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Cai KuiView profile

Associate Professor

• Coding Theory
• Communication Systems
• Information Theory
• +3 more

Cai Kui is an Associate Professor at Singapore University of Technology and Design (SUTD) and the Programme Director of the SUTD Technology Entrepreneurship Programme (STEP). Her work focuses on coding theory, communication systems, and data storage technologies. She holds a joint PhD in Electrical Engineering from Technical University of Eindhoven and National University of Singapore, along with prior degrees from Shanghai Jiao Tong University and National University of Singapore. Research interests include advanced channel coding, signal processing for emerging storage systems (e.g., DNA storage, non-volatile memory), and machine learning applications. She leads multiple projects funded by A*STAR, Temasek Labs, and SUTD-MIT collaborations, addressing challenges in high-density storage and communication. Her awards include the 2024 ScholarGPS™ Top 0.05% Scholars, 2023 China Annual Symposium Best Poster, and multiple IEEE recognitions. She is an IEEE Senior Member and has served in leadership roles at IEEE conferences. Current openings include PhD scholarships and postdoc positions in coding for data storage systems. Key labs/teams include her research group focusing on FPGA implementations, DNA storage, and memory systems.

Peter DesnoyersView profile

Associate Professor

• Data Storage Systems
• Cloud Computing
• Object Storage
• +3 more

Peter Desnoyers is an Associate Professor at the Khoury College of Computer Sciences at Northeastern University and an Affiliated Faculty member in the Department of Electrical and Computer Engineering . He co-leads the Mass Open Cloud initiative, focusing on hybrid cloud systems combining multiple tenants and providers. PhD, University of Massachusetts Amherst (2003-2008) SB and SM, MIT (1982-1988) His research spans data storage systems , cloud computing , object storage and caching , and SMR translation layers . Recent work includes hybrid caching mechanisms, flash storage optimization, and secure multi-provider cloud environments. Publications highlight trends in distributed storage, translation layer frameworks, and non-intrusive software introspection. Scientific awards include a $1.55M NSF Grant (2019) for cloud technologies innovation. His advisees include Mohammad Hossein Hajkazemi and Mania Abdi , with collaborations across institutions like MIT, Boston University, and University of Massachusetts Amherst.

Daniel S. BergerView profile

Adjunct Assistant Professor

• Cloud Computing
• Sustainability
• System Reliability
• +7 more

Daniel S. Berger is a Principal Researcher at Microsoft's Azure Systems Research Group in Redmond, focusing on the efficiency, sustainability, and reliability of cloud platforms . He is also an Affiliate Assistant Professor at the Paul G. Allen School of Computer Science at the University of Washington, where he teaches graduate classes. His research spans systems stack innovations for sustainability , including work on memory tiering , repair operations , and cooling systems (Zissou). He leverages system prototyping , simulation , and statistical modeling in his work, often collaborating with PhD students and postdocs from institutions like Columbia, University of Toronto, CMU, and Princeton. Recent publications highlight his leadership in CXL-based memory management , carbon-efficient cloud design , and latency-aware caching . His tools, such as Belatedly and FOO , have demonstrated significant improvements in cache performance and latency optimization. Best Paper Awards: USENIX OSDI 2023, HotCarbon 2023, ACM SOSP 2021. Distinguished Paper: ASPLOS 2023. His work has been integrated into Apache Traffic Server and Microsoft production systems , with open-source tools and datasets released for reproducibility. Collaborations include hardware and OS development teams within Azure and academia.

• Optimizing Compilers
• Runtime Systems
• Embedded Systems
• +5 more

Summary Role & Affiliation: Mahmut Taylan Kandemir is a Professor in the Department of Computer Science and Engineering at Pennsylvania State University. He is a member of the Microsystems Design Lab and has held roles such as Graduate Program Coordinator. His research focuses on optimizing compilers, runtime systems, embedded systems, and storage technologies. Education: B.S. and M.S. from Istanbul Technical University (Computer Engineering), Ph.D. from Syracuse University (Computer Science). Research Interests: Kandemir’s work spans compiler optimization, energy-efficient architectures, non-volatile memory systems, and cloud storage. His research has been funded by NSF, DARPA, and industry partners like Intel and Microsoft. Articles & Contributions: Over 150 journal papers and 650+ conference publications, covering topics like compiler-driven storage efficiency, 3D NAND SSD optimization, and approximate computing frameworks. Awards: NSF Career Award, Penn State Premier Research Award, IEEE Fellow, and multiple best paper awards. His contributions include compiler tools and storage systems for high-performance computing. Advising & Grants: Advised 32 Ph.D. and 20 M.S. students. Active in program committees for conferences like MICRO, ISCA, and HPCA. Current grants focus on cloud resource management and emerging memory technologies. Labs & Collaborations: Leads projects in collaboration with Argonne National Lab, NVIDIA, and Intel. His work bridges academia and industry, addressing challenges in multicore and embedded systems.

Bingzhe LiView profile

Assistant Professor

• DNA Storage Systems
• Machine Learning Systems
• Low-Power Computing
• +3 more

Dr. Bingzhe Li is an Assistant Professor in Computer Science at UT Dallas' Erik Jonsson School of Engineering. His research at the Lab for Intelligent Storage and Computing (Lab4ISC) focuses on DNA storage systems, machine learning infrastructure, and energy-efficient computing architectures. Awarded the NSF CAREER Award (2025) and recognized for Best Paper nominations at leading conferences. Research spans DNA storage capacity optimization, reinforcement learning for hybrid SSDs, Kubernetes storage optimization, and stochastic computing architectures. Recent publications demonstrate innovations in out-of-core graph processing and blockchain storage systems. Leads multiple NSF/NASA-funded projects on DNA storage and convertible SSDs. Teaches Digital Logic and Computer Architecture courses. Supervises 8 PhD students and 3 master's candidates in storage systems and low-power computing research.

André BrinkmannView profile

Professor

• Data Center Management
• Cloud Computing
• Storage Systems
• +5 more

André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.

Greg EisenhauerView profile

Researcher

• High Performance Computing
• Systems
• Enterprise Computing
• +3 more

Dr. Greg Eisenhauer is a Senior Research Scientist at the Georgia Institute of Technology's School of Computer Science and affiliated with the Center for Experimental Research in Computer Systems (CERCS). His research focuses on high-performance computing (HPC), systems, and enterprise computing, with an emphasis on program monitoring, dynamic adaptation, performance evaluation, and I/O systems like ADIOS. Supported by NSF, DOE, DARPA, and industry grants, his work addresses challenges in HPC workflows, data management, and streaming analytics. He leads efforts in scalable data environments, metadata optimization, and exascale computing resilience.

François TrahayView profile

Professor

• High Performance Computing
• Runtime Systems
• Performance Analysis
• +4 more

François Trahay is a Full Professor in the Computer Science department at Télécom SudParis (Institut Mines-Télécom) and a member of the Benagil Inria team. He leads research in high-performance systems, runtime systems, and performance analysis for HPC and distributed systems. He holds an HDR from Institut Polytechnique de Paris and a PhD from University of Bordeaux (2009). His work includes the EZTrace framework for performance analysis and contributions to storage systems optimization. Education: 2021: Habilitation à Diriger des Recherches (HDR), Institut Polytechnique de Paris 2010: PostDoc at Riken, University of Tokyo 2009: PhD in Computer Science, University of Bordeaux 2006: MS in Computer Science, University of Bordeaux Research focuses on runtime system design, HPC trace analysis, and storage efficiency. Recent projects include PALLAS trace format (IPDPS 2025) and GPU performance prediction (Euro-Par 2024). He advises 4 current PhD students and has supervised 2 former students now in industry. Key contributions include: Co-developer of EZTrace performance analysis framework Co-author of 60+ peer-reviewed papers in top venues (IPDPS, IEEE Cluster, ICPP) Technical leadership in Inria's Benagil team and Samovar Lab

Francois TrahayView profile

Associate Professor

• High Performance Computing
• Storage Systems
• Parallel and Distributed Systems
• +2 more

François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.

Anrin ChakrabortiView profile

Assistant Professor

• Cryptography
• Systems Security
• Cloud Computing Security
• +7 more

Anrin Chakraborti is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago, affiliated with the College of Engineering. His work bridges cryptography, systems, and theory to build privacy-preserving technologies for real-world challenges. Ph.D. in Computer Science from Stony Brook University (2020) Postdoctoral Researcher at Duke University B.Tech in Computer Science from Jadavpur University, India Chakraborti’s research focuses on three core areas: Secure Cloud Computing: Developing cryptographic mechanisms to protect data confidentiality and integrity in cloud environments against side-channels and malicious providers. Privacy in Collaborative Systems: Designing tools for private information sharing in cyber-physical systems and collaborative learning pipelines, including autonomous vehicle communication. Deniable Communication & Storage: Creating storage and messaging systems resistant to coercion, backdoors, and key compromise through plausibly deniable encryption. His publications demonstrate a consistent focus on privacy-enhancing technologies, oblivious protocols, and secure storage solutions. Notable contributions include Wink (deniable secure messaging), INVISILINE (plausibly-deniable storage), and ORAM-based protocols for cloud data privacy. He teaches graduate courses like CS 588 (Security & Privacy in Networked Systems) and CS 594 (Applied Cryptography), emphasizing hands-on implementation of secure systems.

Chen PanView profile

Assistant Professor

• Energy-Harvesting Embedded Systems
• Internet of Things (IoT)
• Low-Power Computing
• +5 more

Chen Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. His research focuses on energy-harvesting embedded systems, low-power computing, and IoT network optimization through machine learning techniques. Ph.D. from University of Pittsburgh Specializes in transient computing for batteryless devices Develops reinforcement learning solutions for UAV-assisted IoT systems Chen's recent publications emphasize energy-aware scheduling, non-volatile memory optimization, and sustainable communication protocols. His work intersects spatiotemporal modeling, fault tolerance, and resource-constrained AI execution across heterogeneous architectures.

Matti VilkkoView profile

Professor

• Control Engineering
• Industrial Process Control
• Energy Systems
• +9 more

Matti Vilkko is a Professor in Control Engineering at Tampere University's Faculty of Engineering and Natural Sciences , specifically within the Automation Technology department. He serves as Head of the Automation and Mechanical Engineering Unit. Research Focus: Industrial process control, mathematical modeling, state estimation, and optimization of metallurgical and energy systems Key Projects: Future Electrified Mobile Machines (FEMMa), Circular Economy of Water (CEIWA), Social Energy Ecosystems (ProCem), Green Electrification (HYGCEL) His work combines control theory with industrial applications, particularly in copper smelting optimization, green hydrogen systems, and smart energy networks. Recent publications show expertise in: Machine learning for wind turbine cybersecurity ASM1 calibration for wastewater treatment EU regulatory impacts on hydrogen infrastructure Finite element analysis of paperboard mechanics Price-based coordination in metallurgical processes He leads interdisciplinary collaborations across Finland, integrating automation technology with energy systems, materials science, and industrial ecology.

Oleksandr TkachView profile

Researcher

• Materials Science & Engineering
• Solid State Physics
• Thermoelectric Materials
• +5 more

Oleksandr Tkach is a Principal Researcher at the University of Aveiro's Department of Materials and Ceramic Engineering, affiliated with CICECO - Aveiro Institute of Materials. His research focuses on advanced oxide electroceramics, including thermoelectric and piezoelectric materials for energy applications, giant permittivity dielectrics, and energy-efficient sintering techniques. He holds a Ph.D. in Materials Science & Engineering from the University of Aveiro (2005) and a Microelectronics degree from Kyiv Polytechnic Institute (1999). His career includes postdoctoral positions at the University of Barcelona, University of Porto, and Johannes Gutenberg University of Mainz, with extensive EU project involvement (e.g., IFOX, MASPIC, FAME). Dr. Tkach has authored over 100 articles, including high-impact publications in Advanced Functional Materials and Nature Physics . His awards include FCT grants, ERC project participation, and featured articles in prestigious journals. He supervises PhD and Master's students, coordinates research projects totaling €48M, and contributes to editorial boards for six journals. Current research emphasizes energy storage, multiferroic heterostructures, and sustainable ceramic processing. His work bridges fundamental material science with industrial applications in sensors, energy harvesting, and electronic devices.

Reza SalkhordehView profile

Lecturer

• Operating Systems
• Solid-State Drives
• Data Storage Systems
• +5 more

Reza Salkhordeh is a Lecturer and Postdoctoral Researcher at Johannes Gutenberg University Mainz, Germany, where he leads the Efficient Computing and Storage Group. He holds a Ph.D. in Computer Engineering from Sharif University of Technology (2018) and has been affiliated with Ferdowsi University of Mashhad and Sharif University of Technology. His research focuses on operating systems, storage systems, and non-volatile memory technologies, with a particular emphasis on high-performance computing and I/O optimization. He has taught courses such as Storage Systems and Advanced Topics in Operating Systems since 2020. His academic journey includes a B.Sc. from Ferdowsi University (2011), M.Sc. and Ph.D. from Sharif University (2013, 2018). He has held roles such as Technical Lead for the High-Performance Data Storage System (HPDS) project in Tehran, Iran, and has mentored 4 M.Sc. and 6 B.Sc. students. His work spans patented technologies like Reconfigurable Cache Architectures and Load Balancers for I/O caching systems. Research interests include heterogeneous memory management, storage system design, and optimizing I/O performance in distributed environments. His recent publications address challenges in NVMM utilization, garbage collection in SSDs, and I/O tracing for HPC systems. He is actively involved in conference committees (e.g., FAST, ARCS, SC) and has reviewed for top journals like IEEE TPDS and ACM Transactions on Storage. Notable recognitions include membership in Iran’s National Elites Foundation (2012–2015) and top rankings in national exams (3rd in PhD, 35th in MSc). His contributions to storage systems have advanced enterprise-grade architectures and decentralized file systems, with a focus on practical implementations for modern computing challenges.

Héctor D. AbruñaView profile

Professor

• Electrochemistry
• Fuel Cells
• Batteries
• +4 more

Héctor D. Abruña is the Emile M. Chamot Professor of Chemistry and Chemical Biology at Cornell University, affiliated with the College of Arts & Sciences. His research focuses on electrochemical systems, energy storage (batteries, supercapacitors), molecular electronics, and fuel cells. He leads the Abruña Group, pioneering studies on nanomaterials for electrocatalysis, including intermetallic nanoparticles and graphene-based systems. Education: BS and MS from Rensselaer Polytechnic Institute, PhD from the University of North Carolina at Chapel Hill, and a postdoc at the University of Texas at Austin. Research emphasizes interdisciplinary approaches using techniques like in-situ X-ray methods, TEM, and electrochemical spectroscopy. Key areas include lithium-sulfur batteries, fuel cell catalysts (e.g., PtBi/PtPb alloys), and molecular electronics for memory devices. Over 30+ publications since 2006 span energy storage, nanomaterials, and electrocatalytic mechanisms. Awards: 2025 Dreyfus Prize, Enrico Fermi Award, Global Energy Prize, and National Academy of Sciences membership. Grants: $8.3M DOE award for fuel cell research, $12.6M energy center funding, and collaborations like the 2030 Project. Labs/Teams: Abruña Group develops advanced materials for energy and electronics, with industry partnerships (e.g., Factorial Energy).