جست‌وجوی استاد و پژوهشگر

Deanna A. Ibrahimمشاهده پروفایل

استادیار

• Critical Consciousness Development
• Social Justice Education
• Arts Education
• +۳ مورد دیگر

Deanna A. Ibrahim is an Assistant Professor of Critical Youth Development in the Department of Health and Human Development at the University of Pittsburgh. Her research focuses on critical consciousness development in youth through arts-based programming and out-of-school contexts. She has collaborated with organizations like the Guggenheim Museum’s Teen Program and Partners for Youth Empowerment to design interventions fostering sociopolitical awareness. Dr. Ibrahim holds a Ph.D. in Applied Psychology from NYU’s Steinhardt School and a BA in Psychology and Theatre from Muhlenberg College. Her interdisciplinary work bridges education, developmental psychology, and creative youth development. Her recent research explores arts-based youth programming’s role in promoting critical consciousness, with studies funded by grants from the Huntington Arts Council and NYSCA. She has authored or co-authored six peer-reviewed publications, focusing on topics like structural inequities, juvenile justice systems, and early childhood interventions. Awards: Statewide Grant for Creative Learning (2023) Chein-Lehmann Dissertation Proposal Award (2023) Felix M. Warburg Memorial Award (2021) Dr. Ibrahim advises EastLine Theatre, developing inclusive youth programming, and serves as a reviewer for journals like American Journal of Community Psychology and Developmental Psychology .

Scott Fraundorfمشاهده پروفایل

دانشیار

• Psycholinguistics
• Memory
• Cognitive Aging
• +۸ مورد دیگر

Scott Fraundorf is an Associate Professor in the Department of Psychology at the University of Pittsburgh, affiliated with The Dietrich School of Arts & Sciences and the MAPLE Lab at the Learning Research and Development Center (LRDC). His research focuses on psycholinguistics, memory systems, cognitive aging, metacognition, and educational technology. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and has been recognized with an NSF Graduate Research Fellowship (2007-2011) and inclusion in the List of Teachers Ranked as Excellent by Students ("Outstanding"). Key research themes include the role of prosody and disfluency in language processing, cognitive aging effects on memory, and the application of statistical modeling to study decision-making and learning strategies. His work bridges experimental psychology with real-world educational interventions, such as adaptive grammar instruction tools and investigations into digital literacy practices among adolescents. Recent publications emphasize cognitive mechanisms underlying expertise retention in medical professionals and the impact of exercise on memory preservation in older adults. He collaborates on projects analyzing how contrastive linguistic cues (e.g., pitch accent, beat gestures) influence online discourse comprehension and long-term memory encoding. Labs/Teams: MAPLE Lab (Memory, Attention, Processing, Learning, Education) Grants: NSF Graduate Research Fellowship Advising: Advises graduate student Jessica Macaluso

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Frank Tongمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Visual Perception
• Visual Cognition
• +۸ مورد دیگر

Frank Tong is a Professor of Psychology at Vanderbilt University in the College of Arts and Science. He leads an active research laboratory investigating the neural mechanisms of human visual perception, cognition, attention, and working memory. His work integrates behavioral experiments, high-resolution fMRI, and computational modeling to decode how visual information is represented and maintained in the brain. Department: Department of Psychology Office: Wilson Hall, Room 531 Email: frank.tong@vanderbilt.edu Phone: 615-322-1780 Education: B.S. in Psychology, Queen's University, Kingston, Canada (Advisor: Barrie Frost) Ph.D. in Psychology, Harvard University (Advisors: Ken Nakayama, Nancy Kanwisher) Postdoctoral Fellow, UCLA (McDonnell-Pew Fellowship, Advisor: Steve Engel) Frank Tong's research centers on understanding how early visual representations interact with higher cognitive functions such as attention and working memory. He has developed pioneering fMRI decoding methods to reconstruct visual features like orientation and object categories from brain activity patterns in the human visual cortex. His lab has demonstrated how these techniques can reveal the neural bases of visual working memory and object-based attentional selection. Current work includes using deep convolutional neural networks as models of human visual processing. His recent publications show a consistent focus on decoding mental states, visual features, and memory contents from brain activity, particularly using fMRI pattern analysis. The research spans visual working memory, attentional modulation, scene perception, and the application of machine learning to neural data. These studies frequently appear in top journals such as Nature , Nature Neuroscience , and Annual Review of Psychology . Scientific Awards: McDonnell-Pew Training Fellowship (1999) Robert K. Root Preceptorship, Princeton (2003) Scientific American Top 50 Award (2004) Young Investigator Award, Cognitive Neuroscience Society (2006) Chancellor's Award for Research, Vanderbilt (2008) Young Investigator Award, Vision Sciences Society (2009) Troland Research Award, National Academy of Sciences Frank Tong has advised numerous graduate students and postdoctoral fellows, many of whom have gone on to successful academic and research careers. His lab has received significant research funding to support its work in cognitive neuroscience and brain imaging. He has also served on the editorial board of the Annual Review of Psychology and as a board member of the Vision Sciences Society, reflecting his leadership in the field. He teaches undergraduate courses including Psy 3760 (Mind and Brain), Psy 3765 (Social Cognition and Neuroscience), and Psy 3780 (The Visual System). His lab continues to explore the interplay between early visual processing and higher cognition using advanced neuroimaging and computational techniques.

Sophie Schwartzمشاهده پروفایل

استاد

• Sleep Neuroscience
• Cognitive Neuroscience
• Neuroimaging (fMRI, hd-EEG, MEG)
• +۷ مورد دیگر

Prof. Sophie Schwartz is a leading neuroscientist at the University of Geneva , where she heads the Sleep & Cognition Lab within the Faculty of Medicine . Her research integrates neuroimaging (fMRI, hd-EEG, MEG) , behavioral testing , and computational modeling to unravel the neural mechanisms underlying memory consolidation , emotion processing , and dreaming during sleep, while also developing clinical interventions to enhance sleep in neurological and psychiatric disorders.

Stefan Leutgebمشاهده پروفایل

استاد

• Neurobiology
• Memory Storage
• Neuronal Mechanisms
• +۵ مورد دیگر

Stefan Leutgeb is a Professor in the Department of Neurobiology at the University of California San Diego (UCSD), affiliated with the School of Biological Sciences. His research focuses on the neural mechanisms underlying long-term memory storage, particularly the role of coordinated neuronal activity and synaptic plasticity in hippocampal and cortical networks. His work investigates how spatial and nonspatial information is encoded, how memory systems degrade in aging and neurodegenerative disorders like dementia, and the translational implications of these findings. Key research areas include hippocampal ensemble dynamics, temporal organization of neuronal activity, and the impact of Alzheimer’s-related proteins (e.g., APP) on neural networks. Leutgeb employs multi-electrode recordings, optogenetics, and computational modeling to study these processes. His lab has discovered critical mechanisms such as pattern separation in the dentate gyrus and the role of theta oscillations in memory encoding. Notable recent contributions include studies on how hippocampal network dysfunction due to APP expression disrupts spike timing ( 2022 ), theta oscillation roles in memory phases ( 2021 ), and the necessity of dentate gyrus activity for spatial working memory ( 2018 ). Despite no explicitly listed awards, his prolific publication record reflects significant contributions to systems neuroscience. Leutgeb’s research also explores cognitive aging and cross-species comparisons of neural processes. His lab emphasizes translational research, aiming to bridge basic neuroscience discoveries with clinical applications for neurodegenerative diseases. Current projects include investigating hippocampal ensemble dynamics during memory retention and developing biomarkers for cognitive flexibility.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +۱۲ مورد دیگر

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

Shuvendu K. Lahiriمشاهده پروفایل

پژوهشگر

• Formal Verification
• Smart Contracts
• Program Synthesis
• +۵ مورد دیگر

Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.

Almira Vazdarjanovaمشاهده پروفایل

استاد

• Neuroscience
• Neuropharmacology
• Behavioral Neuroscience
• +۷ مورد دیگر

Almira Vazdarjanova is a Professor at the Medical College of Georgia, Augusta University, with academic appointments in the Department of Pharmacology and Toxicology and the Department of Neurology. Her work bridges neuroscience, pharmacology, and behavioral research, focusing on translational models of stress, memory, and feeding behavior. Education: Ph.D. in Biological and Biomedical Sciences, University of California - Irvine, 2000 M.S. in Biology/Biological Sciences, University of California - Irvine, 2000 B.S. in Biology/Biological Sciences, University of California - Irvine, 1995 Her research interests include neuropharmacology, behavioral neuroscience, sex differences in anxiety and fear, PTSD modeling, memory mechanisms, and hypothalamic regulation of feeding . She uses rodent and primate models to explore neural circuits underlying cognition and emotion. Her work integrates molecular, systems, and behavioral approaches to understand brain function and dysfunction. Recent publications highlight her focus on fear extinction, anxiety assessment tools, working memory enhancement via neuromodulation, and predictive models of PTSD . These studies reflect a strong trend toward translational neuroscience with implications for psychiatric and neurological disorders. Scientific Awards: VA Merit Award, VHA (2018) Integrity Award, AU Core Values, Augusta University (2017) Authentic Leadership for Women Academy, Augusta University (2017) VA Merit Award, VHA (2013) National Research Service Award, NIH (2003) Dr. Vazdarjanova has served on multiple graduate and faculty committees, including the Graduate Student Committee, MCG Faculty Affairs Committee, and Grievance Committee (where she served as Chair). She has been an ad hoc reviewer for the Society for Neuroscience. She teaches core medical and graduate courses such as Pharmacology & Therapeutics , Medical Physiology , Neuroscience I , and Neuropharmacology . While specific students are not listed, her role in seminars and tutorials suggests active mentorship. She is affiliated with the The Graduate School and participates in institutional research governance through the Partnership Council Committee on Research. There is no indication of part-time status, retirement, or emeritus designation.

Andreas Moshovosمشاهده پروفایل

استاد

• Computer Architecture
• Deep Learning Acceleration
• Energy-Efficient Computing
• +۲ مورد دیگر

Andreas Moshovos is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds a Bachelor's and Master's degree from the University of Crete, and a PhD from the University of Wisconsin-Madison. Previously, he taught at Northwestern University, École Polytechnique Fédérale de Lausanne, University of Athens, and Hellenic Open University. His research focuses on designing optimized computing hardware for performance, energy efficiency, and cost. Primary interests include: Deep Learning acceleration through value-based optimization (exploiting sparsity/precision variability) Hardware specialization for neural networks Efficient data management systems High-performance processor/memory architecture His publications demonstrate consistent focus on hardware acceleration techniques for deep learning, particularly leveraging value sparsity, dynamic precision adaptation, and ineffectual computation elimination to boost performance and energy efficiency in neural network processing. Significant Awards: ACM SIGARCH Maurice-Wilkes Award (2010) 2× IEEE MICRO Top Picks Awards (2006, 2010) 2× IBM Faculty Awards (2008, 2009) NSF CAREER Award (2000) MICRO Hall of Fame Award He leads the NSERC COHESA Network on Machine Learning Hardware Acceleration (19 researchers across 7 universities) and advises multiple PhD/Master's students in computer architecture and deep learning acceleration. His lab develops specialized hardware for computational imaging, gene sequencing, and neural network optimization.

Reetuparna Dasمشاهده پروفایل

دانشیار

• Computer Architecture
• In-Memory Computing
• Precision Health
• +۷ مورد دیگر

Reetuparna Das is an Associate Professor at the University of Michigan in the Department of Computer Science and Engineering, School of Electrical Engineering and Computer Science (EECS). She previously worked as a research scientist at Intel Labs and as researcher-in-residence for the Center for Future Architectures Research (C-FAR). She co-founded the precision medicine start-up Sequal Inc. and leads the M-Bits research group, which is part of the Computer Engineering Lab at Michigan. Her research focuses on computer architecture and its intersections with software systems and device/VLSI technologies . Key projects include in-memory computing for BigData and ML, fine-grain heterogeneous architectures for mobile systems, and energy-efficient network-on-chip (NoC) designs for many-core processors. Her work has been funded by the NSF, C-FAR, Semiconductor Research Corporation, and Intel. She has authored over 50 papers, filed 7 patents, and received numerous awards, including the Sloan Foundation Faculty Fellowship CRA-W Borg Early Career Award NSF CAREER IEEE Top Picks MICRO/ISCA Hall of Fame inductions Das has served on over 40 program committees, is associate editor for TACO, and co-founded initiatives like WiCArch (Women In Computer Architecture) to promote diversity. She mentors students through outreach programs like Girls Encoded and Ada Lovelace opera events.

Shweta Agarwalمشاهده پروفایل

استاد بالینی

• Pathology
• Head and Neck Pathology
• Cytopathology
• +۳ مورد دیگر

Shweta Agarwal is a board-certified clinical pathologist specializing in Head and Neck pathology and Cytopathology. Currently affiliated with Mayo Clinic in Jacksonville, Florida, she focuses on diagnosing salivary gland, thyroid, oral cavity, and sinonasal cancers. Her research integrates artificial intelligence for metastasis prediction and explores PD-L1 expression in rare tumors. 2018 Fellowship in Cytopathology - University of California San Francisco Medical Center 2017 Fellowship in Head and Neck Pathology - Massachusetts General Hospital 2016 Residency in Anatomic Pathology - University of Oklahoma Health Sciences Center 2011 Senior Residency in Oncopathology - Tate Memorial Hospital 2003 MBBS - GSVM Medical College, Kanpur University Her research bridges clinical pathology with computational tools, investigating salivary gland cancers, thyroid neoplasms, and oral squamous cell carcinoma. Key publications include AI-driven nodal metastasis predictions and PD-L1 analysis in rare cancers. Scientific awards include the 2025 Mayo Clinic 'Healing' recognition, 2022 Presidential Teaching Fellow nomination, and multiple best paper awards. She serves on committees for the College of American Pathologists and Papanicolaou Society of Cytopathology.

Mohammed J. Zakiمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Graph-structured Data Analysis
• +۶ مورد دیگر

Mohammed J. Zaki is a Professor and Department Head at the Department of Computer Science , Rensselaer Polytechnic Institute . He co-directs the NSF IUCRC Center for Research Towards Advancing Financial Technologies (CRAFT) and has authored the textbook Data Mining and Machine Learning (2nd Ed, Cambridge University Press, 2020). Research Focus : Novel data mining techniques for graph-structured and textual data with applications in bioinformatics, personal health, and financial analytics. Leadership : Co-chair of BIOKDD workshops, Associate Editor for multiple journals, and Program Co-chair for SIGKDD, SDM, ICDM, and other top conferences. Scientific Awards include: EDBT'24 Test of Time Award NSF CAREER Award DOE Early Career Award HP Innovation Research Award Google Faculty Research Award Fellowships: IEEE, ACM, AAAS, SIAM Recent Publications highlight advancements in: Knowledge Graph Completion with Directed Attention Multi-Sense Embeddings for Language Models Financial Trend Prediction via Graph Neural Networks Health-Guided Recipe Recommendation Systems Triplet Interaction in Molecular Graph Learning Temporal Personal Health Data Analysis

• Nonvolatile Memory
• Neuromorphic Computing
• Radiation Effects on Electronics
• +۳ مورد دیگر

Matthew J. Marinella serves as an Associate Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University, where his research bridges semiconductor device physics and next-generation computing architectures. His work focuses on enabling reliable computing systems for extreme environments through novel memory technologies. His academic foundation includes: Ph.D. in Electrical Engineering, Arizona State University (2008) Marinella's research centers on nonvolatile memory devices (particularly ECRAM and SONOS technologies), neuromorphic computing systems, and radiation effects characterization. He pioneers analog in-memory computing solutions resilient to space radiation, with expertise spanning electrochemical memory physics, radiation-hardened circuit design, and emerging device applications for artificial intelligence. His experimental work combines nanoscale imaging with computational modeling to understand device degradation mechanisms under ionizing radiation. Analysis of his 2023-2025 publications reveals a dominant focus on radiation-tolerant neuromorphic systems, with 70% of recent work addressing radiation effects on emerging memories. Key thematic clusters include TaOx ECRAM characterization under gamma/heavy-ion exposure (25% of publications), analog in-memory computing fault tolerance (30%), and novel test platforms for memory device benchmarking (20%). This research directly enables space-based computing applications where radiation resilience is non-negotiable. As a technical leader, Marinella chairs the Emerging Memory Devices Section for the IRDS Roadmap Beyond CMOS Chapter and serves on the SRC Decadal Plan Executive Committee. His Sandia legacy includes founding the Secure, Efficient, Extreme Environment Computing (SEEEC) Grand Challenge. At ASU, he mentors graduate researchers through thesis supervision in EEE 599/799 courses and directs laboratory work on memory device characterization, though specific student names and grant awards aren't publicly enumerated. His laboratory operations emphasize radiation testing infrastructure and analog computing testbeds, supporting collaborative projects with national labs on space electronics hardening. Current efforts integrate magnetic domain wall devices with resistive memories to create hybrid neuromorphic systems capable of operating in extreme environments where conventional CMOS fails.

Joergen Kornfeldمشاهده پروفایل

پژوهشگر

• Connectomics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish