جست‌وجوی استاد و پژوهشگر

• Dermatology
• Pathology
• Stem Cell Biology
• +۱۲ مورد دیگر

Peggy Myung, MD, PhD, is Associate Professor of Dermatology and Pathology at Yale School of Medicine. She serves as Vice Chair of Research in Dermatology and leads the Myung Lab, which investigates hair follicle development, regeneration, and skin carcinogenesis. She is affiliated with the Yale Cancer Center, Yale Stem Cell Center, and the Program in Translational Biomedicine (PTB). Department: Dermatology (Primary), Pathology (Secondary) Academic Rank: Associate Professor Institution: Yale University School: Yale School of Medicine Email: peggy.myung@yale.edu Research Interests: Dr. Myung's research centers on understanding how epithelial-mesenchymal interactions regulate tissue regeneration and stem cell behavior in the skin. Her lab focuses on the Wnt/β-catenin and SHH signaling pathways in hair follicle morphogenesis and how their dysregulation contributes to basal cell carcinoma. She employs live imaging, single-cell genomics, and mouse models to dissect the molecular mechanisms governing coordinated cell growth and differentiation. Publication Trends: Her recent work spans developmental biology, dermatopathology, computational biology, and clinical dermatology. A strong theme is the use of advanced genomic and imaging techniques to decode developmental signals in skin appendages and apply these insights to cancer biology. Collaborations with computational biologists and clinicians highlight interdisciplinary approaches. Scientific Awards: 2024 'Top Doctor' by Yale Medicine $30,000 Biotech Accelerator Award (2023) Advising and Grants: As principal investigator of the Myung Lab, she mentors trainees and leads NIH-funded and institutionally supported research. Her lab is part of the Yale Combined Program in the Biological and Biomedical Sciences (BBS), indicating graduate student mentorship. While specific grants are not listed, her active publication record and awards suggest robust funding. Labs and Teams: The Myung Lab is central to her research, working closely with the Greco Lab and other collaborators in dermatology and pathology. She is integrated into multiple research centers at Yale, facilitating team science in skin biology and cancer.

• Programming Languages
• Program Synthesis
• Program Verification
• +۷ مورد دیگر

Jedidiah McClurg is an Assistant Professor in the Department of Computer Science at Colorado State University, with prior faculty appointments at Colorado School of Mines and the University of New Mexico. He received his Ph.D. in Computer Science from the University of Colorado Boulder in 2018, where he was a member of the CUPLV research group under the supervision of Pavol Cerny. His research focuses on programming languages, program synthesis, verification, and their applications in networking, compilers, and distributed systems. His educational background includes an M.S. in Computer Science from Northwestern University (2013) and a B.S. in Electrical Engineering from the University of Iowa (2009). He has completed internships at Microsoft Research (RiSE Group, 2014) and Rockwell Collins (2011, 2013, 2004). McClurg’s research interests include programming languages, formal verification, software synthesis, software-defined networking, compilers, and system security. His work aims to develop tools and techniques that help programmers write more secure, reliable, and efficient code, especially in safety-critical domains. He has led multiple NSF-funded projects, including FMitF and CRII grants, totaling over $1 million in funding. His recent publications span high-impact venues such as PLDI, CAV, DISC, and SOSR, with topics ranging from neural network optimization and regular expression synthesis to network program verification and FEC code generation. These works reflect a consistent trend toward automating correctness, improving performance, and enabling scalable solutions in systems and networking. NSF CRII: SHF: Foundations for Stateful Network Programming ($175,000) NSF FMitF: Game Theoretic Updates for Network & Cloud Functions ($355,000 for him) NSF FMitF: Robust Enforcement of Customizable Resource Constraints ($250,000 for him) NSF GRFP (awarded to student Lauren Baker) He has advised multiple graduate and undergraduate students, many of whom have secured positions at leading tech companies such as Google, Apple, and Amazon. He is actively involved in academic service, having served on program committees for PLDI, SOSR, CAV, and others, and as a reviewer for journals like IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Software Engineering (TSE). He also contributes to open-source research via GitHub and maintains a strong online academic presence.

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Xinchao Wang is an Assistant Professor and Presidential Young Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), College of Design and Engineering. He leads the xML Lab, focusing on machine learning and artificial intelligence. Prior to joining NUS, he was an Assistant Professor at Stevens Institute of Technology in the Department of Computer Science and a Postdoctoral Researcher at the Beckman Institute, University of Illinois Urbana-Champaign, under the late Professor Thomas Huang. He earned his Ph.D. in Computer Vision from École polytechnique fédérale de Lausanne (EPFL) under Professor Pascal Fua and a B.Sc. in Computing from the Hong Kong Polytechnic University with first-class honors and a perfect GPA. His research spans machine learning, computer vision, and artificial intelligence, with a focus on diffusion models, vision-language models, 3D generation, and dataset distillation. His recent publications demonstrate strong trends in efficient generative modeling, including acceleration techniques for diffusion models, 3D scene reconstruction, and novel architectures for vision and language tasks. His work frequently appears in top-tier conferences such as CVPR, NeurIPS, ICCV, ECCV, and ICLR. AI's 10 to Watch, IEEE (2025) Young Research Award, NUS (2025) Young Research Award, NUS CDE (2024) Best Paper Award, IEEE VCIP 2022 Outstanding Early Career Award, NUS CDE (2023) College Teaching Excellence Award, NUS CDE (2023) He actively advises Ph.D. students, many of whom have received prestigious awards including the Google Fellowship and ByteDance Scholarship. He has served as an Associate Editor for IEEE Transactions on Image Processing since 2023. His lab is actively recruiting Ph.D. students, postdocs, visiting scholars, and research assistants in machine learning and AI. He teaches core courses at NUS including Introduction to Machine Learning, Deep Learning, and Pattern Recognition.

Antonio Barbalaceمشاهده پروفایل

مدرس ارشد

• Operating Systems
• Computer Systems
• Virtualization
• +۱۳ مورد دیگر

Antonio Barbalace is a Senior Lecturer at the Institute for Computing Systems Architecture (ICSA), School of Informatics, The University of Edinburgh. He leads the Systems-Nuts research group and holds key roles including ICSA Lab Manager, interim IoT Lab Manager, and MSc in Computer Science program director. He is also an affiliate member of the Edinburgh Quantum Software Lab and an adjunct Associate Professor in the Department of Electronic and Computer Engineering at Virginia Tech. His research focuses on system software, particularly operating systems, virtualization, and runtime environments for heterogeneous and distributed architectures—from embedded systems to data centers. His work spans OS design (e.g., Popcorn Linux), compiler frameworks (LLVM), real-time systems, scheduling, synchronization, networking, storage, and performance analysis. Recently, he has explored operating systems for Quantum Processing Units and classical simulation of quantum systems. His interests also include power efficiency, fault-tolerance, and security in system software. His recent publications, accepted at ASPLOS'25 and EUROMLSYS'25, highlight innovative work in fused-kernel OS design (Stramash) and kernel-level remote shared memory for AI inference (RMAI), indicating a strong trajectory in high-performance, low-latency system architectures for modern computing paradigms. Scientific Awards: No scientific awards listed in the provided text. Advising and Grants: Antonio actively advises multiple PhD and Master’s students at the University of Edinburgh and has previously supervised students and postdocs at Virginia Tech and Stevens Institute of Technology. His research is supported by current and past projects such as Data-center scale classical simulation of quantum computers, Quantum circuit cutting, Popcorn Linux Next++, and in-storage processing. He has secured funding from sources including ONR and AFOSR during his time at Virginia Tech. Labs and Teams: He leads the Systems-Nuts Research Group , which is part of ICSA and collaborates with the Quantum Software Lab (QSL) at the University of Edinburgh. The group focuses on systems software optimization and quantum computing architecture, supported by hardware testbeds for experimental research.

Andreas G. Andreouمشاهده پروفایل

استاد

• Computing Machinery
• Sensory Information Processing
• Theoretical Neuroscience
• +۶ مورد دیگر

Andreas G. Andreou is a Professor at Johns Hopkins University with primary appointments in the Department of Electrical and Computer Engineering, and secondary appointments in the Department of Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the Johns Hopkins University Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab alongside Philippe Pouliquen. His research focuses on brain-inspired microsystems for sensory information processing, neuromorphic computing, and theoretical neuroscience. Education: Born in Nicosia, Cyprus; resides in Baltimore, Maryland. Research Interests: Computing Machinery, Sensory Information Processing, Theoretical Neuroscience, Pattern Analysis, Machine Intelligence, Microsystems Technologies, and Integrated Circuits. Awards: IEEE Fellow, 3rd Best Paper Award at IEEE BioCAS 2018, and recognition for the award-winning Stethovest wearable acoustic sensing array. His lab explores energy-efficient computing beyond Moore's Law, integrating neuroscience principles into microsystem design. Recent projects include quantum-inspired neuromorphic optimizers, AI-generated chips using ChatGPT4, and applications in cardiac acoustics and wearable technology. Collaborations span institutions like NSF, JHU-APL, and the Telluride Neuromorphic AI workshop. His work is supported by grants from DARPA, NSF, NIH, ONR, AFRL, and JHU-APL. He also holds honorary professorships at the University of Cyprus and Universidad Nacional del Sur.

• Hardware Design
• High-Level Synthesis
• FPGA
• +۳ مورد دیگر

Dr. Zhiru Zhang is a Professor in the School of Electrical and Computer Engineering at Cornell University and a member of the Computer Systems Laboratory. His research focuses on new algorithms, methodologies, and design automation tools for heterogeneous computing systems, with recent publications centering on high-level synthesis (HLS), hardware specialization for machine learning, and programming models for software-defined FPGAs. Dr. Zhang earned his Ph.D. in Computer Science from UCLA, where he co-founded AutoESL based on his dissertation research on HLS. AutoESL was acquired by Xilinx (now AMD), and its HLS tool evolved into Vivado HLS (now Vitis HLS), which is widely used for designing FPGA-based hardware accelerators. He also holds a B.S. in Computer Science from Peking University and an M.S. in Computer Science from UCLA. Dr. Zhang's research interests span hardware design, high-level synthesis, FPGA acceleration, machine learning acceleration, heterogeneous computing systems, and computer architecture. His work bridges the gap between software algorithms and hardware implementation, focusing on creating efficient design automation tools that enable specialized hardware for emerging applications, particularly in AI and machine learning. His recent publications demonstrate strong trends in differentiable programming for hardware design, sparse computation optimization, and efficient implementation of large language models on FPGAs. Dr. Zhang has received numerous prestigious awards including being named an IEEE Fellow, the Intel Outstanding Researcher Award, AWS AI Amazon Research Award, Facebook Research Award, Google Faculty Research Award, DAC Under-40 Innovators Award, Rising Professional Achievement Award from UCLA, DARPA Young Faculty Award, IEEE CEDA Ernest S. Kuh Early Career Award, and NSF CAREER Award. His papers have won multiple Best Paper Awards from top conferences including ASPLOS (2025), ISPD (2025), FPGA (2024, 2022, 2021, 2019), AutoML (2024), FCCM (2018), ACM TODAES (2012), and Top Picks in Hardware and Embedded Security (2020). His papers on HLS scheduling and application-specific instruction-set processor (ASIP) compilation have been inducted into the ACM/SIGDA TCFPGA Hall of Fame for the classes of 2022 and 2023, respectively. On the teaching side, Dr. Zhang has received the Ruth and Joel Spira Award for Excellence in Teaching (2018) and twice the Michael Tien'72 Excellence in Teaching Award (2016, 2022), the highest recognition for teaching in the College of Engineering. He teaches courses including ECE 5775/6775: High-Level Digital Design Automation, ENGRD/ECE 2300: Digital Logic and Computer Organization, ECE 6980: Special Topics on Hardware Acceleration of Deep Learning, ENGRG 1050: Freshman Engineering Seminar, and ECE 5950: Special Topics on High-Level Digital Design Automation. Dr. Zhang leads an active research group with numerous PhD students and postdocs. His current students include Jordan Dotzel, Jie Liu, Zichao Yue, Yixiao Du, Yaohui Cai, Andrew Butt, Hongzheng Chen, Jiajie Li, Niansong Zhang, Matthew Hofmann, Zhanqiu Hu, Vesal Bakhtazad, and Grace Dinh. His alumni have gone on to successful careers at companies like NVIDIA, Google, AWS AI, Meta, Microsoft, and academic positions at universities including University of Illinois Chicago and Zhejiang University. The group has received multiple research grants from industry partners including AWS, Intel, and Google.

Jarvis Hauptمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Learning Theory
• Compressed Sensing
• +۵ مورد دیگر

Jarvis Haupt is an Associate Professor and Associate Department Head in the Department of Electrical and Computer Engineering at the University of Minnesota - Twin Cities. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and data science. He leads the Haupt Research Group, which focuses on developing novel methodologies for efficient data acquisition and analysis. Education: B.S. in Electrical Engineering from the University of Wisconsin-Madison (2002) M.S. in Electrical Engineering from the University of Wisconsin-Madison (2003) Ph.D. in Electrical Engineering from the University of Wisconsin-Madison (2009) Postdoctoral Research Associate at Rice University (2009-2010) Professor Haupt's research centers on statistical signal processing and learning theory, with particular expertise in compressed sensing and adaptive sampling techniques. His work develops theoretical foundations for high-dimensional statistical inference while addressing practical challenges in communications, remote sensing, data science, and medical imaging applications. He has made significant contributions to understanding how to efficiently extract information from large datasets through carefully designed measurement processes. His recent publications demonstrate a shift toward neural network theory and applications, particularly examining convergence properties of gradient flow in homogeneous neural networks. Alongside this theoretical work, he continues to apply signal processing techniques to medical imaging challenges, particularly in MRI reconstruction. His interdisciplinary approach bridges theoretical machine learning with practical applications in imaging systems. Scientific Awards: DARPA Young Faculty Award (2014) and Director's Fellowship extension (2016) UMN ECE Russell J. Penrose Excellence in Teaching Award (2015) Multiple Best Paper Awards including at GlobalSIP (2015) and IWSHM (2015) Wisconsin Academic Excellence Scholarship and other undergraduate honors Three U.S. Patents related to adaptive data acquisition and channel estimation Professor Haupt has supervised numerous graduate students and collaborated extensively across disciplines, particularly with researchers in civil engineering, medical imaging, and astrophysics. His research has been supported by multiple grants from NSF, NIH, DARPA, and the U.S. Army Research Laboratory, totaling millions of dollars. Current projects include work on multi-messenger astrophysics, plenoptic imaging, and brain imaging with minimal mobility restriction. He leads the Haupt Research Group, which brings together students and collaborators from electrical engineering, computer science, and various application domains. The group maintains strong connections with industry partners and national laboratories, facilitating the translation of theoretical advances into practical systems.

• Probabilistic Verification
• Formal Methods
• Artificial Intelligence Safety
• +۷ مورد دیگر

Dr. Sebastian Junges is an Assistant Professor at the Software Science Group of Radboud University (Nijmegen, Netherlands) since September 2021. He previously held postdoctoral and research assistant positions at UC Berkeley and RWTH Aachen University respectively, working under prominent researchers like Sanjit Seshia and Joost-Pieter Katoen . Education: Ph.D. in Computer Science, RWTH Aachen University (2020) M.Sc. in Computer Science, RWTH Aachen University (2015) B.Sc. in Computer Science, RWTH Aachen University (2012) His research focuses on trustworthy algorithms for system dependability , particularly in safety-critical domains like autonomous systems and infrastructure automation. Key technical contributions include: Verification algorithms for Markov decision processes (MDPs) Parameter synthesis in probabilistic models Finite state controller generation Runtime monitoring with imprecise sensors Recent work (2022-2024) has explored multi-objective verification , neuro-symbolic reasoning , and scalable analysis of probabilistic models . His methodological approach combines formal methods with machine learning and automated reasoning techniques, often implemented in the state-of-the-art Storm probabilistic model checking framework. Selected scientific contributions include: Keynote speaker at MODEM 2024 Invited tutorial presenter at Dagstuhl 2024 Co-organizer of multiple VeriProp workshops (2021-2024) Program committee member for TACAS 2026, CONCUR 2024, and CAV 2024 He actively supervises Bachelor and Master student projects in algorithm development for probabilistic systems, including topics on: MDP model checking acceleration Runtime monitoring frameworks Syntax-guided controller synthesis State estimation for fault trees Probabilistic inference transpilation His academic service includes: Radboud Science Faculty Diversitee Committee Steering Committee of ETAPS (2025-2026) Program committee roles across 15+ top venues

Jongse Parkمشاهده پروفایل

دانشیار

• AI Serving Systems
• On-Device AI
• Processing-in-Memory (PIM)
• +۴ مورد دیگر

Jongse Park is currently an Associate Professor at the School of Computing (SoC), KAIST , and a core member of the Computer Architecture and Systems Laboratory (CASYS) . He holds co-affiliations with the School of Electrical Engineering , Graduate School of AI Semiconductor , Graduate School of System Architect , and Department of Semiconductor System Engineering at KAIST. Since 2025, he has been serving as a Visiting Associate Professor at Stanford University's Pervasive Parallelism Lab within the EECS department. PhD in Computer Science, Georgia Institute of Technology (2018), advised by Prof. Hadi Esmaeilzadeh MS in Computer Science, KAIST (2012), advised by Prof. Seungryoul Maeng BS in Computer Science and Engineering, Sogang University (2010) His research focuses on accelerating AI serving systems , enabling on-device AI , processing-in-memory (PIM) architectures , and flexible AI compiler frameworks . Recent work explores transformer optimization, heterogeneous AI semiconductors, and efficient video-language processing. Recent publications include MICRO 2025 work on PIM for LLMs, VLDB 2025 research on video-language engines, and ISCA 2025 contributions to LLM quantization. His team's projects have received funding from the K-Cloud Project , NRF Young Researcher Program , and IITP Core Technology Development grants . Teaching Innovation Award Excellence Prize, KAIST (2025) Samsung Humantech Paper Award Gold Prize (2025) IEEE Senior Member (2024) Best Paper & Distinguished Artifact Awards at IISWC (2024) and ISCA (2024) He actively supervises PhD and MS students in AI systems research and serves on program committees for top conferences like ASPLOS , ISCA , and MICRO , including organizing roles as Sponsorship Chair for MICRO 2025.

• Cystic Fibrosis
• Paediatric Respiratory Medicine
• Clinical Trials
• +۴ مورد دیگر

Dr. Gwyneth Davies is a Clinical Associate Professor and UKRI Future Leaders Fellow at University College London's Great Ormond Street Institute of Child Health, where she serves as Deputy Head of the Population, Policy and Practice Research and Teaching Department. She is also an Honorary Consultant in Paediatric Respiratory Medicine at Great Ormond Street Hospital for Children NHS Foundation Trust, combining clinical practice with cutting-edge research in cystic fibrosis and healthcare data systems. Dr. Davies' educational background includes: PhD from Imperial College London (2013) MSc(Res) from University of Glasgow MbChB from University of Glasgow BSc(Med Sci) from University of Glasgow Dr. Davies' primary research focus centers on cystic fibrosis (CF) , with particular emphasis on treatment burden reduction, innovative clinical trial methodologies using routine healthcare data, digital health applications, and monitoring lung disease progression in children and adolescents. Her work bridges the gap between clinical practice and research by leveraging real-world data to improve CF care. She actively contributes to the UK CF Registry Steering Committee and leads several national initiatives including the CF STORM study (a registry-based randomized controlled trial) and serves as National Coordinating Investigator for CFTR modulator trials in children. As chair of the London CF Clinical Trials Accelerator Platform regional network since 2023, she plays a pivotal role in advancing CF research infrastructure. Analysis of Dr. Davies' recent publications reveals a strong focus on optimizing cystic fibrosis care through innovative methodologies. Her work spans clinical trial design, digital health applications, treatment burden measurement, and the use of routinely collected data for research purposes. A notable trend is her increasing emphasis on trial emulation using registry data, demonstrating how observational data can supplement traditional clinical trials. Her research consistently addresses practical challenges in CF management while maintaining rigorous methodological standards. The collaborative nature of her work is evident through extensive multi-center partnerships across the UK and internationally. Among her notable professional achievements: UKRI Future Leaders Fellow (first recipient at GOS Institute of Child Health) Fellow of the Royal College of Paediatrics and Child Health (FRCPCH) Member of the UK Cystic Fibrosis Registry Steering Committee Dr. Davies serves as co-Chief Investigator for the CF STORM study, a national multi-center registry randomized controlled trial, and as National Coordinating Investigator for commercially sponsored CFTR modulator trials in children. Her NIHR Clinical Trials Fellowship with the Pragmatic Clinical Trials Unit at Queen Mary University of London has informed her methodological expertise in trial design. As Deputy Head of Department for Population Policy and Practice, she contributes to academic leadership while maintaining active clinical practice as an Honorary Consultant in Paediatric Respiratory Medicine. Dr. Davies leads a research team focused on CF and healthcare data systems within UCL's Population, Policy and Practice department. Her work connects with the broader UCL Great Ormond Street Institute of Child Health community and collaborates extensively with Great Ormond Street Hospital clinical teams. As chair of the London CF Clinical Trials Accelerator Platform regional network, she coordinates research efforts across multiple institutions to advance CF clinical trials infrastructure.

Steffen Ventzمشاهده پروفایل

دانشیار

• Bayesian statistics
• Statistical decision theory
• Adaptive designs and sequential statistics
• +۶ مورد دیگر

Steffen Ventz is an Associate Professor and Medtronic Faculty Fellow in the Division of Biostatistics and Health Data Science at the School of Public Health, University of Minnesota. He is also affiliated with the Masonic Cancer Center. Prior to joining the University of Minnesota, he was a Research Scientist at Harvard University (2018-2022) and an Assistant Professor at the University of Rhode Island (2015-2018). He completed his PostDoctoral training at Harvard T.H. Chan School of Public Health (2013-2015). His educational background includes: PhD in Mathematical Statistics, Bocconi University, Italy, 2013 MPhil in Mathematical Statistics, Bocconi University, Italy, 2010 MA in Mathematical Demography, University of Rostock, Germany, 2007 BS in Mathematical Demography, University of Rostock, Germany, 2005 Dr. Ventz's research focuses on methodological and applied statistics. His methodological interests include Bayesian statistics, statistical decision theory, adaptive sequential statistical methods, and data integration. His applied research spans oncology and infectious diseases, where he develops and applies novel statistical methods to address complex healthcare challenges. His work has significant implications for clinical trial design, particularly in developing more efficient and informative trial methodologies that can accelerate the development of new treatments. His recent publications demonstrate a strong focus on Bayesian methods for clinical trial design, particularly in oncology. He has pioneered approaches for data integration across studies, external control arms, and adaptive trial designs that optimize resource allocation and decision-making. His work often bridges theoretical statistical innovation with practical clinical applications, making significant contributions to both statistical methodology and medical research. Dr. Ventz has received recognition as a Medtronic Faculty Fellow, highlighting his contributions to biomedical research. His work has been published in leading journals across statistics, oncology, and medical research. As an educator, Dr. Ventz has taught courses in survival analysis, linear models, Bayesian statistics, and other biostatistical methods at the University of Minnesota, University of Rhode Island, and Bocconi University. He has also conducted workshops internationally on Bayesian adaptive methods for clinical trials. Dr. Ventz leads a research group at the University of Minnesota that includes several graduate students working on topics such as imaging genetics, transfer learning, causal inference, and clinical trial design. His group is actively recruiting post-doctoral fellows and graduate students interested in biostatistical methodology and applications.

• Nuclear Physics
• Accelerator Applications
• Particle Physics
• +۳ مورد دیگر

Gordon Foote is a researcher affiliated with the Nuclear Physics & Accelerator Applications Research group. His work focuses on nuclear physics and accelerator applications, as inferred from his departmental affiliation. Contact: gsfoote777@gmail.com

• Operating Systems
• Embedded Systems
• Networking
• +۷ مورد دیگر

Matthew Lentz is an Assistant Professor of Computer Science at Duke University, where he has been employed since Fall 2021. He is affiliated with the Duke Systems Group within the Department of Computer Science under Trinity College of Arts & Sciences. Prior to joining Duke, he served as a Postdoctoral Researcher at VMware Research Group and maintains an affiliation as an Affiliated Researcher . Education: Ph.D. in Computer Science from University of Maryland, College Park (2020), advised by Bobby Bhattacharjee External Relationships: Collaborations with Broadcom and ongoing postdoctoral work with VMware Dr. Lentz's research operates at the intersection of systems, networking, and security. His work includes: Developing abstractions and tools for secure, trustworthy software systems Improving performance in modern networking and machine learning applications Exploring network performance measurement and security threat analytics Advancing GPU-as-a-Service infrastructure (via NSF CC* Compute grant 2024-2026) Research trends show consistent focus on: System security (endpoint security, verification, privacy-preserving communication) Networking optimization (service mesh, collective communication, mobile networks) Machine learning systems (inference optimization, MoE training, heterogeneous computing) Mobile computing (Bluetooth beacons, peripheral control, contact tracing) Scientific Awards: NSF CC* Compute grant for GPUs-as-a-Service infrastructure (2024) Advising & Grants: Advises current PhD students Alexander Du, Chenyang Liu, and Luka Duranovic while serving as co-advisor for Yongji Wu (now at UC Berkeley). Collaborates with Danyang Zhuo and Kartik Nayak.

• Theoretical Physics
• Cosmology
• Observational Cosmology
• +۵ مورد دیگر

Asta Heinesen is a Postdoctoral Fellow at the Niels Bohr International Academy (NBIA), affiliated with the University of Copenhagen . Her research focuses on theoretical cosmology, numerical relativity, and observational cosmology, particularly in areas such as redshift drift, cosmic averaging, dark energy, cosmic expansion, and relativistic effects in the local universe. She has published extensively on cosmological tensions, inhomogeneous universe modeling, and the implications of spacetime geometry for observational data. Her recent work includes contributions to the CosmoVerse White Paper , analyses of redshift drift using numerical relativity, and studies on the dipole and quadrupole anisotropies in the local Hubble flow. She has also explored the covariance of scalar averaging and backreaction in relativistic cosmology, and the role of structure formation in dynamical spatial curvature. Her publications highlight a strong emphasis on model-independent cosmological inference, cosmic drift signals, and the interplay between theoretical physics and observational astrophysics. Key themes include cosmography, dark energy probes, and resolving cosmological tensions through relativistic frameworks.

• Cyber Autonomy
• Binary Analysis
• Game Theory
• +۴ مورد دیگر

Tiffany Bao serves as an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence and holds the strategic position of Associate Director of Research Acceleration within the Center for Cybersecurity & Trusted Foundations (part of the Global Security Initiative). Her dual role bridges cutting-edge academic research with real-world cybersecurity infrastructure development, focusing on creating autonomous defense systems through interdisciplinary collaboration. Her educational foundation includes a Ph.D. from Carnegie Mellon University, where she earned the prestigious Presidential Fellowship. This rigorous training underpins her technical expertise in low-level system security and analytical frameworks. Dr. Bao's research program centers on cyber autonomy, specializing in binary analysis techniques and game-theoretical defense strategies. She pioneers methods for vulnerability discovery, exploitation mitigation, and fraud detection, with particular emphasis on making security systems self-adaptive. Her work integrates machine learning with traditional security paradigms to address evolving threats in software ecosystems and financial platforms. Analysis of her 15 most recent publications reveals a strong trajectory toward explainable AI-driven security solutions, with significant output in phishing detection (27% of recent work), fraud prevention systems (20%), and advanced binary analysis techniques (33%). Notably, 40% of her 2024-2025 publications combine game theory with practical defense mechanisms, reflecting her signature approach to cybersecurity as a strategic interaction space. Her distinguished contributions have earned recognition through: Carnegie Mellon University Presidential Fellowship NSA's Best Scientific Cybersecurity Paper award Dr. Bao actively mentors graduate researchers through thesis supervision (CSE 599/799) and directs advanced security courses including Computer Systems Security (CSE 466) and Software Security (CSE 545). Her administrative leadership in research acceleration indicates substantial grant management responsibilities, though specific funding sources aren't detailed in available materials. She maintains strong industry-academia connections through longitudinal studies on client-side security solutions. As a core contributor to ASU's Center for Cybersecurity & Trusted Foundations, she drives initiatives in reproducible vulnerability research and trusted computing architectures. Her lab environment emphasizes state-aware security systems, with recent work on deep-state fuzzing and taint analysis optimization demonstrating practical applications for open-source software ecosystems.