جستجوی استاد راهنمای دانشگاهی

Corey Clarkمشاهده پروفایل

استادیار

• Artificial Intelligence
• Game Development
• Human Computation
• +۴ مورد دیگر

Corey Clark, Ph.D., serves as Deputy Director of Research and Assistant Professor in the Department of Computer Science and Engineering at Southern Methodist University's Lyle School of Engineering. He leads the Human and Machine Intelligence (HuMIn) Game Lab, pioneering research at the intersection of gaming, artificial intelligence, and human computation to solve large-scale problems in healthcare, education, and national security. Dr. Clark holds a Ph.D., M.S., and B.S. in Electrical Engineering from The University of Texas at Arlington, where he graduated Magna Cum Laude for his undergraduate degree. His doctoral research focused on nanoscale modeling and simulation techniques for Molecular Beam Epitaxy and Chemical Vapor Deposition of exotic materials. His research spans Artificial Intelligence, Game Development, Human Computation, Distributed Computing, Machine Learning, Computational Biology, and Educational Technology. Clark is renowned for transforming commercial video games into distributed computing platforms through human computation, enabling breakthroughs in medical diagnostics and educational technology. His technical innovations in HTML5/JavaScript multithreading have been featured at major international game conferences. Analysis of Clark's 15 most recent publications (2022-2024) reveals a dominant focus on generative AI applications integrated with gaming mechanics, particularly for knowledge graph enhancement, medical diagnostics, and computational thinking education. Key trends include explainable AI systems, human-AI collaboration through gameplay, blockchain applications, and privacy-preserving machine learning, demonstrating consistent innovation in applying game-based approaches to real-world problems. Dr. Clark has secured over $1.7 million in competitive research funding, including: Human Computation for Ocular Tomography Analysis ($62,924) with Retina Foundation Cryptocurrency Transactional Analysis via Gaming ($60,000) with Raytheon Game-Based Adult Literacy (XPrize) ($480,000) Flexible Electronics for Airborne Laser ($820,000) with Missile Defense Agency Networked C4ISR Chip ($850,000) with US Army As CTO for Dallas-based game technology companies, Clark has helped raise over $12 million in startup funding. His HuMIn Game Lab currently develops immersive gameplay systems for cancer treatment research using crowdsourced computation and machine learning, with recent projects including therapeutic discovery platforms and blockchain-based task completion systems.

David Levineمشاهده پروفایل

استاد

• Intellectual Property Law
• Cybersecurity
• Technology Law
• +۶ مورد دیگر

David Levine is a Professor of Law at Elon University School of Law and Associate Dean of Faculty Development (2024–2025). He holds affiliations as an Affiliate Scholar at Stanford Law School’s Center for Internet and Society, Fellow at UNC’s Center for Information, Technology, and Public Life, and Fellow at the University of Milan’s Information Society Law Center. Education: BS in Industrial and Labor Relations (Cornell University), JD (Case Western Reserve University School of Law) Research Focus: Intersection of intellectual property law, technology, and public life, particularly trade secrecy, transparency, and AI regulation Policy Influence: Testified before the Library of Congress, advised White House and Congress on trade secret policy, and participated in Trans Pacific Partnership negotiations Recognition: Co-founded award-winning Hearsay Culture radio program and republished in Edward Elgar’s 2014 anthology His scholarship explores how trade secrecy impacts public infrastructure, climate change mitigation, and pandemic response, emphasizing the need for balanced regulation that preserves innovation while ensuring accountability. Recent work addresses generative AI and compelled technology transfer frameworks.

• Distributed Computing
• Distributed Systems
• Computer Security
• +۴ مورد دیگر

Giuseppe Antonio Di Luna is an Associate Professor at the Dipartimento di Ingegneria Informatica, Automatica e Gestionale (DIAG), Sapienza University of Rome . His research spans critical areas of Distributed Computing, Distributed Systems , and Computer Security , with a focus on Dynamic Networks, Mobile Agents, Anonymous Communication , and NLP techniques applied to binary analysis . Current research themes include anonymity in distributed environments Security challenges in confidential computing Algorithm design for mobile robots and dynamic networks Applying NLP to enhance binary analysis security His recent publications across top venues like DSN, EuroS&P, and JPDC reflect these interdisciplinary interests, with notable work on: Black hole detection in dynamic rings Robustness of binary similarity systems Confidential virtual machine evaluation tools Self-stabilizing computation in anonymous networks He has received prestigious awards including the Axa Fellowship (2020-2022) , ASPLOS 2019 Distinguished Paper Award , and DIMVA 2019 Best Paper Runner-Up . His collaborative efforts include organizing the EuroSys 2023 conference in Rome.

• Trustworthy Artificial Intelligence
• Privacy-Preserving Machine Learning
• Federated Learning
• +۴ مورد دیگر

Georgios Kaissis is an Adjunct Assistant Professor at the Technical University of Munich , where he holds a senior research scientist role at the Institute of Artificial Intelligence in Medicine and serves as an attending radiologist at the Institute of Radiology . His work focuses on developing next-generation trustworthy artificial intelligence algorithms for medical applications, integrating advanced deep learning techniques and probabilistic machine learning . Education: Medical and doctoral degrees from LMU Munich , Master’s in Healthcare Business Administration from FAU Nuremberg , radiology specialization from TU Munich , postdoctoral training in Machine Learning at Imperial College London . Research Interests : Georgios leads the Reliable AI group, concentrating on trustworthy AI and privacy-preserving algorithms in medical imaging. His work bridges machine learning and clinical radiology , with a focus on federated learning and secure data integration to enhance diagnostic accuracy while maintaining patient confidentiality. Networks and Affiliations span institutions like Imperial College London , Technical University of Munich , and LMU Munich , alongside collaborations in mathematical departments and radiology across Europe.

Dipti Kapoor Sarmahمشاهده پروفایل

مدرس

• Cryptography
• Steganography
• Cybersecurity
• +۴ مورد دیگر

Dipti Kapoor Sarmah is a Lecturer specializing in cybersecurity, steganography, and cryptography. Her work bridges theoretical and applied aspects of secure data transmission, digital forensics, and cyber threat mitigation. Current role: Lecturer in Semantics, Cybersecurity & Services Key research areas: Cryptography, Steganography, Phishing analysis, IoT security Her research explores hybrid cryptographic-steganographic systems, gamified cybersecurity education, and innovative approaches to combat MageCart e-skimming attacks using reverse proxy architectures. She has systematically analyzed zero-knowledge protocols (zk-SNARK, zk-STARK, Bulletproofs) for privacy-preserving authentication. Recent publications address pandemic-era phishing trends, consumer IoT security standards, and data mining applications in insurance fraud detection. Her work frequently intersects with artificial intelligence and machine learning techniques for secure image processing. Notable recognition includes the EMMSAD 2025 Best Paper Award for collaborative research on phishing attack ontologies. Her activity spans diverse formats including peer-reviewed journal articles, conference contributions, and systematic literature reviews. With a Scopus h-index of 5 and over 128 citations, her research output spans 2008-2025, showing consistent contributions to cybersecurity, particularly in steganography optimization algorithms and digital forensics.

• Health data science
• Linked health and social care data
• Epidemiology
• +۷ مورد دیگر

Elizabeth Ford is an Associate Professor in Health Data Science at Brighton and Sussex Medical School, with expertise in analyzing primary care data and linked routinely collected health data to identify areas for healthcare improvement. She serves as Lead for Data Science in the NIHR Applied Research Collaboration in Kent, Surrey, and Sussex (ARC KSS), where she works to enhance health data science capacity across the region. Her research focuses on developing early detection models for mental health conditions and dementia, and understanding groups at risk of delayed diagnosis of long-term conditions. Professor Ford's academic journey began with a BA in Psychology from Oxford University, followed by an MRC-funded DPhil in clinical health psychology at the University of Sussex. She has held progressive academic positions at Brighton and Sussex Medical School, advancing from Research Fellow to her current role as Associate Professor. Her career demonstrates a clear trajectory in health data science, epidemiology, and public engagement with health data. Her research spans health data science methodology, ethical considerations in data use, and practical applications for healthcare improvement. Professor Ford specializes in risk prediction modeling, data quality assessment using free text, Bayesian methods for handling missing data, and public engagement approaches to build social license for data sharing. Her work addresses critical challenges in using routinely collected health data for research while maintaining public trust. Professor Ford's recent publications reflect her leadership in navigating the technical, ethical, and governance dimensions of health data science. Her work addresses data privacy concerns, quality assessment of electronic health records, development of risk prediction models, and public perspectives on data sharing - all critical areas for advancing responsible health data research. Future Leader in Health Data Science by the Farr Institute of Health Informatics Research (2016) As an educator, Professor Ford oversees undergraduate research methods curriculum and teaches epidemiology and health data science to Masters students. She runs a specialized course on AI in healthcare for medical undergraduates and regularly supervises research projects at all levels. Her teaching reflects her commitment to developing the next generation of health data scientists who can navigate both technical and ethical challenges in the field. Professor Ford actively collaborates with NHS, public health, and public stakeholders across Sussex, Kent, and Surrey to support responsible use of routinely collected data in research. Her leadership in developing secure regional data environments and understanding public perspectives on data sharing positions her at the forefront of health data science infrastructure development in the UK.

• Competition Law
• Intellectual Property Law
• Digital Regulation
• +۶ مورد دیگر

Peter Georg Picht is Professor for Commercial Law at the University of Zurich's Institute of Law, where he conducts research at the intersection of competition law, digital regulation, and intellectual property. His work focuses on the rapidly evolving legal landscape governing digital markets, with particular expertise in Standard Essential Patents (SEPs), FRAND licensing, and the regulatory implications of artificial intelligence. Professor Picht's research interests span several interconnected domains: Digital market regulation under the Digital Markets Act (DMA), Digital Services Act (DSA), and Data Act Competition law implications of algorithms and artificial intelligence Standard Essential Patents and FRAND licensing frameworks Data governance and access requirements in competitive markets The intersection of intellectual property law with emerging AI technologies His recent publications reveal a consistent focus on the practical implementation challenges of EU digital regulations and the adaptation of competition law frameworks to address algorithmic markets. Professor Picht has published extensively on FRAND licensing disputes, contributing to the scholarly discourse on how courts should determine fair, reasonable, and non-discriminatory royalty rates for standard-essential patents. Professor Picht maintains active research collaborations with leading institutions including the Max Planck Institute for Innovation & Competition, ETH Zurich Center for Law and Economics, and the Swiss Federal Institute of Intellectual Property. His work bridges theoretical legal scholarship with practical regulatory challenges facing digital markets today, making significant contributions to both academic discourse and policy development in European competition law.

Myoungsoo Jungمشاهده پروفایل

استاد

• Computer Architecture
• Operating Systems
• Solid State Drives
• +۶ مورد دیگر

Myoungsoo Jung is the KAIST Endowed Chair Professor and Full Professor at Korea Advanced Institute of Science and Technology, holding primary appointment in the School of Electrical Engineering with additional affiliations in the School of Semiconductor System Engineering, Graduate School of AI Semiconductor, Graduate School of System Architect, and Graduate School of AI. His research focuses on cutting-edge computer architecture and operating systems with specialization in memory and storage systems. Professor Jung's research interests span computer architecture, operating systems, flash memory, solid-state drives, non-volatile memory, file systems, parallel processing, and heterogeneous computing. He has pioneered work in CXL-based memory expansion, computational SSDs, and memory disaggregation technologies that are transforming modern data centers and AI infrastructure. His recent publications demonstrate significant advancements in CXL-driven architectures, computational storage, and memory systems. The research trends show increasing integration of storage and memory technologies with AI workloads, particularly in large-scale graph processing, federated learning, and billion-scale data management. His team's work frequently appears in top-tier venues including ISCA, HPCA, SOSP, and USENIX ATC. Hall of Fame, IEEE/ACM ISCA (2024) Digital Innovation Award from Minister of Science and ICT (2024) CES Innovation Award Winner, CXL-Enabled AI Accelerator (2025) Korea Innovative Startup Award, Ministry of Science and ICT (2025) Samsung Best Paper Award Winner (Grand Prize) (2022) Professor Jung has successfully advised numerous PhD students including Miryeong Kwon (recipient of KAIST Outstanding PhD Dissertation Award) and Donghyun Gouk. His CAMEL research lab has secured over $13M in funding from sources including DOE, NSF, and Korean government agencies. The lab maintains strong industry partnerships with Samsung, SK Hynix, and Panmnesia, focusing on translating research into practical systems. Current projects include CXL-based memory expansion, computational SSDs for AI acceleration, and next-generation storage architectures for hyperscale data centers.

Anitha Gollamudiمشاهده پروفایل

استادیار

• Language-based Security
• Trusted Execution Environments
• Formal Methods
• +۴ مورد دیگر

Anitha Gollamudi is an Assistant Professor in the Department of Computer Science at the Miner School of Computer and Information Sciences, University of Massachusetts Lowell. She teaches core courses including Systems Security (COMP 5300), Compiler Construction (COMP 4060/5340), and Organization of Programming Languages (COMP 3010). Her research focuses on language-based security through hardware-assisted mechanisms, cryptography, and formal methods. Current projects include: Automatic compartmentalization of Trusted Execution Environment (TEE) programs Formal foundations of Fully Homomorphic Encryption (FHE) compilers Privacy-preserving machine learning with encrypted learning Analysis of her 8 recent publications (2016-2025) reveals dominant themes in TEE security and secure compilation. Key contributions address memory safety in WebAssembly, authorization logic for computation principals, and formal verification of cryptographic systems. Her work consistently bridges theoretical foundations with practical implementations for real-world security challenges. No scientific awards were mentioned in the available information. Professor Gollamudi actively mentors students across levels, currently advising PhD candidates Wesley B. Nuzzo and Samuel Dodson alongside undergraduate Benjamin Houle. Her former students include Nam Bui, James Chen, Yuka Akiyama (honors thesis), and Andrew Eggleston. She emphasizes collaborative research and encourages prospective students to contact her with research interests and academic background. She leads a research group focused on applying programming language techniques to security problems, with ongoing projects targeting enclave placement optimization, FHE correctness verification, and encrypted machine learning frameworks.