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Angshuman KarmakarView profile

Assistant Professor

• Post-Quantum Cryptography
• Computation On Encrypted Data
• Side-Channel Analysis
• +4 more

Angshuman Karmakar is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur, India. His research focuses primarily on Post-Quantum Cryptography (PQC) and Computation On Encrypted Data (COED), which are critical areas in modern cryptography and computer security. Dr. Karmakar received his Ph.D. from Katholieke Universiteit Leuven (KU Leuven), Belgium, where he worked under Prof. Ingrid Verbauwhede in the COSIC research group. He was awarded the prestigious Erasmus Mundus fellowship for his doctoral studies and the FWO (Fonds voor Wetenschappelijk Onderzoek – Vlaanderen) fellowship for his post-doctoral research at KU Leuven. His research spans theoretical development of cryptographic schemes, implementation algorithms, side-channel and fault attack analysis, and countermeasure development. Dr. Karmakar has established extensive international collaborations with researchers and engineers worldwide to address complex challenges in cryptography and security. Recent publications demonstrate a strong focus on practical post-quantum cryptographic implementations with particular attention to hardware and software efficiency, side-channel resistance, and novel attack methodologies. His work bridges theoretical cryptography with real-world implementation challenges across diverse platforms from IoT devices to high-performance computing systems. Erasmus Mundus fellowship for doctoral studies at KU Leuven FWO fellowship for post-doctoral study at KU Leuven Google India Research Award for work on practical transition to post-quantum cryptography Dr. Karmakar is actively seeking graduate students and postdoctoral researchers to collaborate on cutting-edge research in cryptography and computer security. His work has significant implications for securing future communication systems against quantum computing threats, with applications spanning blockchain technologies, IoT security, and general-purpose computing systems.

Wenfeng ZhaoView profile

Assistant Professor

• Neural Engineering
• Compressed Sensing
• Ultra-low Power VLSI
• +3 more

Wenfeng Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at Binghamton University. He holds a PhD from the National University of Singapore (2014) and BS/MS degrees from Huazhong University of Science and Technology (2007-2009). Prior to this role, he conducted postdoctoral research at the University of Minnesota's Biomedical Engineering Department. His research focuses on neural engineering, compressed sensing, ultra-low-power VLSI systems, and in-memory computing. Key areas include hardware security, biomedical signal processing, and energy-efficient computing architectures. His work spans applications in neural interfaces, cryptographic hardware, and IoT edge devices. Recent publications highlight advancements in block-cipher-in-memory architectures, emotion recognition via EEG analysis, and energy-efficient FPGA accelerators for neural networks. His research also addresses challenges in cryogenic memory systems and MRI-compatible neural recording devices. Zhao's contributions emphasize interdisciplinary approaches at the intersection of hardware design, signal processing, and cybersecurity. His lab develops novel solutions for low-power embedded systems and trustworthy IoT infrastructure.

Marco SerafiniView profile

Assistant Professor

• Database Systems
• Distributed Systems
• Data Science
• +3 more

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +6 more

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

Zia SaadatniaView profile

Assistant Professor

• Smart Structures and Materials
• Nonlinear Vibration and Structural Dynamics
• Energy Harvesting
• +5 more

Dr. Zia Saadatnia is an Assistant Professor at Ontario Tech University's Department of Mechanical and Manufacturing Engineering, part of the Faculty of Engineering and Applied Science. He holds affiliations with the KITE Research Institute (University Health Network) as an Affiliate Scientist and the University of Toronto's Department of Mechanical and Industrial Engineering as an Assistant Professor (Status-only). His research focuses on advanced materials, energy harvesting, and biomedical devices, with notable contributions to aerogel composites, triboelectric nanogenerators, and functional electrical stimulation technologies. Education: Ph.D., Mechanical Engineering, University of Toronto (2019) M.Sc., Mechanical Engineering, Ontario Tech University (2015) B.Sc. (Hons), University of Science and Technology, Iran (2007) Research Interests: Smart structures and materials Nonlinear vibration dynamics Energy harvesting systems Sensors and actuators Biomedical devices Polymer composites and aerogel fabrication Awards and Honors: Mitacs Accelerate Fellowship (2021-2024) William Dunbar Memorial Scholarship (2019) Pierre Rivard Hydrogenics Graduate Fellowship (2018) Ranked First in Undergraduate Class (2011) Advising and Grants: Dr. Saadatnia has led research projects supported by grants from Mitacs and the Government of Canada. His work integrates interdisciplinary approaches to address challenges in energy systems, biomedical engineering, and material innovation. Labs and Teams: Collaborates with KITE Research Institute and University of Toronto teams on advanced materials and biomedical applications. His lab focuses on experimental and computational studies of energy harvesting and smart materials.

Kaiqun FuView profile

Assistant Professor

• Spatial Data Mining
• Machine Learning
• Deep Learning
• +3 more

Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.

Wendy MaoView profile

Professor

• Materials Science
• Planetary Science
• Geophysics
• +3 more

Wendy Mao is a Professor of Earth and Planetary Sciences, Photon Science, and (by courtesy) Geophysics at Stanford University, affiliated with SLAC National Accelerator Laboratory. Her research focuses on materials under extreme conditions, particularly high pressure, to understand planetary interiors, energy materials, and novel phases. Key interests include phase transitions in minerals, silicate melts, and light-element alloys, with applications in planetary core modeling and hydrogen storage. Education: Ph.D. in Geophysical Sciences from the University of Chicago (2005). Teaching includes Earth's interior dynamics, mineralogy, and a freshman seminar on diamonds. Research emphasizes high-pressure experimentation using diamond anvil cells and synchrotron X-ray techniques. Recent work explores metallic hydrogen, iron spin states in super-Earths, and amorphization in halide perovskites. Collaborations leverage machine learning and advanced imaging for material characterization. Her lab develops methods to stabilize metastable phases and study ultrafast structural responses under shock compression. The group also investigates defects in quantum sensors and novel synthesis pathways for energy materials.

Barzan MozafariView profile

Associate Professor

• Database Systems
• Data Management
• Machine Learning
• +3 more

Barzan Mozafari is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and leads a research group focused on scalable database systems and approximate computing. He holds a PhD from UCLA (2011) and was a Postdoctoral Associate at MIT. His research emphasizes data-intensive systems, combining statistical models, optimization, and machine learning to enhance database performance and predictability. Notable projects include BlinkDB (approximate query processing), DBSeer (database diagnosis), and VerdictDB (platform-independent AQP). He co-founded Keebo, advancing data learning technologies, and contributed to SnappyData (acquired by TIBCO). His work spans technical innovations like CATS/VATS scheduling algorithms (adopted in MySQL/MariaDB) and grants such as ConFlux (NSF-funded supercomputing-big data integration) and a smart black-box for autonomous vehicles. Awards include the NSF CAREER Award and Best Paper recognitions at SIGMOD and EuroSys. Teaching includes EECS 484/584 (Database Management Systems) and advanced topics courses. He advises students like Yongjoo Park (SIGMOD Dissertation Runner-Up), Jiamin Huang, and Boyu Tian.

• Computational Fluid Dynamics
• Turbomachinery
• Aerodynamics
• +4 more

Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.

• Computer Architecture
• Memory Systems
• Solid-State Drives
• +2 more

Dr. Seyyedmohammad Sadrosadati is a Lecturer in the Department of Information Technology and Electrical Engineering at ETH Zurich. His expertise centers on storage systems and computer architecture, with particular focus on solid-state drive (SSD) design and optimization. Research focuses on: Modern SSD architecture and performance enhancement Memory hierarchy optimization Hardware accelerators for data-intensive applications Energy-efficient storage solutions His teaching includes courses on SSD design and advanced computer architecture.

Joel TownView profile

Adjunct Professor

• Psychotherapy Efficacy
• Short-Term Psychodynamic Psychotherapy
• Emotional Processing in Mental Health
• +6 more

Dr. Joel Town is an Adjunct Professor in the Department of Psychiatry and cross-appointed in the Department of Psychology and Neuroscience at Dalhousie University . He holds a BSc (Hons) from the University of York and a Doctorate in Clinical Psychology (DClinPsy) from the University of Sheffield. Education: BSc (Hons) in Psychology, University of York, England Doctorate in Clinical Psychology (DClinPsy), University of Sheffield, England Dr. Town's research focuses on psychotherapy outcome research , mechanisms of change in psychotherapy , and psychological treatments for depression and medically unexplained symptoms . He specializes in Intensive Short-Term Dynamic Psychotherapy (ISTDP) , examining its efficacy for conditions like treatment-resistant depression, post-traumatic stress disorder, and somatic symptom disorders. His work includes clinical trials , systematic reviews , and meta-analyses to evaluate therapeutic processes and outcomes. His recent publications highlight ISTDP applications for personality disorders, anxiety, chronic pain, and eating disorders. Key trends include emotional processing , therapeutic alliance dynamics , and cost-effectiveness of brief interventions . Dr. Town also explores how unconscious emotional conflicts influence treatment outcomes. As a clinician, he serves as the lead clinical psychologist at the Medically Unexplained Symptoms Clinic, QE-II Health Science Centre, and as Director of Dynamic Health Psychological Services, Halifax . He supervises clinical psychology interns and practicum students and contributes as a Consulting Editor for journals like Psychotherapy and Psychotherapy Research .

Aurèle MaheoView profile

Research Fellow

• Distributed Systems
• Parallel Computing
• Machine Learning Systems
• +3 more

Aurèle Maheo is a Research Fellow at the Computer Science Department of Telecom SudParis, affiliated with the Parallel and Distributed Systems (PDS) group. His research focuses on distributed systems, parallel computing, and hardware acceleration, with applications in machine learning and high-performance computing. He has contributed to projects such as SpeedyLoader for optimizing machine learning pipelines and FPGA memory bandwidth improvements. His work bridges theoretical distributed systems principles with practical implementation challenges in large-scale environments. Affiliations: PDS Group, Computer Science Department, Telecom SudParis Research interests include distributed consensus algorithms, system scalability, and hardware-software co-design for accelerating computational workflows. Recent work highlights include presentations on machine learning pipelining techniques and FPGA-based memory optimization strategies.

Jiannan WangView profile

Associate Professor

• Database Systems
• Data Management
• Data Cleaning
• +5 more

Jiannan Wang is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). He holds a Ph.D. from Tsinghua University (2013) and a B.Sc. from Harbin Institute of Technology (2008). His research focuses on database systems, data management, and data science, with particular emphasis on data cleaning, crowdsourcing, and big data technologies. He leads the SFU Data Science Research Group, aiming to accelerate data science workflows through innovative tools like DataPrep and ConnectorX. Education: Ph.D. in Computer Science and Technology, Tsinghua University, China (2013) B.Sc. in Computer Science and Technology, Harbin Institute of Technology, China (2008) Research Interests: Dr. Wang's work spans database systems, data cleaning, crowdsourcing, and big data education. He develops open-source tools for data scientists to streamline data preparation and analysis. His lab's mission is to make data science more efficient through technologies like DataPrep and ConnectorX . Awards: IEEE TCDE Rising Star Award (2018) CS-Can|Info-Can Outstanding Early Career Researcher Award (2020) VLDB Best Experiments, Analysis & Benchmark Paper Award (2021) PVLDB Distinguished Review Board Member Award (2020) Advising & Leadership: Director of SFU's Professional Master's Program in Big Data and Visual Computing. Supervised over 20 graduate and undergraduate students, many of whom have gone on to roles at top companies like Google, Amazon, and Huawei. Lab & Teams: Part of the SFU Data Science Research Group and the SFU Big Data Academic Advisory Committee. His lab collaborates with industry partners and contributes to open-source projects in data management and machine learning.

Marc PollefeysView profile

Professor

• 3D Computer Vision
• Robotics
• Machine Learning
• +5 more

Marc Pollefeys is a Full Professor of Computer Science at ETH Zurich and Director of the Microsoft Mixed Reality and AI Zurich Lab. He has held roles such as Visiting Professor at Stanford University (2007) and Assistant/Associate Professor at UNC-Chapel Hill (2002–2009). His research focuses on 3D computer vision, robotics, machine learning, and augmented reality. Education: PhD in Computer Science from KU Leuven (1999), followed by postdoctoral research there until 2002. He transitioned to academic roles at UNC-Chapel Hill before joining ETH Zurich in 2007. Research interests include 3D reconstruction, visual localization, SLAM, and applications in archaeology, urban modeling, and robotics. Notable projects include real-time 3D scanning, city-scale reconstruction, and autonomous vision-based drones. Key awards include ACM Fellow (2022), IEEE Fellow (2012), and ERC Starting Grant (2008). He advises numerous PhD students and collaborates with institutions like Google and Microsoft. Labs and teams: Leads the Computer Vision and Geometry (CVG) lab at ETH Zurich and directs the Microsoft Mixed Reality and AI Lab. His work bridges academia and industry, focusing on perception for mixed reality and autonomous systems.

• Superconductivity
• High-Field Magnets
• Particle Accelerators
• +3 more

Tiina Salmi is an Academy Research Fellow specializing in superconductivity and high-field magnet systems. She holds a Doctor of Science (Technology) in Electrical Engineering (2015) and a Master of Science in the same field (2009). Her research focuses on quench protection systems, superconducting magnet design for particle accelerators (e.g., Future Circular Collider, Muon Collider, and LHC upgrades), and material behavior under extreme conditions. Affiliations: Academy of Finland, collaborating with institutions like CERN and LARP. Key Projects: FCC dipole development, Muon Collider storage ring magnets, and HTS tape mechanical analysis. Editorial Roles: Editorial board member for IEEE Transactions on Applied Superconductivity (2020–present). Research Interests Her work addresses critical challenges in superconducting magnet technology, including quench dynamics, thermal management, and high-field stability. She employs computational models (e.g., FEM simulations) and AI-driven approaches to optimize magnet performance and safety. Recent focus areas include: Superconducting materials (Nb 3 Sn, ReBCO, HTS) under mechanical/thermal stress CLIQ protection systems for FCC-hh dipoles Surrogate models for predicting heater delays Mechanical behavior of 2G HTS tapes Grants & Activities Active in international collaborations like the Muon Collider Project and the LARP (LHC Accelerator Research Program). Conducted over 80 peer-reviewed publications (2011–2025) and presented at conferences since 2013. Her work aligns with SDGs 9 (Industry, Innovation) and 12 (Sustainable Consumption). Future Work Ongoing projects include optimizing superconducting magnets for next-gen colliders and advancing AI applications in magnet design. Exploring sustainable materials and cost-effective high-field magnet solutions for future accelerators.