Find Academic Supervisors

• Data Science
• Artificial Intelligence
• Machine Learning
• +7 more

Prof. habil. dr. Gintautas Dzemyda is a leading Lithuanian computer scientist, Professor and Senior Researcher at Vilnius University Institute of Data Science and Digital Technologies (VU DMSTI), and Head of the Cognitive Computing Group . He is simultaneously affiliated with the Institute of Mathematics and Informatics (MII) in Vilnius, where he has built an internationally recognized scientific school in visual data analysis. Education & Qualifications 1984 – Candidate of Technical Sciences (PhD equivalent), thesis on “Problem Structure Analysis – a Tool for More Effective Optimization”. 1997 – Habilitation Doctor of Technical Sciences, dissertation on “Isolation of Necessary Knowledge to Improve Optimization Efficiency”. 1992 – Associate Professor, Institute of Mathematics and Informatics, Vilnius. 1998 – Professor, Kaunas University of Technology. Research Interests Prof. Dzemyda’s research integrates data science, artificial intelligence, optimization, and cognitive computing . Core topics include dimensionality reduction, multidimensional data visualization, neural-network–based analytics, parallel and distributed computing, multi-criteria decision support, and advanced AI applications in medicine (ophthalmology, cardiology, oncology). His work has pioneered Lithuanian capabilities in visual analytics and large-scale data exploration. Publication Trends Across 270+ refereed works and 2 Springer monographs (2013, 2023), recent outputs (2021–2025) emphasize geometric multidimensional scaling for big-data visualization, deep learning for pancreatic-cancer detection on CT images, reinforcement learning for autonomous navigation, and fraud-detection techniques for highly imbalanced financial datasets. These contributions appear in Springer LNCS/LNNS, Informatica, Journal of Global Optimization, Engineering Applications of Artificial Intelligence , and other top venues. Awards & Recognition Lithuanian State Science Prize (2001 & 2021) Honorary Doctor of the University of Latvia (2019) Knight's Cross of the Order “For Merit to Lithuania” (2007) Doctoral Supervision & Committees He has mentored 28 doctoral graduates (15 direct, 13 through academic descendants) and currently supervises: Dalia Breskuvienė – Classifier training-set optimization Modestas Motiejauskas – Emotion recognition in photographs Victor Bulava – Machine-learning methods for cyber-incident early detection He also chairs or serves on doctoral and habilitation committees at VU, KTU, VGTU, VMU and MII. Laboratory & Projects As Head of the Cognitive Computing Group , Prof. Dzemyda coordinates several national and EU projects, including the current Lithuanian Research Council grant “Geometric Method for Multidimensional Scaling” (S-MIP-20-19, 2020-2022) and the SMART programme project “CognitiveSTATS” (2021-2023) focused on combating misinformation during pandemics. His team develops open-access tools for large-scale data visualization and contributes to the MIDAS national research-data archive.

Marc van OostrumView profile

Researcher

• Molecular Neuroscience
• Synaptic Proteomics
• Proteome Dynamics
• +2 more

Marc van Oostrum is a molecular neuroscientist and proteomics researcher at the Biozentrum , University of Basel. His work bridges molecular neuroscience , synaptic proteomics , and systems biology to unravel the protein networks governing synaptic function and neuronal circuits. Institution: University of Basel (Biozentrum) Academic Rank: Researcher Key Grants: SNSF Ambizione Fellowship Research Focus: Van Oostrum's group investigates how synaptic protein dynamics determine neuronal connectivity, circuit development, and disease mechanisms. Their approach combines mass spectrometry-based proteomics , fluorescence-activated sorting , and in vivo labeling of synaptic populations. Notably, they explore synaptic diversity in healthy brain function and its dysregulation in neurological disorders. Scientific Contributions: His recent publications address: Proteasome maturation factors' role in transcriptional stress responses Molecular diversity of synapses through spatial proteomics Tissue-specific protein interaction atlases for disease gene discovery Synaptic proteome changes in neurodegenerative disease models Awards & Recognition: Van Oostrum has received the Best Talk Prize (2025) at the Swiss Proteomics Meeting and holds a prestigious SNSF Ambizione Fellowship . Collaborative Network: He collaborates with leaders in proteomics and neuroscience, including Biozentrum colleagues , Erin Schuman , and international teams in synaptic and proteomic research.

Dejan LavbičView profile

Associate Professor

• Intelligent agents
• Multi-agent systems
• Ontologies
• +7 more

Assoc. Prof. Dejan Lavbič is an Associate Professor at the University of Ljubljana, Faculty of Computer and Information Science with 15+ years of academic experience. His research focuses on intelligent agents, multi-agent systems, ontologies, and blockchain-based smart contracts , particularly in semantic web technologies, AI services ecosystems, and information quality assessment . Doctor of Philosophy in Computer Science, University of Ljubljana (2010) Bachelor of Science in Computer Systems and Informatics, University of Ljubljana (2004) His scientific contributions span semantic web frameworks, blockchain applications, and machine learning systems, with 20+ peer-reviewed publications. Recent works include: Smart contract classification with AI Cardano blockchain identity systems Information quality metrics with gamification Awards include Cambridge CAE certification and multiple industry certifications. He mentors students in decentralized applications, AI development, and smart city ecosystems , having guided 6+ diploma/master theses on topics like automated essay grading and air quality data collection.

Vince LyzinskiView profile

Associate Professor

• Statistical Network Inference
• Graph Matching
• Vertex Nomination
• +6 more

Vince Lyzinski is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, with additional affiliations in the Applied Mathematics, Statistics, and Scientific Computation (AMSC) program. His work spans statistical network inference, graph matching algorithms, and machine learning applications to complex networks. Ph.D. in Applied Mathematics and Statistics (2013), M.S.E. (2011), and M.A. (2007) from Johns Hopkins University B.S. in Mathematics from University of Notre Dame (2006) His research focuses on statistical network inference , particularly graph matching and vertex nomination , with applications to connectomics and adversarial activity analytics. Supported by DARPA MAA , AFOSR , and JHU HLTCOE , he develops algorithms like iGraphMatch for graph alignment and analysis. Recent publications (2025-2023) explore vertex label error impacts, network trimming for robustness, and stochastic blockmodel extensions. Key papers include "Asymptotically Perfect Seeded Graph Matching" and "ACRONYM: Augmented Degree-Corrected Network Models" . Scientific Awards : Best Paper Award at GTA3 2018 Workshop He has advised 13 Ph.D. students and 1 postdoc across UMD , JHU , and Texas A&M , including Keith Levin (now at UW-Madison) and Jesus Arroyo (now at Texas A&M). His DARPA-funded team includes researchers from UMass, UMD, BU, and JHU.

• Computer Science
• Information Theory
• Machine Learning
• +3 more

Daniel Cullina is an Assistant Professor in Electrical Engineering, specializing in theoretical computer science and machine learning. His research explores fundamental aspects of adversarial robustness, graph alignment, and information theory, with applications spanning cybersecurity and data science. Research Focus: Adversarial machine learning: Robustness guarantees, attack/defense strategies for classifiers Graph algorithms: Alignment and recovery in random graph models like Erdős-Rényi Information theory: Fundamental limits of database matching and Gaussian alignment Coding theory: Deletion error correction and converse bounds His publications (28+ with 575+ Scopus citations) demonstrate consistent contributions to understanding adversarial vulnerabilities in ML systems and combinatorial algorithms for graph/data matching. Recent work (2020-2023) focuses on theoretical characterization of optimal losses under attacks and database alignment frameworks. With an h-index of 12, his research output shows sustained productivity since 2012, peaking in 2016 (8 publications) and maintaining 3-5 annual publications in recent years.

• Machine Learning
• Optimization
• Statistical Learning Theory
• +5 more

Purushottam Kar is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He completed his PhD from IIT Kanpur in 2014 and his B.Tech. from the same institution in 2008. His academic career includes a post-doctoral research position at Microsoft Research India from July 2013 to August 2015, following a doctoral research internship there from May to August 2012. Dr. Kar's research focuses on the theoretical and practical aspects of machine learning. His primary interests include Machine Learning , Optimization , and Statistical Learning Theory . More specifically, he works on online and stochastic learning and optimization, multi-label classification, high dimensional statistics, kernel methods, and statistical learning theory. His work bridges theoretical foundations with practical applications in artificial intelligence. His recent publications demonstrate a strong focus on advancing machine learning methodologies, particularly in robust regression, non-decomposable performance measures, precision optimization, multi-label classification, and the theoretical foundations of online learning. His research spans both theoretical developments in statistical learning and practical implementations for complex classification problems. International Conference on Machine Learning (ICML) Reviewer Award (2015) Indian Unit of Pattern Recognition and Artificial Intelligence (IUPRAI) Doctoral Dissertation Award (2014) Yahoo! Key Scientific Challenges (Yahoo! KSC) Program Honourable Mention (2012) Microsoft Research (MSR India) PhD Fellowship (2010) Academic Excellence Awards by IIT Kanpur (2005, 2006) Dr. Kar teaches courses in Machine Learning, Online Learning, and Statistical Learning Theory. His research profile suggests he mentors PhD and Master's students in machine learning and related fields. His work with Microsoft Research indicates significant industry collaborations and grant funding in artificial intelligence research. His office is located in Room 509 of the CSE Extension building (Rajeev Motwani Building) at IIT Kanpur.

• Data Mining
• Data Science
• Data Analytics
• +4 more

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +9 more

Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)

Kristian HildebrandView profile

Professor

• Computer Graphics
• Interactive Systems
• Augmented Reality
• +7 more

Prof. Dr. Kristian Hildebrand is a Professor of Computer Graphics and Interactive Systems at Berlin University of Applied Sciences and Technology since 2015, leading the Intelligent Interactive Systems research group. His work bridges computer graphics, VR/AR, computer vision, and machine learning. PhD in Computer Graphics (Technical University of Berlin, 2013) Diploma in Computer Science and Media (Bauhaus University Weimar) Academic experience: University of British Columbia, Max Planck Institute Saarbrücken Industry experience: ART+COM Studios Berlin, Disney Research Zurich, co-founder of kunstmatrix Research Interests span computer graphics , AR/VR systems , computer vision , and digital fabrication , with applications in: Medical therapy (AnorexiaVR, PAN-Assistant, VITALAB.mobile) Human-robot interaction (Digit gestures, Non-verbal communication) Machine learning (Domain adaptation, GAN-based medical image synthesis) Digital fabrication (Optimized 3D printing, crdbrd fabrication) Publications show trends in: Medical VR applications for mental health and geriatrics GAN-based medical image generation and segmentation Redirected walking and spatial navigation techniques Domain adaptation for industrial object classification Scientific Contributions : Principal investigator in multiple DFG-funded projects (tele.interaction, Manipulation of virtual self-perception) Recipient of Best Student Paper Award (ECML-PKDD 2023) Key role in BMBF projects: Vitalab.mobile, BewARe, SynthNet Academic Leadership includes: Supervision of PhD students (Christopher Kümmel, Tabea Kossen) Teaching: Computer Vision, Game Programming, Scientific Computing Founding director of Human.VR.Lab (interdisciplinary computer science/life science collaboration)

Mahdi KhodadadzadehView profile

Assistant Professor

• Machine Learning
• Data Mining
• Process Mining
• +9 more

Mahdi Khodadadzadeh is an Assistant Professor in the Department of Geo-information Processing, specializing in machine learning, data mining, and geospatial analysis. His work bridges traditional and deep learning methods with applications in mineral exploration, environmental modeling, and circular economy initiatives. Research outputs include novel cross-validation techniques for geospatial machine learning (Spatial+, dissimilarity-adaptive methods), hyperspectral mineral mapping for ore characterization, and hybrid models combining artificial neural networks with optimization algorithms. He actively contributes to open-access datasets and methodologies, particularly in drill-core analysis and multi-source data fusion. Collaborations with researchers like R. Zurita-Milla and R. Gloaguen are evident through co-authored publications and shared datasets. His work impacts domains such as mineral exploration, geospatial modeling, and sustainable resource management, with a focus on robust statistical evaluation frameworks and spectral-spatial analysis.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science and Engineering
• +7 more

Janardhan Rao (Jana) Doppa is a Huie-Rogers Endowed Chair Professor and Berry Distinguished Professor in Engineering at Washington State University (WSU), where he chairs the EECS Graduate Studies committee in the School of Electrical Engineering and Computer Science. His research spans artificial intelligence, machine learning, and data-driven science, with emphasis on: AI-driven adaptive experimental design for scientific discovery Sequential decision-making under uncertainty for agriculture and cyber-physical systems Robust machine learning for high-stakes applications Machine learning for electronic design automation and sustainable computing Current projects include NSF-funded work on agricultural AI and materials discovery. Publication analysis (2019-2026) reveals strong focus on Bayesian optimization methods (37% of recent papers), conformal prediction for uncertainty quantification (21%), and cross-domain applications in materials science (19%), healthcare (15%), and sustainable systems (12%). Major awards include: WSU Faculty Mid-Career Award (2024) Voiland College Outstanding Researcher Award (2024) 4 Best Paper Awards (2021-2023) NSF CAREER Award (2019) Outstanding Paper Award, AAAI (2013) He actively advises 8 PhD students and has graduated 21 PhDs who now hold positions at universities (Minnesota, Houston) and industry labs (Meta, Stanford). Research is supported by NSF, USDA, and industry grants exceeding $5M. He leads a dynamic research group in AI/ML at WSU with collaborations spanning Duke, Stanford, and Oregon State. Group outputs include 100+ publications with 25+ Best Paper nominations/awards since 2013.

• Artificial Intelligence
• Data Mining
• Natural Language Processing
• +4 more

Andrés Jonathan Abeliuk Kimelman is an Assistant Professor at the University of Chile's Faculty of Physical Sciences and Mathematics , affiliated with the Department of Computer Science . He holds a PhD in Computer Science (University of Melbourne, 2017) and a Civil Engineering degree in Computing (University of Chile, 2012). Research Focus : AI ethics, network analysis, natural language processing, and machine learning applications in social systems. Teaching : Leads undergraduate and postgraduate courses in Discrete Mathematics, Data Mining, and Computational Theory. Students : Advises multiple thesis and research projects, including works on AI-assisted urban planning, misinformation analysis, and algorithmic bias. Collaborations : Participates in the National Center for Artificial Intelligence (CENIA) as a co-investigator (2022-2027). Key Publications explore polarization detection, networked public spheres, and computational models for social systems. Current projects include unsupervised topic quantification and extreme multi-label classification in multilingual contexts.

Jifeng XuanView profile

Professor

• Software Analysis and Testing
• Software Testing
• Software Debugging
• +2 more

Jifeng Xuan is a Professor and Deputy Dean at the School of Computer Science, Wuhan University, China. He leads the CSTAR (Centre of Software Testing, Analysis and Reliability) research group and has made significant contributions to software engineering, particularly in software testing, debugging, and analysis. Dr. Xuan completed his bachelor's degree and PhD at OSCAR Lab, School of Software, Dalian University of Technology, China, under the supervision of Prof. He Jiang. He also conducted postdoctoral research at SPIRALS Team in INRIA Lille - Nord Europe, France, working with Dr. Martin Monperrus and Prof. Lionel Seinturier. His research focuses on Software Analysis and Testing , with specific interests in software testing and debugging, software data analysis, and search-based software engineering. Dr. Xuan's work bridges theoretical foundations with practical applications, addressing real-world challenges in software quality assurance. His research has led to numerous publications in top-tier software engineering venues and has influenced both academic research and industrial practices. Dr. Xuan's recent publications demonstrate a strong focus on innovative approaches to software testing, debugging, and analysis. His work spans multiple subfields including log analysis, fuzz testing, bug triage, and automated program repair. He has made significant contributions to understanding software crashes, improving bug report quality, and developing techniques for more effective software testing and analysis. 2025 ACM SIGSOFT Distinguished Paper Award 2025 IEEE TCSE Distinguished Paper Award 2024 CCF NASAC Youth Software Innovation Award 2018 ACM SIGSOFT Distinguished Paper Award 2015 Young Talent Development Program of CAST and CCF 2015 Luojia Young Scholar Program of Wuhan University 2014 Outstanding Doctoral Dissertation of CCF Dr. Xuan actively mentors PhD and master's students through his CSTAR research group at Wuhan University. He has served on numerous program committees for major software engineering conferences including ICSE, FSE, ASE, and ISSTA. His editorial service includes roles on the editorial boards of Empirical Software Engineering (EMSE), PLOS One, and Frontiers of Computer Science. Dr. Xuan has also organized several workshops and conferences, demonstrating his leadership in the software engineering community. As founder of CSTAR (Centre of Software Testing, Analysis and Reliability), Dr. Xuan leads a research team focused on advancing the state of the art in software quality assurance. The group's work spans theoretical foundations and practical applications, with strong connections to industry challenges. CSTAR has established collaborations with both domestic and international research institutions, contributing to a vibrant research ecosystem in software engineering.

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +6 more

Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.