Paul S. Blaer is a Senior Lecturer in Discipline at Columbia University's Computer Science Department, with adjunct roles in the Robotics Laboratory. He directs computing research facilities and teaches courses in data structures, robotics, and computer programming. His research develops autonomous systems for 3D site modeling and robotic navigation. Key projects include view planning algorithms for large-scale environments (e.g., Governors Island), hybrid topological localization using vision and WiFi, and educational tools for Java/MATLAB programming. His AVENUE robot platform enabled automated urban mapping. Teaching awards include the Columbia Engineering Alumni Distinguished Teaching Award (2018) and multiple Outstanding Teaching Assistant awards. Grants support robotics curriculum development and NSF-funded research in geometric computation.
Kathlén Kohn is an Associate Professor in Mathematics at KTH Royal Institute of Technology in Stockholm, Sweden (since December 2024). She holds a PhD from Technische Universität Berlin (2018) and dual Master's degrees in Mathematics and Computer Science from Paderborn University (2015). Her research bridges algebraic geometry, geometric deep learning, and computer vision, focusing on algebraic structures in neural networks and geometric problems in AI. Education: PhD in Mathematics, TU Berlin (2015–2018) Master of Science in Mathematics & Computer Science, Paderborn University (2013–2015) Bachelor of Science in Mathematics & Computer Science, Paderborn University (2009–2013) Research: Kohn explores neural algebraic geometry , applying algebraic techniques to analyze deep learning architectures like polynomial neural networks and self-attention mechanisms. Her work includes minimal problems in computer vision (e.g., PLMP framework), metric algebraic geometry, and invariant theory connections to maximum likelihood estimation. She co-authored the book Metric Algebraic Geometry (2024) and leads the WASP-funded project on 3D scene perception. Recent Articles: Focus on geometric neural network analysis, self-attention mechanisms, and structure-from-motion problems. Key venues include ICML, ICLR, CVPR, and SIAM Journal on Applied Algebra and Geometry. Awards: Wallenberg Prize (2025), SIAM SIGEST Award (2024), Swedish L'Oréal-Unesco Award (2023), Göran Gustafsson Prize (2021), and multiple fellowships including Marie Skłodowska-Curie. Teaching & Outreach: Lectures on algebraic vision, nonlinear algebra, and cryptography. Active in promoting gender equality in STEM through ELLIS and Swedish Young Academy.
Dr. Cam Minh Tri Tien is a Postdoctoral Research Fellow at the University of Southern Queensland (USQ), affiliated with the Centre for Future Materials and the iLAuNCH research group, within the School of Engineering. His expertise spans composite materials, structural engineering, and numerical methods. He holds a BEng from HoChiMinhUT (2008), an MSc from Konkuk University (2010), and a PhD from USQ (2016). His research focuses on pipeline rehabilitation systems, advanced fluid dynamics simulations, and structural behavior analysis of composite materials. His work includes developing predictive models for internal replacement pipe systems under various loading conditions and advancing numerical techniques like integrated radial basis functions (RBFs) for solving complex engineering problems. Recent contributions address debonding effects in structural repairs, temperature-induced axial responses, and digital twin applications in composite component lifecycle management. Dr. Tien has supervised multiple doctoral and master’s projects, including principal supervision of a doctoral thesis on digitizing composite component workflows. His research outputs total 18312 views and 1840 downloads, with active engagement in international conferences and peer-reviewed journals.
Pradeep Sen is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara, where he directs both the UCSB MIRAGE Lab and the UCSB Gaucho Game Lab. His research spans computer graphics, computational photography, and real-time rendering, with significant contributions to high dynamic range imaging and Monte Carlo rendering techniques. Sen has established himself as a leading researcher in computer graphics with numerous publications in top venues including SIGGRAPH and IEEE Transactions on Visualization and Computer Graphics. Ph.D. in Electrical Engineering, Stanford University (2006) M.S. in Electrical Engineering, Stanford University (1998) B.S. in Computer and Electrical Engineering, Purdue University (1996) Dr. Sen's research primarily focuses on novel rendering algorithms for accelerating global illumination and image-based techniques for capturing light transport. His work bridges theoretical advances in computer graphics with practical applications in video game development and immersive technologies. He has made significant contributions to patch-based high dynamic range video, computational zoom, and denoising Monte Carlo renderings using machine learning approaches. His research has important applications in virtual reality, augmented reality, and medical imaging. Analysis of Sen's recent publications reveals a strong trend toward integrating machine learning techniques with traditional computer graphics algorithms. His work has evolved from fundamental rendering techniques to more complex applications involving neural networks for image synthesis, 3D reconstruction, and content creation. There's a clear progression from his early work on dual photography and HDR imaging to more recent work on virtual try-on systems, diffusion models for image editing, and cosmic ray detection frameworks. Symposium on Computer Animation (SCA) Best Paper Award (2016) High Performance Graphics (HPG) Best Paper Finalist (2015) CVPR 2015 Outstanding Reviewer Award (2015) National Science Foundation CAREER award (2009) SIGGRAPH/Eurographics Graphics Hardware Conference Best Paper Award (2004) Dr. Sen has successfully mentored numerous PhD and Master's students who have gone on to prominent positions at companies like Adobe, Facebook Reality Labs, and Pinterest. His research has been generously funded through multiple NSF grants totaling over $1.6 million, including a prestigious CAREER award. He has also secured funding from NVIDIA, Sandia National Laboratories, and other sources to support his innovative work in computer graphics and visualization. As Director of both the UCSB MIRAGE Lab and the UCSB Gaucho Game Lab, Sen leads research teams focused on advancing computer graphics, imaging, visualization, and computer vision technologies. The MIRAGE Lab conducts fundamental research in rendering algorithms and computational photography, while the Gaucho Game Lab focuses on video game development and immersive experiences. These labs foster interdisciplinary collaboration between computer science, electrical engineering, and media arts students and faculty.
Zoe Likoudis serves as an Adjunct Faculty member in the College of Professional Studies and Advancement at National Louis University, teaching core mathematics courses including MTH 101 (Introduction to Mathematical Concepts), MTH 102 (Statistical Foundations), MTH 105/115 (Math for Educators I/II), MTH 307 (Investigatory Geometry), MTH 308 (Exploratory Probabilities), and LAM 309 (Theory of Numbers). With over two decades of Information Technology experience primarily in banking since 1994, she integrates industry expertise into her academic instruction while specializing in online pedagogy since 2002. Her academic credentials feature dual Master of Science degrees from the University of Illinois, Chicago: one in Mathematics, Statistics and Computer Science (Object Oriented Programming) earned as a University Fellow, and another in Computer Engineering (Computer Graphics, Animation and Virtual Reality) as a College of Engineering Fellow. She completed her Bachelor of Science in Computer Engineering (Artificial Intelligence) with the highest GPA in the College of Engineering. Ms. Likoudis' research centers on transformative online learning methodologies where she champions the philosophy that "all students can learn if they view it as an enjoyable task," supported by her expertise in Statistics Software Development and Banking Industry Technical Administration. Her secondary research streams explore Computer Graphics and Virtual Reality applications alongside IT migration projects, reflecting her dual-degree background in computer science and engineering. She has received formal recognition through an Outstanding Faculty Award specifically for structured and clear online instruction. As an educator specializing in working-adult cohorts since 2002, she emphasizes the advantages of online flexibility and global student engagement, noting adult learners are "highly motivated, smart and well organized." Her professional approach combines curriculum development with live internet-based teaching across multiple online universities.
Ren Ng is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. He is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) and the Visual Computing Lab (VCL). His research focuses on imaging, graphics, computer vision, human vision, and artificial intelligence, with notable contributions to computational photography, light field cameras, and neural radiance fields (NeRF). Ng previously founded and served as CEO of Lytro, Inc., which commercialized his Ph.D. research in light field photography. Ng holds a Ph.D., M.S., and B.S. in Computer Science and Mathematical Sciences from Stanford University. His awards include the ACM Doctoral Dissertation Award, Sloan Research Fellowship, and multiple industry recognitions such as MIT Tech Review's TR35 and Fast Company's 100 Most Creative People in Business. His research spans cutting-edge topics like color vision modeling, neural rendering, and lensless 3D imaging. Recent work explores stimulating photoreceptors for novel color perception and developing frameworks for human brain-based color vision emergence. Ng advises students in advanced imaging and computational techniques, with publications in top venues like Science Advances , ACM Transactions on Graphics , and NeurIPS . Key Contributions: Pioneered the Lytro light field camera technology Co-developed the NeRF framework for 3D scene representation Advanced computational imaging techniques (e.g., DiffuserCam) Grants & Labs: Active in NSF-funded projects and collaborates with institutions like Stanford and UC Berkeley labs. His research impacts fields from medical imaging to consumer electronics.
Caroline Conti is an Assistant Professor in the Department of Information Science and Technology (ISTA) at ISCTE – University Institute of Lisbon, and an Associate Researcher at the Institute of Telecommunications - IUL, where she is part of the Multimedia Signal Processing Group. She holds a PhD in Information Science and Technology from Iscte (2017), a specialization from Instituto Superior Técnico (2013), and a Bachelor’s in Electrical Engineering from the University of São Paulo (2010). Her research focuses on image and video processing , particularly in light field coding , 3D holoscopic video , and immersive visual technologies . She is a pioneer in light field research in Portugal and has contributed extensively to scalable and robust coding frameworks. Her recent publications emphasize deep learning-based segmentation, disparity estimation, and adaptive over-segmentation for 4D light fields, published in top journals like IEEE Transactions on Image Processing and Signal Processing: Image Communication . IBM Scientific Prize (2017) Scientific Awards ISCTE-IUL (2016, 2014) Best Poster Award at COST Interaction 2014 She has supervised multiple PhD and Master’s students in areas such as deep learning for light field inpainting and saliency detection. She has led and participated in European and national research projects, including the European 3D-ConTourNet and FCT-funded LIMESA. She is actively involved in the academic community as a Guest Editor for Signal Processing: Image Communication and as an Area Chair for IEEE ICIP 2025. She is a founding member and secretary of the Portuguese Chapter of the IEEE Signal Processing Society.
Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.
Roland Perko is a university lecturer, project manager, and key researcher at the Institute of Geodesy at Graz University of Technology. His work focuses on remote sensing, photogrammetry, and computer vision, with specific expertise in stereo matching, SAR and optical imagery analysis, and vision-based localization. R&D areas include multiple view geometry for SAR and optical imagery Specializations: subpixel techniques, digital aerial cameras, image registration
Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.
Professor Yi Ma is a faculty member at the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley, and holds the Chair Professor in AI at the School of Computing and Data Science (CDS), University of Hong Kong. He serves as Director of both CDS and the Institute of Data Science (IDS) at HKU since 2024 and 2023, respectively. His research spans artificial intelligence , computer vision , compressive sensing , and machine learning , with a focus on low-dimensional models for high-dimensional data analysis . He has pioneered work in robust principal component analysis , sparse representation , and deep learning theory , as reflected in his textbook High-Dimensional Data Analysis with Low-Dimensional Models (Cambridge, 2021) and recent position papers. Key contributions include organizing conferences like the Conference on Parsimony and Learning (CPAL) and developing course EECS 208 at Berkeley, which explores computational principles for AI and data analysis. His work is affiliated with research hubs such as the Berkeley Artificial Intelligence Research (BAIR) , Center for Augmented Cognition , and Vive Center for Enhanced Reality . Scientific Honors: Society for Industrial & Applied Mathematics (SIAM) Fellow (2020) Association for Computing Machinery (ACM) Fellow (2017) Institute of Electrical & Electronics Engineers (IEEE) Fellow (2013) Office of Naval Research Young Investigator (2005) NSF CAREER Award (2004) CS TCPAMI ICCV Best Paper Award/David Marr Prize (1999) He has held leadership roles at institutions including ShanghaiTech University and Microsoft Research Asia, blending theoretical and applied research in structured data modeling and vision-based robotics .
Dr. Krista Hands is a Professor of Mathematics at Oklahoma Baptist University (OBU), where she has served since Fall 2010. She previously held a position at Ashland University in Ohio. Her academic roles include teaching mathematics education courses for pre-service teachers, service courses for general mathematics requirements, and advanced courses in Calculus, Algebra, and Geometry. She served as Mathematics Department Chair from Fall 2013 to Spring 2016 and acts as the University Supervisor for Secondary Mathematics Education majors. Education: Ph.D. in Mathematics, University of Oklahoma M.A. in Mathematics Education, University of Kansas B.S. in Mathematics, Southern Nazarene University Her research focuses on mathematics pedagogy, particularly improving teaching methods in business calculus and understanding student preferences for challenging courses. She has integrated tablets and flipped classroom techniques into her teaching and has delivered presentations at national and regional conferences like the National Council of Teachers of Mathematics (NCTM) and Mathematics Association of America (MAA) meetings. Dr. Hands is actively involved in mission work, co-founding the 501C3 organization Mission 10:10 to support orphans and street children in Ethiopia. She has led multiple mission trips to Ethiopia and advocates for adoption, having adopted five children with her husband, Jeff, through international and foster care adoptions. Scientific Awards: Information Technology Gold Award for Exemplary Use of Educational Technology (Spring 2015) She maintains professional memberships with the National Council of Teachers of Mathematics and has contributed extensively to educational outreach through YouTube, hosting over 4,109 instructional videos with 1.1 million viewings as of May 2023.
Peter Claes is a Professor at KU Leuven with dual appointments in the Faculty of Engineering Sciences and Faculty of Medicine. He serves as head of the Laboratory for Image Processing in Genetics and is affiliated with both the Department of Electrical Engineering (ESAT) and Department of Human Genetics. His institutional roles include membership in the ESAT Division - Processing of Speech and Images, iSi Health - KU Leuven Institute for Physics-based Modeling for In Silico Health, and Leuven.AI - KU Leuven Institute for Artificial Intelligence. His research focuses on: Biological shape analysis Genetics of the human face Genetics of the brain Medical image analysis 3D facial morphometry Imaging genetics Prof. Claes leads multiple major research projects funded through KU Leuven, including studies on craniofacial morphology, genetics of facial features, and AI applications in healthcare. His work integrates advanced imaging techniques with genetic analysis to understand genotype-phenotype relationships, particularly in craniofacial development. His publication record demonstrates a strong trend toward integrating multi-omics data with geometric deep learning approaches. His research spans genetics, computer vision, bioinformatics, and clinical medicine, with applications in syndrome diagnosis, forensic science, and personalized medicine. As Program Director of the POC Artificial Intelligence for the Faculty of Engineering Sciences, he plays a key role in shaping AI education at KU Leuven. His teaching portfolio includes courses on biometrics, AI in healthcare, and medical imaging analysis. He leads the Laboratory for Image Processing in Genetics, which develops and applies advanced image processing techniques to genetic research, particularly in craniofacial morphology. His work has significant implications for clinical genetics, orthodontics, and forensic identification.
Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.
Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.
