Noura Limam is a Research Assistant Professor at the University of Waterloo's Cheriton School of Computer Science. Her research spans network operations, with emphases on software-defined networking (SDN), 5G/6G architectures, network security, and autonomous network management. Recent work focuses on AI-driven solutions for encrypted traffic analysis, network slicing security, and satellite communication systems. She develops frameworks like Monarch for network slice monitoring and 5Guard for secure slicing. Contributions include blockchain-assisted authentication protocols, meta-reinforcement learning for threat mitigation, and novel handover mechanisms for non-terrestrial networks. Her publications demonstrate consistent innovation in making networks more adaptive, secure, and efficient.
Dr. Hongli (Julie) Zhu is an Associate Professor in the Department of Mechanical and Industrial Engineering at Northeastern University's College of Engineering. Her research focuses on sustainable energy storage, multifunctional materials, and advanced manufacturing, with emphasis on developing environmentally friendly biomass-derived materials, all solid-state batteries, and flow batteries. She leads the ZHU Lab at Northeastern University, which is dedicated to creating safer, cheaper, and higher performance energy storage solutions while exploring multifunctional materials derived from nature. Dr. Zhu received her PhD from South China University of Technology and Western Michigan University (2004-2009). She conducted postdoctoral research at KTH Royal Institute of Technology in Sweden (2009-2011), focusing on biodegradable and renewable biomaterials from natural wood, followed by additional postdoctoral work at the University of Maryland (2012-2015), where she researched nanocellulose and energy storage. Dr. Zhu's research spans multiple disciplines at the intersection of materials science, energy storage, and sustainable manufacturing. Her work addresses critical challenges in energy storage technology, including developing all solid-state batteries, flow batteries, and high energy density battery systems. She has pioneered research in sustainable biomass-derived materials, particularly investigating cellulose, hemicellulose, and lignin for applications in bendable, implantable, and biocompatible electronics. Her lab also focuses on advanced manufacturing techniques, including high-speed roll-to-roll processing for emerging advanced materials and devices. Analysis of Dr. Zhu's publication record reveals a strong focus on next-generation battery technologies, particularly solid-state systems. Her research demonstrates significant contributions to understanding and improving lithium dendrite suppression, electrode architecture optimization, and interface stabilization in solid-state batteries. She has also made substantial advances in sustainable materials derived from natural resources, developing applications for cellulose nanostructured fibers, paper, and aerogel/hydrogel systems. MRS Communications Early Career Distinguished Presenters and JMR Distinguished Invited Speakers (2024) Selected in Stanford University List of Top 2% Scientists Worldwide (2021-2024) College of Engineering Faculty Fellow (2023) Soren Buus Outstanding Research Award (2022) Women in Materials Science, Advanced Materials (2021 and 2022) Women Scientists at the Forefront of Energy Research, ACS Energy Letters (2020) Innovator of the Year 2013, Maryland Jakob Wallenberg Scholarship, Sweden Dr. Zhu has secured significant research funding from various sources, including the National Science Foundation and Department of Energy. Her current projects include "Uncovering the mechano-electro-chemo mechanism of fresh Li in sulfide based all solid-state batteries through operando studies" (NSF), "Enabling Advanced Electrode Architecture through Printing Technique" (DOE), and "Engineering the Metal Sulfide Interface in All Solid State Batteries through Operando Study" (NSF). She collaborates with industry partners including Rogers Corporation and has developed patented technologies related to sustainable materials and energy storage. Dr. Zhu serves as Codirector of Advanced & Intelligent Manufacturing, Editor of Progress in Materials Science, and on the Editorial Advisory Board of Chemical Society Reviews. The ZHU Lab at Northeastern University is a highly interdisciplinary research group that bridges scales from the nanoscopic to macroscopic and system level. The lab's work has led to numerous patents, including "Natural fiber composites as a low-cost plastic alternative" and "Fire-retardant Nanocellulose Aerogel, and Methods of Preparation and Uses Thereof." The group focuses on making energy storage safer, cheaper, and higher performing while exploring multifunctional materials derived from nature, with particular emphasis on applying high-speed roll-to-roll manufacturing to emerging advanced materials and devices.
Alex Shestopaloff is a Lecturer in Statistics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. Previously, he was a Research Fellow at the Alan Turing Institute (2017–2020) and a Junior Research Fellow at Campion Hall, Oxford. He holds a PhD in Statistics from the University of Toronto (2016), supervised by Radford M. Neal. His research focuses on developing efficient MCMC methods, high-dimensional time series analysis, network science, and applications in financial market microstructure. Education: PhD in Statistics, University of Toronto (2016) Supervisor: Radford M. Neal Research Interests: Bayesian online learning in non-stationary environments Limit order book modeling and trading strategies Graph clustering and network analysis Statistical methods for high-dimensional data Algorithmic trading and cryptocurrency markets His recent work spans financial engineering, machine learning, and statistical methodologies. Notable contributions include cluster-based trading strategies (ClusterLOB), generalized Bayesian filtering frameworks, and scalable graph analysis techniques. Collaborations with industry partners (e.g., Wise Plc) highlight applied research in financial systems. Advising & Alumni: Current advisees include Yichi Zhang (Oxford), Maria Fernanda Pintado (QMUL), and Dave Lui (Oxford) Alumni: Gerardo Duran-Martin (Postdoc at Oxford-Man Institute), Claudio Bellani (Citadel Securities) Labs/Teams: Leads interdisciplinary projects at QMUL and collaborates with the Alan Turing Institute on financial and network science initiatives.
Dr. Farhad Merchant is an Assistant Professor of Innovative Computer Architecture at the Bernoulli Institute, University of Groningen, since July 2024. Previously, he served as a Lecturer (Assistant Professor) at Newcastle University (2022–2024) and held research roles at Bosch Research, NTU, and RWTH Aachen University. His research focuses on emerging technology-based computing and hardware-oriented security, including neuromorphic architectures, in-memory computing, and secure hardware design. Education: PhD in Electronics Engineering from the Indian Institute of Science, Bangalore, with a DAAD-funded visit to RWTH Aachen University. He also holds industry experience from Bosch Research. Research Interests : - Hardware Security - Neuromorphic Computing - Algorithm-Architecture Co-design - Reconfigurable Computing - Computer Arithmetic Projects : - Coordinator for the REACT project (2025–2029): Focuses on self-aware neuromorphic architectures. - Principal Investigator for Privacy-Preserving Computer Architectures (CogniGron, 2025–2029). - Completed BioNanoLock project (DFG-funded, focusing on bio-nanoelectronic security). Awards : - Best Paper Awards at ISQED 2022, NEWCAS 2023, and VLSI-DAT 2024. - Minerva Fellowship (Technion, Israel), HiPEAC Technology Transfer Award (2019). He co-founded the SeHAS workshop (since 2019) and serves on editorial and program committees for major conferences like DAC, ISLPED, and VLSI-SoC.
Yu Meng is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), part of the School of Engineering and Applied Science. He joined UVA in 2024 as a tenure-track faculty member. His research focuses on machine learning, natural language processing (NLP), and data mining, with recent emphasis on large language models (LLMs), alignment, reliability, and ethical AI development. Educated at the University of Illinois Urbana-Champaign (UIUC), Meng earned his Ph.D. in 2023 under advisor Jiawei Han. His doctoral thesis, Efficient and Effective Learning of Text Representations , received the ACM SIGKDD 2024 Dissertation Award. He also held a visiting researcher position at Princeton University under Danqi Chen and was a Google PhD Fellow. His work has been recognized with awards including the Superalignment Fast Grant from OpenAI and notable publications at venues like NeurIPS, ICLR, and ACL. Meng’s research explores topics such as preference optimization (SimPO), retrieval-augmented generation (InstructRAG), and zero-shot learning. He actively serves on program committees for top conferences (ICLR, ICML, NeurIPS) and as an action editor for Transactions of Machine Learning Research (TMLR) . He teaches graduate-level courses on NLP, emphasizing cutting-edge LLM topics like architecture design, instruction tuning, and ethical considerations. Key achievements include contributions to LLM alignment via retrieval optimization, efficient pretraining techniques, and foundational work on weakly supervised learning. His research bridges theory and practice, addressing both technical challenges and societal impacts of AI systems.
Omkant Pandey is an Associate Professor at Stony Brook University, affiliated with the National Security Institute. He holds a PhD from the University of California, Los Angeles (UCLA). His research focuses on Cryptography, Security, and Privacy, with a particular emphasis on post-quantum cryptographic systems, secure computation protocols, and privacy-preserving technologies. Education: PhD in Computer Science, University of California Los Angeles (UCLA) Research interests span theoretical and applied cryptography, including the design of secure multi-party computation protocols, non-malleable codes, and post-quantum cryptographic primitives. His work also addresses challenges in privacy-preserving data sharing and IoT security in enterprise environments. His recent publications highlight advancements in post-quantum secure computation, zero-knowledge proofs, and cryptographic protocol design. Notable projects include grants such as the CAREER award for concurrent security against quantum adversaries and foundational work on black-box constructions in cryptography. Advising and grants: He advises graduate students in cryptography and has secured grants focusing on post-quantum security and cryptographic protocol development. His work is supported by initiatives like the National Science Foundation’s SaTC and CAREER programs. Labs/Teams: His affiliation with the National Security Institute reflects his contributions to applied security research and collaboration with interdisciplinary teams.
Ruixiang Tang is an Assistant Professor at Rutgers, The State University of New Jersey. His research focuses on artificial intelligence, machine learning, and natural language processing, with an emphasis on multimodal learning, model security, and ethical AI. He explores topics such as adversarial robustness, bias mitigation, and applications in healthcare and robotics. Key research interests include developing robust algorithms for vision-language models, analyzing model vulnerabilities like backdoors and hallucinations, and designing trustworthy AI systems. His work bridges theoretical advancements and practical applications, addressing challenges in healthcare data augmentation, copyright infringement detection, and cognitive reasoning. His recent publications highlight contributions to multimodal in-context learning, counterfactual reasoning benchmarks, and secure model optimization. Tang's research also intersects with fairness in AI, such as mitigating bias in NLP models and ensuring equitable outcomes in medical applications.
Professor Lei Zhou is a faculty member in the Department of Mechanical Engineering at the University of Wisconsin-Madison, with an affiliate appointment in Electrical & Computer Engineering. His research focuses on Precision Mechatronics, integrating precision mechanical design, electromagnetics, and control engineering to address challenges in electric machines, motion control, and precision systems. He holds a PhD from MIT (2019), an MS from MIT (2014), and a BE from Tsinghua University (2012). Research interests include: High-performance motion systems and electric motor design Control solutions for precision positioning and robotic actuation Magnetic levitation technologies for manufacturing and medical applications Recent work emphasizes magnetic levitation systems (e.g., LevCube nanopositioning stage) and over-actuated precision stages that break traditional performance trade-offs. His publications span 2020-2025, reflecting advancements in motor design, control algorithms, and mechatronics integration. Notable achievements include the 2023 ASPE Early Career Award and Meta Research Award. Teaching includes courses on mechatronics (ME 376), automatic controls (ME 577/ECE 577), and advanced research supervision (ECE 790/ME 890). He leads the FlexLab and LevLab, focusing on portable mechatronics education and precision motion systems. Current research explores lightweight stages for semiconductor manufacturing and magnetic catheter systems for medical treatments.
Neelakantan R. Krishnaswami is a Professor of Computer Science at the University of Cambridge's Computer Laboratory , and a Fellow of Trinity College . His research focuses on the intersection of program verification, programming language design, and foundational topics like type theory and semantics. His work spans areas such as refinement types, parser design, separation logic for systems software, and the semantics of reactive programming. Notable contributions include the Datafun language for higher-order Datalog and the λert type theory for explicit refinement types. He has also developed foundational frameworks for verifying imperative programs using advanced type systems and logical relations. Key publications include 'Explicit Refinement Types' (ICFP 2023), 'flap: A Deterministic Parser with Fused Lexing' (PLDI 2023), and 'CN: Verifying Systems C Code' (POPL 2023). His work frequently addresses challenges in efficiency, correctness, and modularity for both functional and imperative systems. His awards include Distinguished Paper Awards at PLDI 2019 and POPL 2020. His research integrates theoretical rigor with practical tooling, exemplified by contributions to languages like Coq, Lean, and Haskell.
Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods
Amir Asif is a Professor at the Lassonde School of Engineering, York University, and concurrently serves as Vice President, Research and Innovation. His academic leadership roles include Dean of the Gina Cody School of Engineering and Computer Science at Concordia University (2014-2020). He specializes in signal processing, communications, and their applications in healthcare, power grids, and distributed systems. Asif holds a PhD from Carnegie Mellon University and a Harvard certification in executive leadership. Education: PhD, Electrical and Computer Engineering, Carnegie Mellon University (1996) MS, Electrical and Computer Engineering, Carnegie Mellon University (1993) BSc, University of Engineering and Technology Lahore (1990) Harvard Certificate in Leadership for Senior Executives (2018) Research Interests: Asif’s work spans signal processing for medical imaging (e.g., ultrasound elastography), smart grid optimization, and cybersecurity in power systems. His recent publications address hydrogen energy systems, EMG-based gesture recognition, and resilient control frameworks against cyberattacks. Grants & Leadership: He leads NSERC-funded projects on federated learning and resilient algorithms. He chairs the Ontario Council of University Research and serves on TRIUMF Innovations and the Richmond Hill Board of Trade. His grants include SSHRC funding for equity initiatives and NSERC support for distributed signal processing. Teaching & Mentorship: Asif has supervised over a dozen graduate students and taught courses like Digital Communications and Statistical Signal Processing Theory. Notable advisees include Arash Mohammadi (PhD, 2014) and Nick Sajadi (PhD, 2017).
Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.
Prof. Dr. Viktor Leis is a Professor in the Department of Computer Science at the Technical University of Munich (TUM), leading the Chair for Decentralized Information Systems and Data Management. His research focuses on cost-efficient data systems, particularly in cloud environments, with expertise in core database topics like query processing, transaction management, and storage optimization. He earned his PhD from TUM in 2016 and previously held professorships at Friedrich Schiller University Jena and Friedrich-Alexander-Universität Erlangen-Nürnberg before returning to TUM in 2022. His work has been recognized with prestigious awards, including the ACM SIGMOD Dissertation Award, VLDB Early Career Research Contribution Award, and an ERC Starting Grant. Research Interests: Cloud computing, database systems, query optimization, storage engines, transaction processing, and NVMe-optimized systems. Key Projects: Developed the LeanStore storage engine and contributed to the Hyper database system. His recent publications emphasize cloud-native architectures, high-performance storage solutions, and hybrid transactional/analytical processing. He actively teaches courses on distributed systems, cloud databases, and blockchain technologies.
Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.
Joseph S. Friedman is an Associate Professor of Electrical & Computer Engineering at the University of Texas at Dallas, leading the NeuroSpinCompute Laboratory within the Erik Jonsson School of Engineering and Computer Science. His research focuses on unconventional computing paradigms leveraging nanotechnology, including neuromorphic systems, spintronics, and memristive devices. He specializes in nanomagnet-based logic architectures, neuromorphic computing with domain walls and skyrmions, and hardware security for emerging technologies. His research explores energy-efficient computing through novel paradigms such as reversible skyrmion logic, neuromorphic networks using magnetic tunnel junctions, and stochastic Bayesian inference circuits. He has pioneered spintronic neurons demonstrating 94% accuracy in handwritten digit recognition and developed secure logic locking mechanisms using nanomagnet logic. His work integrates experimental fabrication with SPICE modeling, emphasizing scalable beyond-CMOS systems. Recent advancements include toggle SOT-MRAM architectures, quantum circuit design for neutral atom systems, and neuromorphic networks leveraging superconducting flux quanta. He advises over 20 graduate and undergraduate students, fostering innovation in AI hardware and unconventional computing. Notable projects include the NeuroSpinCompute Lab's domain wall neuromorphic networks, secure logic locking schemes, and collaborations with institutions like Sandia National Labs on neuromorphic reservoir computing. Current research trends emphasize low-energy spintronic architectures, hybrid quantum-classical systems, and neuromorphic applications in edge computing. His research is supported by NSF grants CCF-1910800 and CCF-2146439, focusing on neuromorphic and spintronic systems. He regularly contributes to conferences like IEEE Rebooting Computing and SPIE Spintronics, showcasing breakthroughs in nanomagnetic logic and neuromorphic inference.
