جست‌وجوی استاد و پژوهشگر

Haoran Qiuمشاهده پروفایل

پژوهشگر

• AI/ML Systems
• ML for Systems/AIOps
• Distributed Systems
• +۳ مورد دیگر

Haoran Qiu is a Systems Researcher at Microsoft Azure Research – Systems (AzRS), focusing on AI-driven cloud systems efficiency. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), advised by Prof. Ravishankar K. Iyer, and a B.Eng. in Computer Science from the University of Hong Kong (HKU). His research interests span AI/ML systems, cloud computing, distributed systems, and sustainability. He has interned at Google, Microsoft Research, and IBM Research. Notable awards include the 2023 ML Systems Rising Stars by MLCommons and the Mavis Future Faculty Fellowship. Education: Ph.D., Computer Science, UIUC (2024) B.Eng., Computer Science, HKU (2019) Visiting Student, University of Wisconsin-Madison (2018) Research Interests: Designing learning-based solutions for cloud systems Energy-efficient LLM serving and resource management Resilience and sustainability in hyperscale datacenters AIOps and ML-driven systems management Recent Contributions Include: Advances in thermal-aware scheduling for LLMs (TAPAS) Green computing strategies balancing SLOs and carbon emissions Queue management for large language models (QLM) Awards: Best Paper Finalist (L4DC 2024) Best Presentation Award (COMPSYS 2022) Multiple Fellowships and Scholarships Labs/Teams: Active in Microsoft Azure Research, contributing to cutting-edge ML-centric cloud systems projects. Collaborates widely with industry (Google, IBM) and academic institutions.

Katia Obraczkaمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• IoT
• +۳ مورد دیگر

Katia Obraczka is a Professor of Computer Science and Engineering at UC Santa Cruz and the director of the Center for Information Technology Research in the Interest of Society (CITRIS) and the Banatao Institute. She leads interdisciplinary research focused on societal and economic impacts of IT, particularly in IoT, edge computing, and cyber-physical systems. Her work emphasizes technology for social good, including environmental monitoring and healthcare applications. Education: B.S. and M.S. in Electrical and Computer Engineering from the Federal University of Rio de Janeiro M.S. and Ph.D. in Computer Science from the University of Southern California Research Interests: IoT and Edge Computing – Developing energy-efficient, decentralized systems for smart agriculture and healthcare monitoring. Cyber-Physical Systems – Designing resilient networks for autonomous vehicles and industrial control. Machine Learning – Applying ML to improve network performance and user mobility modeling. Healthcare Technology – Innovations in pressure injury prevention and real-time patient monitoring. Articles Trends: Recent publications emphasize IoT multipath routing, edge intelligence architectures, and energy-harvesting smart systems. Over 80% of her 2023-2025 work addresses healthcare and environmental applications, with notable contributions to autonomous vehicle platooning and QoS-aware quantum networks. Labs & Initiatives: Directs CITRIS UCSC programs like the CITRIS Initiative for Drone Education and Research and oversees the Internet Research Group , which explores edge computing and network security. Grants & Collaborations: Active in cross-campus and industry partnerships, including drone-based wildfire monitoring and solar-powered IoT deployments with UC Berkeley and Merced.

• Cybersecurity
• Artificial Intelligence
• Computer Networks
• +۷ مورد دیگر

Pere Barlet-Ros is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Barcelona Neural Networking Center (BNN-UPC). He leads research in AI-driven network technologies and is a key member of the UPC CBA and IDEAI-UPC research groups. Ph.D. in Computer Science, UPC (2008) M.Sc. in Computer Science, UPC (2003) His research focuses on applying artificial intelligence, particularly graph neural networks, to solve critical challenges in network monitoring, traffic classification, congestion control, and cybersecurity. His work bridges theoretical innovation with practical deployment, as evidenced by integration into commercial tools like Auvik TrafficInsights and Talaia Polygraph. He explores digital twins for network optimization, privacy-preserving technologies, and next-generation web tracking detection. The recent publications highlight a strong trend toward leveraging graph-based machine learning for network modeling, anomaly detection, and traffic compression. His work spans top-tier journals like IEEE/ACM Transactions on Networking and conferences such as IEEE EuroS&P and ACM SIGCOMM workshops, emphasizing robust, real-world AI/ML applications in networking. 2nd VALORTEC prize for best business plan (2014) Fiber Entrepreneurs award, best entrepreneur at FIB (2015) Pere Barlet-Ros has served as Principal Investigator on seven competitive research projects and has advised at least 14 PhD students, including Paul Almasan, Miquel Ferriol, and Ismael Castell. His lab, BNN-UPC, fosters interdisciplinary collaboration in neural networking and AI for infrastructure. His entrepreneurial experience with Talaia Networks underscores a commitment to translating research into impactful technology.

Sara Aloufمشاهده پروفایل

پژوهشگر

• Stochastic Modeling
• Communication Networks
• Caching Systems
• +۱۱ مورد دیگر

Sara Alouf is an Associate Researcher and Vice-Head of the Network Engineering and Operations (NEO) project team at Inria Sophia Antipolis Méditerranée. She holds a Habilitation from Université Côte d'Azur (2017), a Ph.D. in Network Sciences (2002), and an M.Sc. (1999) from Université Nice Sophia Antipolis, along with an Electrical and Electronics Engineering Degree from Lebanese University (1998). Her research focuses on stochastic modeling , caching systems , and performance evaluation of communication networks , with past work in green networking , peer-to-peer storage , and delay-tolerant networks . Education: Habilitation (2017) - Université Côte d'Azur Ph.D. (2002), M.Sc. (1999) - Université Nice Sophia Antipolis Electrical Engineering (1998) - Lebanese University Her recent work involves no-regret caching algorithms , stochastic solar energy models , and count-min sketch analysis , while earlier research addressed power saving in cellular networks and WiMAX sleep mode optimization . Articles span topics like multi-level cache hierarchies , peer-to-peer storage reliability , and evolutionary network protocols . She has co-authored three patents related to congestion avoidance and RTT estimation . Scientific awards include the Best Paper Award at IFIP Wireless Days 2009 . Professional service highlights: Board of Directors - ACM SIGMETRICS (2019-2023) Editorial roles at IEEE/ACM Transactions on Networking and Elsevier journals Conference organization for ACM Sigmetrics/IFIP Performance (2016, 2024) and ITC (2015, 2023) She has taught Performance Evaluation of Networks at Université Nice Sophia Antipolis, Probabilities and Statistics (with Giovanni Neglia), and Optimization of Business Processes at Vrije Universiteit Amsterdam. Her lab (NEO) investigates network stochastic models , green IT solutions , and train control communication systems .

Edouard Bugnionمشاهده پروفایل

استاد

• Operating Systems
• Datacenter Infrastructure
• Systems Networking
• +۲ مورد دیگر

Edouard Bugnion is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he joined in 2012. His research focuses on datacenter systems through the Data Center Systems Lab (DCSL). Professor Bugnion's research interests span Operating Systems , Datacenter Infrastructure , Systems Networking , and Computer Architecture . His work aims to combine low latency, high throughput, and efficiency in protected dataplane systems, with a particular focus on virtualized datacenters and operating system design. His publication record shows a consistent focus on operating systems and virtualization technologies, with significant contributions spanning from the late 1990s to the present. His work demonstrates a progression from foundational virtualization research to practical datacenter systems engineering, particularly in optimizing networking performance while maintaining kernel protection benefits. Professor Bugnion's scientific achievements have been recognized with several prestigious awards: ACM Software System Award for VMware 1.0 (2009) Best Paper Award at SOSP '97 for "Disco: Running Commodity Operating Systems on Scalable Multiprocessors" ACM SIGOPS Hall of Fame Award (2008) OSDI 2014 Best Paper Award for work on the IX dataplane operating system Before joining EPFL, Professor Bugnion co-founded two successful startups: VMware and Nuova Systems (acquired by Cisco). At VMware (1998-2005), he served in various roles including CTO. At Nuova/Cisco (2005-2011), he helped build the core engineering team and became VP/CTO of Cisco's Server, Access, and Virtualization Technology Group, which developed Cisco's Unified Computing System (UCS) platform for virtualized datacenters. Professor Bugnion leads research in the Data Center Systems Lab (DCSL) at EPFL, which focuses on next-generation datacenter technologies. His lab maintains a strong collaboration with Stanford University, as evidenced by the joint development of the IX Operating System, demonstrating his continued engagement with top academic institutions and industry partners in advancing datacenter infrastructure.

• High Performance Computing
• Distributed Systems
• Cloud Computing
• +۲ مورد دیگر

Zhuozhao Li is an Assistant Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in August 2021 after serving as a Postdoctoral Scholar at the University of Chicago from July 2018 to July 2021. His research focuses on high-performance computing, distributed systems, and cloud/edge computing, with applications across scientific domains. Ph.D. in Computer Science, University of Virginia, May 2018 M.S. in Computer Science, University of Southern California, May 2012 B.E. in Computer Science, Zhejiang University, July 2010 Dr. Li's research spans several critical areas in modern computing infrastructure. His primary focus is on High Performance Computing , where he develops novel approaches to optimize computational efficiency in scientific applications. In Distributed Systems , his work addresses challenges in large-scale system coordination and resource management. His contributions to Cloud/Edge Computing focus on improving virtualization techniques and resource allocation strategies. Additionally, he explores applications in the Internet of Things , examining how distributed computing paradigms can enhance IoT infrastructure. Dr. Li's publication record demonstrates a consistent focus on advancing distributed computing systems and their scientific applications. His recent work shows a clear progression from foundational research on data-parallel frameworks and job scheduling toward more applied systems for scientific computing. His contributions to projects like funcX and DLHub represent significant advancements in making distributed computing more accessible for scientific workflows. A notable trend is the increasing interdisciplinary nature of his work, with applications spanning from computational biology (SARS-CoV-2 research) to privacy-preserving spatial crowdsourcing. ACM HPDC Best Paper Nominees, 2019 IEEEMASS Service Award, 2019 Outstanding Graduate Research Assistant, University of Virginia, 2018 Dr. Li actively mentors students and researchers, currently recruiting PhD students, Master's students, postdocs, and research assistants for his research group at SUSTech. His research has been supported through various collaborative projects, including significant contributions to the U.S. Department of Energy National Virtual Biotechnology Laboratory Project about COVID-19, which was awarded the Secretary of Energy Achievement Award. His work on funcX, DLHub, and Parsl represents major software infrastructure development efforts with broad scientific impact. Dr. Li is affiliated with multiple research initiatives, including the Globus Lab (during his postdoc at University of Chicago) and various collaborative projects involving high-performance computing resources. At SUSTech, he leads a research group focused on distributed systems and high-performance computing. His work often involves collaboration with researchers across institutions, particularly in projects related to scientific computing infrastructure like funcX and DLHub.

• Computer networks and communications
• Cybersecurity
• Industry 4.0
• +۷ مورد دیگر

Guido Marchetto is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino , where he conducts cutting-edge research in computer networks, cybersecurity, machine learning, and Industry 4.0. He is a member of the NETGROUP - Computer Networks Group , the SmartData@PoliTO interdepartmental center, and leads research at ACS LAB and LABINF . He serves as Deputy Coordinator of the Doctoral College in Computer and Systems Engineering and is the Scientific Advisor for the European Technology Platform for High Performance Computing (ETP4HPC). PhD Students: Federico Rinaudi, Doriana Monaco, Antonino Angi Research Groups: NETGROUP, SmartData@PoliTO Labs: ACS LAB, LABINF His research focuses on intelligent network management, explainable AI for networking, reinforcement learning, federated learning, and softwarized networks. He has led multiple EU and commercial research projects such as MIRANDA and DESIRE , with applications in cybersecurity, edge computing, and digital twins. Marchetto’s recent publications reveal a strong trend toward integrating AI/ML techniques—especially reinforcement learning and language models—into network automation, traffic engineering, and IoT systems. His work bridges theoretical innovation with practical implementation in cloud, edge, and telco environments. Scientific Awards: Best Paper Award Finalist, IEEE HPSR (2014) Best Paper Award, IEEE ICC (2007) He is a Senior Member of IEEE and serves on the editorial board of IEEE Transactions on Vehicular Technology . He has also been a Contract Professor at the Turin Polytechnic University in Tashkent from 2012 to 2022. His extensive grant leadership includes EU Horizon projects and numerous commercial research contracts with a focus on network intelligence and automation. He is actively involved in doctoral education and curriculum development, contributing to multiple degree programs and supervising ongoing PhD research in AI-driven networking and cybersecurity.

• Computer Networking
• Networked Systems
• Datacenter Networks
• +۵ مورد دیگر

Vishal Shrivastav is an Assistant Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. He is affiliated with Max W and Maileen Brown Family Hall in West Lafayette, Indiana. His research focuses on Computer Networking and Networked Systems , with a primary emphasis on Computer Engineering . His work spans areas including programmable switches, datacenter networks, and privacy-preserving reconfigurable systems. Shrivastav's recent research trends highlight advancements in ultra-low latency networking fabrics, scalable oblivious reconfiguration mechanisms, and machine learning-driven traffic analysis. His projects often involve hardware-software co-design for high-performance networked systems. He maintains an active research webpage at https://engineering.purdue.edu/~vshriva/ and collaborates with industry on real-time IoT analytics and next-generation network platforms. His office is located in BHEE 334B at the West Lafayette campus.

Robert René Maria Birkeمشاهده پروفایل

استادیار

• Virtual Resource Management
• Network Design
• Workload Characterization
• +۶ مورد دیگر

Robert René Maria Birke is a tenured assistant professor in the Department of Computer Science at the University of Turin, leading research in the Parallel Computing group. His expertise spans virtual resource management, network design, workload characterization, and optimization of AI/big-data applications. Previously, he served as a visiting researcher at IBM Research Zurich and Principal Scientist at ABB Corporate Research, combining industry experience with academic rigor since earning his Ph.D. from Politecnico di Torino in 2009. His educational background includes: Ph.D. in Electronics and Communications Engineering, Politecnico di Torino (2009) Dr. Birke's research centers on systems-level challenges in distributed AI, with current projects investigating federated learning architectures, confidential computing via Trusted Execution Environments, and RISC-V processor optimizations for decentralized machine learning. His work bridges theoretical foundations with practical deployments, particularly in edge computing scenarios and high-performance data synthesis applications. Recent publications reveal growing emphasis on securing generative models against forgery attacks and optimizing tabular data synthesis techniques. Analysis of his 15 most recent publications (2024-2026) shows dominant themes in confidential federated learning (33% of works), RISC-V system optimizations (27%), and generative model security/synthesis (40%). This output spans premier venues including IEEE Transactions, ACM Computing Surveys, and SIGCOMM-affiliated conferences, demonstrating consistent contributions to systems-AI intersection research. Professional recognition includes: IEEE Senior Member While the text confirms extensive collaboration through co-authorships (notably with Marco Aldinucci, Lydia Chen, and Giulio Malenza), no specific student advisees or grant details are provided. His work appears embedded within European initiatives like ICS and EUPilot projects, focusing on compute continuum challenges. Dr. Birke actively contributes to the Parallel Computing group's mission through projects including HPC4AI@UNITO (datacenter digital twins) and Cross-Facility Federated Learning frameworks. His research ecosystem involves multi-institutional teams across Italy, Switzerland, and the EU, with recent talks addressing FLaaS implementations and generative model impacts on system design.

Mohammad Alizadehمشاهده پروفایل

دانشیار

• Computer Networks
• Systems
• Machine Learning
• +۶ مورد دیگر

Mohammad Alizadeh is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds additional roles as Director of the 6-A MEng Thesis Program and Industry Officer for EECS, overseeing the EECS Alliance industry outreach program. His research focuses on computer networks, systems, and their applications in datacenters, cloud computing, and edge computing. Notable areas include machine learning for systems, congestion control, and network protocols. Education: PhD (Stanford University), MS in Electrical Engineering (Stanford), BS (Sharif University of Technology). Industry Experience: Former roles at Microsoft Research, Insieme Networks, and Cisco Systems. Research Interests: Alizadeh’s work emphasizes improving network performance, robustness, and management in large-scale systems. Key projects include DCTCP, CONGA, and Pensieve (adaptive video streaming with reinforcement learning). His contributions span network architecture, programmable switches, and learning-based systems. Awards: NSF CAREER Award Alfred P. Sloan Research Fellowship SIGCOMM Rising Star Award Microsoft Research Faculty Fellowship Teaching: Courses include 6.888 (Advanced Topics in Networking), 6.829 (Computer Networks), and 6.02 (Digital Communication Systems). His teaching emphasizes cutting-edge networking research and practical system design. Labs/Teams: Active member of CSAIL, leading projects in distributed systems, network protocols, and cloud computing.

Yannis Viniotisمشاهده پروفایل

استاد

• Networking
• Service Engineering
• High Speed Networks
• +۷ مورد دیگر

Yannis Viniotis is a Professor in the Department of Electrical and Computer Engineering at NC State University. His research focuses on networking, service engineering, and high-speed network technologies. He has authored textbooks on probability theory and mathematical principles for engineers, and co-founded Orologic, a semiconductor company acquired by Vitesse in 2000. Education: Ph.D. in Electrical Engineering, University of Maryland (1988) M.S. in Electrical Engineering, University of Maryland (1985) B.S. in Electrical Engineering, University of Patras, Greece (1981) Research Interests: Dr. Viniotis specializes in Quality of Service (QoS), transport protocols, ASIC implementations, and network algorithm analysis. His work bridges theoretical foundations with practical applications in cloud computing, IoT systems, and vehicular networks. Awards: 2009 Best Paper Award (ICSOFT) Multiple Outstanding Instructor Awards (1989–1995) 1992 Best Paper Award (Institute of Management Sciences) 1985 George Corcoran Graduate Assistant Award Advising & Industry: Co-founded Orologic, where he designed high-speed network chips. His recent work focuses on cloud resource allocation and IoT security. He has advised multiple senior design projects, including work on TCP Live Play extended by Yazan Siam (2012). Labs/Teams: Active in NC State’s networking and cloud computing research groups, contributing to both academic and industry-driven projects.

Stavros Kalafatisمشاهده پروفایل

استاد

• Datacenter System Optimization
• SDN/SDS/SDI Technologies
• Robotics in Manufacturing
• +۴ مورد دیگر

Stavros Kalafatis is an Associate Department Head and Professor of Practice in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds an M.S.E.E. from the University of Arizona (1991) and a B.S.E.E. from the University of Surrey (1989). His research focuses on datacenter system optimization, SDI/SDN/SDS improvements for server efficiency, robotics in manufacturing, and sensor systems for agricultural applications. He has received multiple awards, including the Intel Achievement Award (2005) and repeated Intel Divisional Recognition Awards (1993, 2000–2010). His work spans cutting-edge technologies like precision agriculture robotics, network performance optimization, and machine learning-driven systems for hydroponics and wildfire prediction. Recent publications emphasize interdisciplinary solutions in agriculture, networking, and environmental modeling. Kalafatis is affiliated with the Computer Engineering and Systems Group at Texas A&M and contributes to advancing practical applications of engineering and computing. Education: M.S.E.E., University of Arizona, 1991 B.S.E.E., University of Surrey, 1989 Awards: Intel Achievement Award (Lynnfield Team), 2005 Intel Divisional Recognition Award (multiple years) His research trends highlight innovations in smart systems, including digital twins for manufacturing, AI-driven crop monitoring, and network optimization for data centers. While no grants or advising records are explicitly listed, his publications reflect active collaboration with industry and academic partners.

Jianer Chenمشاهده پروفایل

استاد

• Algorithms and complexity
• Computer graphics
• Bioinformatics
• +۶ مورد دیگر

Jianer Chen is a Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. He holds multiple academic positions and has contributed significantly to research in algorithms, computer graphics, bioinformatics, and computer networks. Educational Background: Ph.D. Mathematics, Columbia University, 1990 Ph.D. Computer Science, New York University, 1987 M.Phil. Mathematics, Columbia University, 1990 M.A. Mathematics, Columbia University, 1989 M.S. Computer Science, New York University, 1984 B.S. Computer Science, CSUT, P.R. China, 1982 Research Interests: His work focuses on Algorithms and Complexity , advancing computational efficiency and theoretical foundations. He explores Computer Graphics , including geometric modeling and Bézier patch subdivision. In Bioinformatics , he develops algorithms for phylogenetic trees and genetic data analysis. His Computer Networks research addresses data center optimization and sensor network reliability. Publications Trends: Recent work emphasizes parameterized algorithms, kernelization techniques, and their applications to big data, graph theory, and geometric problems. He also contributes to optimization in scheduling and network protocols, reflecting interdisciplinary engagement with both theoretical and applied computer science. Awards & Honors: AFS Distinguished Faculty Achievement Award (university level), 2007 E.D. Brockett Professorship Award, 2005 Janet Fabri Award (Best Ph.D. Dissertation), 1988 Multiple recognitions for teaching and research excellence Advising & Grants: Chen has secured grants such as the NSF Research Initiation Award (1991) and Amoco Faculty Award (1998). Though no advisees are listed, his work spans mentoring doctoral and master’s students in algorithm design and computational theory. His research initiatives often address practical challenges in high-performance computing and network reliability. Labs/Teams: No specific labs or teams are explicitly mentioned in the provided texts. However, his collaborations suggest involvement in interdisciplinary groups focused on bioinformatics, data center networks, and geometric modeling.

Kevin Xueمشاهده پروفایل

پژوهشگر

• Computer Systems
• Resource Management
• Virtualization
• +۴ مورد دیگر

Kevin Xue is a Ph.D. student at the Computer Science and Engineering Division of the University of Michigan , advised by Professors Mosharaf Chowdhury and Ryan Huang . He focuses on computer systems , particularly efficient and fair application scheduling on personal devices . Education: Master's from Carnegie Mellon University , joint Bachelor's from Columbia University and City University of Hong Kong His research spans systems , virtualization , and real-time computing , with publications on topics like Arm-based confidential VMs , memory contiguity in datacenters , and video conferencing optimization . His work at ISCA '23 earned the Best Paper Award and IEEE Micro Top Pick Selection . Kevin is an active contributor to open-source projects, with 294 contributions in the last year. His GitHub profile highlights collaborations with institutions like Apple , Databricks , and Meta . He maintains connections with peers at Columbia University , Stanford , and Carnegie Mellon University , and his ORCID is 0009-0002-5311-2826 .

Sagar Karandikarمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware/Software Co-Design
• Hyperscale Cloud Data Centers
• +۵ مورد دیگر

Sagar Karandikar is an Assistant Professor in the Electrical Engineering and Computer Sciences (EECS) department at UC Berkeley and the Jean and Hing Wong Foundation Faculty Fellow. He is also a Visiting Faculty Researcher at Google. His research focuses on hardware/software co-design for hyperscale cloud data centers powering critical applications including AI/ML, scalable data processing, and web services. His educational background includes: Ph.D. in Computer Science from UC Berkeley (2024) M.S. in Computer Science from UC Berkeley (2018) B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2015) Dr. Karandikar's research spans hardware accelerator and server system-on-chip design, full-stack system optimization, and agile, open-source hardware development methodologies. His work addresses system-level inefficiencies in warehouse-scale computers through data-driven hardware/software co-design. His group is part of the SpeciaLIzed Computing Ecosystems (SLICE) Lab at Berkeley. His publications reveal a consistent focus on improving hyperscale datacenter performance through hardware acceleration and system optimization. Key themes include FPGA-accelerated simulation (FireSim), hardware accelerators for specific workloads (like protocol buffers and compression), and open-source frameworks for agile hardware development (Chipyard). His work bridges the gap between theoretical architecture research and practical deployment in large-scale datacenters. Dr. Karandikar has received numerous prestigious awards for his research: 2025 ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award ISCA@50 25-year Retrospective selection IEEE Micro Top Picks selection and honorable mention MICRO and ISCA Distinguished Artifact Awards David J. Sakrison Memorial Prize for outstanding graduate research UC Berkeley Outstanding Graduate Student Instructor Award DARPA Riser selection Dr. Karandikar actively mentors students and leads research projects including FireSim (used in over 70 peer-reviewed publications from institutions worldwide), Hyperscale SoC, and Chipyard. His work has been adopted by both academic institutions and industry for developing commercially available chips and as standard platforms for DARPA/IARPA programs. He is accepting new students for his research group. His research group is part of the SpeciaLIzed Computing Ecosystems (SLICE) Lab at Berkeley, focusing on hardware/software co-design for hyperscale cloud systems. The group develops frameworks like FireSim for FPGA-accelerated cycle-accurate simulation and Chipyard for agile RISC-V SoC design, enabling rapid prototyping and evaluation of specialized computing systems.