جست‌وجوی استاد و پژوهشگر

James Duncan Summerfordمشاهده پروفایل

دانشیار

• Metaphysics
• Epistemology
• Philosophy of Science
• +۶ مورد دیگر

James Duncan Summerford is an Associate Professor of Philosophy at the Ohio University–Lancaster campus, part of the College of Arts and Sciences. He has been a core faculty member since 1999, contributing to undergraduate education and philosophical scholarship in metaphysics and epistemology. Education: Ph.D. in Philosophy, The Ohio State University (1997) M.A. in Philosophy, The Ohio State University (1990) B.A. in English & Philosophy, Oberlin College (1988) His research centers on metaphysics and epistemology, with a focus on the problem of universals, virtue epistemology, and the philosophical foundations of science and religion. He has critically engaged with the work of David Armstrong, particularly on realism, instantiation, and laws of nature. His interdisciplinary interests extend into logic, ethics, philosophy of religion, and the metaphysics of death. The published articles reflect a sustained engagement with analytic metaphysics and epistemology, particularly in the tradition of Armstrong and contemporary virtue-based theories. His work bridges technical metaphysical debates with broader epistemic and religious questions, showing a consistent interest in how abstract entities and epistemic norms function in philosophical explanation. Scientific Awards and Recognitions: Phi Theta Kappa Professor of the Year (2004) Included in Who’s Who Among America’s Teachers (2004, 2002) Pan Hellenic Award for Teaching Excellence, Auburn University (1999) William H. Fink Award (Second Prize) for philosophical writing (1997) Graduate Fellowship, The Ohio State University (1995) He has served as a referee for the Ohio Philosophical Association and has delivered commentaries on peer work. He has also held leadership roles in academic and community service, including co-chairing the United Way Campaign at Ohio University–Lancaster. He has taught a wide array of courses including Symbolic Logic, Environmental Ethics, and Philosophy of Science, demonstrating a strong commitment to liberal arts education. While there is no mention of a research lab or formal team, his academic service, conference participation, and collaborative work (e.g., co-authoring with James Barron) indicate active engagement within the philosophical community.

• Formal Verification
• Automated Reasoning
• SAT and SMT Solving
• +۷ مورد دیگر

Bernd Becker is a Professor in the Department of Computer Science at the Faculty of Engineering, University of Freiburg, Germany. He has been actively publishing in the fields of formal methods, hardware verification, and automated reasoning since the 1980s, with a sustained record of high-impact publications up to 2025. His research interests span formal verification, SAT and SMT solving, hardware testing, probabilistic systems, and embedded systems security. His work bridges theoretical advances in automated reasoning with practical applications in processor design, RISC-V verification, and safety-critical systems. The recent articles highlight a strong focus on applying formal methods to emerging challenges in hardware and AI, including POMDPs for robot planning, RISC-V security, GPU verification, and energy-efficient monitoring. There is a clear trend toward integrating symbolic computation with machine learning and probabilistic reasoning. Bernd Becker has collaborated extensively with researchers such as Ralf Wimmer, Ilia Polian, and Matthias Sauer, indicating leadership in a large, interdisciplinary research group. His work has been supported by numerous grants, though specific details are not listed in the source text. He has contributed to major conferences such as DATE, FMCAD, and SAT, and has mentored several students and researchers, though specific names are not provided. He is involved in projects related to secure scan networks, physical unclonable functions, and smart home systems.

• Hematopathology
• Molecular Diagnostic Pathology
• Lymphoma Research
• +۲ مورد دیگر

Dr. Krasimira Rozenova, MD, PhD, is an Assistant Professor of Anatomic Pathology at Yale University, specializing in hematopathology and molecular diagnostic pathology. She completed her PhD in Physiology at the University of Kentucky, medical training at St. George's University, residency at Mayo Clinic, and fellowships at Memorial Sloan Kettering Cancer Center and Yale. Her research focuses on Lymphoma diagnostics Short Tandem Repeat (STR) testing applications Molecular genetic pathology Immunophenotyping techniques Clinical-pathological correlation She has presented at international conferences and co-authored multiple peer-reviewed publications analyzing diagnostic challenges in hematology-oncology. Scientific awards include First place Trainee Presentation in Pulmonary Pathology (2022) Physician-in-Training Award (2021) ASH Abstract Achievement Award (2013) T32 Pediatric Hematology grant (2012) Dr. Rozenova actively contributes to clinical diagnostics, resident education, and advancing molecular pathology techniques at Yale Medicine. Her recent publications demonstrate expertise in both hematologic malignancies and diagnostic method validation.

Uwe Egbert Wolterمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Category Theory
• Algebraic Specification
• +۱۲ مورد دیگر

Uwe Egbert Wolter is an Associate Professor in the Department of Informatics at the University of Bergen, Faculty of Mathematics and Natural Sciences. He is actively engaged in research at the intersection of theoretical computer science, category theory, and formal methods in software engineering. His research interests include: Theoretical Computer Science Category Theory Algebraic Specification Graph Transformations Formal Methods Coalgebras Diagrammatic Modeling Model-Driven Engineering Multilevel Modeling Logic in Computer Science Wolter’s recent publications focus on diagrammatic specification frameworks, generalized sketches, and coalgebraic modeling. His work explores the use of categorical and algebraic structures to formalize software models, knowledge representation, and logical systems. He frequently collaborates with researchers such as Harald König, Adrian Rutle, Zinovy Diskin, and Alfio Martini. The research trends show a consistent focus on unifying logical and structural aspects of systems using category-theoretic tools. He has no listed scientific awards in the provided text. Wolter has advised or collaborated with several researchers, though formal students are not explicitly listed. His work has been supported through academic collaborations and research projects, including funding from the Research Council of Norway (e.g., project 194521). He has contributed to workshops and conferences in formal methods, algebraic development techniques, and model-driven engineering. He is involved in research groups and collaborative efforts centered on formal modeling, particularly through the Diagram Predicate Framework and multilevel typed graph transformations. His work contributes to foundational aspects of software specification and knowledge representation.

Vinayak S. Prabhuمشاهده پروفایل

استادیار

• Formal Methods
• Formal Verification
• Model Checking
• +۱۵ مورد دیگر

Vinayak S. Prabhu is an Assistant Professor in the Department of Computer Science at Colorado State University, where he conducts research in formal methods for cyber-physical systems. He holds a Ph.D. from the University of California at Berkeley and is supported by an NSF CAREER award. His research integrates formal verification, control theory, and software analysis to ensure the reliability of embedded and cyber-physical systems. His research interests include formal verification, model checking, temporal logics (especially Signal Temporal Logic), robustness in cyber-physical systems, conformance checking, hybrid and timed systems, multi-agent systems, scheduling, and security. He employs techniques from automata theory, logic, and software analysis to synthesize tools and algorithms for system development. His recent publications focus on monitoring and robustness of temporal logic specifications, with several papers published in top venues like HSCC, MEMOCODE, ECAI, and IEEE Transactions. His work demonstrates a strong trend in developing efficient, scalable, and quantitative methods for verifying complex real-time and hybrid systems. NSF CAREER Award Best paper award at MEMOCODE 2024 He advises several Ph.D. and M.S. students, including Bassem Ghorbel and Alperen Tercan, and has mentored numerous undergraduate researchers who have gone on to graduate programs at institutions like Duke and Michigan. He serves on program committees for major conferences like ICCPS and VMCAI and teaches courses such as Automata, Logic, and Computation, and Foundations of Formal Methods and Cyber-Physical Systems.

• Programming Languages
• Program Synthesis
• Program Verification
• +۷ مورد دیگر

Jedidiah McClurg is an Assistant Professor in the Department of Computer Science at Colorado State University, with prior faculty appointments at Colorado School of Mines and the University of New Mexico. He received his Ph.D. in Computer Science from the University of Colorado Boulder in 2018, where he was a member of the CUPLV research group under the supervision of Pavol Cerny. His research focuses on programming languages, program synthesis, verification, and their applications in networking, compilers, and distributed systems. His educational background includes an M.S. in Computer Science from Northwestern University (2013) and a B.S. in Electrical Engineering from the University of Iowa (2009). He has completed internships at Microsoft Research (RiSE Group, 2014) and Rockwell Collins (2011, 2013, 2004). McClurg’s research interests include programming languages, formal verification, software synthesis, software-defined networking, compilers, and system security. His work aims to develop tools and techniques that help programmers write more secure, reliable, and efficient code, especially in safety-critical domains. He has led multiple NSF-funded projects, including FMitF and CRII grants, totaling over $1 million in funding. His recent publications span high-impact venues such as PLDI, CAV, DISC, and SOSR, with topics ranging from neural network optimization and regular expression synthesis to network program verification and FEC code generation. These works reflect a consistent trend toward automating correctness, improving performance, and enabling scalable solutions in systems and networking. NSF CRII: SHF: Foundations for Stateful Network Programming ($175,000) NSF FMitF: Game Theoretic Updates for Network & Cloud Functions ($355,000 for him) NSF FMitF: Robust Enforcement of Customizable Resource Constraints ($250,000 for him) NSF GRFP (awarded to student Lauren Baker) He has advised multiple graduate and undergraduate students, many of whom have secured positions at leading tech companies such as Google, Apple, and Amazon. He is actively involved in academic service, having served on program committees for PLDI, SOSR, CAV, and others, and as a reviewer for journals like IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Software Engineering (TSE). He also contributes to open-source research via GitHub and maintains a strong online academic presence.

• Artificial Intelligence
• Machine Learning
• Abstract Argumentation
• +۲ مورد دیگر

Gianvincenzo ALFANO is a Researcher at the Department of Computer Engineering, Modelling, Electronics and Systematics (DIMES) at the University of Calabria. His work focuses on abstract argumentation frameworks, epistemic reasoning, and computational complexity. University: University of Calabria Department: Department of Computer Engineering, Modelling, Electronics and Systematics Academic Rank: Researcher Research Interests: Gianvincenzo specializes in abstract argumentation, exploring formal foundations, complexity analysis, and applications in artificial intelligence. His research spans epistemic reasoning, preference handling, and incremental computation in dynamic knowledge bases. Recent Publications: Over the past two years, he has published extensively on topics such as physics-informed neural networks in federated learning, credulous acceptance in high-order frameworks, and complexity of epistemic argumentation. His work emphasizes formal logic, algorithm design, and uncertainty handling in AI systems.

Oliver Thielمشاهده پروفایل

استاد

• Motivation in Mathematics Education
• Preschool Teachers' Perspectives on Mathematics
• Children's Mathematical Development
• +۷ مورد دیگر

Oliver Thiel is a Professor of Early Childhood Mathematics Education at Queen Maud University College in Trondheim, Norway. With a PhD in Philosophy (Pedagogy) from Humboldt-University of Berlin, he has dedicated over two decades to researching and teaching mathematics education, focusing on preschool and kindergarten contexts. Current role since 2024: Professor at Queen Maud University College 2011–2024: Associate Professor at same institution 2010–2011: Substitute Professor at Schwäbisch Gmünd University College 1998–2005: Assistant Professor at Humboldt-University His research spans teacher beliefs about mathematics, children's mathematical development, and innovative pedagogy through video production and automata construction . He co-developed Norway's first Mathematics Room for experiential learning and co-authored the textbook Matematikkens kjerne , widely used in early childhood teacher training. Key research areas include: Quantitative methods in educational research Spatial reasoning in children Stochastic concepts in play Math anxiety among preservice teachers STEM integration through mechanical toys His recent publications focus on video-based learning, automata for STEM, and affective-motivational teacher training. Notable projects include ViduKids (video production) and AutoSTEM (mechanical learning). Academic achievements: Summa cum Laude doctoral distinction (Humboldt-University, 2005) He has supervised both bachelor's and master's students, emphasizing the integration of teaching and research through student participation in active projects. The Mathematics Room at Queen Maud University College exemplifies his approach, combining Bishop's six fundamental mathematical activities with Dewey's experiential learning theory in a social constructivist framework.

Xin Chenمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Formal Methods
• Safety and Security of Machine Learning Systems
• +۷ مورد دیگر

Xin Chen is an Assistant Professor in the Department of Computer Science at the University of New Mexico , where he conducts research at the intersection of cyber-physical systems, formal methods, and machine learning. His work focuses on safety and security verification for learning-enabled systems. Research Interests: Safety and security of Cyber-Physical Systems (CPS) with machine learning components Formal verification and reachability analysis of hybrid and neural network-controlled systems Real-time monitoring and attack recovery in CPS Development of verification tools for safety-critical autonomous systems Publication Trends: His recent publications (2020–2025) emphasize formal methods for neural network verification, attack recovery in CPS, and reachability analysis. Key themes include the use of polynomial arithmetic, linear approximations, and abstraction techniques to ensure safety in systems using deep learning and reinforcement learning. Scientific Service: Program Committee Member: DAC 2025, VMCAI 2025, RTSS, AAAI, CAV, HSCC, ARCH, and others Grants and Advising: Dr. Chen is the Principal Investigator (PI) on multiple grants, including an NSF-funded project on safety-directed abstraction and correction of learning-enabled CPS. While no formal students are listed, he collaborates extensively with researchers and students on funded projects and tool development. He leads research in formal verification and has mentored work on tools like Flow* and POLAR. Labs and Tools: Flow* : A tool for reachability analysis and safety verification of nonlinear hybrid systems, with a reengineered high-performance version available on GitHub. POLAR : A framework for formal verification of neural network-controlled systems.

Felipe Werndl Trevizanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Automated Planning and Scheduling
• Reasoning under Uncertainty
• +۹ مورد دیگر

Felipe Werndl Trevizan is a Senior Lecturer in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of Artificial Intelligence, Operations Research, and Machine Learning. His work focuses on automated planning and scheduling, reasoning under uncertainty, heuristic search, and machine learning for planning. He is actively involved in research projects related to uncertainty-centric pilot assistance systems and probabilistic planning. His research interests include: Automated Planning and Scheduling Probabilistic and Multi-Objective Planning Heuristic Search and Admissible Heuristics Deep Learning for Generalized Planning Constraint-Based and Risk-Aware Planning Learning Heuristics from Data His recent publications demonstrate a strong trend toward integrating machine learning with classical planning techniques, particularly in learning reliable, domain-independent heuristics using both deep and classical machine learning methods. He has developed novel algorithms for constrained and stochastic shortest path problems, multi-objective probabilistic planning, and generalized planning using neural networks. His scientific awards include: Best Paper Award at ICAPS 2016 Best Paper Award at ICAPS 2017 2016 Kikuchi-Karlaftis Best Paper Award from the Transport Research Board Felipe Werndl Trevizan is registered to supervise research students and has advised several successful projects in AI planning. He has secured research grants for projects such as 'Integrated Planning for Uncertainty-Centric Pilot Assistance Systems' and 'Chance-Constrained POMDP for Pilot Assistance Systems' at ANU. He is also organizing key community events including the 'Reliability In Planning and Learning (RIPL)' workshop and a tutorial on 'Learning for Planning' at ICAPS 2025. He is affiliated with the School of Computing at ANU and previously worked as a Senior Research Scientist at NICTA and Data61/CSIRO. His research is supported by active collaborations with leading researchers such as Sylvie Thiébaux, Hanna Kurniawati, and others.

Ian Starkمشاهده پروفایل

مدرس ارشد

• Programming Languages
• Theoretical Computer Science
• Concurrency Theory
• +۱۵ مورد دیگر

Ian Stark is a Senior Lecturer in Computer Science at the School of Informatics , The University of Edinburgh , and a key member of the Laboratory for Foundations of Computer Science (LFCS) . His research centers on mathematical models of programming languages and concurrent systems, with a focus on reasoning about name generation and secure mobile code. Research Interests: Ian Stark's work lies at the intersection of theoretical computer science and programming language semantics. He investigates foundational models such as the π-calculus and nu-calculus, applying them to problems in security, resource management, and systems biology. His research in denotational and operational semantics provides rigorous frameworks for understanding program behavior, particularly in systems with dynamic name creation and local state. He has made significant contributions to proof-carrying code and resource guarantees for mobile and embedded systems, ensuring safety and security through formal methods. The trends in his publications over the last 15 years show a consistent focus on foundational models for concurrency and security. Early work established semantic models for the π-calculus, while later research applied these to practical domains like mobile code security and systems biology. His articles frequently appear in top venues like LICS, FOSSACS, and TCS, reflecting a strong theoretical grounding with applications in verification and language design. Scientific Recognition: Advising and Funding: Stark has supervised several PhD students, including Marek Kwiatkowski, Chris Banks, Uli Schöpp, Sam Lindley, and Tom Chothia. He has been involved in major collaborative research projects such as REMS (Rigorous Engineering for Mainstream Systems), App Guarden (Resilient Application Stores), CerCo (Certified Complexity), and Mobius (Proof-Carrying Code), funded by the EPSRC and the European Union. He actively recruits PhD students, particularly through the Centre for Doctoral Training in Pervasive Parallelism. Labs and Teams: He is a core member of the Laboratory for Foundations of Computer Science (LFCS) , a world-renowned research group at the University of Edinburgh focused on the mathematical foundations of computing. His work often involves collaboration with researchers across Europe in the areas of programming languages, semantics, and formal verification.

• Formal Verification
• Theorem Proving
• Hybrid Systems
• +۱۲ مورد دیگر

Paul Jackson is a Senior Lecturer in the School of Informatics at the University of Edinburgh and serves as Director of the Artificial Intelligence and its Applications Institute (AIAI). He is also affiliated with the Laboratory for Foundations of Computer Science (LFCS) and is an associate member of the Institute for Computing Systems Architecture (ICSA). His work bridges theoretical and applied aspects of formal methods in computer science. Education PhD and MS in Computer Science, Cornell University (1988–1995) MS in Physics, Cornell University (1986–1988) Undergraduate in Engineering, University of Cambridge (1981–1984) Paul Jackson's research focuses on formal verification, particularly the development and application of tools for verifying software, hardware, and hybrid systems. His primary interests include interactive theorem proving (using Lean, Isabelle, and KeYmaera), formal verification of hybrid and cyber-physical systems, non-linear arithmetic, and the integration of symbolic computation with deduction. He has worked extensively with the Nuprl theorem prover and contributed to tools like Victor for SPARK/Ada verification. His recent publications emphasize compositional verification, validated integration using Taylor models, invariant generation for polynomial systems, and dynamic proof presentation. He has been involved in multiple EPSRC and UKRI-funded projects, including work on trustworthy autonomous systems and cache coherence protocols. Scientific Awards and Recognition No specific awards are listed in the provided text. Paul Jackson has advised several PhD students, including Ramon Fernández Mir, Kristjan Liiva, Andrew Sogokon, and Grant Olney Passmore, many of whom have gone on to influential roles in academia and industry. He has held significant administrative roles, including Senior Tutor and Senior Director of Studies, and has contributed to major conferences such as CAV, CICM, and CADE as program committee member or chair. His teaching includes courses on formal verification, automated reasoning, and software engineering.

• Mathematics Education
• Algebra Education
• Analysis Education
• +۲ مورد دیگر

Reinhard Oldenburg is Professor of Mathematics Education at the University of Augsburg, holding a chair position since 2014 in the Institute of Mathematics within the Faculty of Mathematics, Natural Science and Technology. His academic career spans university teaching, educational research, and development of innovative teaching approaches that integrate technology with mathematical concepts. He maintains an active research program while teaching courses in algebra education, analysis education, and digital tools for mathematics instruction. His educational background includes: 2000: Second State Examination in Mathematics/Physics 2000: Examination in Computer Science (additional subject) 1998: First State Examination in Mathematics/Physics 1998: PhD in Mathematics with thesis 'Birman-Murakami-Wenzl Algebren des Coxeter Typs B' 1993: Diploma in Mathematics Studied Mathematics and Physics at Goethe University Frankfurt Professor Oldenburg's research focuses on the didactics of mathematics with special emphasis on algebra education, analysis education, and the integration of digital technologies in mathematics teaching. He investigates how students develop mathematical understanding, particularly in conceptual areas of algebra and calculus, and examines how digital tools can enhance mathematical learning. His work bridges theoretical educational frameworks with practical classroom applications, addressing challenges in both school and university mathematics education. He has made significant contributions to understanding how technology can support mathematical reasoning and problem-solving. His recent publications reveal a strong trajectory toward the intersection of mathematics education and technological innovation. The articles demonstrate consistent exploration of how digital tools can enhance conceptual understanding in algebra and analysis, with increasing attention to emerging areas like AI applications in mathematics education. His work spans from theoretical examinations of mathematical concepts to practical classroom implementations, showing particular strength in developing technological approaches to traditional mathematical topics while maintaining rigorous mathematical foundations. Professor Oldenburg leads the Mathematics Education department at the University of Augsburg, collaborating with researchers including Barbara Chikh (secretary), Sabrina Bersch, Ruth Dietl, and others. His work extends to the 'impulse für guten Mathematikunterricht' journal and various research projects focused on mathematics education innovation. He has been instrumental in developing the FeliX project, which integrates computer algebra and dynamic geometry systems, representing a significant contribution to educational technology in mathematics instruction.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Polyhedral Compilation
• Performance Programming
• +۷ مورد دیگر

Tobias Grosser is an Associate Professor in Compiler Design at the Department of Computer Science and Technology, University of Cambridge. His research spans compilation, programming language design, and performance programming with applications to climate science, quantum computing, and hardware design. He leads an active research group focusing on making compilers more modular, predictable, and trustworthy while bridging the gap between developers and compilers. His research interests include: Compilers and polyhedral compilation Static and dynamic program analysis High-performance computing and loop optimization Domain-specific compilation for accelerators (GPU, FPGA) Formal verification for compiler correctness Machine learning applications to compiler design His recent publications demonstrate significant contributions to multi-level intermediate representations (MLIR), compiler verification using Lean, and performance modeling. His work on xDSL provides a Python-native compiler framework that enables rapid prototyping of compiler infrastructure. His research group has developed tools like FPL (Fast Presburger Library) for loop optimization in deep learning and scientific computing. Scientific awards include two Amazon Research Awards (2023-2024) for work in automated reasoning, a Google PhD Fellowship, and an Ambizione Fellowship at ETH Zurich. He has advised numerous PhD students and researchers who have gone on to positions at Google, ETH Zurich, NVIDIA, and Intel. He actively collaborates with industry through the Amazon Scholars program and hosts regular Compiler Social events in Cambridge. His research vision emphasizes connecting compiler technology with societal challenges like climate change while maintaining a strong commitment to open-source development and diversity in computer science.

• Computer Vision
• Machine Learning
• Healthcare AI
• +۷ مورد دیگر

Jianbo Jiao is an Associate Professor in Computer Vision and Machine Learning at the School of Computer Science, University of Birmingham, where he leads the MIx (Machine Intelligence + x) research group. Previously, he was a Postdoctoral Researcher in the Biomedical Image Analysis (BioMedIA) group and Visual Geometry Group (VGG) at the University of Oxford, working on the SeeBiByte and VisualAI projects. MSc from Peking University (2015) PhD in Computer Science from City University of Hong Kong (2018), supported by Hong Kong PhD Fellowship Scheme Visiting scholar at Beckman Institute, University of Illinois at Urbana-Champaign (2017-2018) Dr. Jiao's research focuses on Computer Vision, Machine Learning, Healthcare AI, and AI for Science. His work primarily investigates learning representations in open-world scenarios by exploring auxiliary information from multiple modalities (image/video, speech/audio, text/NLP, depth/stereo, gaze/saliency, motion) and reducing dependency on supervision through self-supervised, weakly-supervised, and semi-supervised learning approaches. His group has made significant contributions to multimodal learning, medical image analysis, and 3D vision. His recent publications demonstrate strong trends in multimodal learning, medical AI applications, and 3D computer vision. The research spans fundamental computer vision tasks like segmentation and scene understanding while addressing practical challenges in healthcare and scientific domains. Notably, there's significant focus on making vision systems more robust to distribution shifts and requiring less labeled data. Amazon Research Award recipient (2024) Royal Society Short Industry Fellow Best Paper Award at MICAD 2024 Best Paper Award at MICCAI 2024 ASMUS Workshop Best Presentation (Runner-Up) Award at MICCAI 2024 ASMUS Workshop Best Paper Award at ECCV 2022 Medical Computer Vision Workshop Multiple Outstanding Reviewer Awards from top conferences Dr. Jiao serves as Associate Editor for The Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), and has been actively contributing to the community as Area Chair for NeurIPS, ICML, ICLR, and ACM MM. His MIx group has secured multiple research grants including the Amazon Research Award for the PCo3D project on Physically Plausible Controllable 3D Generative Models. Current PhD students include Isaac Akintaro, Hao Ai, and Kangning Zhang. The MIx group maintains active collaborations with industry partners including Amazon and Meta, and has developed notable datasets such as 360+x for panoptic multi-modal scene understanding and DyMVHumans for dynamic human modeling. Their research bridges fundamental computer vision with practical applications in healthcare, physics, chemistry, and other scientific domains.