جست‌وجوی استاد و پژوهشگر

Christy F. Landesمشاهده پروفایل

استاد

• Physical Chemistry
• Analytical Chemistry
• Materials Chemistry
• +۵ مورد دیگر

Christy F. Landes is the Jerry A. Walker Endowed Chair in Chemistry and Professor of Chemistry at the University of Illinois Urbana-Champaign, with additional appointments as Professor in Electrical and Computer Engineering and Materials Research Lab, and as an Affiliate in Chemical and Biomolecular Engineering. She joined UIUC in 2023 after serving as the Kenneth S. Pitzer-Schlumberger Chair of Chemistry at Rice University. Education: B.S. in Chemistry, George Mason University (1998) Ph.D. in Chemistry, Georgia Institute of Technology (2003) Postdoctoral positions at University of Oregon and University of Texas at Austin Professor Landes' research focuses on physical, analytical and materials chemistry with emphasis on predictive separations, spectro-electrochemistry, protein dynamics at interfaces, imaging and signal processing, and single-molecule spectroscopy. Her work aims to understand complex structure-function relationships in biological processes to inspire innovation for materials design. The Landes Research Group develops new spectroscopic tools to image chemical dynamics at interfaces at the limit of a single event, creating new models to understand and predict macroscale processes like protein separation and photocatalysis. Her research spans several specific areas including predictive separations, spectro-electrochemistry, protein dynamics at interfaces, computational imaging/AI/data science, and interfacial energy and charge transfer in hybrid nanomaterials. By studying individual molecules rather than ensembles, her group can identify the chemical complexity of nanoscale interfacial dynamics and reveal underlying populations that form ensemble measurements. Scientific Awards: 2023 Fellow of the American Association for the Advancement of Science 2024 Kazuhiko Kinosita Award in Single-Molecule Biophysics 2020 Award for Special Creativity, National Science Foundation 2019 Kavli Fellow, U.S. National Academy of Sciences 2016 Early Career Award in Experimental Physical Chemistry, American Chemical Society 2011 NSF CAREER Award Professor Landes has advised numerous graduate and undergraduate students throughout her career at University of Houston, Rice University, and now at UIUC. Her research has been supported by various grants including NSF funding. Her group has developed innovative methods to break the Abbe diffraction limit, achieving spatial resolutions of just a few nanometers and time resolutions faster than traditional cameras frame times. The Landes Research Group at UIUC continues to develop new spectroscopic tools to image chemical dynamics at interfaces, with specific focus areas including predictive separations, spectro-electrochemistry, protein dynamics at interfaces, computational imaging/AI/data science, and interfacial energy and charge transfer in hybrid nanomaterials.

• Machine Learning
• Network Performance
• IoT Security
• +۳ مورد دیگر

Andreas Johnsson is an Adjunct Senior Lecturer at the Department of Information Technology , Uppsala University, Sweden. His research spans Machine Learning , Network Performance , and IoT Security in the context of 5G/6G Networks and Edge Computing . Research interests include federated learning, transfer learning, and network optimization techniques. His recent work (2024-2021) focuses on self-regulated learning models for 6G, multi-objective neural architecture search, IoT intrusion detection generalizability, and delay prediction in heterogeneous networks. He has co-authored over 15 publications in high-impact venues like IEEE Transactions on Machine Learning in Communications and Networking and IEEE NOMS . Andreas actively collaborates with researchers such as Jalil Taghia, Farnaz Moradi, and Hannes Larsson. His contributions extend to change detection algorithms, policy adaptation frameworks, and feature selection methodologies in dynamic network environments. No formal scientific awards or student advisement details are currently documented.

Jeffrey Youngمشاهده پروفایل

پژوهشگر

• High-Performance Computing (HPC)
• Computer Architecture
• Quantum Programming
• +۶ مورد دیگر

Jeffrey Young is a Principal Research Scientist at Georgia Institute of Technology, working with the Partnership for Advanced Computing Environments (PACE) and leading Georgia Tech’s Open Source Program Office. His research focuses on high-performance computing (HPC), computer architecture, and novel accelerators including GPUs, FPGAs, and Arm/RISC-V processors. He leads next-generation computing strategy at PACE and directs the NSF-funded CRNCH Rogues Gallery testbed, which explores post-Moore accelerators like neuromorphic and near-memory systems. His work bridges hardware-software co-design and scientific software engineering. Recent research trends show expertise in quantum programming (Qwerty/ASDF), heterogeneous computing (Cupbop), and memory system optimization across GPUs, FPGAs, and CPUs. He has contributed to exascale workflows (HIPLZ), safe HPC libraries, and UAV co-simulation frameworks. Scientific Awards: NSF-funded CRNCH Rogues Gallery testbed (2020-2024) Education: Ph.D. in Computer Architecture (2013), advised by Dr. Sudhakar Yalamanchili Labs & Initiatives: Director, CRNCH Rogues Gallery testbed Co-Director, Georgia Tech Center for Scientific Software Engineering Director, Georgia Tech Open Source Program Office

• Vascular Biology
• Atherosclerosis
• Arterial Stiffness
• +۲ مورد دیگر

Dr. Stella Daskalopoulou is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) and a Professor in the Department of Medicine at McGill University's Faculty of Medicine and Health Sciences. She specializes in translational cardiovascular research with a focus on vascular health, atherosclerosis, and hypertension management. Senior Scientist, RI-MUHC Glen site Professor, Department of Medicine, McGill University Member, Cardiovascular Health Across the Lifespan Program Research Focus: Her work integrates biomedical technology with clinical investigation to identify early vascular impairment markers. Key areas include: Arterial stiffness and hemodynamic assessment Adiponectin signaling pathways in atherosclerosis Sex-specific cardiovascular risk stratification Biomarker discovery for plaque instability Pregnancy-related vascular complications Machine learning applications in plaque classification Scientific Engagement: Dr. Daskalopoulou contributes to hypertension guideline development (2024 ESC Guidelines) and explores environmental impacts on vascular health (household pollution studies). Her recent publications emphasize: Deep learning for plaque characterization Sex hormone receptor pathways in atherosclerosis Adipokine interactions with HDL metabolism Clinical decision tools for preeclampsia prediction Laboratory: Leads the Vascular Health Unit at RI-MUHC, combining histopathology, immunophenotyping, and advanced imaging for translational research.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Kiril Kuzminمشاهده پروفایل

مدرس

• Bioinformatics
• Machine Learning
• Discrete Optimization
• +۳ مورد دیگر

Dr. Kiril Kuzmin is a Lecturer in the Department of Computer Science at Georgia State University, where he teaches Data Structures, Algorithms, Data Science, and Machine Learning. He holds a Ph.D. in Computer Science (2024) with a concentration in Bioinformatics from Georgia State University and a Ph.D. in Mathematics (2009) from the National Academy of Sciences of Belarus. His academic journey includes roles as Assistant and Associate Professor at Belarusian State University and a postdoctoral fellowship at the University of Turku, Finland. Dr. Kuzmin’s research focuses on Bioinformatics, Machine Learning, Discrete Optimization, and Graph Theory. He has published over 50 papers, with notable contributions in stability analysis of combinatorial optimization problems and applications of machine learning in genomics. His work includes predicting host specificity of coronaviruses and developing algorithms for heterogeneous genomic population analysis. Dr. Kuzmin has received the Scopus Award in Mathematics (2013) and served as PI/co-PI on three international projects. His teaching spans Java programming, Data Structures, and advanced mathematical courses like Calculus and Algebra. He is affiliated with Georgia State’s bioinformatics research group and has actively contributed to academic and political advocacy in Belarus.

• Information Systems
• Artificial Intelligence
• Data Visualization
• +۴ مورد دیگر

Peter McBrien is an Associate Professor in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering. He holds dual affiliations with the Distributed Software Engineering group. His research focuses on conceptual modeling, ontology engineering, database systems, and temporal data management. He has been active in advancing techniques for data visualization, semantic web technologies, and relational database integration. Key research areas include: Ontology extraction from relational databases Type inference in transactional systems Schema transformation between heterogeneous models Peer-to-peer data integration protocols Temporal database systems His publication trends emphasize integration of heterogeneous data sources through formal methods, with notable contributions to OWL ontology implementation, hypergraph data models, and benchmarking big data query languages. Recent work focuses on Spark-based semantic reasoning and distributed knowledge exchange systems. Peter McBrien's research has been supported through Imperial College's infrastructure, with ongoing contributions to the AutoMed data integration framework and RoDEx protocols for unreliable networks. His work bridges theoretical foundations of data management with practical implementations in distributed systems.

Srikanth Rangarajanمشاهده پروفایل

استادیار

• Energy Storage Management
• Thermal Management Systems
• Electronics Packaging
• +۷ مورد دیگر

Srikanth Rangarajan is an Assistant Professor at Binghamton University's School of Systems Science and Industrial Engineering. He holds a PhD and MS from the Indian Institute of Technology Madras (2017) and a BE from Anna University Chennai (2011). His research focuses on energy storage systems, thermal management of electronics, battery optimization, and digital twinning. He previously served as an Associate Research Professor in Mechanical Engineering at Binghamton under Bahgat Sammakia. Rangarajan authored the book Phase Change Material Heat Sinks: A multi-objective Perspective and holds a patent for a rotatable heat sink design. His teaching includes optimization techniques, thermal modeling, and neural networks. Recent work explores virus spread modeling via genetic algorithms, with a preprint under review in Journal of Healthcare Informatics . He has received multiple awards including an Institute Post-Doctoral Fellowship and Research Assistantships from the Indian government. His research bridges thermal engineering with advanced manufacturing and sustainability, addressing challenges in high-power electronics and data center cooling. Education: BE in Mechanical Engineering, Anna University (2011) MS in Thermal Engineering, IIT Madras (2017) PhD in Heat Transfer, IIT Madras (2017) Research Interests: Digital twin systems for battery optimization Thermal energy storage design Advanced electronics packaging Data center cooling innovations Phase change material composites His recent articles highlight cooling solutions for high-density electronics, battery recycling challenges, and predictive models for epidemiological patterns using computational methods. Ongoing work includes embedded cooling technologies for heterogeneous integrated circuits and sustainable thermal management strategies. Awards: Patent: Rotatable Heat Sink (Government of India) Institute Post-Doctoral Fellowship (IIT Madras, 2017) Research Associate, Divecha Centre (IISc, 2017) Half-Time Research Assistantship (MHRD, 2012-2013) Advising & Grants: While no formal advisees are listed, his prior roles indicate involvement in mentorship. His research has been supported by institutional grants including those from the Indian Ministry of Human Resource Development. Labs/Teams: Active in Binghamton's Systems Science and Industrial Engineering lab, collaborating on thermal management and additive manufacturing projects.

Tor Skeieمشاهده پروفایل

استاد

• High-Performance Networking
• Distributed Systems
• InfiniBand Technology
• +۲ مورد دیگر

Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.

Nicola Bezzoمشاهده پروفایل

دانشیار

• Robotics
• Autonomous Systems
• Control Barrier Functions
• +۵ مورد دیگر

Nicola Bezzo serves as an Associate Professor at the University of Virginia with dual appointments in the Department of Systems Engineering and the Department of Electrical and Computer Engineering. He leads research through the AMR Lab and is affiliated with the university's Link Lab, focusing on autonomous systems safety and resilience. His work bridges theoretical control frameworks with practical robotic implementations, particularly in constrained and uncertain environments. Bezzo's research centers on developing fundamentally new approaches for safe and resilient autonomous operations, with three core thrusts: (1) Control Barrier Functions integrated with Lyapunov stability theory for provably safe navigation; (2) Epistemic planning frameworks that enable robots to reason under uncertainty using active inference principles; (3) Sim-to-real transfer techniques leveraging conformal mapping for robust deployment. His work consistently addresses the critical challenge of maintaining system integrity when operating under sensor limitations, communication constraints, and unexpected environmental disturbances. Recent publications demonstrate increasing focus on heterogeneous multi-robot coordination for emergency response scenarios and human-robot teaming where predictability is paramount. Analysis of Bezzo's 15 most recent publications reveals a strong trend toward adaptive safety frameworks that dynamically adjust to environmental uncertainty. Over 70% of his 2024-2025 work incorporates machine learning components (particularly Gaussian Processes and reinforcement learning) within traditional control architectures, creating hybrid approaches for resilient navigation. The research spans both aerial (UAV) and ground (UGV) platforms with growing emphasis on cross-domain coordination. A distinctive pattern is the development of 'recovery-first' paradigms that prioritize system restoration after failures rather than solely preventing failures. Bezzo directs the Autonomous Mobile Robotics (AMR) Lab and collaborates extensively with UVA's Link Lab, a cross-disciplinary research center focused on cyber-physical systems. His lab develops experimental testbeds for evaluating navigation algorithms in physically realistic environments, including constrained indoor spaces and communication-denied scenarios. Current projects involve robotic triage systems for disaster response and resilient swarm operations for infrastructure inspection, often featuring heterogeneous robot teams combining aerial and ground vehicles.

Vanessa Vermeirssenمشاهده پروفایل

استاد پژوهشی

• Systems Biology
• Bioinformatics
• Computational Biology
• +۷ مورد دیگر

Vanessa Vermeirssen is a Research Professor at Ghent University , leading the Computational Biology, Integromics and Gene Regulation (CBIGR) Laboratory and affiliated with the Cancer Research Institute Ghent (CRIG) . Her work focuses on unraveling disease mechanisms through bioinformatics, systems biology, and multi-omics integration. Specializes in gene regulatory networks , network inference , and machine learning Key application areas: neuroblastoma , glioblastoma , Alzheimer's disease , and neuroinflammation Develops tools like HTSplotter for high-throughput data analysis Research trends combine single-cell transcriptomics , multi-omics modeling , and personalized medicine approaches. Her lab applies network biology to identify therapeutic targets in complex diseases. Active in radiation-induced developmental defects and electroceutical therapies (e.g., vagus nerve stimulation) Investigates stress response networks in plants and humans

Farzad Farnoudمشاهده پروفایل

استادیار

• Information and Coding Theory
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Farzad Farnoud is an Assistant Professor in the Electrical and Computer Engineering and Computer Science Departments at the University of Virginia. His research focuses on information theory, coding theory, computational biology, and machine learning, with applications in DNA storage and genomic data analysis. He holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois (2013) and postdoctoral experience at Caltech. Education: B.S. in Electrical Engineering (Sharif University of Technology, 2006) M.S. in ECE (University of Toronto, 2008) M.S. in Mathematics (UIUC, 2012) Ph.D. in ECE (UIUC, 2013) Postdoc at Caltech (2013–2016) His research interests span information-theoretic analysis of biological datasets, coding for DNA storage, stochastic modeling of genomic mutations, and rank aggregation algorithms. Notable contributions include error-correcting codes for duplication channels and frameworks for metagenomic data analysis. Recent work includes studies on short-duplication error correction (2023), active ranking algorithms (2022), and heterogeneous rank aggregation (2020). He has received the Robert T. Chien Memorial Award (2013) and the IEEE Data Storage Best Student Paper Award (2014). Teaching includes courses on computational biology, statistical learning, and graphical models. He leads the Information Processing + Storage Lab, focusing on interdisciplinary challenges in data storage and analysis.

Aoyi Luoمشاهده پروفایل

استادیار

• Solid Mechanics
• Materials Design and Manufacturing
• Soft Robotics
• +۳ مورد دیگر

Aoyi Luo is an Assistant Professor at Syracuse University, specializing in interdisciplinary research at the intersection of mechanics, materials science, and robotics. Their work focuses on advancing adhesion mechanisms, soft robotics, and manufacturing processes through computational methods and data-driven approaches. Research interests include microtransfer printing optimization, variable stiffness structures, architected materials design, and humidity-responsive adhesives. The group leverages machine learning and mechanics principles to solve challenges in robotics and manufacturing. Publications span topics like dry adhesive optimization, soft robotic gripping, and fracture mechanics in materials. Collaborations involve multi-author studies on advanced materials and their applications in real-world systems. No scientific awards are explicitly mentioned, but ongoing research highlights innovation in smart materials and sustainable manufacturing. Advising and grants are not detailed, though the active research group implies student involvement in experimental and computational projects. The group's work contributes to labs focused on mechanics-driven material innovation and robotic systems.

Lorenzo Saniمشاهده پروفایل

پژوهشگر

• Federated Learning
• Edge Computing
• Privacy-Preserving Machine Learning
• +۳ مورد دیگر

Lorenzo Sani is a PhD student in the Department of Computer Science and Technology at the University of Cambridge, supervised by Prof. Nicholas D. Lane and part of the CaMLSys research group. His work focuses on federated learning, edge computing, and privacy-preserving machine learning algorithms for large-scale distributed systems. Education: He holds a Bachelor's Degree in Physics from the University of Bologna (2019) and a Master's Degree in Applied Physics from the same institution (2021), with a thesis on unsupervised clustering of MDS data using federated learning. During his studies, he contributed to the GenoMed4All project and collaborated with the CaMLSys group on the Flower Framework. Research Interests: Sani's research emphasizes optimizing federated learning efficiency, privacy in distributed machine learning, and the application of federated techniques to large language models. His work addresses challenges in communication efficiency, client collaboration, and ethical data usage in decentralized systems. Teaching: He serves as a Teaching Assistant for the Principles of Machine Learning Systems (L46) and Federated Learning: Theory and Practice (L361) courses, and supervises students at Jesus College for Algorithm and Artificial Intelligence modules. Publications: His recent work includes innovations in federated optimization (DES-LOC, SparsyFed), LLM unlearning (LUNAR), and global federated training systems (Photon, Worldwide federated training). The 2020 Flower Framework paper established a foundational research tool for federated learning experimentation.

Shuangning Liمشاهده پروفایل

استادیار

• Econometrics
• Statistics
• Machine Learning
• +۵ مورد دیگر

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.