جست‌وجوی استاد و پژوهشگر

Ambuj K. Singhمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Network Science
• +۷ مورد دیگر

Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.

Damien Charrierasمشاهده پروفایل

دانشیار

• Cultural Studies
• Media Theory
• Digital Arts
• +۷ مورد دیگر

Dr. Damien Charrieras is an Associate Professor at the School of Creative Media , City University of Hong Kong, where he serves as Program Leader for the Master of Art in Creative Media and deputy program leader for the PhD in Creative Media. His work bridges cultural studies, new media theory, and the intersection of financial technologies with artistic practices through the ACIM Laboratory for Interventions in Speculative Finance . Doctoral research on digital artists' trajectories in Montreal FRQSC Postdoctoral Fellowship at McGill University Research spans game engine design , datafication of creativity , blockchain in art , and post-anthropocentric creative processes . With over HK$3.14M in grants as Principal Investigator, he explores situated digital creativity in urban contexts, critical worldbuilding pedagogy , and the mediation of technical systems in cultural production. His publications in Cities , Organized Sound , and Human Relations analyze the reconfiguration of artistic practices through software infrastructures and automation paradigms. Scientific awards include: FRQSC Postdoctoral Fellowship

Kuldeep S. Meelمشاهده پروفایل

دانشیار

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۶ مورد دیگر

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at the School of Computer Science, Georgia Institute of Technology, and an Associate Professor at the University of Toronto (on leave). He previously held a NUS Presidential Young Professorship at the National University of Singapore. His research focuses on automated reasoning, aiming to enable computing systems to handle uncertain real-world environments through scalable techniques integrating randomized algorithms, statistical inference, formal methods, distribution testing, and software engineering. Core research areas: Automated Reasoning, Formal Methods, Approximate Model Counting, Probabilistic Inference, Constraint Solving His research group has achieved significant recognition in both individual awards and publications. Key trends in his recent work include advancing model counting algorithms, developing frameworks for probabilistic explanations, and improving scalability in formal verification and constraint satisfaction. His tools have consistently ranked top in international competitions, demonstrating practical impact in automated reasoning. 2019 NRF Fellowship for AI 2022 ACP Early Career Researcher Award 2020 IEEE Intelligent Systems AI's 10 to Watch Top placements in Model Counting, SAT, and CAV competitions He mentors a diverse group of PhD and Master's students and collaborates with institutions worldwide. His group's publications span premier conferences in AI, formal methods, and design automation, reflecting interdisciplinary contributions to theoretical and applied computer science.

David Bindelمشاهده پروفایل

دانشیار

• Applied Numerical Linear Algebra
• Eigenvalue Problems
• Network Analysis
• +۵ مورد دیگر

David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.

Mohamed Ibrahemمشاهده پروفایل

استادیار

• Security and Privacy in IoT Environments
• Applied Cryptography
• Privacy-preserving Machine Learning
• +۵ مورد دیگر

Dr. Mohamed Ibrahem is an Assistant Professor at the School of Computer and Cyber Sciences, Augusta University, USA. His research focuses on Cyber-Security, IoT Privacy, and Machine Learning applications in Smart Grids and Cyber-Physical Systems. He received a Ph.D. in Electrical and Computer Engineering from Tennessee Tech University in 2021 and has been recognized with prestigious awards including the IEEE Senior Member (2024) and the Eminence Award for Best Ph.D. Paper (2021). Education: Ph.D., Electrical and Computer Engineering, Tennessee Tech University (2021) Research Interests: Security and Privacy in IoT, Applied Cryptography, Privacy-preserving Machine Learning, Secure Federated Learning, Smart Grid Cyber-Security, and Traffic Analysis Attacks. Publications: His work emphasizes privacy-preserving techniques in smart grids, federated learning for anomaly detection, and robust countermeasures against cyber-attacks. Recent trends include leveraging deep learning for real-time fraud detection in AMI systems and developing efficient decentralized learning frameworks. Awards: IEEE Senior Member (2024) Top 2% Scientists List (Stanford/Elsevier, 2024) Eminence Award for Best Ph.D. Paper (Tennessee Tech University, 2021) Advising & Grants: Supervises graduate research in Dissertation and Master's Thesis projects. Active in securing grants for IoT and Cyber-Security initiatives. Collaborates with industry partners on edge computing and secure AMI networks.

Illya V. Hicksمشاهده پروفایل

استاد

• Combinatorial Optimization
• Integer Programming
• Graph Theory
• +۵ مورد دیگر

Illya V. Hicks is a Professor in the Computational and Applied Mathematics Department at Rice University. He holds a PhD from Rice University (2000) and a BS from Texas State University (1995). His research focuses on combinatorial optimization, integer programming, graph theory, and matroid theory, with applications in social networks, cancer treatment, and network design. He has advised numerous doctoral, post-doctoral, and masters students. Education: PhD and MA in Computational and Applied Mathematics, Rice University, 2000 BS in Mathematics, Texas State University, 1995 Research Interests: Utilizing graph decomposition techniques to solve NP-complete problems, including branch decompositions and matroid circuit problems. Applications include sensor network design, healthcare logistics, and algorithmic graph theory. Awards: Recognized with the 2015 Presidential Mentoring Award (Rice University), 2010 Forum Moving Spirit Award (INFORMS), and the 2005 Optimization Prize for Young Researchers. Grants and Projects: Includes NSF-funded research on branch decomposition techniques, submodular optimization, and healthcare service distribution. Active in promoting minority participation in operations research through travel grants and mentoring initiatives. Labs/Teams: Engaged in collaborative research on graph algorithms, combinatorial optimization, and interdisciplinary applications in healthcare and engineering.

Andrea Ferrariمشاهده پروفایل

استاد

• Nanoscience
• Graphene and related materials
• Raman spectroscopy
• +۳ مورد دیگر

Andrea C. Ferrari is a Professor of Nanotechnology in the Department of Engineering at the University of Cambridge, UK. He currently holds multiple leadership roles, including Science and Technology Officer and Chair of the Management Panel for the EU Graphene Flagship, as well as Director of both the EPSRC Centre for Doctoral Training in Graphene Technology and the Cambridge Graphene Centre. PhD, University of Cambridge (24 March 2001) Laurea in Nuclear Engineering, Politecnico di Milano (24 July 1997) His research focuses on nanoscience, graphene and related materials, Raman spectroscopy, and optoelectronic applications of nanotechnology. He has authored over 390 papers, amassed >116,000 citations, and delivered >450 invited/keynote talks globally. He has received numerous accolades including: IMR-Lee Hsun Lecture Award (2018) Nanosmat Award (2017) ERC Synergy grant (2013) Philip Leverhulme Prize (2005) As a leading figure in graphene research, he has secured >£60M in research funding, led major EU Flagship projects, and commercialized innovations through patents licensed to companies like Nokia, IBM, and Huawei. He chairs the Executive Board of the EU Graphene Flagship and serves as Academic lead for the Royce Institute's e-beam lithography initiatives.

Jeannette Bohgمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Previously, she was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems (2012-2017). She holds a PhD from KTH Royal Institute of Technology (Stockholm) and degrees from Chalmers University and TU Dresden. Her research focuses on perception, learning, and real-time multi-modal methods for autonomous robotic manipulation and grasping, aiming to bridge principles of human sensorimotor coordination with robotic implementation. Education: PhD in Robotics (KTH), MSc in Art & Technology (Chalmers), Diploma in Computer Science (TU Dresden) Research interests include developing goal-directed, real-time robotic systems capable of meaningful feedback for execution and learning. Key areas are dexterous manipulation, imitation learning, and cross-embodiment policy transfer. Notable contributions include the TidyBot platform and work on force-aware surgical robotics. Awards include the 2019 IEEE ICRA Best Paper Award, 2019 IEEE RA Early Career Award, and 2020 RSS Early Career Award. Her lab explores intersections of robotics, ML, and computer vision. Advising: Actively mentoring students/postdocs in manipulation, perception, and learning. Grants and collaborations span NSF, Stanford AI Lab, and industry partnerships. Future work emphasizes robust real-world deployment and human-robot collaboration. Labs/Teams: Leads the Interactive Perception and Robot Learning Lab, contributing to Stanford’s AI ecosystem. Previously managed the MPI AMD group, fostering interdisciplinary research in autonomous systems.

John Evansمشاهده پروفایل

دانشیار

• Computational Mechanics
• Fluid Dynamics
• Fluid-Structure Interaction
• +۳ مورد دیگر

John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.

Nidhi Homey Parayilمشاهده پروفایل

پژوهشگر

• Path Planning
• Robotic Manipulation
• Agricultural Robotics
• +۳ مورد دیگر

Nidhi Homey Parayil is a PhD researcher at the QUT Centre for Robotics , affiliated with Queensland University of Technology. She holds a B.Tech in Production Engineering from the National Institute of Technology, Calicut (India), and a Master's in Robotics with a minor in AI from Oregon State University (USA). Research focus on path planning and manipulation of robotic arms in agricultural environments Experience with dual-arm robots for human assistance and six-axis robots for prenatal scanning Her work bridges computer vision and robotics in uncertain environments. While no formal awards or publications are mentioned in the available text, she is actively engaged in robotics research at QUT.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Daniel Sierra Ramos is an Adjunct Professor at IE University, Spain, and Co-Founder of Fitizens, a startup developing AI-driven wearables for physical activity quantification. He has over 6 years of experience as a Lead Data Scientist at Telefónica and Synergic Partners, specializing in AI applications for industries like banking, retail, and telecommunications. His expertise includes machine learning models for customer segmentation, predictive maintenance, and demand forecasting, alongside Big Data and cloud technologies (AWS/Azure). Education: Master in Telecommunication Engineering (Carlos III University, 2015) Master in Multimedia and Communications (Carlos III University, 2015) Bachelor in Telematic Engineering (Carlos III University, 2013) His research focuses on integrating AI into wearable devices and industry-driven data solutions. He trains professionals in AI from technical and business perspectives, emphasizing autonomy in data-centric environments. Active in corporate roles since 2015, he bridges technological innovation with practical business applications.

Clifton R. Haiderمشاهده پروفایل

عضو هیئت علمی

• Biomedical Engineering
• Physiological Monitoring
• Medical Device Design
• +۵ مورد دیگر

Clifton R. Haider, Ph.D., is a Professor of Biomedical Engineering at Mayo Clinic, holding primary appointments as a Consultant in the Department of Physiology & Biomedical Engineering and the Department of Pediatric and Adolescent Medicine. His research focuses on accelerating medical device development through a discovery platform integrating mathematical modeling, algorithmic design, and physiological sensor validation. This platform aims to identify predictive biomarkers for conditions like cardiovascular collapse, sleep disorders, and neurodegenerative diseases. Dr. Haider’s work emphasizes noninvasive, autonomous monitoring and real-time predictive algorithms, with a particular focus on pediatric care. Education: PhD, Physiology & Biomedical Engineering, Mayo Graduate School, Mayo Clinic College of Medicine Predoctoral Studies in Biomedical Engineering and Physiology, Mayo Graduate School BSc (Hons), Biomedical Engineering, University of Iowa Research Interests: Dr. Haider’s work bridges engineering and clinical practice, targeting unmet needs in pediatric and adult healthcare. His discovery platform combines statistical modeling, sensor design, and validation to streamline medical device prototyping. Key focus areas include: Accelerated medical device design Quantitative predictive biomarkers Remote pediatric monitoring Real-time algorithm development This platform seeks to predict treatment responses and identify patients requiring advanced care, enhancing clinical decision-making. Awards: Office of Naval Research Young Investigator Program (2014–present) Grants & Labs: His research is supported by interdisciplinary collaborations at Mayo Clinic, leveraging laboratory facilities for sensor validation and algorithm testing. While specific grant details are not listed, his work aligns with Mayo’s emphasis on translational biomedical engineering.

Shiri Azenkotمشاهده پروفایل

دانشیار

• Accessibility
• Human-Computer Interaction
• Assistive Technology
• +۵ مورد دیگر

Shiri Azenkot is an Associate Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and Cornell University, affiliated with the Information Science Department and Technion’s Computer Science Department. Her research focuses on accessibility innovations in emerging technologies, particularly addressing needs of people with vision impairments and neurodiverse populations. She holds a PhD from the University of Washington, advised by Richard Ladner and Jacob Wobbrock, and has been recognized with prestigious awards including the NSF Graduate Research Fellowship. Education: PhD in Computer Science & Engineering from University of Washington (advisors: Richard Ladner, Jacob Wobbrock). Research interests include VR/AR accessibility, assistive technology design, inclusive education tools, and ethical AI applications. Current projects explore social VR avatars for invisible disabilities, AR navigation aids, and AI moderation systems against ableist hate speech. Her work is funded by NSF, AOL, Verizon, and Facebook. Recent publications address video accessibility for ADHD users, AI-driven scene descriptions for blind users, and tactile learning materials co-designed with educators. She leads the XR Access initiative promoting inclusive virtual/augmented reality. Awards include the UW Graduate Medal (2020), NSF CRII Award (2017), and AT&T Labs Fellowship (2014). Her research bridges academic innovation with industry impact through collaborations with tech companies and disability advocacy groups.

• Reconfigurable Computing
• FPGA Architectures
• Computer Security
• +۶ مورد دیگر

André DeHon is the Oliver C. Boileau Jr. and Nan Eleze Boileau Professor of Electrical Engineering at the University of Pennsylvania, affiliated with Electrical and Systems Engineering (ESE) and Computer and Information Science (CIS). He chairs the Computer Engineering (CMPE) program and directs the CyberSavvy Security Center. His research focuses on reconfigurable computing, FPGA architectures, computer security, and energy-efficient hardware design. He holds a Ph.D. and M.S. from MIT and a B.S. from MIT's Lincoln School. Research interests include reconfigurable computing, FPGA interconnect, hardware security, and fault-tolerant systems. Notable achievements include the SEVER & PROTECT DARPA award and IEEE/ACM Fellowships. Recent work addresses fast FPGA compilation (HiPR, PLD), security through compartmentalization (μSCOPE, SCALPEL), and partial reconfiguration techniques. Teaching includes courses on hardware security, system-on-chip architecture, and digital audio basics. Over 200 publications span FPGA design, network-on-chip optimization, and molecular-scale computing. His labs (Implementation of Computation Group) explore physical implementation of computations through hardware-software co-design.

Michael S. Brownمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Computer Graphics
• +۴ مورد دیگر

Michael S. Brown is a Professor and Canada Research Chair at York University's Department of Electrical Engineering and Computer Science (EECS). He also serves as Senior Director at the Samsung AI Center in Toronto, Canada. His research focuses on computer vision, image processing, and computer graphics, with a deep specialization in camera imaging pipelines, color theory, and AI-driven ISP optimization. He has organized major conferences like ProCams, eHeritage, WACV, and ICCV, and holds editorial roles in journals like TPAMI and IJCV. He is renowned for his work on computational color constancy, ISP hardware algorithms, and AI-based image enhancement techniques. His recent ICCV 2023 tutorial detailed modern camera pipelines and AI applications in ISP components. His research is supported by grants from NSERC, Samsung, Adobe, Google, and Microsoft. Brown advises numerous graduate students and has mentored over 30 alumni now in academia and industry roles at Meta, Samsung, Microsoft, and startups. His lab focuses on camera systems, noise modeling, and cross-platform color management, with contributions to open-source datasets and software platforms for ISP experimentation.