Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۳۱-۴۵ از ۱۹٬۶۵۱ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.
Tushar Krishnaمشاهده پروفایل
دانشیار
Tushar Krishna is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology, with a courtesy appointment in the School of Computer Science. He earned his PhD in Electrical Engineering and Computer Science from MIT in 2014, an MSE in Electrical Engineering from Princeton University in 2009, and a B.Tech in Electrical Engineering from IIT Delhi in 2007. His research spans computer architecture, interconnection networks, networks-on-chip (NoC), and AI/ML accelerator systems, with a focus on optimizing data movement in modern computing platforms. His work is funded by NSF, DARPA, IARPA, SRC, Department of Energy, Intel, Google, Meta, Qualcomm, and TSMC. His papers have been cited over 17,000 times, with three receiving IEEE Micro's Top Picks recognition, one earning an honorable mention, and four winning best paper awards. Dr. Krishna leads the Synergy Lab at Georgia Tech and has developed several influential tools including ASTRA-sim for distributed AI/ML training, MAESTRO and SCALE-sim for accelerator design space exploration, and Garnet2.0 for NoC simulation. His recent work focuses on large language model acceleration, distributed training systems, and neuro-symbolic AI architectures. He has received numerous teaching and research awards including induction into the HPCA Hall of Fame (2022), the Class of 1940 Teaching Effectiveness Award (2018), and the Roger P. Webb Outstanding Mid-career Faculty Award (2024). HPCA Hall of Fame Inductee (2022) Roger P. Webb Outstanding Mid-career Faculty Award (2024) Richard M. Bass/Eta Kappa Nu Outstanding Junior Teacher Award (2023) Roger P. Webb Outstanding Junior Faculty Award (2021) Class of 1940 Course Survey Teaching Effectiveness Award (2018) Dr. Krishna currently serves as Associate Director for the Center for Research into Novel Computing Hierarchies (CRNCH) and co-chair of the Chakra Execution Traces and Benchmarks Working Group. He has held the ON Semiconductor (Endowed) Junior Professorship at Georgia Tech (2019-2021) and has been a visiting professor at MIT EECS, Harvard University CS, and a researcher at Intel's VSSAD group.
Minseok Ryuمشاهده پروفایل
استادیار
Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.
Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.
Clifford Stein is a Professor of Industrial Engineering and Operations Research (IEOR) and Computer Science at Columbia University, and Associate Director for Research at the Data Science Institute. He holds a Ph.D. (1992), M.S. (1989), and B.S.E. (1987) from MIT and Princeton University, respectively. His research focuses on algorithms, combinatorial optimization, operations research, scheduling, and computational biology. A co-author of the best-selling textbook Introduction to Algorithms , Stein has published widely in top venues and holds prestigious awards like ACM Fellow and NSF Career Award. His work includes foundational contributions to minimum cut algorithms, scheduling theory, and network optimization, supported by NSF and Sloan Foundation grants. Stein has advised over 40 graduate and undergraduate students, many now in academia and industry.
Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.
Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies and recognized as an ACM Distinguished Member. He leads the Safe AI Lab, which focuses on developing methods and tools for the verification of AI systems to ensure their safe and secure deployment in applications of societal importance. His research spans verification and robust learning for neural networks and decision trees, robust machine learning in aviation and finance, monitoring of machine learning systems, assurance for autonomous systems and AI, and verification and validation of neuro-symbolic systems. Lomuscio has made significant contributions to formal verification methods for AI systems, particularly in the context of safety-critical applications. His recent publications demonstrate a strong focus on neural network verification techniques, with applications across multiple domains including finance, aviation, and autonomous systems. His work bridges theoretical advances in formal methods with practical applications in real-world AI systems, addressing critical challenges in AI safety and trustworthiness. Scientific Awards: Royal Academy of Engineering Chair in Emerging Technologies ACM Distinguished Member Lomuscio has served in numerous leadership roles, including as Co-Director (2023-present) and Deputy Director (2019-2023) of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. He has also held positions as Director of Strategy and Planning (2017-2020), Member of Management Committee (2013-2020), and Deputy Head of Department (2016-2017). His editorial service includes roles as Associate Editor for Artificial Intelligence Journal and Editorial Board Member for Journal of Artificial Intelligence Research and Journal of Autonomous Agents and Multi-agent Systems. His research group actively mentors students and researchers, with current openings for PhD and postdoctoral positions focused on AI verification and safety. Lomuscio's work has established him as a leading figure in the field of safe and verifiable AI systems, with significant contributions to both theoretical foundations and practical applications.
Prof. Dr. Jörg E. Drewes is a Full Professor and Chair at the Research Institute for Urban Water Management, Technical University of Munich (TUM), Germany. He also serves as Academic Program Director for Environmental Engineering at TUM and is a co-founder of Waterloop Solutions GmbH. His work spans advanced water treatment technologies, wastewater reuse, and environmental health surveillance. Affiliations : TUM, Colorado School of Mines, King Abdullah University of Science and Technology, UNSW Water Research Centre Research focuses on advanced oxidation processes , membrane fouling mitigation , micropollutant removal , and wastewater epidemiology for public health. His team develops integrated systems for water-energy-food nexus applications and graphene oxide-based treatment solutions. Recent publications address PFAS remediation , UVC-LED pre-treatment for biofouling control, and GIS-based wastewater surveillance models . Awards include the Bayerische Staatsmedaille (2023) and William Dunbar Medal (2022). 2023 : Bayerische Staatsmedaille 2022 : William Dunbar Medal 2018 : IWA Fellow He leads the Nexus@TUM initiative and contributes to EU water resilience strategies. Collaborations include projects in Germany, China, India, and Spain, focusing on climate-resilient water systems and resource recovery .
Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing (SoC). He holds a B.Sc. (1st Class Honors) and Ph.D. in Computer Science from Monash University. His research focuses on database systems, large-scale analytics, and distributed computing. He has held leadership roles including Dean of School of Computing (2007–2013) and Director of Smart Systems Institute (2011–2021). Key achievements include the Singapore President’s Science Award (2011), ACM Fellow (2011), IEEE Fellow (2009), and multiple best paper awards. His work emphasizes scalable data management, blockchain systems, and healthcare data analytics. Education: Monash University (B.Sc., Ph.D.) Leadership: Dean (SoC), Director (Smart Systems Institute) Awards: Over 15 major honors including ACM SIGMOD E.F. Codd Innovations Award (2020) His research spans distributed databases, big data systems, and innovative applications of blockchain technology. Recent work includes NASI (neural architecture search) and Rafiki (ML-as-a-service).
Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.
Gilbert Fridgen is Full Professor in Digital Financial Services and holder of the PayPal-FNR PEARL Chair at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the FINATRAX research group focusing on blockchain, digital identity, and sustainable digital solutions. His research explores decentralized technologies in finance, energy systems, and public administration. Current projects investigate blockchain applications in industrial demand response, NFT markets, EU digital identity wallets, and DeFi institutional adoption. Prof. Fridgen examines the intersection of technology with regulatory frameworks and user behavior in energy and financial systems. He collaborates extensively with industry partners including PayPal and Spuerkeess. Prof. Fridgen actively contributes to EU digital policy discussions, particularly regarding the European Digital Identity Wallet framework and decentralized finance governance.
Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.
Alexandra Livada is a Professor at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business (AUEB). She holds office at two locations: 12 Codringtonos Street, 2nd Floor and 76 Patision Street, Antoniadou Wing, 3rd Floor in Athens, Greece. Her contact information includes email livada@aueb.gr and phone number +30 210-8203521. Dr. Livada earned her BA and MA in Economics from Athens School of Economics and Business followed by a PhD in Economics from Essex University, UK in 1988. Her academic career has spanned several decades with extensive teaching experience at both undergraduate and postgraduate levels. Her research interests encompass a diverse range of quantitative fields: Quantitative economics and applied econometrics Time series analysis and forecasting techniques Income distribution and inequality measurement Applied financial econometrics Business cycles analysis Medical statistics Index numbers and official statistics Professor Livada's publication record demonstrates consistent scholarly productivity across multiple disciplines, with a noticeable trend toward interdisciplinary work connecting economics with healthcare and social policy. Her recent research shows increasing focus on income inequality across different geographic regions, economic sentiment during crises, and the intersection of medical conditions with statistical analysis. Her scholarly contributions have been recognized through numerous citations in leading journals and books. She has served as a referee for prestigious journals including the European Journal of Political Economy, Journal of Public Economics, and Journal of Insurance, Mathematics and Economics. Professional service highlights include: Member of multiple project teams Marie-Curie project supervisor External evaluator for the Greek State Scholarship Foundation (IKY) External evaluator for the Social Sciences and Humanities Research Council of Canada Co-author of the book "Index Numbers and Official Statistics" Professor Livada maintains an active research agenda with collaborations spanning economics, statistics, and medical fields, demonstrating the interdisciplinary nature of contemporary quantitative research.
Ian Goldberg is a Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. He holds the Canada Research Chair in Privacy Enhancing Technologies and leads the Cryptography, Security, and Privacy (CrySP) group . He is affiliated with the Cybersecurity and Privacy Institute and the Institute for Quantum Computing . Research Focus: Security, privacy, and privacy-enhancing technologies (PETs) for the Internet, including Off-the-Record (OTR) Messaging , censorship resistance , private information retrieval , and zero-knowledge proofs . Recent Publications: His work addresses metadata protection , deniable authentication , and secure multi-party computation , with applications to anonymous communication and censorship resistance. Scientific Awards: Canada Research Chair in Privacy Enhancing Technologies Students: He advises current and former PhD/Master’s students in projects like Lox , PRSONA , Walkie-Talkie , and SkypeMorph . Notable advisees include Sajin Sasy (Privacy-Preserving Communications), Justin Tracey (PETs Development), and Tao Wang (Website Fingerprinting Defenses). Labs & Teams: Goldberg is a core member of the CrySP group and contributes to the Tor Project and Off-the-Record Messaging open-source initiatives. His research bridges theoretical and applied cryptography, with deployments in real-world systems like Tor and PIR protocols.