Babak Falsafi is a Full Professor at the School of Computer and Communication Sciences (IC) at EPFL, leading the Parallel Systems Architecture Laboratory (PARSA). He is a renowned expert in computer architecture, datacenter systems, and cloud-native server design. His research focuses on post-Moore era computing, emphasizing heterogeneous architectures, energy efficiency, and scalable IT infrastructure. Falsafi is the founder of EcoCloud, an EC-sponsored industrial-academic consortium investigating sustainable information technology. He holds ACM and IEEE fellowships, a Sloan Research Fellowship, and has contributed to major projects like Optimus Prime (data transformation acceleration), AstriFlash (flash-based online service systems), and Midgard (virtual memory re-design). His work spans hardware-software co-design, memory systems, and security. Falsafi advises numerous PhD students and collaborates with industry partners such as Google and Cavium. Key achievements include pioneering scalable multiprocessor architectures, snoop filters in IBM BlueGene, and spatial memory streaming in ARM cores. His lab develops open-source tools like QFlex for server simulation. He frequently presents at top conferences (HPCA, ISCA, MICRO) and chairs workshops on post-Moore infrastructure. Teaching roles include leading courses in computer architecture and parallel systems across multiple EPFL departments (SIN, EDIC, SSC, SMA). His work addresses datacenter challenges like the 'data tax' and mitigating latency through specialized accelerators.
Alenda Y. Chang serves as an Assistant Professor in Film and Media Studies at the University of California, Santa Barbara. With a multidisciplinary background spanning biology, literature, and film, she integrates ecocritical theory with contemporary media analysis. Her scholarly work appears in journals including Interdisciplinary Studies in Literature and Environment , Qui Parle , and electronic book review , focusing on sustainable media practices and ecological frameworks for digital engagement. Her educational background includes: M.A. and Ph.D. in Rhetoric from the University of California, Berkeley M.A. in English Language and Literature from the University of Maryland Chang specializes in environmental media and game studies, developing theoretical frameworks for ecological game design. Her research examines how digital games model environmental systems and foster player engagement with climate change through concepts like "slow violence" and "rambunctious play." She advocates for sustainable design patterns that address gaming's carbon footprint while creating immersive ecological narratives. Analysis of her 15 most recent publications reveals a consistent trajectory from foundational ecological game theory (2013-2018) to current work on thermal contexts of play and sustainable infrastructure (2022-2024). Her scholarship increasingly bridges data-driven activism with critical game design, emphasizing multispecies entanglements and infrastructural play as tools for environmental awareness. As co-founder of Wireframe—a collaborative media studio at UCSB—Chang cultivates critical game design practices through initiatives like growinggames.net . The studio supports data-driven global media art, environmental activism, and experimental pedagogy, serving as a hub for translating ecological theory into tangible digital interventions that challenge anthropocentric perspectives in gaming.
Amit Kumar is the Jaswinder and Tarwinder Chadha Chair Professor in the Department of Computer Science and Engineering at IIT Delhi. His research focuses on combinatorial optimization, online algorithms, and algorithmic fairness. He has taught courses such as Approximation Algorithms (COL 754), Design and Analysis of Algorithms (COL 351), and Numerical Analysis (COL 726). His work spans theoretical computer science with applications to clustering, scheduling, and fairness in evaluation processes. Research Interests Kumar's research emphasizes developing efficient algorithms for online and dynamic settings, particularly in constrained optimization and biased evaluation systems. He explores theoretical foundations of clustering, load balancing, and resource allocation, with recent contributions to fair food delivery systems and coreset constructions. Publications His recent work includes advancements in online convex paging (STOC 2025), consensus clustering (SODA 2025), and fairness-aware algorithms (AAAI 2024). Over 70 papers across top venues like STOC, SODA, and ICML reflect his expertise in algorithm design and analysis. Awards Best Paper Award at ISAAC 2023 for 'Clustering What Matters in Constrained Settings' Teaching & Mentorship Kumar instructs graduate and undergraduate courses in algorithms, data structures, and numerical methods. He advises students through these courses and collaborates with researchers on NSF-funded projects related to approximation algorithms and streaming systems.
Anwar Hithnawi is an Assistant Professor of Computer Science at the University of Toronto, where he leads the Privacy Preserving Systems Lab (PPS Lab). His research focuses on data privacy, applied cryptography, and secure systems, with emphasis on privacy-preserving machine learning, federated learning, and encrypted data processing. He holds a Ph.D. in Computer Science from ETH Zurich and was a postdoctoral researcher at UC Berkeley. Previously, he served as an Ambizione Fellow and research group leader at ETH Zurich. Research Interests: Data Privacy & Security Applied Cryptography (Homomorphic Encryption, Zero-Knowledge Proofs) Privacy-Preserving Systems (Federated Learning, Secure Analytics) IoT Security & Privacy Secure Collaborative Learning Awards: Google Research Award SNF Ambizione Grant ETH Medal for Outstanding Master Thesis (student Lukas Burkhalter) Microsoft Research Ph.D. Award (student Lukas Burkhalter) Lab Activities: The PPS Lab develops systems for privacy-preserving computation, secure collaborative learning, and encrypted data stream processing. Notable projects include Zeph, HECO, and Cohere. Recent achievements include acceptance of DPolicy at IEEE S&P 2025 and RoFL at Oakland 2023.
Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.
James C. Hoe is Professor of Electrical and Computer Engineering at Carnegie Mellon University (College of Engineering). He is on sabbatical at MangoBoost and directs research in computer architecture, reconfigurable computing, and high-level hardware design. Education Ph.D., Electrical Engineering and Computer Science, MIT (2000) M.S., Electrical Engineering and Computer Science, MIT (1994) B.S., Electrical Engineering and Computer Science, UC Berkeley (1992) Research Interests Professor Hoe’s work spans computer architecture , reconfigurable computing , FPGA architectures , and high-level hardware synthesis . His group created the CoRAM abstraction for virtualized FPGA computing and leads efforts in power-efficient accelerators, in-network computing, and security-oriented FPGA systems. Scientific Awards IEEE Fellow (2013) Intel Outstanding Researcher Award (2021) Research Funding & Projects Intel / VMware Crossroads 3D-FPGA Academic Research Center – co-leading exploration of FPGA roles in future datacenters. DARPA BRASS program ($2.7 M, 4 years) – ensuring long-lived software systems remain robust to resource changes. Pigasus open-source IDS – world’s fastest FPGA-accelerated intrusion-detection system (100 Gb/s on one server). Labs & Teams He heads activities within the Computer Architecture Lab at Carnegie Mellon (CALCM) , supervising graduate researchers on CoRAM++, SPIRAL autotuning, and FPGA overlays for stream processing.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Zachary Ives is the Adani President's Distinguished Professor and Department Chair of the Computer and Information Science Department at the University of Pennsylvania. He holds affiliations with the ASSET Center for Safe, Explainable and Trustworthy AI, the Warren Center for Network and Data Science, the Center for Neuroengineering and Therapeutics, and serves as a Distinguished Research Fellow at the Annenberg Center for Public Policy. His research focuses on data integration and sharing, data provenance and trustworthiness, and machine learning systems. He develops data science platforms at the intersection of databases, machine learning, and distributed systems, with applications in Web question answering and scientific domains like genetics and neuroscience. His work addresses fundamental challenges in integrating heterogeneous data, ensuring trustworthy results, and facilitating collaborative data science. His recent publications demonstrate a strong focus on data lakes, learned database systems, fine-grained provenance, and question answering systems. These works span top conferences including SIGMOD (where his paper was selected as Best Paper in 2024), VLDB, ACL, and PODS, showing the breadth of his contributions across database systems, natural language processing, and data management. NSF CAREER award recipient Fellow of the ACM Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching IEEE Technical Committee on Data Engineering Education Award SIGMOD Best Paper Award ICDE 2013 ten-year Most Influential Paper award As Department Chair, Ives has overseen significant departmental growth, hiring 25 new faculty since 2018. He advises numerous PhD students and postdocs, and maintains extensive collaborations across Penn and with external institutions. His research has been funded by NSF, NIH, DARPA, Google, Amazon, and other organizations. He has developed courses including NETS 212 'Scalable and Cloud Computing' and teaches Big Data Analytics. His research group, the Penn Database Group, works on projects including data lake management, data provenance, and collaborative data science platforms. His work with neuroscientists on seizure prediction has received significant attention, including a competition with 504 teams achieving 82% accuracy.
Prof. Hayden Kwok Hay SO is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Electrical and Electronic Engineering. He currently serves as Acting Director of the School of Innovation and previously co-directed the Computer Engineering Program. His research focuses on reconfigurable computing systems, FPGA-based architectures, and their applications in AIoT, medical imaging, and high-performance computing. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (1998–2007). Prof. So has been recognized with awards such as the IEEE-HKN Teaching Award (2021), Croucher Innovation Award (2013), and multiple teaching excellence awards. He leads the Computer Architecture & System Research Lab (CASR) and co-founded the Joint Lab on Future Cities (JLFC). His work spans FPGA overlay architectures, graph processing systems, and hardware-software co-design for efficient computing. Key research contributions include advancements in FPGA-based reconfigurable systems, sparse dataflow architectures, and medical imaging accelerators. He has secured grants for projects like 'Advanced machine vision guided aquatic surface vehicles' and 'Efficient and Productive Parallel Data Processing in Hybrid FPGA-CPU Clusters.' Prof. So has advised numerous students and researchers, contributing to over 150 peer-reviewed publications. His current projects explore AI hardware acceleration, neuromorphic computing, and FPGA-driven solutions for big data challenges.
John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.
Giulio Jacucci is a Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Helsinki Information Technology Research Institute. He serves as a Supervisor in the Doctoral Programme in Computer Science, mentoring PhD candidates in human-computer interaction and related fields. His academic career spans over two decades with continuous research productivity from 2006 through 2025. Professor Jacucci's research interests center on Human-Computer Interaction , particularly focusing on Virtual Reality , Brain-Computer Interfaces , and Neuroadaptive Systems . His work explores how technology can adapt to users' cognitive and physiological states, with significant contributions to social virtual reality environments, embodied agents, and information retrieval systems. He investigates how virtual representations affect social behavior, time perception, and communication in digital spaces. His publication record shows a clear evolution from foundational work in information retrieval toward increasingly sophisticated neuroadaptive systems. Recent publications (2024-2025) demonstrate his leadership in examining social dynamics within virtual reality platforms, including votekicking mechanisms, mirror watching behaviors, and communication tools like mutes. His work bridges technical innovation with deep understanding of human social behavior in digital environments. Professor Jacucci currently leads multiple research projects funded by the Academy of Finland, including DataLit: Datalukutaito ja vastuullinen päätöksenteko (2023-2026) and MyModel: Selitettävyys suositusten hallinnassa (2023-2027). These projects focus on data literacy, responsible decision-making, and explainability in recommendation systems, reflecting his commitment to ethical and user-centered technology development. His research has received media attention, particularly around brain-computer interfaces and virtual reality applications, with coverage in outlets discussing how deep learning transforms mobile applications and sensor landscapes. He has presented his work at major conferences including CHI 2017 and continues to be an active contributor to the international research community.
Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.
Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Thomas Hacker is a Professor in the Department of Computer and Information Technology at Purdue Polytechnic Institute, Purdue University. His research focuses on cloud computing, high-performance computing, operating systems, computer networking, and cyber infrastructure . He holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Michigan, along with dual B.S. degrees in Computer Science and Physics from Oakland University. Education: PhD (Computer Science & Engineering), University of Michigan (2004) MS (Computer Science & Engineering), University of Michigan (1993) BS (Computer Science, Mathematics Minor), Oakland University (1989) BS (Physics), Oakland University (1989) Dr. Hacker's research spans cloud and grid computing, operating systems, and distributed systems , with applications in earthquake engineering data systems and AI-driven infrastructure analysis. His recent work explores extended layer 2 networking for bare-metal provisioning ( 2023 IEEE Cloud Summit ) and machine-supported bridge inspection using artificial intelligence ( Transportation Research Record, 2023 ). Notable scientific contributions include 15+ publications on topics like cyberinfrastructure for earthquake engineering, container-based virtualization, and data-intensive systems. His work has been recognized with awards such as the NSF CAREER Award (2010) and multiple Purdue Seed for Success Awards . Key Scientific Awards: NSF CAREER Award (2010) Purdue Seed for Success Awards (2008-2013) ASEE Information Systems Division Best Paper Award (2012) College of Technology Outstanding Faculty in Discovery Award (2010) He has held leadership roles at Purdue, including Department Head (2018-2021) and Interim Department Head (2011-2016) . His career spans academic positions at Indiana University, University of Michigan, and industry roles at Storage Technology Corporation.
