Massachusetts Institute of TechnologyUnited States
Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.
Changxi Zheng is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science (SEAS). He directs Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC). After receiving his PhD from Cornell University, he joined the faculty of Computer Science Department at Columbia, where he has established himself as a leading researcher in computer graphics and scientific computing. Dr. Zheng's research spans multiple areas of applied computer science with a particular focus on computer graphics and scientific computing. His work centers around developing numerical models for simulating physical phenomena involving complex motions such as fluids, bubbles, and thin rods, along with their resulting acoustic waves. Leveraging computational insights from these models, he devises methods for improving tangible object creation, enabling novel human-computer interactions, and developing software tools for acoustic and photonic devices. His research has attracted significant public interest and media coverage, including projects like FontCode, AirCode, and Computational Metallophone Design. His recent publications reveal a strong interdisciplinary approach, bridging computer graphics, physics simulation, machine learning, and hardware design. His work demonstrates consistent innovation in computational methods for simulating physical phenomena and applying these techniques to practical problems in 3D printing, acoustic modeling, and interactive systems. The breadth of his research spans from fundamental physics-based simulations to practical applications in industry. Columbia SEAS Dean's Fellow (for advised students) NSF Graduate Research Fellow (for Ruilin Xu) Snap Research Fellow (for Rundi Wu) CKGSB Fellow (for Yun Fei) Adobe Research Fellow (for Gabriel Cirio) Marie Sklodowska-Curie Individual Fellow (for Rundi Wu) Best Paper Award at ACM International Conference on Multimedia (ACMMM), 2019 Dr. Zheng actively mentors a diverse group of students, including current PhD candidates and postdoctoral researchers. His research group has received support from various sources that enable their innovative work in computational graphics and physics-based simulation. He has supervised numerous successful students who have gone on to positions at leading technology companies including Adobe, Tencent, Facebook, and academic institutions. As director of Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC), Dr. Zheng leads a vibrant research team focused on advancing the state of the art in computer graphics, physics-based simulation, and their applications. The group maintains strong collaborations with industry partners and academic institutions worldwide, fostering an environment of innovation and practical application of theoretical concepts.
Adriana Schulz is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She leads a research group focused on computational design, computer-aided design (CAD), and digital fabrication. Her work bridges computer science with practical applications in manufacturing, robotics, and sustainable design. Dr. Schulz received her Ph.D. in Computer Science from MIT in 2018 under the supervision of Professor Wojciech Matusik. Prior to her doctoral studies, she earned a Master's degree in Mathematics from IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro, where she worked with Professor Luiz Velho, and a Bachelor's degree in Electronics Engineering from UFRJ (Federal University of Rio de Janeiro). Her research interests center around computational tools that enhance design and manufacturing processes. She develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches. Her work spans multiple domains including robotics, textiles, electronics, and architecture, with a strong emphasis on creating practical tools that designers and engineers can use in real-world applications. She explores how machine learning, particularly neurosymbolic approaches, can improve design workflows and enable new capabilities in computational design systems. Analysis of her recent publications reveals a strong trend toward more intelligent and user-centered design tools. Her research increasingly integrates machine learning with traditional CAD systems to create more intuitive interfaces, supports sustainable design practices with computational tools, and develops novel fabrication techniques that push the boundaries of what's possible with digital manufacturing. She has made significant contributions to zero-waste fashion design, immersion cooling for high-performance computing, and CAD program understanding through novel representation learning techniques. Innovators Under 35 - MIT Technology Review Bolsa Aluno Nota 10 from FAPERJ Engineer 20000 award Dr. Schulz actively mentors several PhD students and postdoctoral researchers, including Haisen Zhao, Ben Jones, Yuxuan Mei, and others, often in collaboration with colleagues across different departments. Her research has attracted significant media attention, with coverage in major outlets including MIT News, BBC, IEEE Spectrum, Wired, and TechCrunch. Her work on Interactive Robogami was noted as the most read article in the International Journal of Robotics Research in its publication year. She leads a vibrant research group at the University of Washington that focuses on computational design systems, with particular emphasis on creating tools that bridge the gap between digital design and physical fabrication. Her team develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches that have practical applications across multiple industries.
Shiyu Su is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on high-speed data converters, wireless transceivers, digital phase-locked loops (PLL), and AI-assisted analog/mixed-signal design automation. He holds a Ph.D. from the University of Southern California (2019) and teaches courses such as ECE 340 (Electronic Circuits 2) and ECE 432 (Radio Frequency Integrated Devices and Circuits). Education: B.S. from Beijing University of Post and Telecommunication (China) and Queen Mary, University of London (UK), 2011; M.S. and Ph.D. from USC, 2013 and 2019, all in electrical engineering. Research Interests: High-speed ADCs/DACs RF/mm-wave transceivers Time-approximation filters (TAF) Analog/mixed-signal design automation Memristor-based computing Biomedical interfaces Key Awards: IEEE SSCS Predoctoral Achievement Award (2017–2018) Best Student Paper Award at IEEE RFIC (2022) Ming Hsieh Institute Scholar (2019–2020) Lab Focus: The Shiyu Su Lab develops integrated circuits for communications, sensing, and computing, with a focus on AI-driven methodologies and digital-analog co-design. Collaborations include work with Prof. Wei Wu (USC) on memristor-based systems.
Massachusetts Institute of TechnologyUnited States
Faez Ahmed is an Associate Professor at the Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), where he serves as the Doherty Chair in Ocean Utilization. He leads the Design Computation and Digital Engineering (DeCoDE) Lab, focusing on integrating machine learning and optimization with engineering design to enhance human-AI collaboration and accelerate design processes. Ph.D., Mechanical Engineering, University of Maryland College Park (2019) B.Tech.-M.Tech., Mechanical Engineering, Indian Institute of Technology Kanpur (2012) His research interests include generative design methodologies, AI-driven optimization techniques, and the development of algorithms that facilitate collaboration between human designers and artificial intelligence systems. This interdisciplinary work spans applications in automotive design , ship hull synthesis , and wind turbine optimization , with a strong emphasis on creating open-source tools and datasets for the engineering community. Recent publications demonstrate his lab's leadership in fields such as 3D CAD generation , multimodal design datasets , and constraint-aware generative models . These works often address challenges in design space exploration , performance prediction , and data-driven design frameworks . Scientific Awards NSF CAREER Award (2025) ASME Young Investigator Award (2024) Google Research Scholar Award (2024) 3M Non-Tenured Faculty Award (2022) University of Maryland Alumni Research Award (2022) Faez Ahmed's lab has trained numerous Ph.D. candidates and postdoctoral researchers, fostering a collaborative research environment that bridges mechanical engineering , artificial intelligence , and computational methods . The DeCoDE Lab actively engages with industry partners and academic institutions, contributing to large-scale datasets and benchmarks that power the next generation of engineering design research.
Laurent Tapie is a Senior Lecturer at Paris Descartes University with a focus on Biomedical Engineering, Mechanical Engineering, and CAD/CAM . As Deputy Director of the URB2i research unit and manager of the PlatiNum platform , he coordinates the 3d4care.org consortium . His academic background includes a Doctorate in Mechanical Engineering from École Normale Supérieure de Cachan and authorization to direct research (HDR) from Université Paris 13. Research Interests: Mechanical Engineering, Biomedical Engineering, Medical Devices, CAD/CAM, Shaping of Biomaterials Theses Supervised: 3D evaluation of dento-prosthetic joints, impact of CAD/CAM on dental prosthesis integrity, and metrological evaluations of prostheses. Publications: His work spans dental CAD/CAM systems, surface integrity of prostheses, additive manufacturing, and 3D printing applications during the COVID-19 pandemic . Recent articles focus on data dispersion in CAD/CAM chains, tool-material influence on roughness, and numerical workflow standardization . Scientific Award: Prix du comité scientifique de la session recherche (2019). Projects: Currently leads initiatives like ProGéoMéca (Labex LaSIPS), Bio-Dents (CNRS Biomimicry), and additive process development for multi-material dental aligners .
David Salesin is an Affiliate Professor in the Department of Computer Science & Engineering at the University of Washington and a Principal Scientist/Director at Google Research since 2019. He has held academic roles at Cornell University (Visiting Assistant Professor, 1991-92) and guest professorships at Zhejiang University. His career spans academia and industry, including leadership at Adobe's Creative Technologies Lab (2005-17) and Microsoft Research (1999-2005). PhD, Stanford University (1991) Sc.B., Brown University (1983) His research focuses on computer graphics, particularly non-photorealistic rendering, digital typography, color science, and adaptive document layout. He pioneered techniques in image-based rendering, pen-and-ink illustration, and facial animation, with applications in multimedia and user interface design. Article Trends : His work bridges procedural content generation, 3D visualization, and artistic computing, emphasizing user-driven tools for creative industries. Key subfields include texture advection, multiresolution modeling, and real-time camera control for virtual cinematography. Scientific Awards : ACM Fellow (2002) ACM SIGGRAPH Achievement Award (2000) Carnegie Foundation Professor of the Year (1998) NSF Presidential Faculty Fellow (1995-98) Alfred P. Sloan Research Fellowship (1995-97) Numerous industry grants and lab donations He has advised over 30 PhD and Master's students, including leaders at Microsoft, Pixar, and Google. His labs at UW and Adobe focused on graphics, imaging, and creativity tools.
Professor Jason H. Anderson is a faculty member in the Department of Electrical & Computer Engineering (ECE) at the University of Toronto, affiliated with the Faculty of Applied Science & Engineering. He specializes in computer engineering, with a focus on field-programmable gate arrays (FPGAs) and related computer-aided design (CAD) tools. BSc in Computer Engineering from the University of Manitoba MASc and PhD in Electrical & Computer Engineering from the University of Toronto His research spans FPGA architecture, circuit design, and CAD tool development. He has contributed to strategic R&D projects through his academic and industry roles. Scientific awards include multiple Best Paper Awards at IEEE and ACM conferences (2017, 2014, 2011, 2010, 2009), the Xilinx Ross Freeman Award (2000), and teaching accolades such as the Gordon Slemon Award (2012) and several ECE Departmental Teaching Awards (2009-2014). He served as General Chair and Program Chair for leading FPGA conferences. As Chief Scientific Advisor and Co-Founder of LegUp Computing Inc., he bridges academic research with industry innovation. He is a licensed Professional Engineer in Ontario and an active IEEE/ACM member.
Prof. Dr.-Ing. Lars Linsen is a full Professor of Computer Science at the Westfälische Wilhelms-Universität (WWU) Münster, leading the VISualization & graphIX (VISIX) group. His primary affiliation is the Institute of Computer Science within the Faculty of Mathematics and Computer Science. He holds adjunct professorships at Jacobs University, Bremen, and has held previous academic roles including Full Professor at Jacobs University (2012–2017) and Associate/Assistant Professor roles in Germany and the U.S. His research focuses on interactive visual analysis, medical visualization, and scientific visualization, with applications in life sciences and engineering. Education: PhD (Dr.-Ing.) in Computer Science from Universität Karlsruhe (2001), M.Sc. (Diplom) in Computer Science (1997), B.Sc. (Vordiplom) in Computer Science (1994). Awards: IEEE Visualization Design Contest Winner (2008, 2022, 2018), Preis des Fördervereins des Forschungszentrum Informatik (2002). Research Highlights: Develops visualization tools for medical imaging (e.g., mass spectrometry imaging, MRI data analysis) and physical simulations (e.g., wildfire spread analysis, asteroid impact modeling). Active in EU-funded projects like Pig-Pro-QuO (surface coatings) and cells-in-motion initiatives. Supervised over 20 PhD/MS advisees, including notable graduates in medical visualization and simulation ensemble analysis. Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Visualization and Computer Graphics, Computers & Graphics, and EuroVis. Key works include SciVis contest-winning wildfire analysis frameworks and medical visualization tools for stenosis detection. Teaching: Offers courses on visualization, computer graphics, and computational science. Actively involved in thesis supervision and curriculum development at both WWU Münster and Jacobs University. Grants & Collaborations: Principal investigator on DFG-funded projects (e.g., hemodynamics simulations, ensemble visualization) and industry collaborations (e.g., Tascon GmbH for coating quality analysis). Member of the Cells-in-Motion Interfaculty Centre and CDH board at WWU.
David Zhigang Pan is a Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin. He also holds the Silicon Laboratories Endowed Chair. Prior to joining UT Austin, he was a Research Staff Member at IBM T. J. Watson Research Center from 2000 to 2003. His academic journey began with a B.S. from Peking University, followed by M.S. and Ph.D. degrees from UCLA. Research Areas: Electronic Design Automation (EDA), Machine Learning Hardware, FPGA Prototyping, Optical Computing, Hardware Security, and CAD for Emerging Technologies Academic Timeline: Assistant Professor (2003-2008), Associate Professor (2008-2013), Full Professor (2013-present) His research focuses on design automation for mixed-signal circuits , GPU-accelerated EDA tools , and hardware-software co-design for AI . Recent work explores FFT-based optical neural networks and deobfuscation techniques for integrated circuits , reflecting his interdisciplinary approach at the intersection of machine learning , computer architecture , and semiconductor manufacturing . Key publication trends reveal expertise in: VLSI design , lithography optimization , and deep learning applications for EDA tools. His work has been recognized with multiple Best Paper Awards at top conferences including DAC , ASP-DAC , and HOST . Awards: IEEE Fellow (2014), SPIE Fellow (2017), ACM SRC Graduate Category Honors for students Patents: 8 U.S. Patents in electronic design and hardware optimization Prof. Pan has mentored 40 PhDs and postdocs who now hold key positions in academia and industry. He leads research initiatives involving GPU acceleration frameworks and optical computing architectures . His lab focuses on vertical integration of architecture, CAD tools, and fabrication technologies for next-generation hardware solutions.
Professor Serdar Özoğuz is a full faculty member at the Department of Electronics and Communication Engineering , Istanbul Technical University . Holding a Ph.D. from ITU (2000) and a M.Sc. from ITU (1993) , he has taught courses like Active Network Synthesis , Basics of Electrical Circuits , and Scientific Research Ethics since 2014. His research focuses on Active RC filters Nonlinear electronic circuits Analog integrated circuit design Network synthesis . His recent publications emphasize machine learning applications in RF/microwave design , quantum computing for CAD tools , and emerging memory devices . The department's Devreler ve Sistemler Laboratuvarı Çok Geniş Ölçekli Tümdevre (VLSI) Tasarımı Laboratuvarı likely support his work. Despite no explicit awards listed, his 15+ recent articles in high-impact journals underscore his technical contributions.
Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .
Georges Gielen is Full Professor in the Department of Electrical Engineering (ESAT) at KU Leuven, Belgium, and part-time Research Director at imec. He has held multiple leadership roles including Chair of ESAT Department (2012-2013, 2020-2024) and Vice-Rector for Science, Engineering & Technology (2013-2017). His academic career spans over 30 years at KU Leuven, progressing from Assistant to Full Professor. His research focuses on analog and mixed-signal integrated circuit design automation , with expertise in CAD tools, design optimization, sensor interfaces, and neuromorphic systems. His work bridges hardware design with machine learning, particularly in hardware-efficient AI implementations and biomedical applications. He has pioneered techniques for automated analog circuit sizing, topology synthesis, and reliability-aware design in nanometer CMOS. Gielen has received numerous accolades including the IEEE CAS Mac Van Valkenburg Award (2015), IEEE CAS Charles Desoer Award (2020), and EDAA Achievement Award (2021). He holds an ERC Advanced Grant AnalogCreate and is an IEEE Fellow since 2002. As a prolific scholar, he has chaired major conferences including DATE (2006), ICCAD (2007), and ESSCIRC (2017). He has graduated over 55 PhD students through the MICAS research group at KU Leuven, currently supervising 13 doctoral candidates. His research team collaborates extensively with imec and industry partners on cutting-edge projects in carbon-aware AI accelerators, uncertainty-aware design, and neuromorphic sensor interfaces.
Prof. Dr.-Ing. Udo Fiedler is a faculty member at the Technical University of Central Hesse (THM), Department of Business Administration and Economics, where he serves as Head of the Production Engineering Laboratory and Member of the Senate. His academic work focuses on manufacturing engineering with specialization in high-speed machining, production processes, and machine tools. His research interests include: High-Speed Machining (HSC) and precision manufacturing Green machining of sintered parts in the green state Process optimization using statistical experimental design Machine tool technology and NC programming Industry 4.0 applications in manufacturing education Process monitoring and control for increased manufacturing safety Prof. Fiedler's publication record demonstrates an evolution from fundamental machining processes toward integrating AI with traditional manufacturing. His recent work shows strong emphasis on applying artificial intelligence to quality prediction, optimizing green machining processes, and implementing Industry 4.0 concepts through learning factory approaches, bridging traditional manufacturing engineering with modern digital technologies. His significant scientific contributions include: Development of methods for NC programming of complex workpieces Research on stability lobe diagrams for milling processes Studies comparing different production methods including HSC, EDM, and generative processes Work on mechatronic tool holders for process monitoring Applications in the ophthalmic industry for precision machining of spectacle lenses Prof. Fiedler teaches multiple courses at THM including Factory Planning/Ergonomics, Handling and Assembly Technology, Innovative Manufacturing Processes, and Machine Tools at the bachelor's level, and Learning Factory 1 and 2 at the master's level. He leads current research projects including Klag-Robotics (2023-2025), Loewe Project OST (2018-2021), and GrünSpan (2014-2015), demonstrating sustained research activity across multiple manufacturing domains.
Antti Martikkala is a Postdoctoral Researcher at Tampere University's Department of Automation Technology and Mechanical Engineering. His research focuses on integrating data-driven and model-driven methods for digital-twin engineering, low-cost IoT development, and Industry 4.0 applications. Education: Master of Science (Technology) in Automation Engineering (2012). Research interests include Internet of Things (IoT), Digital Twins, Generative AI in CAD, and Laser-Wire Direct Energy Deposition (LWDED). His recent work explores interoperability of IoT platforms, dynamic route optimization for waste collection, and real-time manufacturing process optimization using AI. Key trends in his publications (2025–2012) span IoT (100% focus), Industry 4.0, CAD, and sustainable manufacturing. Notable collaborations involve A. Daareyni, A. Ylä-Autio, H. Mokhtarian, and I.F. Ituarte. He employs open-source tools and low-cost technologies to democratize IoT systems, with expertise in Arduino-based sensor development, multilayer height detection, and smart textile waste collection optimization.
