Find Academic Supervisors

• Bioinformatics
• Systems Biology
• Microscopy
• +5 more

Kevin Dean is an Assistant Professor at the UT Southwestern Medical Center , affiliated with the Lyda Hill Department of Bioinformatics and the Cecil H. and Ida Green Center for Systems Biology . His research focuses on developing and applying advanced microscopy techniques to address complex biological challenges, particularly in cancer metastasis and cellular mechanobiology. Co-developer of autonomous 'self-driving' microscopes using computer vision Expert in spectral unmixing and cyclic immunofluorescence for molecular multiplexing Creator of high-throughput histopathology systems with 300 nm resolution Collaborator with leading scientists (Drs. Danuser, Fiolka, Morrison, Amatruda, Sorger) Principal Investigator for NIH/NCI-funded cancer imaging programs Research Themes: The Dean Lab specializes in cutting-edge imaging solutions that bridge technology development and biological discovery. Their work spans: Cancer Biology: Metastatic colonization mechanisms, tumor microenvironment imaging Biomedical Technology: Light-sheet microscopy, optical probes, time-series analysis software Cellular Mechanobiology: Force-driven morphogenesis, matrix deformation quantification Neuroscience: Stress-induced anhedonia, lateral habenula neuronal signaling Grant Leadership: Dean serves as Principal Investigator for the NCI-funded Imaging Mechanisms of Metastatic Tumor Formation and the NIGMS-supported UTSW-UNC Center for Cell Signaling Analysis , where his team develops open-source platforms like Navigate for smart light-sheet microscopy. Collaborative Networks: Active in multiple interdisciplinary programs including: Prune Belly Syndrome research with Filamin A mutations Drosophila germband extension mechanobiology with micro-cantilevers Lateral habenula functional mapping with multi-modal imaging

• VLSI Design
• Computer-Aided Design
• Electronic Design Automation
• +4 more

Matthew R. Guthaus is a Professor in the Electrical and Computer Engineering Department at the University of California Santa Cruz's Baskin School of Engineering. With an active research career spanning over two decades, his work focuses on VLSI design, Electronic Design Automation, and memory systems. His research interests include VLSI Design, Computer-Aided Design, Electronic Design Automation, Memory Design, Clock Distribution, Resonant Clocking, and Machine Learning for EDA. Guthaus has made significant contributions to open-source EDA tools, most notably OpenRAM, which has become an important resource for memory compiler development in academia and industry. Analysis of his recent publications (2021-2025) reveals a strategic evolution in his research, incorporating machine learning techniques into traditional EDA problems while maintaining strong foundations in circuit design. His work spans from fundamental circuit design to high-level system considerations, with particular emphasis on open-source frameworks that enable broader collaboration in the semiconductor community. Guthaus has been actively involved in national initiatives addressing semiconductor research and workforce development, as evidenced by his contributions to NSF workshops on integrated circuits. His recent publications show continued innovation in memory design, clock distribution, and the application of AI to traditional EDA challenges. His research has practical applications in low-power circuit design, high-performance computing systems, and the growing field of neuromorphic computing. The consistent publication record through 2025 demonstrates ongoing active research and leadership in the EDA community.

Jonathan TimmisView profile

Professor

• Robotics
• Artificial Immune Systems
• Swarm Robotics
• +7 more

Jonathan Timmis is a Professor at Aberystwyth University, actively contributing to research in robotics, artificial immune systems, and bio-inspired computing. He has authored over 280 research outputs and is recognized for his work in swarm robotics, evolutionary algorithms, and formal methods in robotic software design. Affiliation: Aberystwyth University Research Focus: Robotics, Artificial Immune Systems, Evolutionary Computation Recent Collaborations: Alan Tyrrell, Emma Hart, Andy Wuensche, Ana Cavalcanti His research interests span robotics, swarm intelligence, bio-inspired algorithms, software verification, and evolutionary computation . He has pioneered work in artificial immune systems applied to robotics and developed formal methods for robotic controller design using tools like RoboChart. His work integrates theoretical modeling with practical robotic implementation. The recent publications reflect a strong trend in formal verification of robotic systems, swarm robotics, and hardware-software integration in evolutionary robotics . His work bridges theoretical computer science with real-world robotic applications, particularly in adaptive and self-repairing systems. Scientific Awards: Fellow of the Learned Society of Wales (2025) Jonathan Timmis has led and participated in numerous research projects, likely involving grant funding, though specific grants are not listed. He collaborates extensively across institutions and has co-authored with PhD students and researchers, indicating an active supervisory and mentoring role. His editorial contributions, including prefaces and workshops, suggest leadership in the academic community. He is associated with research groups focused on robotics, bio-inspired computing, and formal software engineering , likely contributing to interdisciplinary teams at Aberystwyth University. His recent work on evolvable hardware and swarm systems indicates ongoing innovation in autonomous robotic systems.

David Black-SchafferView profile

Professor

• Computer Architecture
• Memory Systems
• Parallel Programming
• +4 more

David Black-Schaffer is a Professor at Uppsala University's Department of Information Technology, specializing in computer systems research. As of 2023, he serves as Dean of Research for the Faculty of Science and Technology. His work bridges software and hardware innovations to enhance data movement efficiency in computer systems, with applications commercialized through a startup and integrated into industry standards like OpenCL. Black-Schaffer earned his PhD in Electrical Engineering from Stanford University in 2008, focusing on many-core processor programming. His career spans roles at Apple Inc. (contributing to OpenCL standards), postdoctoral research at Uppsala University, and academic progression from assistant to full professor (2010–2017). He has held leadership roles including Head of the Division of Computer Systems (2022) and department representative on the faculty Advisory Committee for Research (2021). His research spans computer architecture, memory systems, parallel programming, and simulation techniques. Recent publications (2024–2020) explore garbage collection, cache optimization, memory contention, NUMA systems, and instruction scheduling. Key trends include software-hardware co-design for power efficiency, reuse-aware data placement, and machine learning for performance modeling. Knut & Alice Wallenberg Foundation: Wallenberg Academy Fellowship Prolongation (2020–2025), Wallenberg Academy Fellow (2016–2021) Swedish Research Council (VR): Project Grant (2019–2024), Young Researcher Grant (2015–2018), Framework Grant (2012–2017) European Research Council: ERC Starting Grant (2017–2022) Teaching Awards: Uppsala Engineering and Science Student Union Pedagogical Prize (2012), Uppsala University Pedagogical Prize (2016), Uppsala Technical Physics Students' Teaching Award (2019) Other Grants: ScalableLearning flipped classroom project (2012–2020), Arm Ltd. collaborations on memory system designs He pioneered flipped-classroom teaching through the ScalableLearning project, impacting over 80,000 students. His research is conducted in collaboration with institutions like Arm Ltd., with past contributions to Apple's OpenCL implementation and UPMARC research center.

• Electronics
• Nanocomputing
• Quantum Technologies
• +12 more

Fabrizio Riente is a Fixed-term Researcher at the Department of Electronics and Telecommunications (DET) , Politecnico di Torino. He holds academic responsibilities in multiple programs and contributes to editorial and conference committees. Member of College of Electronic, Telecommunications and Physics Engineering Invited Member of College of Computer, Film, and Mechatronics Engineering His research spans Nanocomputing , Quantum Technologies , and Machine Learning for environmental and healthcare applications, with a focus on: Qubit Control & Readout Systems IoT for Bee Health Monitoring 3D Audio-Visual Reproduction for Hearing Research Hybrid Simulation and Emerging Nanotechnologies Low-Power Electronic Systems His recent publications demonstrate interdisciplinary work bridging Quantum Electronics and Acoustic Ecology , particularly in developing VLSI architectures for novel applications. Notable scientific awards include Associate Editorship at Frontiers in Electronics and program committee roles at international conferences. He supervises PhD students in Quantum Engineering and Electronic Engineering , directing research projects funded by commercial contracts on quantum architectures and automotive technologies. His teaching portfolio includes courses on Microelectronic Systems and Low-Power Electronics .

David DefourView profile

Professor

• Computer Arithmetic
• High Performance Computing
• Computer Architecture
• +11 more

David Defour is a Professor in Computer Science at the University of Perpignan, affiliated with the Faculty of Science and Engineering and the Mathematics and Computer Science Department. He conducts research at the Multidisciplinary Modeling and Simulation Laboratory (LAMPS) and serves as Scientific Coordinator of the MESO@LR Computing Center. His research focuses on the critical intersection of computer arithmetic and computer architecture, with emphasis on: Unconventional arithmetic systems (interval, fuzzy, high/reduced precision) Numerical reproducibility and error estimation in computational workflows Hardware-software co-design for GPUs, multicore systems, and embedded architectures Reverse engineering of computational units and power consumption optimization Defour actively mentors master's students through thesis proposals on numerical bug detection using LLMs, SAT solvers for numerical challenges, soft error resilience in floating-point units, and reverse execution debugging for HPC. He leads the ANR InterFlop project (2021-2025) and previously managed the BPS collaboration contract (2021-2022), while serving as IT expert for the High Court of Perpignan. The LAMPS laboratory under his research activities structures work around two core thematic axes: Characterization of Digital and Discrete Systems, and Mathematical and Numerical Modeling for Mechanics.

Changhee JungView profile

Associate Professor

• Compilers
• Computer Architecture
• Performance Optimization
• +7 more

Changhee Jung is the Samuel D. Conte Associate Professor of Computer Science at Purdue University, Department of Computer Science. His research focuses on compilers and computer architecture with an emphasis on performance, reliability, and security. He joined Purdue in Fall 2019 after serving as an Assistant Professor at Virginia Tech (2013–2019). Prior academic roles include a post at ETRI (2005–2008) and internships at Google (2010–2012). Education: PhD in Computer Science from Georgia Institute of Technology (2013) under Prof. Santosh Pande. His work spans compiler-architecture co-design for intermittent computing, nonvolatile memory systems, and security frameworks like PeX for Linux kernel permission checks. Notable recognitions include the NSF CAREER Award (2018) and induction into the MICRO Hall of Fame (2021). Research interests include energy-efficient computing, fault resilience, and compiler-driven hardware optimizations. His lab, CompArch, emphasizes cross-layer solutions for performance, reliability, and security challenges. Recent projects include IPEX (ISCA 2025), Write-Light Cache (ISCA 2023), and DevFuzz (Oakland 2023). Awards: NSF CAREER, MICRO Hall of Fame, Memorable Paper Finalist (NVMW 2024), Best Student Paper Finalist (SC 2016), and multiple industry awards. Active in service roles, including program chair for LCTES 2020 and editorial roles for ACM journals. Teaching: Courses include CS 352 (Compilers), CS 502 (Compiling Systems), and advanced special topics. Supervised over 15 graduate students and postdocs, with alumni in academia and industry (e.g., Samsung, Intel, Xilinx).

• Digital Design Education
• Low-Power Electronics
• Hardware-Software Co-Design
• +3 more

Dr. Mark Johnson is a Senior Lecturer at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette, Indiana. His role focuses on advancing educational methodologies and curriculum development in electrical and computer engineering. He is affiliated with Purdue’s Brown Family Hall and maintains an active research agenda centered on digital design education, low-power electronics, and hardware-software co-design. Research interests include integrating industry standards (e.g., ABET) into teaching, FPGA applications, and fostering student success through innovative pedagogical frameworks. His work emphasizes practical tools like SOC prototyping and embedded systems design, alongside active learning strategies and technology integration in classrooms. Publications highlight a consistent focus on educational innovations, such as tablet-based documentation, dual-core prototyping challenges, and FPGA performance analysis. Despite no listed formal awards, his contributions to curriculum design and student project resources have significantly impacted undergraduate education in electrical engineering. No formal advisees or grants are listed, but his teaching portfolio includes senior project courses on x86 motherboards and RTL code structures. His work reflects a commitment to bridging academic theory with hands-on engineering practice.

Branden GhenaView profile

Assistant Professor

• Embedded Systems
• Networking Protocols
• Internet of Things
• +3 more

Branden Ghena is an Assistant Professor of Instruction in the Department of Computer Science at Northwestern University, part of the McCormick School of Engineering. His research focuses on embedded systems, networking protocols for IoT, and operating systems, particularly through the Tock project. He holds a PhD in Electrical Engineering and Computer Science from UC Berkeley (2020), an MSE in Computer Science and Engineering from the University of Michigan (2017), and a BS in Computer Engineering and Electrical Engineering from Michigan Technological University (2013). His research interests include resource-constrained embedded hardware/software, wireless protocols like Bluetooth Low Energy, LoRaWAN, and LTE-M, and energy-harvesting platforms such as Signpost and PowerBlade. He emphasizes open-source contributions and practical embedded systems education. Teaching includes courses like CS211 (C/C++), CS213 (Computer Systems), CS343 (Operating Systems), CE346/CS346 (Microcontroller Design), and CS433 (Wireless IoT Protocols). He has received notable awards, including the 2022 Cole-Higgins Excellence in Teaching Award and the Instructor of the Year award. His teaching philosophy centers on bridging theory to real-world engineering challenges. Awards : Cole-Higgins Award for Excellence in Teaching (2022) Instructor of the Year (2022) Outstanding Graduate Student Instructor (UC Berkeley, 2018) NSF Graduate Research Fellowship (2014–2017) Labs/Teams : Principal contributor to Tock OS, an open-source embedded OS Signpost platform for city-scale sensing Design of PowerBlade energy meters and Buckler development boards His advising includes supporting undergraduate research, with several students proceeding to PhD programs. He has also contributed to curriculum design, including redesigning labs for embedded systems courses using modern tools like Git, Make, and Rust. Branden actively participates in academic service roles, such as faculty search committees and workshop program committees.

Sihang LiuView profile

Assistant Professor

• Computer Architecture
• Computer Systems
• Persistent Memory Systems
• +3 more

Dr. Sihang Liu is an Assistant Professor in the School of Computer Science at the University of Waterloo. Prior to this role, he served as a visiting faculty member at SystemsResearch@Google. He holds a PhD from the University of Virginia, where his research was supported by the Google Fellowship Award. His academic career has been marked by contributions to computer architecture, systems, and persistent memory technologies. Dr. Liu's research interests span computer architecture, systems, and cybersecurity, with a focus on persistent memory systems, energy-efficient computing, and data center optimization. His work bridges hardware-software co-design and explores challenges in scalable computing, fault tolerance, and sustainable AI systems. His recent publications highlight advancements in adaptive memory tiering (HybridTier), carbon intensity forecasting (EnsembleCI), and securing persistent memory (Side-Channel Attacks on Optane). These contributions underscore his commitment to addressing critical issues in modern computing systems' performance, security, and environmental impact. Awards: Google Fellowship Award (PhD), NVMW Memorable Paper Award Finalist (2019), 2019 MICRO Top Picks Honorable Mention Professional Service: Program committees for ISCA, MICRO, ASPLOS; Artifact evaluation roles in leading conferences As an advisor, Dr. Liu mentors a diverse group of students, including current PhD candidates Henry Tian and Desen Sun, and numerous undergraduates. His lab focuses on innovation in sustainable computing and resilient systems design.

Samee U. KhanView profile

Professor

• Quantum Computing
• Edge Computing
• Fog Computing
• +4 more

Samee U. Khan is a Professor in the Department of Electrical & Computer Engineering at Mississippi State University (MSU), affiliated with the Bagley College of Engineering. He holds a Ph.D. from the University of Texas (2007) and a B.S. from GIK Institute of Engineering & Technology (1999). His research focuses on optimization, robustness, and security of computer systems, with recent emphasis on quantum computing, edge/fog computing, and machine learning applications in neuroscience and cybersecurity. Key research interests include quantum algorithm development, edge computing architectures, and neuro-inspired systems. His work integrates quantum computing with traditional machine learning to address challenges in visual perception modeling and medical imaging analysis. He also explores fog computing frameworks for IoT and distributed systems, emphasizing security and efficiency in heterogeneous environments. Publications highlight advancements in quantum noise mitigation, hybrid quantum optimization, and brain-computer interface models. His contributions span simulation toolkits like iFogSim and frameworks for vehicular networks and blockchain-based resource management. Khan’s research bridges theoretical computer science with practical applications in smart homes, healthcare, and autonomous systems.

Guillermo Payá VayáView profile

Professor

• Embedded Systems
• Processor Architecture
• FPGA Design
• +5 more

Prof. Dr.-Ing. Guillermo Payá Vayá leads the Chair for Chip Design for Embedded Computing at Technical University of Braunschweig's Faculty of Electrical Engineering, Information Technology, and Physics. His research focuses on processor architecture design, FPGA/ASIC implementations, and optimization techniques for embedded systems, particularly in high-performance, low-power, and radiation-hardened computing domains. Primary research interests include: Application-Specific Instruction Set Processors (ASIPs) and compiler co-design Radiation effects characterization and fault-tolerant hardware Ultra-low-power processor architectures for embedded AI Hardware acceleration of neural networks and computer vision algorithms Memory subsystem optimization and parallel computing techniques Recent publications demonstrate strong emphasis on radiation-hardened electronics (35% of recent works), AI accelerator design (27%), and ultra-low-power systems (20%), with growing interest in biomedical applications. Experimental validation through FPGA prototyping and semiconductor testing is a consistent methodology across research domains. Leads research team investigating: Radiation-tolerant FPGA architectures (Trumann, Weide-Zaage) Vector processor optimization (Gesper, Thieu) Nano-scale controller design (Weißbrich) AI-hardware co-design (Kautz, Beyer)

Xiaozhe HuView profile

Professor

• Scientific Computing
• Numerical Analysis
• Multigrid Methods
• +7 more

Xiaozhe Hu is a full-time Professor in the Department of Mathematics at Tufts University since July 2024. Previously held positions include Associate Professor (2019-2024) and Assistant Professor (2014-2019) at Tufts, and Adjunct Associate Professor (2020-2022) at the University of Bergen, Norway. Education : PhD in Computational Mathematics (Zhejiang University, 2009); BS in Information and Computer Science (Zhejiang University, 2004) His research focuses on scientific computing and numerical analysis , particularly: Development of adaptive and parallel numerical methods for PDEs and graph problems Multigrid/multilevel solvers for large-scale coupled systems Quantum algorithms and spectral graph theory Applications in poromechanics , reservoir simulation , and bioinformatics Recent publications demonstrate expertise in preconditioning techniques for Biot’s model, meshless methods for fluid-structure interaction, and data-driven discretization approaches . Awards include the Reimann-Louville Award (2016) and outstanding PhD graduate recognition (Zhejiang Province, 2009). PhD Students : Junyuan Lin (Loyola Marymount), Peter Ohm (RIKEN), Casey Cavanaugh (LSU), Kaiyi Wu, Eoghan O'Keefe Master Students : Charles Colley (Purdue PhD), Yue Shen (Florida State PhD), Samuel Rabinowitz, Phong Huang, Samuel Hocking Co-organizer of the Computational and Applied Mathematics Seminar and contributor to open-source HAZmath finite element library.

Hany ElgalaView profile

Assistant Professor

• Telecommunications
• Digital Signal Processing
• Embedded Systems
• +6 more

Dr. Hany Elgala is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York, and Director of the Signals and Networks (SINE) Lab. Previously, he served as a Research Professor at Boston University, co-leading the Multimedia Communications Lab and acting as Communications Testbed leader at the NSF Smart Lighting ERC. His research focuses on telecommunications, digital signal processing, and embedded systems, with specialization in visible light communications (VLC), LiFi networks, IoT security, and AI-driven wireless solutions. He has coordinated industrial projects with Airbus and EADS on optical wireless networks in aircraft cabins. Elgala's work emphasizes heterogeneous wireless networks, backscatter communication, and security in IoT. He has authored/co-authored ~50 publications/patents, contributing to fields like spectral efficiency, energy optimization, and network coexistence. His leadership in the SINE Lab drives innovations in 6G networks, UAV-based systems, and hybrid LiFi-WiFi architectures. His research portfolio includes breakthroughs in VLC system design, machine learning applications for physical layer optimization, and jamming-resistant IoT networks. Recent efforts focus on indoor flying networks and low-complexity VLC frameworks leveraging multi-task learning and autoencoder-based modulation.

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.