Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Dr. Jing Li is an Associate Professor and Eduardo D. Glandt Faculty Fellow at the University of Pennsylvania , holding dual appointments in the Electrical and Systems Engineering and Computer and Information Science departments. As co-director of the CyberSavvy nationwide security research center and director of the Penn Computational Intelligence Lab (PennCIL) , she pioneers innovations in non-von Neumann computing paradigms. Her research spans post-CMOS technologies, in-memory computing, and hardware-software co-design for security and AI applications. PhD in Computer Engineering, Purdue University (2009) BSc in Electrical Engineering, Shanghai Jiaotong University (2004) Research Focus: Dr. Li's work addresses fundamental challenges in computer systems across the stack. Key areas include: In-Memory Computing: Liquid Silicon architecture combining RRAM with silicon CMOS through monolithic 3D integration Security Engineering: Transforming computer security from "Art" to formal "Engineering" discipline within CyberSavvy Virtualization: Cloud FPGA abstraction layers decoupling compilation from runtime resource management Graph Analytics: Degree-aware optimization techniques for massive-scale graph processing Deep Learning Systems: Roofline model extensions for FPGA-based CNN acceleration Scientific Impact: Awarded DARPA Young Faculty Award , NSF CAREER Award , and IBM CEO Milestone Award , her team has achieved world records in energy-efficient computing (ENIAD supercomputer). With 46 U.S. patents and over 80 publications, she leads ecosystem development for emerging computing architectures through initiatives like the open-source MEG simulation platform . Community Leadership: Dr. Li serves on program committees for flagship conferences ( ISCA , FPGA Symposium ), chairs the International Memory Workshop , and contributes to the MLsys conference's inaugural committee. She actively mentors through multiple PhD openings and industry collaborations.
Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.
Michael John Janik is a Professor in the Department of Chemical Engineering at Pennsylvania State University, with significant affiliation to the Institute of Energy and the Environment (IEE). His academic profile demonstrates exceptional research productivity with 270 research outputs, 25 funded projects, and substantial scholarly impact reflected in 17,238 citations and an h-index of 61. His research expertise centers on computational chemistry with particular focus on Density Functional Theory applications to catalysis and electrocatalysis. The fingerprint analysis of his work reveals strong concentrations in Density Functional Theory (76%), Oxidation Reactions (36%), Carbon Dioxide research (29%), Adsorption phenomena (27%), and First Principles Chemistry (22%). His work significantly contributes to UN Sustainable Development Goals related to clean energy and climate action. Analysis of his recent publications (2020-2025) reveals a strong research trajectory in electrocatalysis, particularly examining cation effects on CO 2 reduction mechanisms, intermetallic catalyst design, and computational modeling of electrochemical systems. His work bridges fundamental computational chemistry with practical applications in sustainable energy conversion. h-index of 61 17,238 total citations Multiple high-impact publications in journals including Nature Catalysis, Journal of the American Chemical Society, and Science Advances Professor Janik actively leads and collaborates on numerous research projects, particularly with Dr. Rioux and other colleagues, focusing on advanced catalyst development and electrochemical energy conversion systems. His current research portfolio includes multiple active NSF-funded projects extending through 2027 that address critical challenges in electrocatalysis, CO 2 reduction, and intermetallic catalyst design. His research group maintains strong connections with the Institute of Energy and the Environment, positioning his work at the intersection of fundamental computational chemistry and applied energy solutions. Current projects include combining DFT with classical simulations to predict solvation effects, developing high-entropy alloys for catalysis, and studying oxide overlayers in CO 2 reaction systems.
Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.
Ping Yang is a Professor and Associate Director for Research and Graduate Programs in the School of Computing at Binghamton University (SUNY). She holds a Ph.D. in Computer Science from Stony Brook University, an ME from the Chinese Academy of Sciences, and a BS from Zhongshan University. Her research focuses on cybersecurity, AI-based security, virtual machine security, privacy policy analysis, and formal methods. She directs the Center for Information Assurance and Cybersecurity and coordinates cybersecurity programs at both undergraduate and graduate levels. Education: BS in Computer Science, Zhongshan University ME in Computer Science, Chinese Academy of Sciences MS and PhD in Computer Science, State University of New York at Stony Brook Research Interests: Dr. Yang's work spans information and systems security, security in virtualized computing, access control mechanisms, privacy policies, and formal methods for security verification. Her projects include blockchain-based provenance storage, real-time anomaly detection in workflows, and privacy-preserving virtual machine migration. She has led NSF-funded initiatives on security in cloud environments and scientific workflows. Awards: Not explicitly listed in the provided materials. Advising & Grants: Advised over 30 PhD/Master’s students and contributed to grants including NSF Scholarship for Service and GenCyber programs. Her team develops tools like RBAC-PAT for access control analysis. Labs/Teams: Leads the Center for Information Assurance and Cybersecurity and collaborates on projects involving secure data workflows and blockchain applications in scientific research.
Nenad Miljkovic is the Founder Professor in the Department of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC), with concurrent appointments in Electrical and Computer Engineering and the Materials Research Laboratory. He directs the Air Conditioning and Refrigeration Center (ACRC) and is affiliated with the International Institute for Carbon Neutral Energy Research (Kyushu University). His research spans thermo-fluid sciences, interfacial phenomena, and renewable energy, focusing on enhancing efficiency in power generation, electronics cooling, and thermal management via micro/nanostructured surfaces. Miljkovic holds a BASc in Mechanical Engineering from the University of Waterloo (2009) and an M.S. (2011) and Ph.D. (2013) from MIT. Prior to UIUC, he was a Postdoctoral Associate at MIT and an Adjunct Assistant Professor at UIUC. His research integrates fundamental studies of phase change and electrokinetics with applied device development, including solar thermal conversion and atmospheric energy harvesting. Recent work explores frost dynamics, battery thermal management, and scalable anti-corrosion coatings. Miljkovic's publications emphasize heat transfer enhancement, droplet dynamics, and sustainable energy systems. His articles frequently address thermal management challenges in electronics and renewable energy applications, leveraging nanostructured surfaces and phase change materials. Honors include: ASME Fellow (2022) Founder Professorship, Grainger College of Engineering (2023) ASME Bergles-Rohsenow Young Investigator Award (2021) ONR Young Investigator Award (2017) NSF CAREER Award (2016) He leads the Energy Transport Research Laboratory, advising graduate students and collaborating with industry on thermal systems. Grants support work on electronics cooling, battery safety, and renewable energy integration.
Andreas Jentys is a Professor at the Department of Technical Chemistry within the TUM School of Natural Sciences at Technische Universität München (TUM). His research focuses on understanding surface reactions and transport phenomena in mesostructured, micro- and mesoporous oxides with acid/base or redox properties, as well as supported metal catalysts for gas and condensed phase reactions. He prepares materials with tailored functionality and studies their sorptive and catalytic properties using in situ electron and vibrational spectroscopy combined with microkinetic experiments. Education: PhD (Dr. techn.) in Chemistry from TU Wien (1991) Postdoctoral: Royal Institution of Great Britain (1992-1993) with Prof. Richard Catlow Academic Career: TU Wien (1998: Associate Professor), TUM (1999: Senior Scientist; 2011: Professor) Teaching: Active in TUM Asia since 2006 Research interests span catalysis, surface chemistry, material science, and chemical reaction engineering. He specializes in zeolites, bimetallic catalysts, and in situ spectroscopic methods. Recent work examines: Hydrogen evolution and carbon-carbon coupling on Cu Methane activation via Co²⁺ sites in ZSM-5 Photocatalytic systems using metal-organic frameworks Solvent effects in palladium-catalyzed hydrogenation CO₂ hydrogenation over bifunctional catalysts Publications trend toward heterogeneous catalysis, with emphasis on: Design of mesoporous and microporous materials Characterization of metal clusters and oxo species Microkinetic modeling of catalytic systems Environmental applications (NOx/CO₂ reduction) Hydrogen and hydrocarbon processing Teaching includes courses on: Reaction Engineering and Kinetics Industrial Chemical Processes I & II Fundamentals of Catalysis TC Praktikum (internship)
Professor Guoxiu Wang is a Distinguished Professor and Industry Laureate Fellow at the University of Technology Sydney (UTS), leading the Centre for Clean Energy Technology. His expertise spans battery technologies, materials chemistry, and electrochemistry, with a focus on lithium-ion, sodium-ion, and other advanced energy storage systems. He holds prestigious fellowships, including from the Royal Society of Chemistry and the European Academy of Sciences. His research has been recognized through numerous awards, including being listed as a Highly Cited Researcher since 2018. Research Interests: Professor Wang’s work addresses challenges in energy storage through innovative materials design, including electrode materials for sodium-ion and lithium-sulfur batteries, MXenes, and electrolyte development. His team explores strategies to enhance battery performance, such as heterostructure engineering and defect-rich catalysts. Publications & Impact: With over 750 refereed papers, including in Nature Energy , Advanced Materials , and Angewandte Chemie , his work has garnered >78,000 citations (H-index 153/165). Recent trends focus on sodium-ion battery materials, MXene-based capacitors, and sustainable energy solutions like osmotic energy harvesting. Awards & Leadership: Awards include Fellowships from the Royal Society of Chemistry (2017), International Society of Electrochemistry (2018), and European Academy of Sciences (2020). He serves as an Associate Editor for Energy Storage Materials and Electrochemical Energy Reviews , and leads international collaborations, including a Royal Society Wolfson Visiting Fellowship at the University of Manchester (2024–2026). Grants & Supervision: Secured significant external grants, with active supervision of PhD/Masters students in battery technologies. His labs prioritize sustainable energy solutions and advanced material synthesis. Labs & Teams: Directs the Centre for Clean Energy Technology, fostering interdisciplinary research to advance clean energy technologies, from novel battery designs to electrochemical catalysts for CO2 and nitrate conversion.
Amro Awad is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at North Carolina State University's College of Engineering. He previously served as an Assistant Professor at the University of Central Florida and as a Senior Member of Technical Staff at Sandia National Laboratories. Dr. Awad earned his Ph.D. and Master's in Computer Engineering from NC State and a Bachelor's from Jordan University of Science and Technology. Research Focus His research spans computer architecture and security, emphasizing secure hardware systems , memory security , and integration of emerging technologies . Key contributions include novel approaches to secure and efficient GPU memory management, FPGA resource scheduling, and DRAM simulation. Publications & Awards Dr. Awad's work appears in top-tier venues like ISCA, MICRO, ASPLOS, and HPCA. He holds six U.S. patents and received the prestigious R. Ray Bennett Faculty Fellow Award and recognition as a Goodnight Early Career Innovator . Funding & Collaborations His research group has been supported by DARPA , Sandia National Laboratories , NSF , Naval Surface Warfare Center , and Air Force Research Lab . Collaborations include AMD Research, Los Alamos National Lab, HP Labs, and Air Force Research Laboratory.
Prof. Dr. Saim Özkar is a distinguished Professor in the Department of Chemistry at Middle East Technical University (METU), Ankara, Turkey. He earned his BS (1972) and MSc (1972) from the Technical University of Istanbul and his PhD (1976) from the Technical University of Munich, Germany. His career includes roles as Assistant Professor (1979–1982), Associate Professor (1982–1988), and full Professor (1988–present) at METU, with visiting positions at the University of Toronto and Colorado State University. His research focuses on homogeneous and heterogeneous catalysis , transition metal nanoparticles , hydrogen storage materials , and organometallic chemistry . Key areas include catalytic hydrogen release from solid-state storage systems and nanoparticle synthesis for sustainable energy solutions. Özkar has published extensively on nanocatalysts for hydrogen generation, with recent work emphasizing ruthenium, rhodium, and nickel nanoparticles. His articles consistently explore catalytic efficiency, stability, and applications in green energy. Awards & Grants: Science Prize, Scientific and Technical Research Council of Turkey (1996) Permanent Member, Turkish Academy of Sciences (1996) Fulbright Scholarship (2000), Volkswagen Foundation Grants (1982–1984, 1992–1994), NATO Research Grant (1981–1983) He has mentored students including Prof. Dr. Yalçın Tonbul and Dr. Joydev Manna. His lab specializes in advanced nanocatalyst design and collaborates with international institutions like Colorado State University.
Stratis Ioannidis is a Professor in the Electrical and Computer Engineering Department at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. His research focuses on distributed systems, networking, machine learning, big data, and privacy. He earned his B.Sc. from the National Technical University of Athens, and M.Sc. and Ph.D. from the University of Toronto. Prior to Northeastern, he worked at Technicolor and Yahoo Labs. Education: B.Sc. in Electrical and Computer Engineering (2002, National Technical University of Athens); M.Sc. and Ph.D. in Computer Science (2004, 2009, University of Toronto). Research interests span machine learning, distributed systems, optimization, and privacy. Key projects include the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), and work on federated learning, continual learning, and privacy-preserving algorithms. His research has been supported by NSF, Google, and Facebook grants. Recent publications emphasize federated learning, continual learning, and edge computing. Awards include the NSF CAREER Award, Søren Buus Outstanding Research Award, and multiple best paper awards. He advises numerous PhD students and collaborates across disciplines, including healthcare and wireless networks. Lab activities include SPIRAL and WIoT labs, focusing on machine learning at the edge and network optimization. Grants include the NSF AI Institute and multiple collaborative projects with industry and academia.
Tianzheng Wang is an Associate Professor and Director of the Dual-Degree and Partnerships Programs at the School of Computing Science, Simon Fraser University. His research focuses on database systems, transaction processing, parallel and distributed computing, and embedded systems. He holds a PhD in Computer Science from the University of Toronto (2017) and a BSc in Computing from Hong Kong Polytechnic University (2012). Research Interests: Database systems optimized for modern hardware, parallel programming, synchronization, and distributed architectures. His work emphasizes high-performance transaction processing and efficient indexing techniques, with applications in cloud and embedded systems. Awards: ACM SIGMOD Best Paper Award (2025), IEEE TCSC Early Career Award (2019), and multiple distinguished reviewing recognitions (SIGMOD/VLDB 2021-2024). His research has been integrated into systems like Amazon Redshift and DragonflyDB. Teaching: Leads courses such as CMPT 454 (Database Systems II), CMPT 300 (Operating Systems), and special topics in databases. Actively mentors graduate and undergraduate students in research projects. Labs & Collaborations: Heads the Data-Intensive Systems Lab, part of SFU's Data Science and Systems groups. Collaborates on tools like PiBench for persistent memory benchmarking and contributes to open-source projects like CoroBase and Tabular.
Sally Gibson is a researcher at the Department of Earth Sciences, University of Cambridge, specializing in mantle geodynamics and volatile cycling processes. Her work integrates field observations, geochemical analysis, and numerical modeling to investigate how deep Earth processes influence surface environments over 3.5 billion years of planetary evolution. Research focuses on volatile cycling (CO₂, H₂O, F, Cl, S) in mantle systems Key projects include mantle plume-ridge interactions with collaborators in the US and Ecuador Operates a LA-ICP-MS laboratory for high-resolution geochemical analyses Supervises PhD students in petrology, geochemistry, and numerical modeling Her research addresses fundamental questions about Earth's habitability through studies of mantle-derived volatiles critical for climate regulation and energy transition metal deposits. Fieldwork in remote regions like Antarctica, Lesotho, and the Galápagos Islands provides empirical data for her interdisciplinary approach. Recent publications highlight her expertise in mantle xenolith analysis, plume dynamics, and volatile quantification in large igneous provinces. Her group's work combines 3He/4He isotopic analysis with seismic tomography to constrain lithospheric evolution and mineral deposit formation. Students under her supervision develop expertise in petrology and geochemical modeling while engaging with environmental and societal impacts of geological research. She actively promotes scientific outreach and community engagement, fostering connections between academia and broader society.
