Vittorio Solina is a Researcher at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria, specializing in industrial systems simulation and digital twin technologies. His work spans Industry 4.0/5.0 applications, supply chain optimization, and human-robot collaboration. Affiliation: University of Calabria Academic Role: Researcher (Legge 240/10) Research Focus: Digital twins, simulation modeling, sustainable manufacturing His research emphasizes advanced simulation tools for decision support, including applications in Industry 4.0 , Smart Manufacturing , and Agri-Food Supply Chains . Recent work explores adaptive simulation optimization for Industry 5.0 and 5G-enabled robotics. Key publication trends include: (1) Digital twin integration for supply chain resilience (2024-2025), (2) Simulation automation methodologies (2025), and (3) Human-robot collaboration frameworks (2024).
Andrea Arpaci-Dusseau is the Susan Beth Horwitz WARF Professor of Computer Sciences and Catherine A. Erickson Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. She has been a professor at UW-Madison since January 2000 and currently serves as Graduate Advising Chair for the Computer Sciences Department. Her research focuses on computer systems with primary emphasis on file and storage systems, but also making significant contributions in distributed systems, virtualization, and scheduling. According to csrankings.org, she has published the fourth-most papers in premier systems conferences (SOSP and OSDI) and the most at the top file and storage conference (FAST). Professor Arpaci-Dusseau's recent publications demonstrate continued leadership in storage systems research, with increasing focus on cloud architectures, persistent memory technologies, and system reliability. Her work bridges theoretical foundations with practical implementations, consistently addressing real-world challenges in modern computing environments. ACM SIGOPS Mark Weiser Award 2018 (highest honor in systems field) UW-Madison Van Hise Outreach Teaching Award 2017 Multiple Best Paper awards at FAST, SOSP, OSDI, and EuroSys conferences Carolyn Rosner Award for Excellence in Teaching (2010, 2017) Co-chaired major conferences including USENIX ATC '04, FAST '07, OSDI'18, and SOSP'24 Professor Arpaci-Dusseau has co-advised 28 Ph.D. students and is deeply committed to educational outreach through the CaTaPuLT project, which connects UW-Madison students with K-12 schools to teach computational thinking using Scratch. This initiative has reached hundreds of students across Madison and earned her significant recognition for teaching excellence. She leads the Arpaci-Dusseau Systems Lab (ADSL) and is involved with the Wisconsin Institute on Software-defined Datacenters in Madison (WISDoM), where her research group continues to advance state-of-the-art solutions in computer systems.
Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.
Dr. Chengmo Yang is a Professor in the Department of Electrical and Computer Engineering at the University of Delaware, with a joint appointment. She received her PhD and MSc in Computer Science from the University of California, San Diego, and a BSc in Microelectronics from Peking University, China. PhD, University of California, San Diego MSc, University of California, San Diego BSc, Peking University, China Her research focuses on enhancing security, reliability, non-volatility, and energy efficiency in embedded systems, cyber-physical systems (CPS), and Internet-of-Things (IoT). She leads the Computer Architecture, Reliability, and Security (CARES) group and has authored over 70 peer-reviewed publications. Recent publications emphasize neural network accelerators, non-volatile memory (NVM) optimization, and security frameworks for embedded systems. Her work explores fault tolerance, wear leveling, and side-channel attack mitigation in NVM-based FPGAs, STT-RAM, and SSDs. National Science Foundation Career Award (2013) University of Delaware Mid-Career Faculty Excellence in Scholarship Award (2020) Best paper award at ICESS (2019) Three best paper nominations at DAC, DATE, and GLSVLSI (2019) Dr. Yang serves as Associate Editor for IEEE TCAD and ACM TODAES. She is the Diversity & Inclusion Representative for IEEE CEDA and has organized numerous conferences including Technical Program Chair for CODES-ISSS 2022. Her lab has hosted current and former students like Fanruo Meng (PhD candidate), Fateme Hosseini (Qualcomm), and Patrick Cronin (Intel), focusing on graduate-level research in hardware security and reliability.
Kaiming He is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), holding the Douglas Ross (1954) Career Development Professor of Software Technology chair. He also works part-time as a Distinguished Scientist at Google DeepMind. Prior to joining MIT in 2024, he was a research scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. Dr. He received his PhD from the Chinese University of Hong Kong in 2011 and his Bachelor of Science from Tsinghua University in 2007. His academic journey reflects a strong foundation in computer science and engineering that has led to transformative contributions in artificial intelligence. His research primarily focuses on computer vision and deep learning, with pioneering work on deep residual networks (ResNets), visual object detection and segmentation, and self-supervised learning. He is best known for his work on Deep Residual Networks (ResNets), recognized as the most-cited paper of the twenty-first century. The residual connections he pioneered are now fundamental components in modern deep learning architectures including Transformers, AlphaGo Zero, AlphaFold, and various generative AI models. His recent publications demonstrate continued innovation across generative models, transformer architectures, and cross-disciplinary AI applications. His work bridges theoretical advances in neural network design with practical implementations that address real-world challenges in physics, biology, and other scientific domains. PAMI Young Researcher Award (2018) Best Paper Award, CVPR (2009, 2016) Best Paper Award, ICCV (2017) Best Student Paper Award, ICCV (2017) Everingham Prize, ICCV (2021) Most-cited paper of the twenty-first century Dr. He advises graduate students including Jake Austin, Xingjian Bai, and Mingyang Deng, and teaches advanced courses such as "6.S978: Deep Generative Models" (Fall 2024) and "6.8300/6.8301: Advances in Computer Vision" (Spring 2024). His research group actively explores how AI can serve as a unifying framework across scientific disciplines, breaking down traditional barriers between fields through shared methodologies and tools.
Shivaram Kalyanakrishnan is an Associate Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay , specialising in Artificial Intelligence and Machine Learning . His research spans sequential decision making , multiagent learning , multi-armed bandits , and humanoid robotics , with applications in robot soccer , computer games , and online advertising . He teaches advanced courses like CS 747: Foundations of Intelligent and Learning Agents and CS 748: Advances in Intelligent and Learning Agents , focusing on end-to-end system design and theoretical analysis. His scientific awards include the Best Student Paper Award at RoboCup International Symposium 2006 and nomination for Best Student Paper Award at AAMAS 2007 . His work on reinforcement learning and policy iteration has been published in leading venues such as IJCAI , ICML , and COLT , with recent contributions to railway scheduling and bandit algorithms. While no explicit list of advisees is provided, his research projects and publications suggest mentorship of students in collaborative efforts. Contact : shivaram@cse.iitb.ac.in .
Prof. Dr. Thomas Hofmann is a Full Professor and Head of the Department of Computer Science at ETH Zurich since 2014. He also leads the Institute for Machine Learning. His research focuses on machine learning, deep learning, natural language understanding, and text understanding. Hofmann holds a Ph.D. from the University of Bonn (1997) and has held academic positions at Brown University (1999–2004) and TU Darmstadt. He transitioned to industry as Director of Engineering at Google (2006–2014), leading the Zurich R&D center, before returning to academia. He co-founded Recommind (2000) and 1plusX (Swiss marketing tech company), currently serving as Chief Scientist and board member at 1plusX. Education: Ph.D. in Computer Science, University of Bonn (1997) Postdoctoral Work: MIT (CBCL/AI Lab), UC Berkeley (EECS/ICSI) His research explores advanced machine learning techniques, including diffusion models, generative adversarial networks, and optimization dynamics. Hofmann’s entrepreneurial ventures reflect his focus on applying AI to real-world challenges, such as e-discovery and marketing technology. His work spans theoretical contributions (e.g., neural network training dynamics, continual learning) and applied innovations (e.g., image editing, portrait generation). Hofmann actively bridges academia and industry, influencing both research and commercial AI applications.
Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.
David B. Shmoys is the Laibe/Acheson Professor of Business Management & Leadership Studies at Cornell University's School of Operations Research and Information Engineering (ORIE) and holds a joint appointment in the Department of Computer Science. He is a leading researcher in approximation algorithms, stochastic optimization, and computational sustainability. His work emphasizes data-driven decision-making in enterprise and society, with applications ranging from healthcare logistics to environmental conservation. Shmoys has advised 27 Ph.D. students and served as an editorial board member for top journals like Operations Research and Mathematics of Operations Research. Education: Ph.D. in Computer Science, University of California, Berkeley (1984). Postdoctoral positions at MSRI and Harvard University, followed by faculty roles at MIT before joining Cornell. Research Interests: Design and analysis of approximation algorithms for NP-hard problems, stochastic optimization, scheduling, facility location, and inventory management. Applications include computational sustainability, epidemiological modeling, and political districting optimization. Articles Trends: Focus on optimization under uncertainty, computational sustainability, and real-world applications like air ambulance routing, bike-sharing systems, and gerrymandering analysis. Notable works include the 2018 Wagner Prize-winning paper on bike-sharing analytics and the 2013 Lanchester Prize-winning textbook on approximation algorithms. Awards: Lanchester Prize (2013), Daniel H. Wagner Prize (2018), Fellowships from ACM, INFORMS, and SIAM, NSF Presidential Young Investigator (1987). Advising & Grants: Over 27 advised Ph.D. students, with former students at MIT, Waterloo, Brown, and D-Wave. Active in grants related to data science and sustainability. Labs/Teams: Involved in the Institute of Computational Sustainability at Cornell, focusing on applying algorithms to environmental challenges. Collaborates with organizations like Motivate (bike-sharing) and Ornge (air ambulance services).
Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.
David Mazières is a Professor at Stanford University , affiliated with the School of Engineering and the Department of Computer Science . He serves as a software engineer at the Stellar Development Foundation . His work bridges academic research and industry applications in distributed systems and security. University: Stanford University Academic Rank: Professor Email: dm@scs.stanford.edu Research Interests: David Mazières' research focuses on distributed systems , cryptocurrencies , and computer security . Key areas include consensus protocols (e.g., Stellar ), low-latency scheduling ( Syrup , Shinjuku ), and cryptographic techniques for privacy and security ( SafetyPin , CCFI ). Key Achievements: His notable works include: TCP-ENO (RFC 8547) for secure transport protocols Stellar Consensus Protocol for decentralized finance SOSP 1999 Best Paper for 'Separating key management from file system security' Teaching Contributions: He has taught core and advanced courses at Stanford since 2005, including CS212 (Operating Systems) , CS240h (Functional Systems in Haskell) , and CS251 (Cryptocurrencies and Blockchain Technologies) . Previously taught courses at NYU (2001-2005).
Sham Kakade is the Rampell Family Professor of Computer Science and Professor of Statistics at Harvard University, co-director of the Kempner Institute. His research focuses on advancing artificial general intelligence through foundational work in reinforcement learning, large-scale learning systems, and autonomous agent architectures. He earned his PhD in 2003 from the Gatsby Computational Neuroscience Unit at University College London. His work emphasizes scalable optimization algorithms, distributed systems for foundation models, and understanding emergent capabilities in neural architectures. Research interests include full-stack training pipelines for foundation models, mathematical principles of large-scale learning systems, and bridging language models with embodied intelligence. He advises prospective students with backgrounds in applied deep learning or theoretical computer science, offering access to the Kempner Institute's computational resources. He serves on committees for the ACM Prize in Computing and Sloan Research Fellowships, co-organizes the Simons Symposium on Theoretical Machine Learning, and chaired COLT 2011. His lab works at the intersection of theory and practice, addressing challenges in AI's societal impact and technical scalability. Labs/Teams: Co-directs the Kempner Institute, fostering collaborations between AI researchers and social scientists. Active in Harvard's SEAS community.
Lutz Schubert is a researcher at the Institute of Computer Science , University of Cologne. He focuses on efficient distributed and parallel execution environments for heterogeneous systems, notably the MyThOS operating system in collaboration with Brandenburg University of Technology, Cottbus-Senftenberg. His interdisciplinary work bridges computer science and digital archaeology , addressing modeling of non-deterministic events from sparse excavation data and exploring human behavior constraints in archaeological contexts. Research Interests Design of modular, scalable operating systems for distributed systems Optimization of execution environments for heterogeneous hardware Application of complex systems modeling to digital archaeology Statistical and probabilistic methods for archaeological interpretation Autonomic resource distribution and adaptation in computing Publication Trends : His work spans operating systems , parallel computing , and digital humanities , with recent emphasis on probabilistic reasoning in archaeology and adaptive OS design for multicore architectures. Labs & Collaborations : He collaborates with Brandenburg University of Technology on MyThOS and leads research in computational archaeology as chair of Computer Applications and Quantitative Methods in Archaeology (CAA) , Germany.
Rachit Agarwal is an Associate Professor in the Department of Computer Science at Cornell University, with research focusing on systems, networking, and theoretical problems arising in practical systems. He leads a research group working on resource disaggregation, host architecture, secure cloud storage, and datacenter design. PhD in Computer Science, Cornell University Undergraduate, IIT Kanpur His research spans three major directions: Resource Disaggregation (with $3M NSF and Google awards), Host Architecture (exploring terabit interconnects), and PANCAKE (secure oblivious cloud storage with $1M NSF award). He also contributed to foundational work in Near-optimal Datacenter Design and Data Plane Monitoring . Awards include the Sloan Fellowship, NSF CAREER, IRTF Applied Networking Prize, and multiple best paper awards. His recent publications focus on host network architecture, congestion control, oblivious data access mechanisms, and secure cloud storage systems. He has advised multiple Ph.D. and postdoctoral researchers who now hold faculty positions at leading institutions. Sloan Research Fellowship NSF CAREER award Kavli Fellowship IRTF Applied Networking Research Prize SIGCOMM Best Student Paper Award Usenix Security Distinguished Paper Award Tau Beta Pi Professor of the Year 2025 Rachit has advised numerous students including current Cornell advisees like Midhul Vuppalapati, Shreyas Kharbanda, and Omar Eqbal. Former advisees include Saksham Agarwal (UIUC), Qizhe Cai (UVA), and Mina Tahmasbi Arashloo (University of Waterloo). His research is supported by large NSF grants and industry awards, with deployments in real-world systems and open-source contributions.
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.
