Tathagata Srimani is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. He previously served as a Postdoctoral Scholar in Electrical Engineering at Stanford University. His academic journey includes a Ph.D. and S.M. in EECS from MIT (2022 and 2018 respectively) and a B.Tech. in E&ECE from IIT Kharagpur (2016). Research Focus: Srimani’s work centers on nanoelectronics and transformative NanoSystems. Key areas include: Carbon nanotube field-effect transistors (CNFETs) and their monolithic 3D integration with silicon Ultra-dense 3D integration of logic and memory to address the 'memory wall' in AI/ML Technology-architecture co-design frameworks for energy-efficient computing Key Achievements: Developed first silicon fab-compatible CNFET processes (TNANO ’18, Nature ’19) Enabled CNFET RISC-V microprocessor and monolithic 3D integration with Analog Devices/SkyWater Recipient of MIT Presidential Fellowship (2016) and Morris Joseph Levin Award (2018) Teaching & Outreach: Teaches semiconductor devices and hardware design, including hands-on 'Hacker Fab' courses. Leads the NEXUS Research Group exploring heterogeneous nanomaterials (e.g., magnetic and oxide semiconductors) and thermal/power management in 3D systems. Future Directions: Expanding into probabilistic computing hardware, co-design frameworks for application-specific systems, and scaling 3D NanoSystem technologies for industrial adoption.
Emily Cooper is an Associate Professor of Optometry & Vision Science at the Herbert Wertheim School of Optometry & Vision Science, University of California, Berkeley. She serves as the Chair of the Vision Science PhD Program and is a co-Director of the Center for Innovation in Vision & Optics. Additionally, she is a member of the Helen Wills Neuroscience Institute and a Visiting Faculty Researcher at Google. Dr. Cooper's research focuses on 3D vision, perceptual graphics, AR/VR, computational neuroscience, visual encoding, and display system design. Her work investigates how the visual system processes information to create our perception of the 3D world, with applications in computer graphics, virtual reality, and assistive technologies for people with low vision. Analysis of Dr. Cooper's recent publications (2023-2025) reveals a strong focus on the intersection of vision science and emerging technologies, particularly in augmented reality and assistive vision systems. Her work spans fundamental research on visual perception mechanisms to applied research developing practical technologies for low vision rehabilitation. A significant portion of her recent work addresses visual discomfort in XR displays, perceptual guidelines for AR/VR systems, and innovative approaches to assistive vision technologies that enhance mobility and independence for visually impaired individuals. Dr. Cooper leads an active research laboratory at UC Berkeley's 391 Minor Hall, where she mentors students and collaborators in vision science research. Her lab investigates both basic questions about how vision works and translational questions about improving visual technologies. She has developed perceptual guidelines for optimizing field of view in stereoscopic augmented reality displays and created assistive technologies such as an augmented reality sign-reading assistant for users with reduced vision. Dr. Cooper is also involved in professional activities including co-organizing the Computational Neuroscience: Vision summer course at Cold Spring Harbor Laboratory and working with Community Resources For Science to promote science education.
Ke Xu is an Assistant Professor at the Department of Finance, Faculty of Business and Economics, University of Victoria. His research bridges finance, econometrics, and cryptocurrency, focusing on market microstructure, high-frequency trading, and price discovery mechanisms. He has extensively studied Bitcoin ETFs, fractional cointegration models, and machine learning applications in financial markets. Key Research Areas: Market Microstructure High-Frequency Trading Cryptocurrency Dynamics Price Discovery Machine Learning in Finance Financial Econometrics Article Trends: Xu’s work spans empirical analyses of Bitcoin ETFs, volatility modeling (e.g., affine GARCH), and algorithmic trading strategies. His recent papers explore mini flash crashes using machine learning, regulatory impacts on market quality, and sustainable crypto portfolios.
Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.
Steve Luck is a Distinguished Professor at the University of California, Davis, holding appointments in the Department of Psychology and the Center for Mind and Brain (CMB). He served as CMB Director from 2009–2019 and is affiliated with the UC Davis MIND Institute and the Center for Neuroscience. His research focuses on attention, working memory, and cognitive dysfunction in psychiatric disorders (e.g., schizophrenia), employing ERP recordings, eye tracking, and behavioral methods. He is a leading developer of ERP methodologies, including the ERPLAB Toolbox and global ERP Boot Camp workshops. Education: Ph.D., Neurosciences, UC San Diego, 1993 M.S., Neurosciences, UC San Diego, 1989 B.A., Psychology, Reed College, 1986 Research Interests: Dr. Luck explores mechanisms of cognitive control, with a focus on working memory's role in guiding attention. His lab investigates ERP correlates of attentional deficits in schizophrenia and develops standardized ERP protocols. Recent work emphasizes multivariate decoding of EEG signals and transdiagnostic neurocognitive biomarkers. Awards: Troland Award (2001) APA Distinguished Scientific Award (1998) McGuigan Young Investigator Prize (2004) Elected Fellow, Society of Experimental Psychologists and AAAS Teaching & Leadership: Professor Luck pioneered hybrid course formats in Cognitive Science and teaches advanced topics in perception and cognitive neuroscience. He co-founded the UC Davis Cognitive Science major and advocates for innovative undergraduate education models. Labs & Collaborations: The Luck Lab integrates clinical and basic research, collaborating globally on ERP method development and schizophrenia biomarker studies. Key projects include ERP Core resources and the CNTRACS consortium for neurocognitive reliability studies.
Dr. Yu Zhong is an Assistant Professor in the Department of Materials Science and Engineering at Cornell University's College of Engineering, where he leads the Yu Zhong Group. His research laboratory focuses on the design and synthesis of novel soft materials and nanomaterials for applications in electronics, energy, healthcare, and sustainability. As a principal investigator, he oversees a dynamic research team comprising postdoctoral associates, graduate students, and undergraduate researchers working on cutting-edge materials science projects. Dr. Zhong received his educational training at prestigious institutions, earning his B.S. in Chemistry from the University of Science and Technology of China (USTC) in 2011, followed by a Ph.D. in Chemistry from Columbia University in 2017 under the supervision of Prof. Colin Nuckolls. His doctoral research centered on designing contorted molecules for electronic and energy applications including organic solar cells, photodetectors, and gas sensors. He then conducted postdoctoral research at the University of Chicago in Prof. Jiwoong Park's group, where he worked on the design and synthesis of 2D polymers for ultrathin electronic circuits and energy conversion. Dr. Zhong's research program spans three primary directions: (1) the bottom-up synthesis of ultrathin nanoporous membranes using techniques like laminar assembly polymerization (LAP) for applications in water desalination, nanofiltration, and gas separation; (2) the study of transport behaviors in hybrid organic-inorganic 2D heterostructures created through layer-by-layer assembly for use in optical, electronic, and thermal management devices; and (3) the development of mixed ionic-electronic materials for bio-inspired and bioelectronic devices. His group employs advanced synthesis methods including organic/polymer synthesis, supramolecular and reticular chemistry, and 2D materials characterization to explore novel scientific phenomena and technological applications. An analysis of Dr. Zhong's recent publications reveals a strong focus on the synthesis and characterization of 2D polymers and organic-inorganic hybrid materials. His work bridges fundamental materials science with practical applications in energy conversion, electronics, and separation technologies. A notable trend is his development of innovative synthesis techniques like laminar assembly polymerization that enable precise control over material structure at the molecular level, leading to breakthroughs in areas such as lithium-ion transport, osmotic power generation, and ultra-narrowband photodetection. Dr. Zhong's scientific achievements have been recognized with several prestigious awards: Pegram Award for Meritorious Graduate Research, Columbia University (2016) Camille and Henry Dreyfus Postdoctoral Fellowship, Dreyfus Foundation (2016) Arun Guthikonda Memorial Fellowship, Columbia University (2015) Jack Miller Award for Excellence in Teaching, Columbia University (2014) As an advisor, Dr. Zhong mentors a diverse group of researchers including postdoctoral associate Qiyi Fang, multiple Ph.D. students (Yuhe Zhang, Kaushik Chivukula, William Xie), M.S. students, and undergraduate researchers. His group has secured funding for research on soft and nanomaterials, with projects spanning organic electronics, 2D materials synthesis, and biomimetic membranes. Dr. Zhong actively seeks motivated graduate students and postdoctoral fellows to join his research team, emphasizing the importance of interdisciplinary collaboration in advancing materials science. The Yu Zhong Group operates state-of-the-art laboratories in Bard Hall at Cornell University, equipped for organic synthesis, materials characterization, and device fabrication. The research team works collaboratively across disciplines, partnering with experts in physics, chemistry, and engineering to tackle complex challenges in materials science. Current projects focus on developing novel synthesis methodologies and exploring structure-property relationships in soft materials to enable next-generation electronic, energy, and healthcare technologies.
Venkatesan Guruswami is a Chancellor's Professor in the Department of Electrical Engineering and Computer Sciences and Professor in the Department of Mathematics at the University of California, Berkeley. He previously served as faculty at Carnegie Mellon University for 13 years and held a Miller Research Fellowship at UC Berkeley. His research focuses on Theoretical Computer Science , particularly in Error-Correcting Codes , Approximation Algorithms , Quantum Computing , and Hardness of Approximation . Guruswami has made groundbreaking contributions to list decoding and quantum code constructions, with works featured in Science Magazine and the Journal of the ACM (where he serves as Editor-in-Chief). Education : B.Tech (1997, IIT Madras), Ph.D. (2001, MIT), Miller Research Fellowship (2001-02, UC Berkeley) Research Areas : Theory of error-correcting codes, approximation algorithms, pseudorandomness, probabilistically checkable proofs, and quantum coding theory Guruswami's recent work explores quantum LDPC codes , parameterized inapproximability , and stream decodable codes . He has received prestigious awards including the NSF CAREER award , David and Lucile Packard Fellowship , and Sloan Research Fellowship . His advising spans a wide range of students and postdocs, with notable contributions to coding theory and computational complexity .
Christopher A. Baldassano is an Associate Professor in the Department of Psychology at Columbia University, maintaining offices in Schermerhorn Hall (370 for office, 312 for lab). Contact is available via email c.baldassano@columbia.edu or phone +1 212 854 1902 by appointment. Education Ph.D., Stanford University, 2015 Research Focus Dr. Baldassano leads the Dynamic Perception and Memory Lab investigating how humans process and recall complex real-world experiences through event segmentation, temporal/spatial structure modeling, and neural representation formation. His work integrates cognitive neuroscience with machine learning approaches to analyze fMRI data during narrative, movie, and virtual reality experiments. Key research themes include event cognition dynamics, memory summarization mechanisms, and how prior knowledge shapes mental representations of everyday experiences. Scientific Awards No awards or fellowships were documented in the provided materials. Advising and Grants While specific student advisees and grant details weren't listed, his lab structure implies active mentorship of graduate researchers in cognitive neuroscience methodologies. Funding likely supports fMRI experimentation and computational modeling infrastructure. Laboratory Operations The Dynamic Perception and Memory Lab employs functional MRI combined with data-driven machine learning techniques to model neural representation variations across stimuli and individuals. Current projects examine event boundaries in continuous experiences using ecologically valid paradigms like movies and virtual environments, with emphasis on how temporal/spatial world structures influence cognitive processing.
Prof. Dr. Estela Suarez is a Professor of High Performance Computing at the Institute for Computer Science, University of Bonn (W2 in the Jülich Model) and Joint Lead of the Division "Novel System Architecture Design" at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH. She also leads the Research Group "Next Generation Architectures and Prototypes" at JSC and serves as Spokesperson of Helmholtz Information Program 1, Topic 2. Currently on sabbatical during the 2024/2025 and 2025 academic years, she remains active in research leadership roles. 2010: PhD in Physics from University of Geneva, Switzerland 2004: Master in Physics, Specialization in Astrophysics, University Complutense of Madrid, Spain Professor Suarez specializes in high performance computing with particular expertise in heterogeneous HPC system architectures and modular supercomputing architecture (MSA). Her research spans hardware prototyping and evaluation, system software development, operational data analysis, and co-design methodologies. She has pioneered approaches to address hardware heterogeneity through system-wide orchestration of diverse computing resources, enabling more efficient scientific computing across multiple domains. Her work bridges theoretical computer architecture with practical implementation challenges in exascale computing environments, focusing on real-world applications that require specialized hardware configurations. Professor Suarez's publication record shows a clear evolution from foundational work on the DEEP project (2016) through the development of modular supercomputing concepts (2019-2021) to current applications across diverse scientific domains (2022-2024). Her recent publications demonstrate how modular architectures can be effectively applied to climate modeling, neuroscience simulations, quantum chemistry calculations, and other computationally intensive fields. This trend highlights her focus on practical implementation challenges and the growing importance of adaptable computing architectures in modern scientific research. 2023/2024 Lehrpreis der Universität Bonn: UniBonn teaching award Professor Suarez has secured significant research funding through major projects including NUMERIQS (Projects A05, B02, and Z02), European Processor Initiative (EPI), DEEP-SEA (Software for Exascale Architectures), IFCES2 (optimization of simulation algorithms for exascale supercomputers), and AIDAS (virtual laboratory between Forschungszentrum Jülich and CEA on AI and data analytics). While currently not accepting new students due to sabbatical, she has previously mentored graduate students in high performance computing techniques and has delivered numerous invited lectures at international conferences. Professor Suarez leads the "Next Generation Architectures and Prototypes" research group at JSC and serves as Joint Lead of the "Novel System Architecture Design" division. She chairs the Research and Innovation Advisory Group (RIAG) from EuroHPC Joint Undertaking since 2024. Her work involves close collaboration with international research teams on advancing supercomputing architectures, including contributions to the University of Bonn's new HPC system "Marvin" which ranks on both the TOP500 and GREEN500 lists.
Sivaraman Balakrishnan is a Professor at Carnegie Mellon University with joint appointments in the Department of Statistics and Data Science and the Machine Learning Department. His research bridges statistical machine learning, algorithmic statistics, and robust inference. Education: Ph.D. in Computer Science from Carnegie Mellon University (Language Technologies Institute, advised by Jaime Carbonell); postdoctoral work at UC Berkeley (Department of Statistics, advised by Martin Wainwright and Bin Yu). Research Interests: Spanning robust statistics, domain adaptation, minimax hypothesis testing, assumption-light inference, causal inference, statistical optimal transport, non-parametric statistics, ranking, crowdsourcing, optimization, and topological data analysis. Key Research Trends: Recent work focuses on domain adaptation under label/misingness shifts, robust gradient estimation, smooth optimal transport maps, and conditional independence testing. He explores minimax optimal methods, univariate mean estimation, and high-dimensional regression with missing data. Scientific Awards: IMS Lawrence D. Brown Student Award (2021, 2020) NVIDIA Pioneer Award (2018) Franklin V. Taylor Memorial Best Paper Award (2018) Grants and Editorial Roles: NSF grants (CCF-1763734, DMS-1713003, DMS-2113684, DMS-2310632), Amazon Research Award (2021), Google Research Scholar Award (2021). Associate Editor for JASA and JRSSB ; Editorial Board member for Foundations and Trends in Statistics . Collaborative Groups: Co-organizes the Statistics and Machine Learning Reading Group and participates in the Causal Inference Working Group at CMU.
Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.
Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.
Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.
Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships
Wang Yingji is a tenured professor and doctoral supervisor at the School of Art and Media, Tongji University. He holds a PhD in Literary Studies and has served as director of the China Culture and Art Communication Research Center, member of the China Artists Association, and editorial board member of "Media Criticism" journal. BA: Chinese Language and Literature, Renmin University of China (1995) MA: Journalism, Beijing Normal University (2004) PhD: Literary Studies, Beijing Normal University (2007) His research focuses on the intersections between Journalism and Communication, Media Phenomenology, Philosophy of Technology, and Traditional Chinese Culture. His work explores embodied cognition in media interaction, technological mediation of human perception, and historical epistemology of media systems. The articles demonstrate his expertise in media phenomenology (Hubert Dreyfus analysis), digital reading behavior studies, VR technology ideologies, and historical reconstruction of Chinese communication theories. His publications span from 2016-2022 with consistent CSSCI indexing. National Social Science Fund General Project: "Chinese Modern Public Opinion Thought History" (2021) Major Project Subtopic: "Chinese Media Archaeology in Civilizational Diversity" (2020) Ministry of Education Project: "Husserl's Media and Communication Thought" (2020) Completed NSFC Project: "Rumor Propagation and Governance in Public Emergencies" (2011) As educator, he teaches courses like "Chinese Classical Media Art", "Media Research Methods", and "Communication Theory". His administrative roles include leadership positions in the Chinese Society of Journalism History and multiple journal editorial boards.
