جستجوی استاد راهنمای دانشگاهی

Tim Conradمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Computational Biology
• Machine Learning
• +۵ مورد دیگر

Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.

Lisa Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware Acceleration
• Hardware-Software Co-Design
• +۵ مورد دیگر

Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.

Riad S. Wahbyمشاهده پروفایل

استادیار

• Cryptography
• Blockchain Security
• Secure Hardware Systems
• +۱۱ مورد دیگر

Riad S. Wahby is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. His work focuses on designing secure hardware and software systems, with recent emphasis on cryptographic proof systems. He actively mentors PhD students and collaborates across disciplines in cybersecurity, blockchain, and formal verification. PhD in Computer Science, Stanford University MEng in Electrical Engineering, Massachusetts Institute of Technology SB in Electrical Engineering, Massachusetts Institute of Technology Wahby's research spans cryptography , blockchain security , zero-knowledge proofs , and secure hardware-software co-design . His work addresses challenges in verifiable computation, privacy-preserving protocols, and hardware subversion resistance. Recent publications reveal trends in zero-knowledge proof systems (SNARKs, MPC), blockchain security (anonymous blocklisting, decentralized auctions), and hardware-crypto integration (weird machines, verifiable ASICs). Technical focus areas include formal verification, side-channel analysis, and cryptographic compilers. Distinguished Student Paper Award, IEEE Symposium on Security and Privacy (Oakland16), 2016 Best Paper Award, USENIX Annual Technical Conference (ATC18), 2018 Wahby collaborates with researchers across institutions and industries, including Dan Boneh at Stanford, Mike Walfish at NYU, and Silicon Labs in industrial roles. His CyLab affiliations connect him to over $400K in seed funding opportunities and blockchain initiatives at CMU.

Cynthia Sturtonمشاهده پروفایل

دانشیار

• Hardware Security
• Formal Methods
• Computer Security
• +۴ مورد دیگر

Cynthia Sturton serves as Associate Professor and Peter Thacher Grauer Scholar in the Department of Computer Science at the University of North Carolina at Chapel Hill. She leads the Hardware Security @ UNC research laboratory focused on developing formal verification tools for hardware security analysis. Her educational background includes a Ph.D. (2013) and M.S. from UC Berkeley, and a B.S.Eng. from Arizona State University. Her research centers on hardware security, applied formal methods, and symbolic execution techniques for identifying security vulnerabilities in processor designs before fabrication. Sturton's research demonstrates consistent innovation in hardware security verification, particularly through tools like Sylvia (symbolic execution for Verilog) and SylQ-SV (SystemVerilog analysis with query caching). Her work bridges theoretical formal methods with practical security applications, addressing critical challenges like path explosion and security property generation at scale. Nominated for Best Paper award at IEEE/ACM MICRO 2018 Intel Hardware Security Academic Award, 2nd place ($50,000) at IEEE Symposium on Security and Privacy 2020 Selected as Top Picks in Hardware and Embedded Security 2021 She advises multiple graduate students including Rui Zhang and Calvin Deutschbein, and has secured significant research funding from NSF (Grants 1816637, 651276), Semiconductor Research Corporation, Intel, Google, and UNC Chapel Hill. Her Hardware Security @ UNC lab develops critical tools for security property generation and vulnerability detection in hardware designs.

Jeff Zhangمشاهده پروفایل

استادیار

• Deep learning
• Computer architecture
• Embedded systems
• +۵ مورد دیگر

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

Vasil Georgiev Tsunizhevمشاهده پروفایل

استاد

• Cloud Computing
• Distributed Systems
• Grid Computing
• +۲ مورد دیگر

Vasil Georgiev Tsunizhev is a Professor of Computer Informatics at the Faculty of Mathematics and Informatics, Sofia University, with office hours Monday and Wednesday 13:00-14:00 in room FMI-110. Contact: v.georgiev@fmi.uni-sofia.bg, +359 2 8161-594. His research spans cloud computing, distributed systems, and grid technologies with emphasis on resource management, load balancing, and service modeling. Key contributions include numerical solutions for cloud servicing, distributed coordination mechanisms, and fault-tolerant information services. His work integrates open-source components and addresses scalability challenges in cloud environments. Analysis of his 2009-2015 publications reveals consistent focus on cloud infrastructure optimization, with recurring themes in resource frameworks, quality-of-service models, and distributed coordination. His research demonstrates strong technical depth in numerical modeling and system architecture while addressing real-world scalability and fault tolerance requirements. Professor Tsunizhev collaborates internationally through projects like CoreGRID (contributing to grid security white papers) and works with researchers including R. Zhelev and L. Kirchev. His laboratory work focuses on commodity grid platforms and lightweight resource management systems for distributed computing environments.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۲ مورد دیگر

Geoffrey Hinton is a Professor in the Department of Computer Science at the University of Toronto , where he has been a pivotal figure in advancing artificial intelligence research. His work focuses on neural networks, deep learning, and machine learning, revolutionizing how machines process information and learn from data. With collaborations spanning institutions like NYU and IIT Mumbai, Hinton’s influence extends beyond academia into public discourse through lectures like the Romanes Lecture (2024) . His research explores Deep Belief Networks , Gradient Methods , Neural Network Architectures , and Probabilistic Models , with recent publications addressing novel algorithms like the Forward-Forward Algorithm and frameworks for Panoptic Segmentation . Though he no longer accepts students, current advisees include Jimmy Lei Ba and Cem Anil. Hinton’s contributions to AI are complemented by media engagements, including CBS 60 Minutes (2023) and CNN Amanpour (2023) , reflecting his role as a thought leader. His technical outputs, such as Nature Deep Learning Review (2015) with Y. LeCun and Y. Bengio, remain foundational texts in the field.

Daniel J. Sorinمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Cache Coherence Protocols
• +۷ مورد دیگر

Daniel J. Sorin is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he also serves as Associate Chair of Education. He holds joint appointments in both the Electrical and Computer Engineering department and Computer Science department, and is recognized as a Bass Fellow for his contributions to education and research. His research focuses on computer architecture with specific expertise in memory systems, cache coherence protocols, fault tolerance, and verification-aware design. Dr. Sorin's work bridges theoretical computer architecture with practical implementations, often incorporating coding theory to solve architectural challenges. His research group has made significant contributions to automated protocol generation, hardware acceleration, and robot motion planning systems. Dr. Sorin's publications reveal a consistent focus on memory consistency models, cache coherence protocols, and verification techniques. His recent work has expanded into robot motion planning acceleration, FPGA resource management, and novel error correction techniques for emerging memory technologies. The trend shows increasing interdisciplinary work connecting computer architecture with robotics and machine learning applications. Program Chair of HiPEAC 2017 Co-chair of IEEE Micro's Top Picks selection committee (2016) Lois and John L. Imhoff Distinguished Teaching Award (2011) NSF CAREER Award recipient IEEE Micro Top Pick awards (2011, 2015) ACM Senior Member As an advisor, Dr. Sorin has mentored numerous PhD students who have gone on to successful careers at leading technology companies including Google, Microsoft, Oracle, and Nvidia. His research group maintains strong industry connections and has produced influential work in cache coherence protocols, memory systems, and fault-tolerant architectures. He has also authored the widely-used textbook 'A Primer on Memory Consistency and Cache Coherence' (2nd edition). Dr. Sorin leads an active research laboratory focused on next-generation computer architecture challenges, with ongoing projects in hardware acceleration, memory systems, and robot motion planning. His group collaborates with researchers across multiple disciplines including robotics, coding theory, and semiconductor design.

Lisa Wu Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware-Software Co-design
• Hardware Accelerators
• +۶ مورد دیگر

Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.

Andrea Bastoniمشاهده پروفایل

پژوهشگر ارشد

• Real-Time Systems
• Cyber-Physical Systems
• Operating System Design
• +۵ مورد دیگر

Dr. Andrea Bastoni is a Postdoctoral Researcher and Research Fellow at the Chair of Cyber-Physical Systems in Production Engineering at Technical University of Munich (TUM), Faculty of Mechanical Engineering. He is also the CTO and co-founder of Minerva Systems , developing operating system solutions for AI-ready embedded applications. His expertise spans real-time operating systems, cyber-physical systems, and predictable system design for heterogeneous platforms. His research focuses on enhancing predictability of memory hierarchies in complex SoCs through techniques like memory bandwidth regulation and cache partitioning. This work has industrial applications in safety-critical domains such as avionics and railways, where he contributes to certifiable hypervisors and operating systems. As former Software Architect of the PikeOS hypervisor at SYSGO GmbH (2012-2020), he specialized in DO-178C, IEC 61508, and EN 50128 standards. His academic background includes a Ph.D. in Computer Engineering from the University of Rome Tor Vergata (2007-2011), where he developed LITMUS^RT as part of UNC's Real-Time Systems Group during a visiting researcher period (2009-2010). His publications reflect ongoing work on Multicore Real-Time Scheduling , Mixed-Criticality Task Isolation, and Arm DynamIQ shared unit analysis. He actively participates in program committees for conferences like RTSS, DSN, and DATE.

Roop Aparajita Subhra Purushottamمشاهده پروفایل

دانشیار

• Machine Learning
• Extreme Classification
• Optimization Techniques
• +۵ مورد دیگر

Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.

Simone Winkoمشاهده پروفایل

استاد

• Modern German Literature
• Literary Theory
• Emotion in Poetry
• +۴ مورد دیگر

Prof. Dr. Simone Winko holds the Chair for Modern German Literature and Literary Theory at the University of Göttingen since 2003. Her research spans literary theory, canonization, praxeology of literary studies, and the intersection of emotions with German poetry around 1900 and digital humanities. Academic Roles: Chair at Göttingen (2003–), DFG Priority Programme 2207 leadership (2017–), Courant Center collaboration (2009–) Key Projects: DFG projects on literary change (2023–), computational literary history (2020–2023), and argumentation practices in interpretations (2018–2020) Her recent work focuses on emotions in German-language poetry , using computational methods to model text similarity and analyze historical shifts between Realism and Modernism. She explores how emotional codes and narrative strategies are embedded in lyrical structures, particularly through projects like Anthologien zeitgenössischer deutschsprachiger Lyrik (2022). Scientific Awards : 2010: 1st prize for best doctoral supervision (KissWin, BMBF-funded) Teaching & Collaboration : Supervises B.A., M.A., and Ph.D. theses; co-edits the Journal of Literary Theory and Revisionen book series. Collaborates with Fotis Jannidis, Gerhard Lauer, and Matías Martínez on computational approaches to literary studies.

• Human-Computer Interaction
• Social Media
• Collective Intelligence
• +۱۱ مورد دیگر

Patrick C. Shih is an Associate Professor of Informatics in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington, where he also serves as the Director of Graduate Studies for Data Science and Co-Director of the Animal Informatics MS and PhD track. He directs the Societal Computing Lab (SoCo Lab) and is a core faculty member of the Health Informatics PhD track. Dr. Shih's research focuses on how to better support health and wellbeing, specifically that of underserved and vulnerable populations, through the design, development, and evaluation of sociotechnical systems and community-based mechanisms. His work also designs technologies to amplify human and animal capabilities in animal-assisted interventions, improve animal welfare, and cultivate empathy for others. His research spans human-computer interaction, social media, collective intelligence, crowdsourcing, and online and geographic communities, with a particular emphasis on leveraging awareness of individual and community activities embedded in social media for civic engagement platforms. His recent publications reveal a strong trend toward health equity research, particularly focused on African American/Black communities, Alzheimer's disease and related dementias, breast cancer survivorship, and autism spectrum disorder. His work increasingly integrates generative AI, mobile health applications, and community-based participatory design approaches to address health disparities in vulnerable populations. ACM Senior Member (2020) NSF CAREER Award recipient Indiana University Trustees Teaching Award (2018-19) IU Groups Scholars Program STEM Mentor of the Year (2018-19) Multiple best paper awards at top-tier conferences Dr. Shih has successfully secured significant funding for his research, including a $3.7 million award to investigate inequities in environmental stressors and cognitive decline in urban and rural older adults. He serves on numerous editorial boards and program committees for leading HCI and computing conferences. His teaching portfolio includes courses on usable AI, mobile and pervasive design, and social computing. He leads the Societal Computing Lab, which focuses on developing technologies that address societal challenges through community engagement and participatory design.

Chester Rebeiroمشاهده پروفایل

دانشیار

• Hardware Security
• Applied Cryptography
• Side Channel Analysis
• +۳ مورد دیگر

Chester Rebeiro is an Associate Professor at the Department of Computer Science and Engineering within the Indian Institute of Technology Madras . His work spans hardware and software security with a focus on cryptographic implementations and microarchitectural vulnerabilities. Research interests include hardware security, applied cryptography, side channel analysis, and operating system security. He develops frameworks for automatic vulnerability detection and mitigation in cryptographic systems. Editorial Board: Associate Editor at Journal of Hardware and Systems Security (Springer, 2021-2024) Conference Leadership: General Co-Chair for SPACE 2024, Program Co-Chair for ATS 2024, and Program Co-Chair for INDOCRYPT 2023 Professional Activities: Organizer of e-CTF Embedded Capture The Flag and contributor to cybersecurity workshops across India and abroad Scientific contributions highlight two major awards: a Distinguished Paper Award at USENIX Security 2024 and a Best Paper Award at IEEE HOST 2020. His research focuses on practical security solutions for processors and cryptographic systems. Advising includes mentoring 11 PhD students and 7 MS by Research candidates, with notable co-guided projects in fault attack detection and side-channel mitigation. He actively contributes to educational initiatives through courses on Secure Processor Microarchitecture and Operating Systems.

Jayne Wallaceمشاهده پروفایل

استاد

• Design
• Digital Creativity
• Wellbeing
• +۸ مورد دیگر

Jayne Wallace is a Professor of Craft, Digital Creativity, and Wellbeing at Northumbria University's School of Design. She leads the CoCreate research community, focusing on design, technology, and participation in human contexts. Her work includes EPSRC-funded projects like Enabling Ongoingness and the EU Marie Curie OpenDoTT initiative. Wallace co-founded the Journal of Jewellery Research and the Research Through Design conference series, emphasizing craft ethos and IoT ethics. Her research spans digital creativity, wellbeing, and participatory design, addressing dementia care, citizenship, and technology's societal impact. Education: PhD in Design Studies (2007). Notable collaborations include work with Beamish Museum on pottery and wellbeing, and projects like Centring and Connected Craft. She actively organizes workshops on ethical internet practices and co-creation methods. Research interests emphasize craft's role in digital innovation, mental health, and intergenerational dialogue. Awards include Best Paper Awards (2020) and an Honourable Mention (2018). Grants include EPSRC, AHRC, and EU funding. Her lab, CoCreate, involves over 50 researchers exploring design's societal applications.