جست‌وجوی استاد و پژوهشگر

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +۳ مورد دیگر

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.

Jingrui Heمشاهده پروفایل

استاد

• heterogeneous machine learning
• active learning
• neural bandits
• +۹ مورد دیگر

Jingrui He is a Professor and MSIM Program Director at the School of Information Sciences, University of Illinois Urbana-Champaign. She holds multiple faculty affiliate positions including with the Department of Computer Science, National Center for Supercomputing Applications (NCSA), Illinois Informatics, Center for Digital Agriculture (CDA), and Mayo Clinic Arizona. Her research spans machine learning with applications in diverse domains including healthcare, agriculture, security, and finance. Dr. He received her PhD in Machine Learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, active learning, neural bandits, and self-supervised learning. She addresses complex data challenges where multiple types of heterogeneity coexist, developing methods for exploring, understanding, characterizing, and predicting real-world data through statistical machine learning techniques. Her recent publications demonstrate a strong focus on graph learning, federated learning, fairness in AI, and neural bandit algorithms. She has developed innovative approaches for class-imbalanced graph learning, Byzantine-robust federated learning, and privacy-preserving graph machine learning. Her work bridges theoretical foundations with practical applications across multiple domains. Her scientific awards include the Amazon Research Award (2025), ACM Distinguished Member (2023), AAAI Senior Member (2023), FAccT Distinguished Paper Award (2022), NSF CAREER award (2016), and multiple IBM Faculty Awards. She has been recognized as an excellent teacher and received Best Paper awards at major conferences including ICDM and SDM. Dr. He directs the iSAIL Lab and leads several major research projects including the AI Institute for Future Agricultural Resilience Management and Sustainability (AIFARMS). She has successfully mentored numerous doctoral students who have become co-authors on her publications. Her research has been funded through prestigious grants including the NSF CAREER award and IBM Faculty Awards.

Oliver Johnsonمشاهده پروفایل

استاد

• Information Theory
• Probability
• Statistics
• +۳ مورد دیگر

Professor Oliver Johnson is a faculty member at the School of Mathematics, University of Bristol, UK, where he serves as Head of School and holds the Professor of Information Theory position. His research bridges information theory, probability, and statistics, focusing on entropy convergence, group testing, and fundamental limits in data analysis. Current PhD students: Kieran Morris, Conor Crilly Ex-PhD students: Matt Aldridge, Leonardo Baldassini, Dan Cowley, Vaia Kalokidou, Tom Kealy, Jennifer Chakravarty, Zichen Gui, Chrys Paschou Ex-postdoc: Erwan Hillion His work includes ORCiD profile and collaborations across information theory, cybersecurity, and ecological modeling.

• Data Mining
• Machine Learning
• Reinforcement Learning
• +۹ مورد دیگر

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).

Travis B. Thompsonمشاهده پروفایل

استادیار

• Mathematical Modeling
• Scientific Computing
• Machine Learning in Medicine
• +۵ مورد دیگر

Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.

David Mountمشاهده پروفایل

استاد

• Computational Geometry
• Algorithms
• Data Structures
• +۷ مورد دیگر

David Mount is a Professor in the Department of Computer Science at the University of Maryland, with an additional appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). His primary research focus is Computational Geometry, particularly in designing, analyzing, and implementing data structures and algorithms for geometric problems. Applications of his work span image processing , pattern recognition , information retrieval , and computer graphics . He is a Fellow of the ACM and has received the ACM Recognition of Service Award twice. A member of the Algorithms and Theory Group, Mount has authored over 200 publications, many of which are available on Google Scholar, DBLP, and ArXiV. Research Focus Computational Geometry Algorithm Design and Analysis Geometric Data Structures Nearest Neighbor and Range Searching Clustering Algorithms Recent Publications Mount's recent publications (2023-2025) emphasize non-Euclidean geometry (e.g., Hilbert metric), dynamic geometric structures , and approximation algorithms for polytopes, Voronoi diagrams, and Delaunay triangulations. Collaborative works with students and researchers address challenges in kinetic data compression , label tracking , and geometric software development (e.g., Ipelets for polygonal geometry). Professional Activities Editorial Board Member, TheoretiCS (2021-present) Senior Associate Editor, ACM Trans. on Spatial Algorithms and Systems (2013-2020) Program Committee Member, FOCS , ESA , SODA , and other major conferences Awards ACM Fellow ACM Recognition of Service Award (twice)

Rafail Ostrovskyمشاهده پروفایل

استاد

• Cryptography
• Secure Computation
• Garbled Circuits
• +۷ مورد دیگر

Rafail Ostrovsky is the Norman E. Friedman Chair in Knowledge Sciences at UCLA Samueli School of Engineering, where he serves as a Distinguished Professor of Computer Science and Mathematics. He also directs the Center for Information and Computation Security at UCLA. His academic leadership extends to his role as a foreign member of Academia Europaea and his fellowship in multiple prestigious organizations including the National Academy of Inventors, AAAS, ACM, IEEE, and IACR. Professor Ostrovsky's research spans multiple domains in theoretical computer science, with primary focus on cryptography, secure computation, and algorithms. His work on garbled circuits, zero-knowledge proofs, and private information retrieval has had significant theoretical and practical impact. He has pioneered research in secure multi-party computation, oblivious RAM, and cryptographic protocols that maintain privacy while enabling complex computations on sensitive data. His research bridges theoretical foundations with practical applications in secure systems, ranging from database security to hardware-based cryptographic primitives. Ostrovsky's publication record shows a consistent trajectory of innovation in cryptographic theory and its applications. His recent work focuses on optimizing secure computation protocols for efficiency while maintaining strong security guarantees, with particular attention to communication complexity, round complexity, and practical implementations. He has made significant contributions to homomorphic encryption, non-malleable commitments, and zero-knowledge proofs, often developing techniques that transform theoretical constructs into practically viable solutions. 1993 Henry Taub Prize 2017 IEEE Computer Society Edward J. McCluskey Technical Achievement Award 2018 RSA Award for Excellence in Mathematics (RSA Prize) 2022 W. Wallace McDowell Award (highest award from IEEE Computer Society) Fellow of National Academy of Inventors, AAAS, ACM, IEEE, and IACR Foreign member of Academia Europaea With over 350 peer-reviewed publications and 16 issued USPTO patents, Professor Ostrovsky has significantly shaped the field of cryptography and secure computation. His mentorship has cultivated numerous students and postdocs who have gone on to make their own contributions to the field. His editorial roles on prestigious journals including Journal of ACM and Algorithmica reflect his standing in the theoretical computer science community. His leadership extends to chairing major conferences including FOCS 2011 and serving on over 40 international conference program committees. As Director of the Center for Information and Computation Security at UCLA, Professor Ostrovsky leads a team focused on advancing the theoretical foundations and practical applications of secure computation. His center serves as a hub for interdisciplinary research connecting cryptography with systems security, network protocols, and hardware security. The center's work spans from foundational cryptographic primitives to real-world applications requiring privacy-preserving computation.

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Giorgio Ottonelloمشاهده پروفایل

استادیار

• Empirical Asset Pricing
• OTC Markets
• Institutional Investors
• +۲ مورد دیگر

Giorgio Ottonello is a tenure-track Assistant Professor of Finance at NOVA School of Business and Economics (NOVA SBE), part of NOVA University Lisbon in Portugal. His research focuses on empirical asset pricing in fixed-income markets, with emphasis on institutional investor behavior, OTC market dynamics, and credit-liquidity risk interactions. Education: PhD in Finance, Vienna Graduate School of Finance (2019) M.Sc. in Quantitative Finance, Vienna University of Economics and Business (2014) Bachelor of Business Administration, University of Genoa (2012) Research Focus: Ottonello's work employs large-scale empirical methods to investigate institutional investor behavior in fixed-income markets, particularly examining OTC market microstructure and the interplay between credit risk and liquidity risk . His research demonstrates how regulatory changes alter credit rating informativeness and how macroeconomic shocks create reverse causality in credit markets, revealing novel transmission channels between real economy developments and financial markets. Publication Trends: His 10 most recent publications (2019-2024) appear in top finance journals including the Journal of Finance and Review of Corporate Finance Studies, showing consistent focus on fixed-income market anomalies. Key themes include inventory constraints in underwriting, benchmarking effects, and cyber risk transmission, with methodology emphasizing causal identification through natural experiments and high-frequency data analysis. Scientific Recognition: RAPS Best Paper of the Year Award (2022) for groundbreaking work on underwriter inventory constraints Academic Leadership: Ottonello supervises Master's theses at NOVA SBE in empirical asset pricing and sustainable finance, while teaching core investments courses. His exceptional teaching evaluation (5.3/6) reflects effective pedagogy in quantitative finance, building on prior experience teaching at WU Vienna where he received perfect scores (1.0/6) for finance paper writing instruction. He actively contributes to academic discourse through frequent presentations at major finance conferences including FIRS, EFA, and SGF.

Richard Margerumمشاهده پروفایل

استاد

• Collaborative governance
• Environmental planning and policy
• Watershed management
• +۳ مورد دیگر

Richard Margerum serves as Professor of Planning, Public Policy, and Management and Associate Dean for Faculty and Academic Affairs in the College of Design at the University of Oregon. His scholarly work focuses on collaborative governance frameworks for resolving complex environmental and planning challenges, with particular expertise in watershed management systems and metropolitan governance structures. His educational foundation includes: PhD in Urban and Regional Planning (with Law minor) from the University of Wisconsin, Madison (1995) MS in Water Resources Management from the University of Wisconsin, Madison (1992) MCRP in Environmental Planning from the University of Cincinnati (1989) BA in Geology from Wittenberg University (1987) Margerum's research examines collaborative decision-making processes through multiple theoretical lenses, developing practical frameworks for implementing collaborative governance in environmental contexts. His seminal MIT Press book Beyond Consensus: Improving Collaboration to Solve Complex Public Problems establishes foundational principles for moving beyond theoretical consensus to actionable solutions. Current investigations explore the professionalization of watershed councils, stakeholder motivations in restoration efforts, and network-based approaches to collaborative planning. Analysis of his publication trajectory (2022-2008) reveals three dominant thematic strands: (1) collaborative hazard mitigation planning informed by enhanced information utilization, (2) professionalization dynamics within watershed management organizations, and (3) metropolitan governance challenges in regional planning contexts. His work consistently bridges academic theory and practical application, often employing comparative international methodologies and mixed-methods evaluation frameworks. Margerum actively mentors graduate students through co-authored research, with recent advisees including Curtis Thomas, Sam Ault, Stephen Dobrinich, Ray McPadden, and Andrew Dutterer. He directs the Oregon Experiential Learning Model and has secured sponsored research funding for collaborative governance initiatives. His Collaboration Curriculum instructional resource demonstrates commitment to pedagogical innovation, providing free teaching materials adopted by institutions worldwide. He leads interdisciplinary research teams working with watershed councils, government agencies, and nonprofit organizations to develop implementable collaborative frameworks. Current projects focus on improving information flows in collaborative hazard mitigation and evaluating network effectiveness in metropolitan governance systems.

Edwin Curranمشاهده پروفایل

مدرس

• Employability
• Student Placement
• Career Development
• +۳ مورد دیگر

Dr Edwin Curran serves as a Lecturer in Computer Science at Ulster University's School of Computing, based at both the Belfast and Jordanstown campuses. His academic focus centers on preparing computing students for professional careers through effective placement programs and employability initiatives. Curran's research interests prominently feature student career development, with particular emphasis on employment interview preparation, industry engagement strategies, and the transition from undergraduate studies to professional IT roles. His work demonstrates a consistent commitment to enhancing student employability through practical, industry-aligned educational approaches. His publications reveal a research trajectory focused on workforce preparation, with recent work examining evolving placement landscapes and earlier contributions exploring telecommunications systems visualization. The thematic consistency across his publications highlights his dedication to bridging academic learning with professional practice in computing fields. Curran actively collaborates with colleagues including Sterritt, Ayre, McChesney, and Hanna on employability research, contributing to Ulster University's recognition as University of the Year (Times Higher Education 2024).

Massimo Franceschettiمشاهده پروفایل

استاد

• Mathematical Engineering
• Networked Control Systems
• Information Theory
• +۷ مورد دیگر

Massimo Franceschetti is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), with faculty affiliation at Calit2. His research spans mathematical engineering, focusing on control, communication, computation, and sensing, particularly in complex networks and systems. He integrates tools from statistical physics, wave propagation, and information theory to analyze and design networked systems. Born in Naples, Italy, he studied at the University of Naples Federico II and the University of Edinburgh (European exchange program), graduating in 1997. He earned his M.Sc. (1999) and PhD (2003) from Caltech, where he received the Walker von Brimer Award and the C.H. Wiltz Prize for outstanding research and thesis. After postdoctoral work at UC Berkeley (2003-2004), he joined UCSD as faculty and held visiting positions at Vrije Universiteit Amsterdam, EPFL (Switzerland), and the University of Trento (Italy). He became an IEEE Fellow in 2018 and was nominated a Guggenheim Fellow in 2019. His research includes networked control systems , stochastic geometry , electromagnetic information theory , and social dynamical systems . Recent work explores non-invasive emotional contagion in social networks, quantum limits on information entropy, and the physics of wave propagation. His publications bridge information theory , machine learning , and network science , often applying percolation theory and random walks to explain scaling laws and wireless signal behavior. Scientific accolades include the S.A. Schelkunoff Transactions Prize , IEEE Communications Society Best Tutorial Paper Award , and the IEEE Ruberti Young Researcher Prize . He co-authored two books: Random Networks for Communication (2007) and Wave Theory of Information (2018). His students have pursued careers in academia (e.g., IIT-Bombay, Notre Dame) and industry (e.g., Google, IBM, Tesla). He teaches courses on network science , information theory , and control systems , emphasizing data-driven analysis and the physical foundations of communication. His group’s work impacts cyber-physical systems , quantum network coding , and epidemic modeling on networks .

Michael Bronsteinمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Pattern Recognition
• +۶ مورد دیگر

Michael Bronstein is a Professor at Università della Svizzera italiana (USI Lugano) in Switzerland and Imperial College London in the UK, where he holds the Chair in Machine Learning and Pattern Recognition. He serves as Head of Graph Learning Research at Twitter following the acquisition of his startup Fabula AI, and maintains a principal engineer position at Intel Perceptual Computing. His research focuses on the interplay between geometry, machine learning, and computer vision, with particular emphasis on non-Euclidean structured data. Professor Bronstein received his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford University, MIT, Harvard University (as a Radcliffe Fellow), and Tel Aviv University, and has been affiliated with multiple Institutes for Advanced Study including TUM-IAS where he was a Rudolf Diesel Industry Fellow (2017). He is a Fellow of IAPR, Senior Member of the IEEE, and a member of the Young Academy of Europe. His research program centers on theoretical and computational methods in spectral and metric geometry applied to computer vision, pattern recognition, and machine learning. He pioneered the field of geometric deep learning, developing novel neural network architectures that process non-Euclidean data structures like graphs and manifolds. His work spans from theoretical foundations to practical applications, with over 100 publications in top scientific journals and conferences, and has been featured in international media including CNN. Analysis of his recent publications reveals a strong trajectory in geometric deep learning with applications spanning computer vision, 3D shape analysis, social network analysis, and bioinformatics. His research consistently bridges theoretical innovation with real-world applications, developing novel neural architectures for processing complex data structures. The work demonstrates increasing interdisciplinary reach, connecting machine learning with fields from particle physics to molecular biology. Dalle Molle Prize (2018) Royal Society Wolfson Research Merit Award (2018) ERC Proof of Concept Grant (2018) Amazon AWS Machine Learning Research Award (2018) Fellow, International Association for Pattern Recognition (IAPR) Google Faculty Research Award (2017) Radcliffe fellowship, Harvard University (2017) Rudolf Diesel industrial fellowship, TU Munich (2017) ERC Consolidator Grant (2016) World Economic Forum Young Scientist (2014) Professor Bronstein has secured multiple ERC grants (Starting Grant 2012, Proof of Concept Grants 2016 and 2018, Consolidator Grant 2016) and has mentored numerous students who have contributed to over 30 granted patents. He has chaired more than a dozen conferences and workshops in his field and served as area chair at major computer vision conferences including ECCV 2016 and ICCV 2017. His research group at USI Lugano collaborates extensively with industry partners including Intel and Twitter. As a serial entrepreneur, Professor Bronstein co-founded Novafora (2005-2009) developing large-scale video analysis, Invision (2009-2012) which created low-cost 3D sensors and was acquired by Intel, and Fabula AI (2018-2019) focused on fake news detection which was acquired by Twitter. His work bridges theoretical research with commercial applications, with his technology contributing to Intel RealSense and Twitter's graph learning infrastructure.

Thuy T. Leمشاهده پروفایل

استاد

• Digital System Design
• Computer Architecture
• Hardware Accelerators
• +۶ مورد دیگر

Thuy T. Le is a Professor of Electrical Engineering at San Jose State University's College of Engineering. With a distinguished career spanning several decades, he teaches graduate and undergraduate courses in digital system design, computer architecture, microprocessor systems, and related fields. His academic journey began with earning B.S., M.S., and Ph.D. degrees from the University of California, Berkeley. Professor Le's research interests encompass a broad spectrum of cutting-edge technological domains. His primary focus areas include System-on-Chip (SoC) and Embedded System Design, Hardware Accelerators for complex algorithms, Quantum Computing, implementation of Probability theory and Monte Carlo simulation, and radiation effects on electronic devices and systems. His work bridges traditional electrical engineering with emerging computational paradigms, demonstrating a consistent ability to adapt to evolving technological landscapes while maintaining strong foundations in core engineering principles. Analysis of Professor Le's publication record reveals a consistent trajectory from nuclear reactor physics and computational methods toward modern hardware acceleration and quantum computing. His early work focused on nuclear reactor simulation and radiation shielding, then evolved to parallel computing and distributed systems, and has recently centered on hardware acceleration for complex algorithms, quantum computing applications, and AI hardware. This progression demonstrates his ability to transition between major technological paradigms while maintaining expertise in computational methods and hardware implementation. Professor Le has demonstrated significant leadership in professional service, having served as keynote speaker, general chair, technical program chair, session chair, reviewer, and committee member for numerous international conferences. His service extends beyond academia through his role as Co-Founder and Advisor of the Vietnamese Strategic Ventures Network and Chairman of the Board of the United States–Vietnam Foundation. In his educational role, Professor Le has made substantial contributions to engineering curriculum development and assessment. He has taught a wide range of courses including EE271 (Advanced Digital System Design), EE210, EE250, and various project/thesis courses. His research advising spans digital system design, ASIC, SOC, and hardware accelerators. He has also collaborated with local companies on projects related to high-performance system architectures, parallel algorithms, digital arithmetic, and System-on-Chip verification.

Eren C Kızıldağمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Probability Theory
• +۵ مورد دیگر

Eren C. Kızıldağ is an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign, with additional affiliations in the Department of Electrical and Computer Engineering. He holds a PhD in Electrical Engineering and Computer Science from MIT, where he was part of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS). Previously, he was a Distinguished Postdoctoral Fellow at Columbia University. His research bridges probability, statistics, and computer science, focusing on statistical-computational trade-offs in random models such as optimization problems and statistical inference. Key interests include understanding algorithmic barriers in high-dimensional problems, discrepancy theory, and neural network theory. His work often explores connections to statistical physics and combinatorial structures. Recent publications highlight contributions to topics like low-rank tensor recovery, algorithmic obstructions in partitioning problems, and geometric barriers in discrepancy minimization. His research also addresses theoretical foundations of overparameterized neural networks and hardness results for partition functions in spin glass models. Kızıldağ’s academic journey includes a Master’s from MIT and a B.S. (summa cum laude) from Bogaziçi University, with early work in MRI technology at ISMRM. His research is supported by collaborations with institutions like LIDS and IDSS, and his work frequently appears in top venues such as Annals of Applied Probability, Mathematics of Operations Research, and IEEE ISIT.