Jeong Joon (JJ) Park is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on computer vision, graphics, and artificial intelligence with applications in 3D/4D reconstruction, generative modeling, robotics, and medical imaging. He holds a position in the College of Engineering and actively seeks PhD students and postdoctoral researchers aligned with his research interests. Dr. Park’s work emphasizes interdisciplinary approaches, combining geometric deep learning with generative models to address challenges in scene understanding, novel view synthesis, and multi-modal perception. His lab explores both foundational techniques and applied systems, often collaborating with industry and academia on real-world problems. Key research directions include diffusion models for sparse data restoration, trajectory-conditioned 4D generation, and uncertainty-aware sensor fusion for autonomous systems. His publications span top-tier conferences like CVPR, ICCV, and NeurIPS, reflecting a strong publication record in computer vision and graphics. He teaches courses in computer vision and advises students on advanced projects requiring significant weekly commitments. Prospective applicants are encouraged to apply through the U-M CSE PhD program and contact him directly for collaboration opportunities.
Zhaoli Song is an Associate Professor at the Department of Management and Organisation within NUS Business School, Singapore. His research bridges behavioral genetics with organizational behavior, focusing on leadership, AI in the workplace, cross-cultural management, and work-family dynamics. PhD in Human Resources and Industrial Relations (2004), University of Minnesota Master in Statistics (2004), University of Minnesota Master in Applied Psychology (1999), Chinese Academy of Sciences Bachelor in Optics (1995), Sichuan University Dr. Song pioneered molecular genetics applications in management research, achieving media recognition in Economist and Washington Post . His work spans AI strategy formulation, pandemic scenario modeling, and team innovation across Asia. He has taught organizational behavior, HRM, and research methods at undergraduate, Master's, EMBA, and executive levels. Recent publications analyze AI adoption frameworks, emotional dynamics in leader-member exchanges, and genetic determinants of creativity. He served as Academic Director for NUS Asian Pacific EMBA (Chinese) program (2013-2017), demonstrating educational leadership alongside scholarly contributions.
Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.
Anand Bhattad is an Assistant Professor in the Department of Computer Science at Johns Hopkins University, starting Fall 2025. Previously, he held positions as a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC) and a visiting scholar at UC Berkeley. His research focuses on the intersection of computer vision, generative modeling, and physical reasoning, aiming to develop perception-driven and physics-aware visual models. His academic journey includes a PhD in Computer Science from the University of Illinois Urbana-Champaign under David Forsyth, with mentorship from Derek Hoiem, Svetlana Lazebnik, Greg Shakhnarovich, and Shenlong Wang. Prior to his PhD, he earned dual master’s degrees in Computer Science and Civil and Environmental Engineering at UIUC and a bachelor’s in Civil Engineering from NITK Surathkal, India. Research interests center on how generative models encode physical and perceptual knowledge, with key contributions in intrinsic image emergence, projective geometry limitations, and physics-aware relighting techniques. His work bridges classical computer vision concepts with modern deep learning, producing state-of-the-art methods for 3D scene synthesis and image editing. Articles span topics like 3P Vision , diffusion models, and 360° video datasets, reflecting interdisciplinary approaches in computer graphics and computational photography. Scientific awards include Outstanding Reviewer at ICCV 2023, CVPR 2022 Best Paper Finalist, and multiple conference service roles as workshop organizer and area chair. He designed the TTIC course Past Meets Present: A Tale of Two Visions , teaching connections between historical and modern computer vision research.
Juergen Dingel is a Professor in the School of Computing at Queen's University, Canada. He joined the faculty in 2000 and holds a PhD in Computer Science from Carnegie Mellon University (1999). His research focuses on software modeling, model-driven engineering, formal methods, and formal verification, with applications in real-time systems and embedded systems. He leads the Modeling and Analysis in Software Engineering (MASE) research group. Education: PhD in Computer Science, Carnegie Mellon University (1999) M.Sc. in Pure and Applied Logic, Berlin University of Technology (1994) M.Sc. in Computer Science, Berlin University of Technology (1992) Research Interests: Model-driven engineering and transformation Formal specification and verification Automated testing and debugging Real-time and embedded systems Service composition and distributed systems His work emphasizes practical tools like Papyrus-RT and MDebugger , integrating formal methods into software development. Grants & Collaborations: Funded by NSERC, OCE, and industry partners (IBM, GM, Ericsson) Focus on automotive systems, IoT, and safety-critical applications Service: Editorial board member for SoSyM , STTT , and JOT Former chair of the MODELS Steering Committee (2016–2018) PC co-chair for MODELS 2014 and FMOODS/FORTE 2011 Labs & Teams: Leads the MASE group, which develops open-source tools for model-driven engineering. Collaborates with industry on automotive and IoT projects.
Valérie Berthé is a Research Director at CNRS, based at the Institut de Recherche en Informatique Fondamentale (IRIF), Université Paris Cité. Her research lies at the crossroads of theoretical computer science and mathematics, with a focus on symbolic dynamics, combinatorics on words, discrete geometry, and numeration systems. She is a Principal Investigator of the prestigious ERC Synergy project DynAMICs, which unites experts from mathematics and computer science to tackle fundamental problems in dynamical systems and model checking. Research Interests: Her primary research areas include symbolic dynamics, combinatorics on words, discrete geometry, tilings, aperiodic order, continued fractions, and their connections to number theory and automata. She is particularly interested in the algorithmic and arithmetic aspects of dynamical systems, aiming to develop automated verification methods and resolve open conjectures such as the Skolem problem on reachability. Scientific Leadership: She leads a major collaborative research program and is involved in significant national and international projects. Grants & Projects: ERC Synergy Grant: DynAMICs (Model checking seen from a dynamic and arithmetic point of view) ANR PRCI: SymDynAr (2024–2027) ANR Blanc: CODYS (2018) Academic Service: Member, CNRS National Committee, Section 6 Member, Steering Committee of INSMI (Institut national des sciences mathématiques et de leurs interactions) Member, Scientific Council of the Société Mathématique de France (SMF) Advising and Editorial Work: Valérie Berthé has supervised numerous PhD students who have gone on to successful academic careers. She is a co-editor of several influential volumes in her field, including works published by Cambridge University Press and Birkhäuser, covering topics in sequences, automata, number theory, and symbolic dynamics. She is also involved in organizing scientific events and seminars, such as the One World Numeration Seminar, fostering international collaboration in her research community.
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Dr. Benjamin Busam is a Senior Research Scientist at the Technical University of Munich , affiliated with the Chair for Computer Science Applications in Medicine under Prof. Nassir Navab. Starting September 2025, he will hold the Professorship for Photogrammetry and Remote Sensing at TUM. His career includes leadership roles at FRAMOS Imaging Systems and Huawei Research in London. Education: Mathematics (TUM), Mathematics and Physics (ParisTech, University of Melbourne), PhD in Mathematics (TUM, 2014) His research focuses on 3D computer vision , multi-modal sensor fusion , and their applications in collaborative robotics and augmented reality . He specializes in projective geometry , 6D pose estimation , and neural radiance fields , with a particular emphasis on photometrically challenging environments. Recent publications highlight advancements in 3D scene understanding , neural rendering , and medical imaging , often leveraging machine learning and vision-language models . His work has been recognized through awards like the EMVA Young Professional Award (2015) and Innovation Pioneer of the Year (2019) , along with multiple Outstanding Reviewer distinctions at leading conferences. Dr. Busam has supervised numerous PhD and MSc students on topics including 6D pose estimation , medical augmented reality , and robotic ultrasound , collaborating with institutions like MIT , École Polytechnique , and University of Padova .
Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.
Bo Chen is a postdoctoral researcher at the Siebel School of Computing and Data Science and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His work focuses on AI-system co-design for immersive computing, particularly in extended reality (XR) and multi-modal content delivery over wireless networks. Ph.D. in Computer Science (2022), advised by Klara Nahrstedt B.S. in Computer Science from Shanghai Jiao Tong University (2016) His research integrates AI techniques with system-level optimizations to address challenges in XR infrastructure , including multi-view video streaming , NeRF-based content delivery , and uncertainty management in video transmission . He has pioneered methods like Loose Frame Referencing for learned codecs and Context-Aware NeRF Serving for mobile XR applications. Bo Chen's recent publications span top venues like ACM MobiSys, ACM SenSys, and USENIX NSDI. Key themes include AI-driven compression , dynamic 3D rendering , and reliable streaming over mobile networks . He has received recognition including the Rising Star Best Presentation Award (ACM MobiSys 2025) and Best Student Paper Award (ACM MMSys 2022). Bayesian optimization for XR systems Multi-view video aggregation at edge networks 3D Gaussian Splatting for immersive media
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.
Xiaohu Guo is a Professor of Computer Science at the University of Texas at Dallas specializing in computer graphics, computer vision, and geometric modeling. His research develops algorithms for 3D/4D reconstruction, virtual reality, medical imaging, and physics-based simulations. Professor Guo has received significant recognition including a Best Paper Award at SIGGRAPH (2023) and an NSF CAREER Award (2012). His current research focuses on dynamic human capture, deformable models, and medical image computation. Education: PhD, Stony Brook University MS, Stony Brook University BS, University of Science and Technology of China Research Funding: Recently secured a $500,000 NSF grant for developing open-source 4D reconstruction frameworks for real-time dynamic human capture (2021). Editorial Roles: Serves on editorial boards of Graphical Models , Computer Animation and Virtual Worlds , and IEEE Transactions on Visualization and Computer Graphics .
Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.
Anna Maria Oberländer is Assistant Professor for Information Systems and Digital Transformation at the University of Bayreuth's Faculty of Law and Economics. She also serves as Deputy Academic Director of the Research Center Finance & Information Management and holds a leading position at Fraunhofer FIT's Branch Business & Information Systems Engineering, where she co-heads a research group and manages the Digital Innovation Lab as one of its co-founders. Dr. Oberländer earned her Master of Science with honors from the Elite Graduate Program Finance & Information Management at the University of Technology Munich and the University of Augsburg in 2015, including a research visit at Queensland University of Technology. She was accepted into the 16th class of the Bavarian EliteAcademy, receiving leadership training across Germany, Belgium, China, and Vietnam. Prior to academia, she worked as a strategy consultant at McKinsey & Company from 2015 to 2018, supporting clients in Advanced Industries and Financial Services with digital transformations. She completed her dissertation at the University of Bayreuth's Law and Economics Faculty in 2020. Her research focuses on understanding and designing digital transformation and innovation from established companies' perspectives. She examines digital transformation processes and patterns, the relationship between organizational digital transformation and digital ecosystems, organizational ambidexterity, resource-based digital opportunities for incumbents, and the socio-technical and economic impacts of IoT and AI technologies. Her work spans theoretical frameworks like the OCO (Orientation, Cooperation, Orchestration) Theory for understanding digital ecosystems' influence on transformation. Dr. Oberländer's recent publications reveal a strong trend toward integrated digital-sustainability research (twin transformation), AI strategy and implementation, digital ecosystem dynamics, and industry-specific digital innovation. Her work bridges theoretical frameworks with practical applications across diverse sectors including public administration, manufacturing, and smart cities. She has developed capability maturity models for twin transformation and explored semantic search adoption in SMEs. Science Award 2020 from Universitätsverein Bayreuth e.V. With extensive industry experience at Siemens, Siemens Financial Services, Allianz, and McKinsey & Company, Dr. Oberländer brings practical insights to her academic work. Her Fraunhofer FIT role connects her research to real-world applications through the Digital Innovation Lab. She has led collaborative research projects across multiple industries, including the print industry's sustainability potential with AI, dairy industry digital innovation, and public administration digitalization. Dr. Oberländer co-heads a research group at Fraunhofer FIT and manages the Digital Innovation Lab, which serves as a hub for collaborative research between academia and industry. Her work with the Research Center Finance & Information Management facilitates interdisciplinary collaboration across finance and information systems domains.
