Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Dr. Ismail Guvenc is a Full Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds an IEEE Fellowship and is a Senior Member of the U.S. National Academy of Inventors. His research focuses on 5G/6G wireless systems, UAV communications, heterogeneous networks, and spectrum management. He leads the NSF AERPAW project and serves as the NC State Site Director for the NSF BWAC I/UCRC center. Dr. Guvenc has authored/co-edited four books and holds over 30 U.S. patents. Education: Ph.D. in Electrical Engineering, University of South Florida (2006) Master's in Electrical and Computer Engineering, University of New Mexico (2003) Bachelor's in Electrical Engineering, Bilkent University, Turkey (2001) Research Interests: Dr. Guvenc’s work spans UAV communication systems, 5G/6G networks, spectrum sensing, and machine learning for network optimization. He emphasizes experimental validation through platforms like AERPAW, focusing on drone-based networks, radio dynamic zones, and testbed-driven innovation. His contributions address challenges in UAV localization, interference management, and energy-efficient networks. Grants & Leadership: PI and Director of NSF AERPAW (2019–Present) NC State Site Director for NSF BWAC I/UCRC (2019–Present) Recipient of NSF CAREER Award (2015) and NC State Faculty Scholar Award (2021) Publications & Awards: He has authored over 130 peer-reviewed papers and contributed to standardization efforts. His awards include the R. Ray Bennett Faculty Fellow Award (2019) and the USF Outstanding Dissertation Award (2006). Labs & Teams: Dr. Guvenc directs research in the ECE’s wireless communications group, leveraging AERPAW’s experimental capabilities to advance UAV networks and 6G technologies. His team collaborates with industry and government on spectrum policy and autonomous systems.
Ben Fisch is an Assistant Professor of Computer Science at Yale University's School of Engineering & Applied Science. He is also the co-founder of Espresso Systems, a company focused on blockchain infrastructure. His research focuses on privacy and verifiability in decentralized systems like Bitcoin and Ethereum, with applications in digital finance and healthcare. Dr. Fisch received his B.A. from the University of Pennsylvania and completed his Ph.D. at Stanford University, where he worked with Dan Boneh in the applied cryptography research group. His educational background provided the foundation for his work at the intersection of cryptography, distributed systems, and economics. His research centers on leveraging cryptographic tools such as succinct non-interactive zero-knowledge proofs (zk-SNARKs), private information retrieval, and homomorphic encryption to address challenges in verifiable computation, verifiable storage, and verifiable fairness. He has made significant contributions to verifiable delay functions (VDFs) and proofs of replication, which have been adopted by major blockchain projects including Ethereum 2.0, Chia, and Filecoin. His work on Filecoin's Proofs of Replication has helped the network reach over 1.5 exabytes of storage capacity. His publication record shows a clear trend toward increasingly sophisticated cryptographic protocols for blockchain applications, with recent work focusing on data availability for Bitcoin rollups, efficient folding schemes for pairing-based arguments, and privacy pools with proof-carrying disclosures. His research bridges theoretical cryptography with practical implementations that have real-world impact in decentralized systems. His notable recognition includes: Best Paper Finalist at ACM CCS 2017 for 'Iron: Functional Encryption using Intel SGX' Dr. Fisch's research has led to significant technology transfer, most notably with his work on Verifiable Delay Functions (VDFs) sparking a multimillion dollar industry initiative through the VDF Alliance. His research on Proofs of Replication forms the basis of Filecoin's incentive layer and consensus protocol. His newer SNARK system Basefold is being used by several commercial products. He maintains active collaborations across academia and industry, with publications spanning top conferences in cryptography and security. As co-founder of Espresso Systems, Dr. Fisch leads a team developing next-generation blockchain infrastructure, particularly focusing on sequencing layers for rollups. His work bridges academic research with practical implementation, ensuring that theoretical advances in cryptography find real-world applications in decentralized systems.
Michael J. Black is a Professor and Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department and serves as Managing Director . He is also an Honorarprofessor at the University of Tübingen 's Faculty of Science . His career spans roles at Brown University (2000-2010), Xerox PARC, and academic-industry collaborations with Amazon and Meshcapade.
Kenneth J. Falconer is the Regius Professor of Mathematics at the University of St Andrews, where he is a member of the School of Mathematics and Statistics and the Analysis Research Group. He has held prestigious positions at the University of Bristol and Corpus Christi College, Cambridge, and has been a visiting professor at institutions including Oregon State University and the Australian National University. Regius Professor of Mathematics, University of St Andrews (2017–present) Professor of Mathematics, University of St Andrews (1993–2017) Reader, University of Bristol Lecturer, University of Bristol Research Fellow, Corpus Christi College, Cambridge His research centers on fractal and multifractal geometry, geometric measure theory, and related fields. He has made seminal contributions to the understanding of fractal projections, dimensional analysis of self-affine sets, and fractal processes. His work includes the concept of the digital sundial and the introduction of the affinity dimension and Falconer’s distance problem. His research spans dimensional analysis, random fractals, PDEs on fractal domains, and combinatorial geometry. The most recent publications show a sustained focus on intermediate dimensions, projections of fractal sets and measures, and the dimensional properties of stochastic processes. His work frequently involves collaboration with leading mathematicians and appears in top journals such as Transactions of the American Mathematical Society , Ergodic Theory and Dynamical Systems , and Journal of Fractal Geometry . Fellow of the Royal Society of Edinburgh (1998) Shephard Prize, London Mathematical Society (2020) CBE, King’s New Year’s Honours (2024) Kenneth Falconer has supervised numerous students and collaborated with many researchers including Jonathan Fraser, Pertti Mattila, and Xiong Jin. He has served on editorial boards for Fractals , Journal of Fractal Geometry , and Mathematical Proceedings of the Cambridge Philosophical Society . He has been active in professional service, including as Chair of the British Mathematical Colloquium 2018 and Publications Secretary of the London Mathematical Society (2006–2009). He organized major programs at the Isaac Newton Institute and Mittag-Leffler Institute. He is also known for his involvement in the Long Distance Walkers Association, where he served as Chairman and Editor of Strider , and for his mathematical poetry featured in publications like the London Mathematical Society Newsletter .
Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.
Nedim Pervan is a Full Professor at the Faculty of Mechanical Engineering, University of Sarajevo, Bosnia and Herzegovina. His academic position focuses on mechanical engineering with emphasis on product design, structural analysis, and biomechanical applications. He maintains an active research profile with numerous publications and collaborations across various engineering disciplines, with office hours every workday from 09:00 to 10:00 in room 314. Professor Pervan's research interests span multiple domains of mechanical engineering. He has made significant contributions to additive manufacturing, particularly in polymer gear production and analysis. His work explores mechanical properties, failure mechanisms, and service life of polymer gears manufactured through additive processes. Additionally, he has conducted extensive research on external fixation devices used in orthopedic treatments, analyzing their biomechanical characteristics and structural stability under various loading conditions. His expertise extends to finite element analysis, structural optimization, and the application of 3D scanning technologies within Industry 4.0 contexts. His research demonstrates a strong connection between theoretical engineering principles and practical applications across automotive, medical devices, and manufacturing industries. His recent publication record reveals a strong trend toward interdisciplinary research bridging mechanical engineering with biomedical applications and advanced manufacturing technologies. A significant portion of his work focuses on polymer gears and additive manufacturing, examining material properties and performance characteristics. Another substantial research stream involves biomechanical engineering, particularly the analysis of external fixation devices. His publications demonstrate a methodological approach combining experimental testing with finite element analysis. More recently, his research has expanded into 3D scanning applications in manufacturing and the electrification of transportation systems in Bosnia and Herzegovina. Professor Pervan has been involved in numerous research projects that have advanced the capabilities of the Faculty of Mechanical Engineering. These include the "Integrated Intelligent CAD System for Interactive Design, Analysis and Prototyping of Compression and Torsion Springs" (2022), "Opremanje Laboratorije za razvoj i dizajn proizvoda" (2020), and "Modernizacija Laboratorije za ispitivanje mašinskih konstrukcija" (2019-2020). These projects have focused on developing advanced laboratory facilities, intelligent CAD systems, and equipment for mechanical design and analysis, with several specifically targeting 3D scanning technology implementation. His collaborative work extends across multiple research teams within the Department of Mechanical Constructions at the University of Sarajevo. He frequently collaborates with researchers including Adis Muminović, Elmedin Mešić, and Muamer Delić on projects related to additive manufacturing, biomechanical engineering, and structural analysis. His research group appears actively involved in both theoretical and applied engineering research with practical industrial and medical applications, contributing significantly to Bosnia and Herzegovina's engineering research landscape.
Jennifer K. Ryan is a Professor and Division Head for Numerical Analysis, Optimization & Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. She is affiliated with the Digital Futures Faculty, a cross-disciplinary research center jointly established by KTH, Stockholm University, and RISE Research Institutes of Sweden. Her research focuses on developing numerical schemes for extracting enhanced accuracy from simulations, with applications in imaging, data analysis, and fluid dynamics. Ryan’s work emphasizes improving computational efficiency through theoretical insights and practical algorithms. Her academic roles include teaching courses like Numerical Methods for Differential Equations II and supervising student projects in numerical analysis. She has contributed to the SIAC MAGIC toolbox, a software package for accuracy-enhancing filtering techniques. Ryan’s research group actively explores discontinuous Galerkin methods, SIAC filtering, and multi-resolution analysis, addressing challenges in computational physics and engineering. Her publications span high-order numerical methods, mesh adaptivity, and applications in plasma physics and wave equations. Projects include error estimation for boundary integral methods and developing filters for noisy data. Ryan collaborates internationally, contributing to both theoretical advancements and practical implementations in computational science.
Rozenn Dahyot is a Professor of Computer Science at Maynooth University within the Faculty of Science & Engineering. She previously held roles as Assistant and Associate Professor in Statistics at Trinity College Dublin (2008-2021) and Lecturer in Computer Science (2005-2008). Her research interests bridge Digital Signal Processing, Computer Vision, Machine Learning, and Statistical Analysis. She organized the European Signal Processing Conference (EUSIPCO2021) in Dublin and served as President of the Irish Pattern Recognition and Classification Society (IPRCS) from 2014-2020. Her work spans topics like semantic scene understanding, CNN compression, and medical image segmentation. Key contributions include advancements in graph-based image analysis, reinforcement learning optimization, and AI-driven systems for disaster management. Dahyot is a member of IEEE, ACM, and EURASIP, contributing to both academic and industrial collaborations.
Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.
Lee Miller is a Professor in the Department of Neurobiology, Physiology, and Behavior at the University of California, Davis, College of Biological Sciences. His research integrates neural engineering, physiology, and computational methods to develop communication restoration technologies and investigate sensory processing mechanisms. His primary research interests include neural engineering for speech neuroprosthetics, electrophysiological analysis of speech production, auditory neuroscience, and geometric approaches to neuromuscular signal decoding. He employs surface electromyography (EMG), electroencephalography (EEG), and computational modeling to study brain-machine interfaces for speech restoration and multisensory integration. Recent publications reveal a dominant focus on EMG-based speech neuroprostheses, with geometric and topological analysis of neuromuscular signals emerging as a key methodology. His lab has pioneered non-invasive approaches to speech articulation decoding, created standardized EMG databases, and investigated neural mechanisms of attention in speech-in-noise processing. This work bridges engineering innovation with fundamental neuroscience to address communication disorders. Professor Miller leads the Miller Lab at UC Davis, which specializes in neural engineering for communication restoration. The lab develops real-time speech synthesis systems from neural signals and investigates the physiological basis of speech production and perception using multimodal recording techniques.
Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650
Angela Di Fulvio is an Associate Professor and Donald Biggar Willett Faculty Scholar at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Nuclear, Plasma, and Radiological Engineering and the Center for Digital Agriculture at NCSA. She leads the Nuclear Measurement Laboratory (NML), focusing on radiation detection technologies for nonproliferation, medical physics, and nuclear security. Her academic journey includes a Ph.D. in Nuclear Engineering and Industrial Safety from the University of Pisa (2012), preceded by M.Sc. and B.Sc. degrees in Bioengineering. Her research emphasizes neutron detection instrumentation, radiation protection in therapy, and safeguards applications. Key areas include next-generation thermal neutron detectors, boron neutron capture therapy dosimetry, and spent nuclear fuel imaging. She has pioneered work on pulse shape discrimination using commercial ASICs and developed algorithms for neutron-gamma discrimination in harsh environments. Di Fulvio’s 15+ peer-reviewed articles span advanced detection systems, Monte Carlo modeling, and machine learning for radiation imaging. Notable contributions include a physics-based forward model for spent fuel imaging and variational autoencoder-based pulse discrimination. Her work has been recognized with the Dean’s Award for Excellence in Research. Professional roles include Associate Editor of Radiation Measurements and editorial board member of Nature Scientific Reports . She chairs APS’s Instrumentation and Measurement Science group and ANS’s Nuclear Nonproliferation Policy Division. Recent courses taught include NPRE 451-452 labs, Nuclear Safeguards, and Student Research Seminars.
Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University, affiliated with the Cornell Machine Learning Group and leading the Relax ML Lab. His research focuses on algorithmic, software, and hardware techniques for high-performance machine learning, particularly relaxed-consistency stochastic algorithms like asynchronous and low-precision stochastic gradient descent (SGD). He earned his Ph.D. from Stanford University under advisors Kunle Olukotun and Chris Ré. His work emphasizes constructing efficient, parallel, and distributed machine learning frameworks for deep learning and data analytics. Education: Ph.D. in Computer Science, Stanford University (2017) Research Interests: Algorithmic techniques for scalable ML, quantization, distributed optimization, hyperbolic geometry in ML, and reliable measurement of ML systems. His group develops frameworks for efficient inference/training and explores the intersection of ML with domains like agriculture and plant science through courses like PLSCI 7202. Recent Highlights: DARPA YFA Grant (2024), NSF CAREER Award, Google Research Scholar Award, and multiple best paper recognitions. Key contributions include QuIP quantization methods, Coneheads attention mechanisms, and theoretical advances in decentralized training. Awards: NSF CAREER Award DARPA YFA Grant (2024) Google Research Scholar Award Mr. & Mrs. Richard F. Tucker Teaching Award Grants & Advising: Advises 8 Ph.D. students (including Ruqi Zhang, Yucheng Lu, A. Feder Cooper) and holds leadership roles in MLSys conferences. Active in grant-funded research (e.g., NSF Robust Intelligence). Labs/Teams: Leads the Relax ML Lab and participates in Cornell’s Institute for Digital Agriculture (CIDA).
Wenping Wang is a Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on computer graphics, computer vision, geometric modeling, and visualization. He holds Fellowships from ACM and IEEE, and has received notable awards including the 2021 AsiaGraphics Outstanding Technical Contributions Award and the 2017 John Gregory Memorial Award. Wang's educational background includes a Ph.D. from the University of Alberta and M.Eng. and B.Sc. degrees from Shandong University. His work spans advancements in neural implicit surfaces, 3D reconstruction, and medical imaging applications such as orthodontic treatment prediction. He has authored numerous influential papers in top-tier conferences like SIGGRAPH and journals like ACM Transactions on Graphics. His research interests emphasize bridging geometric modeling with machine learning, particularly in neural rendering, surface parameterization, and medical visualization. Recent projects include developing frameworks for automatic tooth alignment and high-fidelity 3D geometry generation. Wang's contributions have significantly impacted both theoretical foundations and practical applications in computer graphics.
