research, and mythic storytelling, blending historical inquiry with futuristic vision. Notable series include the Opera-Masquerades , Americana Suites , and Numbers Station works, which transform databases of violence into sonic experiences. His honors include: Rockefeller New Media Arts Fellowship (2004) Louis Comfort Tiffany Biennial Award (2015) New York Foundation for the Arts Fellowship in Fiction New Music USA Creator Development Fund (2022) Obadike has received commissions and support from institutions such as the Vera List Center, Harlem Stage, Times Square Arts, the American Composers Orchestra, and the Cornell Mui Ho Center for Cities. He has exhibited and performed at The New Museum, The Whitney Museum of American Art, MoMA, and The Studio Museum in Harlem. His writings and publications include contributions to Studies into Darkness (2022), and artist books such as Four Electric Ghosts and Big House / Disclosure (both 2014). He has also released albums like Crosstalk (2008) and created internet artworks such as Blackness for Sale (2001) and The Interaction of Coloreds (2002), which are foundational in new media and critical race art. While specific advisees are not listed, his role as Director of Undergraduate Studies and Professor indicates active mentorship. His work is deeply collaborative, often co-created with Mendi Obadike, and supported by interdisciplinary teams and institutions.

Benedikt WarthView profile

Professor

• Exposomics
• Metabolomics
• Analytical Chemistry
• +4 more

Benedikt Warth is a Full Professor for Food Chemistry and Exposome Research at the Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna (since 2022). He coordinates the Austrian node of the ESFRI research infrastructure EIRENE and leads the 'Global Exposomics and Biomonitoring Laboratory' since 2017. His work bridges chemistry and toxicology, focusing on exposomics and metabolomics to assess chemical exposure and its health impacts. Education: PhD in Analytical Chemistry (2012), Master’s in Biotechnology (2009), Bachelor’s in Food Science and Biotechnology (2007) Research interests center on exposomics , metabolomics , and analytical chemistry , particularly for environmental and food-related toxicants. Recent articles highlight advancements in mass spectrometry for exposome-scale analysis, combining targeted and non-targeted approaches, and exploring chemical interactions (e.g., xenoestrogens, mycotoxins, flame retardants) in biological systems. Key trends in his publications include: Development of hybrid LC-MS methods (targeted/untargeted) for sensitive exposomics Integration of AI and cognitive computing for pathway analysis Global biomonitoring of mycotoxins and xenobiotics in vulnerable populations Standardization frameworks for non-targeted analysis (SRT) Notable awards include the 2025 Chemical Research in Toxicology Young Investigator Award , Brigitte Gedek-Science Award , and multiple grants (ERC Consolidator, Erwin-Schrödinger Fellowship). He has delivered over 20 invited talks on exposomics, including sessions at ACS Fall Meetings and international conferences. His laboratory's projects span: Breast cancer exposomics (linking environmental exposures to disease onset) High-throughput sample preparation for exposome-wide studies Systems toxicology of chemical mixtures (e.g., flame retardants, mycotoxins) Development of global metabolomic assays for toxicity prediction

• Computational Linguistics
• Historical Linguistics
• Phylogenetic Linguistics
• +8 more

Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .

CHAN Chee YongView profile

Associate Professor

• Database Systems
• Query Processing
• Query Optimization
• +3 more

CHAN Chee Yong is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS) . He earned his Ph.D. in Computer Science from the University of Wisconsin-Madison and holds B.Sc. and M.Sc. degrees in Computer Science from NUS. Education : Ph.D., Computer Science, University of Wisconsin-Madison M.Sc., Computer Science, NUS B.Sc., Computer Science (1st Class Honours), NUS His research focuses on database systems , emphasizing query processing and optimization , transaction management , and database usability . He has contributed extensively to XML data dissemination, skyline computation, and multicore database performance optimization, with publications in venues like ACM SIGMOD, VLDB, and IEEE ICDE. Recent publications show increasing emphasis on join optimization , transaction healing , and spatial-keyword queries , reflecting trends in multicore systems, complex query processing, and XML data management. His work combines theoretical rigor with practical applications in distributed databases and data engineering. Notable professional roles include Associate Editor for the VLDB Journal , ACM SIGMOD Record , and IEEE Transactions on Knowledge and Data Engineering . He has served on program committees for major conferences like SIGMOD, ICDE, and VLDB across 2003-2026. Dr. Chan has supervised 9 PhD students and 10 M.Sc./M.Comp. students , including WANG TaiNing (2021), LI Meiying (2020), and TRAN Quoc Trung (2011). His advisees have been placed in institutions like the Institute for Infocomm Research and Huawei Shannon Lab.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .