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Shantanu DuttView profile

Professor

• VLSI CAD
• Discrete Optimization
• FPGA Testing and Trusted Design
• +2 more

Shantanu Dutt is a full Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago , located in Chicago, IL. His office is in 930 SEO at 851 S. Morgan St, and he can be reached at dutt@uic.edu . Education Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (1990) M.Tech. in Computer Engineering, Indian Institute of Technology Kharagpur (1984) B.E. in Electronics and Communication Engineering, Maharaja Sayajirao University of Baroda (1983) Research Interests Professor Dutt’s research agenda centers on VLSI Computer-Aided Design (CAD) , with particular emphasis on physical design and incremental synthesis targeting both ASICs and FPGAs. He explores discrete optimization techniques to solve placement, routing, and partitioning problems. Another major thrust is FPGA built-in self-test (BIST) and trusted design , ensuring provable diagnosability and security against hardware Trojans. He also investigates fault-tolerant computing at both chip and system levels, and develops parallel and distributed computing algorithms for scalable performance. Scientific Awards 1996 Best Paper Award , ACM/IEEE Design Automation Conference, for “A probability-based approach to VLSI circuit partitioning” 1995 Most Influential Paper Award , Fault-Tolerant Computing Symposium, for “Design and reconfiguration strategies for near-optimal k-fault-tolerant tree architectures” Research Impact and Funding Professor Dutt has published extensively in top-tier journals (IEEE TVLSI, ACM TRETS, IEEE TCAD) and premier conferences (ICCAD, DAC, DATE, FTCS). His work has shaped incremental placement, timing-driven routing, FPGA security, and fault-tolerant multiprocessor architectures. While specific grant numbers are not listed, the breadth and longevity of his publication record indicate sustained funding from NSF, industry, and other agencies. Laboratory and Collaborations Although no formal laboratory name is provided, Professor Dutt’s research is conducted within the ECE department’s VLSI CAD and Fault-Tolerance groups, collaborating with graduate students and colleagues across the US and internationally.

• Computer Engineering
• Embedded Systems
• Fault Tolerance
• +4 more

Corrado De Sio is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN) , Politecnico di Torino , affiliated with the College of Computer, Film, and Mechatronics Engineering . His academic roles include course instruction and collaboration for Reconfigurable Computing , High Performance Computing (HPC) , and Operating Systems for High-Performance Supercomputers across multiple academic years (2020-2025). Research Interests focus on: Reliability of reconfigurable systems and FPGAs under radiation effects Hardware-software co-design for fault tolerance Embedded systems in aerospace and safety-critical applications Machine learning acceleration on reconfigurable hardware Radiation effects on real-time operating systems and CNN implementations Recent publications address: 2025: Selective hardening of RISCV soft-processors for space applications 2025: Real-time 'signal for help' gesture recognition systems 2024: Reliability analysis of RISC-V processors and CNN placement algorithms 2023: Fault tolerance in FPGA-based CNNs and radiation effects on RTOS Patents include: PyXEL - Python Toolkit for Reconfigurable Hardware Surveillance Software for Real-Time Violence Detection Academic Supervision : Co-supervisor for PhD candidate Arash Amini Bardpareh in Computer and Systems Engineering .

Silvia VignoliniView profile

Professor

• Biomaterials
• Sustainable Materials
• Biophysics
• +6 more

Prof. Silvia Vignolini is a leading researcher in sustainable and bio-inspired materials. Since January 2023, she has served as Director at the Max Planck Institute of Colloids and Interfaces , where she leads the department of Sustainable and Bio-inspired Materials . Her academic career includes a Lecturer in Physics at University College London (2013-2017) and a Professor of Biomaterials and Sustainability at the University of Cambridge (2020-2022). Education: University of Florence (Physics, PhD) Postdoctoral Research: University of Florence , University of Cambridge Her research bridges chemistry , soft matter physics , optics , and biology , focusing on the self-assembly of natural materials into functional architectures. She pioneered work on cellulose-based photonic materials with applications in displays, pigments, and radiative cooling. The selected articles highlight her work on cellulose nanocrystals , structural coloration , and sustainable fabrication techniques . Key trends include mechanochromic hydroxypropyl cellulose systems, bio-inspired light management, and applications in microalgae growth and bacterial symbiosis. Scientific Awards : Philip Leverhululme Prize (2019) ACS Lectureship in Sustainable Chemistry (2018) Ipazia Prize for Women in Science (2012) PhD Thesis Award (University of Florence, 2009) At the Max Planck Institute, her interdisciplinary research group explores bio-inspired design principles for sustainable materials, combining experimental and computational approaches to create functional materials from natural resources.

Yuxin ChenView profile

Professor

• statistics
• optimization
• reinforcement learning
• +5 more

Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .

Emmett WitchelView profile

Professor

• Computer Architecture
• Systems and Networking
• Security and Privacy
• +2 more

Emmett Witchel is a Professor of Computer Science at the University of Texas at Austin , with research spanning computer architecture, systems, networking, security, and privacy . His work focuses on low-level systems optimization and secure concurrent execution. Research Interests: Concurrent systems, secure execution environments, GPU integration, distributed systems. Teaching: CS 380L (Advanced Operating Systems), CS 371M (Mobile Computing). Scientific Awards: Runner-up Best Paper, ASPLOS 2013 Runner-up Award for Outstanding Research, USENIX Symposium 2012 IEEE Micro Top Pick Award 2007 ACM Honorable Mention 2004 George M. Sprowls Award, MIT EECS 2004 Recent Publications demonstrate leadership in CXL pod databases ( Tigon ), stateful serverless computing ( Boki ), SmartNIC-accelerated file systems ( LineFS ), and GPU security ( Telekine ). His work bridges hardware-software co-design and practical systems implementation.

Sarah ShirazyanView profile

Lecturer

• Technology Law and Policy
• Misinformation Governance
• Responsible AI Development
• +3 more

Sarah Shirazyan is a Lecturer in Law at Stanford Law School , where she designed the Interpol-Stanford Policy Lab. She also serves as Director and Head of Meta's GenAI Product Policy work, overseeing responsible AI development across Meta's platforms. Doctor of Juridical Sciences (J.S.D.) from Stanford Law School LL.M. from SOAS/UCL, University of London Her research focuses on the intersection of national security , public policy , and international law , particularly examining: Effectiveness of UN Security Council responses to WMD terrorism Framework for regional organization collaboration in WMD prevention Platform content policy development and misinformation governance Responsible AI implementation under legal frameworks Key publication themes include: Platform algorithmic accountability Media capture in digital domains Content policy development during crises UN Security Council institutional limitations Regional organization synergies in security Recipient of Gerald J. Lieberman Award for research, teaching, and community service Professional affiliations: Stanford Law School Stanford Center for International Security and Cooperation (CISAC) INTERPOL European Court of Human Rights Council of Europe United Nations

Paul Pu LiangView profile

Assistant Professor

• Multimodal Machine Learning
• Human-AI Interaction
• Generative AI
• +3 more

Paul Pu Liang is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Media Lab and Department of Electrical Engineering and Computer Science (EECS). He directs the Multisensory Intelligence research group, focusing on building AI systems that integrate diverse sensory inputs to enhance human-AI symbiosis. His work spans theoretical foundations, large-scale resources, and neural architectures for multisensory learning. Education: PhD in Machine Learning (Carnegie Mellon University), MS in Machine Learning (Carnegie Mellon), BS with University Honors in Computer Science and Neural Computation (Carnegie Mellon) Research Interests: Multimodal machine learning, human-AI interaction, clinical AI, generative models, and responsible deployment of AI systems Key Contributions: MultiBench, HEMM evaluation framework, CLIMB clinical data foundations, and multimodal transformer architectures Recent publications emphasize multimodal foundation models , clinical applications , and socially responsible AI . His work has been recognized with multiple best paper awards and fellowships from Siebel, Facebook, and other institutions. Scientific Awards Siebel Scholars Award Waibel Presidential Fellowship Facebook PhD Fellowship Center for ML and Health Fellowship Rising Stars in Data Science Four best paper awards Paul teaches courses on machine learning and multimodal AI at MIT and CMU. He mentors students across multiple programs including Media Arts & Sciences, EECS, and IDSS, with former advisees now at institutions like OpenAI, UC Berkeley, and Princeton.

Giorgio AscoliView profile

Professor

• Computational Neuroanatomy
• Neuroinformatics
• Neuronal Morphology
• +5 more

Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.

Thomas PeyrinView profile

Professor

• Symmetric Cryptography
• Cryptanalysis
• Lightweight Cryptography
• +5 more

Thomas Peyrin is a Full Professor at Nanyang Technological University (NTU) in Singapore, leading the Symmetric Key and Lightweight Cryptography Lab (SyLLab) since 2012. He co-founded TT-logic.ai, focusing on cryptographic innovations. His academic journey includes a Ph.D. in Cryptology from Orange Labs and University of Versailles (2008), a Master in Computer Science from MPRI (2005), and engineering training at CPE Lyon (2004). Education : Ph.D. (2008), MPRI Master (2005), EPFL Exchange (2003-2004), CPE Lyon (1999-2004) Career : Full Professor at NTU (2022-present), Associate Professor (2017-2022), Nanyang Assistant Professor and NRF Fellow (2012-2017), Research Fellow at NTU (2010-2012), Cryptography Expert at Ingenico (2008-2010) Thomas specializes in symmetric cryptography, cryptanalysis of hash functions and block ciphers, and machine learning applications to security. His work includes groundbreaking research on SHA-1 collisions, lightweight cryptographic primitives (e.g., GIFT, PHOTON), and automated cryptanalysis frameworks. Recent projects explore integrating AI with cryptographic verification and fault attack resistance. His publications span top conferences like CRYPTO, EUROCRYPT, and CHES, with awards including the FSE Test of Time Award (2025), FSE Best Paper (2023), and NRF Investigatorship (2022). He has advised numerous students in cryptographic research and co-founded SyLLab, a leading lab for symmetric cryptography innovation. Scientific Awards : Test of Time Award at FSE 2025 Best Paper Award at FSE 2023/ToSC NRF Investigatorship (2022) Lee Kuan Yew Postdoctoral Fellowship Best Paper Awards at FSE 2012 and ASIACRYPT 2007

Gianni FranchiView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Uncertainty Quantification
• +2 more

Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.

Leslie ValiantView profile

Professor

• Complexity Theory
• Machine Learning
• Parallel Computation
• +4 more

Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in Harvard University's School of Engineering and Applied Sciences, where he has held a faculty position since 1982. A foundational figure in theoretical computer science, his work bridges artificial and natural computational phenomena across multiple disciplines. His academic background includes education at: King's College, Cambridge Imperial College, London Ph.D. in Computer Science from Warwick University (1974) Valiant's research spans computational complexity , machine learning theory , parallel systems , and computational neuroscience . He pioneered the PAC (Probably Approximately Correct) learning framework that established computational learning theory as a rigorous field. His holographic algorithms work revealed deep connections between computational complexity and statistical physics, while his neuroidal model and evolvability theory provide computational explanations for cognitive processes and biological evolution. Current investigations focus on cortical computation primitives and knowledge infusion architectures. His publication trends show increasing integration of neuroscience with computational theory since 2010, with dominant themes in holographic computation (2006-2018), cortical modeling (2012-2018), and evolvability (2009-2017). The work consistently applies computational complexity analysis to biological and cognitive systems. Major recognitions include: Nevanlinna Prize (1986) for mathematical aspects of computer science Knuth Award (1997) for foundational algorithms contributions EATCS Award (2008) for theoretical computer science impact Turing Award (2010) for computational learning theory and complexity Fellowship in the Royal Society and National Academy of Sciences Valiant's research has been supported by NSF and international grants enabling cross-disciplinary work in computational neuroscience and evolutionary algorithms. While specific advisees aren't documented in source materials, his theoretical frameworks have shaped generations of researchers in machine learning and complexity theory. His current research group explores neuroidal architectures for cognitive computation, investigating how cortical circuits achieve robust information processing through in-circuit testing methodologies. Ongoing projects aim to identify fundamental computational primitives in neural systems and develop biologically inspired AI frameworks.

Jiaxin LinView profile

Assistant Professor

• Computer Systems
• Networking
• Computer Architecture
• +5 more

Jiaxin Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at Cornell University , affiliated with the Computer Systems Laboratory . She earned her Ph.D. in Computer Science from UT Austin (2025) , preceded by an M.S. from University of Wisconsin-Madison and a B.S. from ShenYuan Honors College at Beihang University. Her research focuses on co-designing software and hardware systems to enable high-performance data center communication, particularly through: Programmable network interface controllers (SmartNICs) Terabit network system stacks Cache/memory interconnects Compilers for in-network computing Chip-to-chip interconnects Her work addresses challenges in portability across heterogeneous SmartNICs, demonstrated through the development of the Alkali compiler framework (NSDI '25). Key themes include hardware abstraction, data center scalability, and network-compute co-design. Scientific Awards: Google Junior Faculty Award (2025) MIT EECS Rising Star (2024) Google Ph.D. Fellowship (2021) Meta Ph.D. Fellowship (2021)

Zsolt KiraView profile

Assistant Professor

• Robotics
• Machine Learning
• Artificial Intelligence
• +6 more

Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.

John C. DoyleView profile

Professor

• Control Theory
• Dynamical Systems
• Network Science
• +4 more

John C. Doyle is the Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering at the California Institute of Technology (Caltech), where he holds appointments in the Division of Engineering and Applied Science with primary affiliation in the Control and Dynamical Systems Department. His research bridges theoretical foundations with applications across biological, technological, medical, and ecological networks. He earned a BS and MS in Electrical Engineering from MIT (1977) and a PhD in Mathematics from UC Berkeley (1984), followed by consultancy at Honeywell Systems and Research Center (1976-1990). MIT: BS & MS in Electrical Engineering (1977) UC Berkeley: PhD in Mathematics (1984) Doyle's research centers on universal laws and architectures in complex systems, emphasizing robustness-efficiency tradeoffs, speed-accuracy tradeoffs (SATs), diversity-enabled sweet spots (DeSS), bowtie/hourglass structures, and evolvability. His work pioneers System Level Synthesis (SLS) for control systems with sparse, local, saturating, delayed, noisy, quantized, and distributed (SLSDNQD) components, integrating control theory, computation, communication, and machine learning to address challenges from neural networks to infrastructure resilience. Key concepts include virtualization, horizontal transfer, and virality in multiscale systems. Analysis of his publication trends reveals consistent interdisciplinary impact across neuroscience (brain connectivity modeling), systems biology (metabolic oscillations), network science (internet topology), and physics (turbulence, earthquakes), with recurring themes of robust-efficiency limits and architectural principles governing complex networks. His work demonstrates exceptional translation from abstract theory to practical tools like the Matlab Robust Control Toolbox and Systems Biology Markup Language (SBML). His scientific recognition includes: 1990 IEEE Baker Prize (ranked among top 10 most important mathematics papers 1981-1993) Three IEEE Automatic Control Transactions Awards (1998, 1999, 2021) ACM Sigcomm Paper Prize (2004) and Test of Time Award (2016) IEEE Control Systems Field Award (2004) Multiple early-career honors including IEEE Centennial Outstanding Young Engineer (1984) Doyle has mentored generations of students whose contributions include foundational software tools adopted globally. His research has secured sustained funding from NSF, NIH, and other agencies supporting theoretical advances in control frameworks and their applications to biomedical systems, network infrastructure, and environmental modeling. The SBML initiative exemplifies his group's impact in standardizing computational biology research. He leads a highly collaborative research ecosystem at Caltech that integrates engineers, biologists, neuroscientists, and computer scientists to develop universal principles for complex networks. Current efforts focus on translating theoretical insights into health technologies, resilient infrastructure, and climate-responsive systems through the application of robust-efficiency frameworks to emerging challenges in cyber-physical and biological domains.

Yi DingView profile

Assistant Professor

• AI/ML Systems
• Sustainable Computing
• Computer Systems
• +3 more

Yi Ding is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, where they lead the STYLE (Sustainable computing Systems and LEarning) Lab. Dr. Ding joined Purdue in August 2023 after completing a postdoctoral fellowship at MIT CSAIL as an NSF Computing Innovation Fellow, mentored by Michael Carbin. During their postdoc, they also held a visiting position at Meta Infra Data Center to improve server maintenance efficiency in hyperscale datacenters. They received their Ph.D. in Computer Science from the University of Chicago, advised by Henry Hoffmann. Dr. Ding's research focuses on computer systems, computer architecture, and AI/ML, with strong emphasis on applications in sustainability and healthcare. Their work spans sustainable computing, including energy efficiency in datacenters and LLM serving, as well as healthcare applications such as mental health prediction and EEG analysis. Their recent publications demonstrate a strong trend toward addressing environmental impacts of computing, particularly in datacenters and AI systems, while also exploring innovative healthcare applications. Their research bridges systems, sustainability, and health domains, creating novel solutions for pressing societal challenges. Dr. Ding has received notable recognition including the Seed Funding for High-Impact Review Papers (2024), the Meta Research Award (2021), and was selected as a Computing Innovation Fellow by CRA/CCC (2020). Seed Funding for High-Impact Review Papers (2024) with Inez Hua 1st Place in Research Talk in CoE at Fall 2024 Undergrad Research Expo (awarded to Gavin Fortwendel) 2020 Computing Innovation Fellow by CRA/CCC Meta Research Award on Statistics for Improving Insights, Models, and Decisions (2021) Dr. Ding is actively recruiting self-motivated Ph.D. students interested in AI/ML systems research. They have secured funding for undergraduate research projects through DUIRI, focusing on sustainable AI computing and energy use in training autonomous vehicles. Their lab collaborates with various institutions including MIT, Meta, and interdisciplinary partners at Purdue. The STYLE Lab under Dr. Ding's leadership is actively engaged in multiple research initiatives addressing sustainable computing and healthcare applications, with strong industry connections and funding support from both internal university sources and external partners.