Find Academic Supervisors

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +3 more

Prof. Tomaso Fontanini is a researcher at the Department of Engineering and Architecture, University of Parma. His academic contributions span multiple disciplines, including computer science, artificial intelligence, and computer vision. 2025/2026: Deep Learning and Generative Models (Master's in Computer Engineering) 2024/2025: Processing Systems (Bachelor's in Prevention Techniques) 2023/2024: Processing Systems (Bachelor's in Prevention Techniques) 2022/2023: Processing Systems (Bachelor's in Prevention Techniques) Research Focus: His work primarily explores generative models, image synthesis, and style transfer with a strong emphasis on semantic control and attention mechanisms. Recent research has advanced state space models for efficient style transfer (Mamba-ST), semantic image synthesis via class-adaptive cross-attention, and diffusion model acceleration through U-shape architectures. Scientific Contributions: Publications include breakthroughs in controllable face synthesis, mask-based generative modeling, and video anomaly detection. His work bridges theoretical advancements in neural architectures with practical applications in remote sensing and educational technology. 2025: FLAV (audio-video generation), Swin2-MoSE (remote sensing) 2024: MARS (text-based person search), MCGM (mask conditioning) 2023: FrankenMask (face part editing), Student attendance systems

Bo LiView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Computer Graphics
• +2 more

Dr. Bo Li serves as an Associate Professor at the University of Southern Mississippi, where he teaches core computer science courses including Artificial Intelligence, Computer Graphics, and Database Management Systems. His academic foundation spans institutions across three countries, reflecting a globally oriented research perspective in visual computing and machine learning. His educational background includes: PhD in Computer Science from Nanyang Technological University (2012) MS in Computer Science from Texas State University (2015) MS in Computer Science from Xi'an Jiaotong University (2005) BS in Computer Science from Xi'an Jiaotong University (2005) Dr. Li's research centers on 3D shape retrieval systems, where he pioneers methods for sketch-based and image-based 3D model search. His work bridges computer vision, graphics, and machine learning through innovative approaches to 3D scene analysis, semantic modeling, and cross-modal translation. Recent investigations extend into social media analysis and speech emotion recognition, demonstrating methodological versatility within artificial intelligence. Analysis of his 15 most recent publications reveals a sustained focus on 3D shape retrieval benchmarking through SHREC competitions, evolving from traditional descriptor methods to deep learning frameworks. Key trends include multimodal query processing, large-scale dataset handling, and applications in real-world image denoising. His research consistently addresses challenges in partial/non-rigid model matching and semantic scene understanding. Dr. Li has not been documented with scientific awards in the provided information. Regarding academic mentorship and funding, no details about student supervision, research grants, or sponsored projects are available in the source material. Similarly, information about laboratory facilities, research teams, or collaborative groups is not provided in the current documentation.

Almut Sophia KoepkeView profile

Researcher

• Multi-Modal Learning
• Computer Vision
• Audio-Visual Processing
• +3 more

Dr. Almut Sophia Koepke is a junior research group leader and TUM Junior Fellow at the Technical University of Munich (TUM) and University of Tübingen. She leads the multi-modal learning research group focusing on video understanding through sound, vision, and text integration. University: Technical University of Munich School: TUM School of Computation, Information and Technology Department: Informatics 9 Academic Rank: Researcher Her research spans multi-modal learning, audio-visual foundation models, and cross-modal attention mechanisms. Key themes include: Advancing zero-shot learning through language-guided audio-visual models Developing explainable AI systems via attention pattern translation in VQA Exploring temporal understanding in video-adverb retrieval Building robust multi-modal representations for self-driving applications Recent publications analyze foundation model capabilities in audio-visual tasks (ICCV 2025), temporal reasoning (ACMMM 2024), and cross-modal attention frameworks (ECCV 2022). She co-organizes CVPR workshops on foundation model evaluations and serves as area chair/reviewer for major conferences.

• Neuroscience
• Neurology
• Psychiatry
• +4 more

Dr. Mazen Kheirbek is a Professor in Psychiatry at the University of California, San Francisco (UCSF) School of Medicine. He leads research at the Kheirbek Lab, focusing on hippocampal circuits in emotional behavior and psychiatric disorders. Education : BA (Washington University), PhD (University of Chicago), Postdoc (Columbia University) Research Interests center on: Neural circuits in anxiety and depression Hippocampal-prefrontal pathways Adult neurogenesis mechanisms Neuroplasticity in mood disorders Optogenetics and circuit mapping Translational psychiatry Publication Trends show consistent contributions to neuroscience journals, particularly in hippocampal function, neural circuits, and psychiatric disorders. His work often employs animal models and advanced imaging techniques. Scientific Awards : McKnight Foundation Memory & Cognitive Disorders Award (2020) Pew Scholar in Biomedical Sciences (2019) Klingenstein-Simons Fellowship (2019) Human Frontier Science Program Young Investigator (2019) NARSAD Young Investigator Award (2012) Grants include multiple NIH-funded projects: NIH/NIDA 1R01DA062018 (2025-2030): Dopaminergic circuits in insight learning NIH/NIMH 1R01MH136270 (2025-2030): Hippocampal stimulus processing NIH/NIDCD R01DC19813 (2021-2026): Dentate gyrus associative learning

Andreas NieklerView profile

Researcher

• Natural Language Processing
• Computational Social Science
• Digital Humanities
• +7 more

Andreas Niekler is a research associate and lecturer in Computational Humanities at the Institute of Computer Science, Leipzig University, Faculty of Mathematics and Computer Science. He develops computational methods for semantic language analysis and applies them to computational social science and humanities research, with focus on machine learning and data management methodologies. His research interests span Natural Language Processing , Computational Social Science , Digital Humanities , Text Mining , Machine Learning , and Conversational AI . Niekler specializes in developing algorithms including Bayesian Models, Topic Models, Deep Learning, and Support Vector Machines for text analysis applications. He is actively involved in the development of the interactive Leipzig Corpus Miner platform and researches how semantic representations can be applied to literary studies and scientometrics. Niekler's recent publications demonstrate expertise across computational linguistics, social science methodology, and practical applications of text mining. His work shows strong interdisciplinary connections between computer science, linguistics, and social sciences, with particular emphasis on developing robust methodologies for automated content analysis. As an educator, Niekler has extensive teaching experience in computational methods for humanities and social sciences, offering courses on text mining, computational linguistics, and programming in R and Python. He serves as a scientific staff member in the Computational Humanities research group led by Prof. Dr. Manuel Burghardt and represents scientific staff in the Faculty Council of Mathematics and Computer Science at Leipzig University.

• Machine Learning
• Statistical Methodology
• Computational Biology
• +5 more

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

Niamh Nic DaeidView profile

Professor

• Forensic Science
• Prison Drug Analysis
• Explosives Research
• +4 more

Niamh Nic Daeid is Professor of Forensic Science and Director of the Leverhulme Research Centre for Forensic Science (LRCFS) at the University of Dundee, leading the £15m Just Tech Institute for Innovation. She holds fellowships with the Royal Society of Edinburgh, Royal Society of Chemistry, and multiple forensic science bodies while serving on committees for INTERPOL, the International Criminal Court, and the United Nations. Her research focuses on forensic chemistry applications in prison drug analysis, explosives detection, and fire investigation. Recent work emphasizes science communication, particularly using comics to improve juror comprehension of forensic testimony. She leads major projects including Clarus (bias prevention in digital forensics) and the Smart Digital Forensic Advisor initiative. Nic Daeid's publications span forensic methodology development, from quantum dots for fingerprint detection to machine learning for footwear impression analysis. Her team's 2025 research includes prison drug studies using seized Scottish evidence and advanced cartridge case imaging techniques. European Network of Forensic Science Institutes Distinguished Forensic Scientist award (2018) Royal Society of Edinburgh Senior Medal for Public Engagement Peter Ganci Award for fire investigation services Gold Engage Watermark for Public Engagement (2019) Best Short Paper Award, International Conference on eXtended Reality (2022) She supervises 13 research students and early-career academics across forensic chemistry, digital forensics, and science communication projects. Current grants include the Leverhulme Trust's £10m LRCFS (2016-2026), UK government's Tay Cities Regional Deal funding, and Dundee City Council's VR/5G initiative. Her team maintains active collaborations with Scottish prisons, international forensic networks, and law enforcement agencies.

Miloš RackovićView profile

Professor

• Artificial Intelligence
• Machine Learning
• Databases
• +4 more

Miloš Racković serves as a full Professor in the Department of Mathematics and Informatics at the University of Novi Sad, Serbia. He maintains active academic engagement through the Laboratory for the development of information systems, with his office located in the Information technologies and systems office (DMI&DF) on the second floor, room 49. Contact is available via telephone (485)-2868 or email rackovic@dmi.uns.ac.rs, and his personal website (http://www.is.pmf.uns.ac.rs/rackovicm/) provides additional resources. His research spans foundational and applied computer science, with seminal contributions in fuzzy database systems including PFSQL query language development and prioritized fuzzy logic for relational databases and XML. He has pioneered deep learning methodologies through innovative classification techniques using negative and missing features in convolutional neural networks. Additional expertise includes high-performance computing implementations of Lattice Boltzmann methods using OpenCL, robotics (symbolic modeling and trajectory planning), and blockchain applications for Industry 4.0 production processes. His sports analytics work applies neural networks to basketball player and referee movement analysis. Analysis of his 2012-2025 publications reveals a strategic evolution toward interdisciplinary applications, particularly in industrial transformation (blockchain-enabled traceability) and sports analytics. His work consistently bridges theoretical computer science with practical implementations, demonstrating increasing focus on real-world problem solving while maintaining strong foundations in database theory and computational methods. Professor Racković leads the Laboratory for the development of information systems, which focuses on advancing information system methodologies through formal modeling extensions (including Petri net innovations) and practical implementations for uncertainty management. The laboratory's work spans from foundational research in fuzzy logic systems to applied projects in high-performance computing and blockchain integration, fostering innovation in information technology development.

Daniel G. BrownView profile

Professor

• Computational Creativity
• Music Information Retrieval
• Bioinformatics
• +7 more

Daniel G. Brown is a Professor at the David R. Cheriton School of Computer Science , University of Waterloo. He holds a Ph.D. in Computer Science (Cornell, 2000) and an S.B. in Mathematics with Computer Science (MIT, 1995). Research interests include: Computational Creativity : Exploring algorithmic modeling of creativity through AI, generative art, and poetry analysis. Bioinformatics : Developing algorithms like QTree and LSHtree for phylogenetic tree reconstruction, sequence correction (PANDASeq2), and solving NP-hard problems in biological sequence analysis. Music and Lyrics : Creating tools like RhymeAnalyzer for rap lyric analysis and cross-cultural music studies. Computational Ethics : Focused on equity, labor rights, and ethics of generative AI. Problem Gambling : Affiliated with Waterloo's Gambling Research Lab, studying slot machine interventions. Teaching includes courses like Computing and Discrimination , Computational Techniques in Biological Sequence Analysis , and Algorithmic Methods in Phylogenetics . He has served in major administrative roles, including FAUW President and Acting Director of the School of Computer Science. Non-academic affiliations : Co-founder of a dog-centric photo archive for his late dog Rover and current dog River. A Quaker, he sings with choirs like the Grand Philharmonic Choir and maintains a food blog with detailed reviews of Kitchener/Waterloo restaurants and grocery stores.

Jing YangView profile

Professor

• Data Visualization
• Visual Analytics
• High Dimensional Data Analysis
• +7 more

Jing Yang is a full Professor and Director of MS CS Program in the Computer Science Department at the University of North Carolina at Charlotte (UNCC). She has been actively involved in data visualization and visual analytics research since joining UNCC in 2005 after completing her PhD at Worcester Polytechnic Institute. Dr. Yang earned her Bachelor's degrees in Engineering Mechanics and Computer Science from TsingHua University in 1997 and completed her Ph.D. in Computer Science from Worcester Polytechnic Institute in May 2005 under advisors Matthew O. Ward and Elke A. Rundensteiner. Her research focuses on developing visual analytics techniques for abstract data including multidimensional data, time-oriented data, networks, hierarchies, text documents, and trajectory data. She conducts design studies for application domains such as sports, bioinformatics, finance, network security, and health, while exploring fundamental visualization topics like interactions, insight management, clustering-based approaches, and animations. Her recent work emphasizes sports analytics, urban data, and multivariate time series visual analytics. Analysis of Dr. Yang's publication record reveals a strong focus on practical applications of visualization techniques across diverse domains. Her work consistently bridges theoretical visualization frameworks with real-world data challenges, particularly in transportation (taxi trajectories), sports (tennis match analysis), financial transactions, and bioinformatics. The publications demonstrate an evolution from foundational visualization techniques to increasingly sophisticated domain-specific applications. Dr. Yang has secured substantial research funding from NSF, EPA, DHS, and industry partners including Google and Bank of America. Her grants portfolio includes significant projects like TrajAnalytics (NSF, $200,950), Visualizing Event Dynamics (NSF EAGER, $75,317), and Visualizing High Dimensional Categorical Datasets (Google Faculty Research Award, $60,000), among others totaling over $4 million in research support. As an educator, Dr. Yang has taught numerous courses including Visual Analytics, Information Visualization, and Database Design. She directs the Charlotte Visualization Center and has mentored numerous students through research projects. Her collaborative approach is evident in her extensive co-authorship network spanning multiple institutions and disciplines. Current research directions include narrative animation for streaming text visualization and advanced techniques for exploring high-dimensional categorical datasets.

Jordan TaylorView profile

Professor

• Motor Control
• Neuroscience
• Computational Motor Learning
• +2 more

Professor Jordan Taylor is affiliated with Princeton University as a faculty member in the Department of Biomedical Engineering within the School of Engineering and Applied Science. His research focuses on unraveling computational processes in motor control and learning, with particular emphasis on interactions between explicit cognitive strategies and implicit motor adaptation during skill acquisition. Taylor leads the Intelligent Performance and Adaptation Laboratory , aiming to develop optimal training protocols for motor rehabilitation post-stroke or disease. Research Interests : Taylor investigates how humans learn motor skills through dual mechanisms of declarative strategy formation and implicit neural adaptation. His work explores the neural systems underlying these processes and their functional consequences, especially in pathological conditions like cerebellar degeneration. Current studies examine working memory constraints, reward modulation of implicit adaptation, and plan-based generalization of motor learning. Publication Trends : Recent articles analyze dual mechanisms in sensorimotor learning, reward-driven adaptation, and contextual influences on motor memory. His computational neuroscience approach combines behavioral experiments, neural imaging, and theoretical modeling to study cognitive-motor interactions across various tasks.

Kalina BontchevaView profile

Researcher

• NLP for social media
• semantic search
• GATE infrastructure
• +8 more

Kalina Bontcheva is a Senior Researcher in the Natural Language Processing Group within the Department of Computer Science at the University of Sheffield. She holds an EPSRC Career Acceleration Fellowship (working part-time since October 2015) focused on personalized summarization of social media content. Her research spans multiple EU-funded projects including PHEME (computing veracity of social media), TrendMiner, DecarboNet, and uComp, with significant contributions to the GATE (General Architecture for Text Engineering) open-source NLP infrastructure since 1999. Dr. Bontcheva's research interests focus on the intersection of natural language processing and social media analysis. Her work encompasses NLP for social media, semantic search, information extraction from social platforms, crowdsourcing of NLP corpora, collaborative text annotation, semantic technologies, and text mining and analytics. She has particular expertise in developing methods for personalized, abstractive multi-document summarization across different social media platforms, addressing the challenges of noisy, jargon-filled and dynamic content. Her interdisciplinary approach combines machine learning, semantic technologies, and social dimension analysis to create systems that adapt to individual users' information seeking goals. Analysis of her recent publications reveals a strong focus on social media processing challenges, with emphasis on Twitter analysis, temporal expression recognition, and handling noisy text. Her work consistently addresses the unique characteristics of social media content and develops specialized techniques for information extraction, sentiment analysis, and user geolocation within these platforms. The GATE framework serves as the foundation for much of her tool development, demonstrating her commitment to creating reusable, open-source NLP infrastructure. Her most significant award is the EPSRC Career Acceleration Fellowship, which supports her work on personalized social media summarization. This prestigious fellowship includes a substantial budget of £560k and involves collaborations with industry partners including The Press Association, British Telecom, and Fizzback. Dr. Bontcheva has led numerous major research projects throughout her career. She was Principal Investigator on three EU-funded projects (MUSING, TAO, and ServiceFinder) between 2006-2009, coordinating the TAO consortium with seven partner institutions. She currently leads the PHEME EU project and serves as PI for TrendMiner and DecarboNet European projects, while also contributing as Co-I on the uComp project. Her project portfolio demonstrates consistent success in securing competitive research funding across multiple domains within NLP and semantic technologies. She works within the Natural Language Processing Group at the University of Sheffield, which has been central to the development of the GATE infrastructure. Her work connects with various initiatives including the GATE Cloud platform and the TextVRE project for e-humanities textual studies. She has established collaborations with organizations including the Press Association, British Telecom, Oxford Internet Institute, and Sheffield's Department of Journalism to ensure her research addresses real-world needs across different user communities.

Greg HueyView profile

Professor

• Atmospheric Chemistry
• Aerosols
• Clouds
• +4 more

Dr. Greg Huey is a Professor in the School of Earth & Atmospheric Sciences at the Georgia Institute of Technology, where he leads research in atmospheric chemistry with expertise in trace gas measurement and aerosol processes. His work bridges laboratory studies, aircraft campaigns, and global modeling to address air quality and climate challenges. His educational background includes a Ph.D. in Physical Chemistry from the University of Wisconsin-Madison (1992) and a B.S. in Chemistry from the University of Virginia (1986). Dr. Huey's research centers on chemical kinetics of ionic and neutral species, heterogeneous chemistry , and aircraft-based sampling of condensable gases. He specializes in chemical ionization mass spectrometry for trace gas analysis, with applications to wildfire smoke, urban pollution, and stratospheric-tropospheric exchange. Key themes include aerosol formation mechanisms, ozone production pathways, and halogen chemistry impacts on oxidation capacity. Analysis of his 2021-2025 publications reveals dominant research threads in Asian pollution transport , wildfire smoke chemistry , and urban ozone formation . His work frequently integrates NASA field campaigns (KORUS-AQ, FIREX-AQ, ATom) with satellite validation and modeling to quantify pollution impacts across scales. His scientific recognition includes: Outstanding Teaching Award, Georgia Tech (2007) Dr. Huey directs major field campaigns and instrumentation development for atmospheric trace gas measurements. His research is supported by NASA and NSF grants focused on aircraft-based studies of wildfire emissions, monsoon-driven transport, and urban air quality. He actively mentors students through Georgia Tech's atmospheric science programs and a multi-institution REU site. He contributes to international efforts like the Asian Monsoon Chemical and Climate Impact Project (ACCLIP), developing advanced chemical ionization mass spectrometers for deployment on NASA aircraft to study global atmospheric composition.

Jon BarkerView profile

Professor

• Noise-Robust Speech Processing
• Machine Listening
• Computational Modeling of Human Hearing
• +4 more

Professor Jon Barker is a faculty member at the University of Sheffield , where he holds a Personal Chair in the School of Computer Science . He leads the Speech and Hearing (SpandH) research group and co-founded the CHiME international workshop series on robust speech recognition. Education : PhD in Computer Science (University of Sheffield, 1999); BA in Electrical and Information Sciences (Cambridge University). Research Focus : His work bridges machine listening and human auditory perception , with key contributions to noise-robust speech recognition , speech intelligibility prediction , and hearing aid signal processing for speech and music. Recent projects include the Clarity Challenges and Cadenza Challenges , large-scale machine learning initiatives to improve accessibility for hearing-impaired users. Publication Trends : Recent articles emphasize machine learning for hearing aid optimization , dysarthric speech recognition , audio-visual integration , and music demixing algorithms . Collaborations span speech processing, psychoacoustics, and biomedical engineering. Scientific Awards : EURASIP Best Paper Award (2009) ISCA Best Paper Award (2008) Grants and Leadership : He has secured major EPSRC grants including EnhanceMusic (2022-2026) and Challenges to Revolutionise Hearing Device Processing (2019-2025). He co-led the TAPAS Marie Curie Training Network (2017-2022) and led projects like AV-COGHEAR (2015-2018) and CHiME (2009-2012). Labs and Teams : Barker collaborates closely with the Speech and Hearing Research Group and contributes to international initiatives like the CHiME Workshop . His lab develops open datasets such as the Clarity Speech Corpus and Audio-Visual Lombard Corpus .

• Information Retrieval Systems
• Machine Learning
• Data Mining
• +1 more

Assoc. Prof. Dr. Yıltan Bitirim is a faculty member at the Computer Engineering Department of Eastern Mediterranean University in North Cyprus. With over two decades of academic experience, he has served in various roles including Vice Chair (2014-2022), Academic Affairs Coordinator (2025-), and committee member for ABET assessment, curriculum development, and faculty recruitment. Current academic rank: Associate Professor Active administrative roles: Senate Member (2023-), Information Technology Commission Member (2023-) Professional memberships: ACM, IEEE Senior Member, Cyprus Turkish Chamber of Computer Engineers Research Interests focus on four primary areas: Information Retrieval Systems – evaluating search engine effectiveness and reverse image search performance Machine Learning – applied to emotion classification, gender recognition, and medical diagnosis Data Mining – used in Turkish word-stemming analysis and user behavior studies Biometrics – specializing in hand/wrist/palm vein recognition systems and voice-based identification Publications demonstrate consistent contributions across disciplines, with recent works (2023-2025) emphasizing: Deep learning applications in biometric authentication Advanced emotion recognition systems Medical AI for diabetes management and retinopathy diagnosis Biometric spoof detection mechanisms Turkish language processing challenges Recommendation system innovations Awards & Recognition : Research Incentive Awards (2020, 2021) Best Paper Award at ICIW 2007 IEEE Senior Member status As an educator, he has supervised numerous thesis committees and taught foundational courses in computer engineering, including CMPE 112 and CMPE 342. His certifications (MCTS, MCITP) reflect technical expertise in Microsoft technologies.