Robert Calderbank is a distinguished academic and researcher at Duke University, holding professorships in Computer Science, Electrical and Computer Engineering, and Mathematics. He serves as Director of the Information Initiative at Duke and is affiliated with the Duke Quantum Center. His interdisciplinary work bridges information theory, quantum computing, and biomedical applications. Education: Ph.D. from California Institute of Technology (1980) Previous Institution: Princeton University (Distinguished Professor) Calderbank's research spans wireless communications, distributed storage systems, machine learning, and quantum information theory. Recent work focuses on two-dimensional magnetic recording, quantum error correction, and biomedical imaging with pump-probe microscopy. His publications demonstrate sustained innovation in constrained coding, matrix completion, and subspace classification. Scientific contributions include grants from the National Science Foundation and collaborative projects with the University of Maryland. Awards include Fellowships from the Royal Society, IEEE, and AAAS. His teaching and mentorship at Duke and Princeton have shaped next-generation signal processing and computer science research.
Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks
Daniel J. Graham is a Professor of Psychological Science at Hobart & William Smith Colleges (HWS), where he has been a faculty member since 2012. He is affiliated with the Department of Psychological Science within the School of Humanities and Sciences, contributing to interdisciplinary research and teaching in vision science, brain networks, and neuroaesthetics. Graham holds a Ph.D. in Psychology and an M.S. in Physics from Cornell University, and a B.A. in Physics from Middlebury College, reflecting his strong foundation in both the natural and cognitive sciences. Ph.D. in Psychology, Cornell University M.S. in Physics, Cornell University B.A. in Physics, Middlebury College His research integrates computational, behavioral, and theoretical approaches to understand how the brain processes visual information, particularly in natural scenes, art, and faces. He is a leading proponent of the 'internet metaphor' for brain function, proposing that neural communication operates similarly to packet-switched networks. His work emphasizes efficiency, statistical regularities, and network dynamics in cortical and whole-brain systems. Key research themes include efficient coding, neuroaesthetics, and models of neural communication. The most recent publications reveal a strong trend toward interdisciplinary synthesis, combining neuroscience, computer science, and psychology. His work increasingly explores machine learning models to predict human affective responses to visual stimuli, critiques of dominant theoretical frameworks like the free energy principle, and educational innovation in perception teaching. The research spans from foundational vision science to philosophical reflections on brain function. Scientific Awards and Recognition: Winner, Outstanding Student Presentation Award at MAA MathFest (2021) Invited speaker at numerous national and international conferences, including the Redwood Neuroscience Institute and the Bernstein Conference Media coverage of PNAS work in Nature , NPR , BBC , and IEEE Spectrum Teaching and Advising: Graham mentors numerous undergraduate students, many of whom co-author his publications. He has developed innovative lab courses involving electrophysiology, perceptual experiments, and creative demonstrations. He teaches core courses such as Introduction to Psychology, Sensation and Perception, and advanced seminars on art and neuroscience. His teaching philosophy emphasizes critical thinking, interdisciplinary reasoning, and active student participation. Research Labs and Collaborations: Graham collaborates closely with Prof. Yan Hao (HWS Mathematics) on modeling neural communication. His research group involves students in computational modeling, data analysis, and experimental design. He is involved in conferences and workshops focused on the mathematics of neuroscience and AI, reflecting his commitment to cross-disciplinary science.
Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.
Peng Jiang is an Assistant Professor in the Computer Science Department at the University of Iowa. His research focuses on machine learning systems, high-performance computing, and graph processing, with a particular emphasis on compiler and programming techniques for GPU acceleration. He earned his Ph.D. in Computer Science from The Ohio State University in 2019 under Dr. Gagan Agrawal. Education: Ph.D., The Ohio State University, 2019 His work spans sparse training, knowledge graph embedding, and subgraph matching, often leveraging fine-grained parameter management and GPU optimization. Key trends in his publications include compiler design for high-performance systems, parallel programming models, and performance-aware weight pruning for neural networks. Scientific Awards 2024 NSF CAREER Award Peng Jiang has collaborated extensively with researchers such as Lihan Hu, Yihua Wei, Shihui Song, and Gagan Agrawal. His contributions to sparse matrix multiplication, distributed learning communication optimization, and PIM architecture-aware frameworks highlight his expertise in bridging machine learning and systems research.
Professor Ram Zamir is a senior faculty member in the School of Electrical Engineering at Tel Aviv University, where he has been a professor since 2009 and a faculty member since 1996. He has held leadership roles including Head of the Electrical Engineering Program (2013–2017) and Head of the School of Electrical Engineering (2020–2023). His research bridges information theory, communication, signal processing, and learning, with a strong emphasis on geometric and lattice-based coding structures. His research interests include: Information Theory and Digital Communications Statistical and Musical Signal Processing Lattice Codes and Analog Coding via Frames Sparse Modeling and Random Matrix Theory His work has led to two influential books—one on lattice codes (Cambridge University Press, 2014) and another on asymptotic frame theory (NOW Publishers, 2021)—and nearly 200 journal and conference publications with significant citation impact. His recent focus includes the intersection of electrical engineering and music, where he promotes curriculum and research integration. Prof. Zamir has served in key roles in the IEEE Information Theory Society, including as editor, branch chair in Israel, and member of the Board of Governors. He organized the ITW 2015 conference in Jerusalem and has consulted for industry leaders such as Orckit, Actelis, and served as Chief Scientist at Celeno Communications (2004–2014), later acquired by Renesas. He advises graduate students and leads research initiatives in coding and signal processing, though specific students are not listed. He also contributes to academic and technological advancement through collaborations, grants, and industrial partnerships. His lab and research group focus on fundamental coding theory and its applications in modern communication and learning systems. Notably, he and his family donated a piano to the Faculty of Engineering in memory of his late mother, Esther Elchanati-Zamir, reflecting his passion for music and interdisciplinary innovation.
Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .
Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.
Leon Derczynski is a researcher at the IT University of Copenhagen with a focus on Natural Language Processing and computational linguistics. His work spans multiple NLP subfields including temporal information extraction , misinformation detection , and social media analysis . He has contributed to the development of NLP resources for Danish and Nordic languages, and created frameworks like garak for model security probing. Research interests include: Temporal relation classification and time expression modeling Social media analysis and misinformation detection Model efficiency and resource-aware NLP Scandinavian language processing Ethical considerations in NLP Publications highlight trends in transformer architecture optimization , set-to-sequence modeling , and abusive language detection . His work frequently appears in top venues like Transactions of the Association for Computational Linguistics , EMNLP , and COLING . Key collaborations include work with Kalina Bontcheva on rumor evaluation, Manuel R. Ciosici on efficient NLP methods, and Erick Galinkin on model security. He has also contributed to datasets like the Danish Gigaword Corpus and evaluation frameworks like Risk Cards for model deployment assessment.
Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.
Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.
Muhammad Abu Bakar Siddique is an Assistant Professor in the Department of Computer Science at the University of Kentucky, part of the Stanley and Karen Pigman College of Engineering. His research focuses on natural language processing, large language models, and machine learning with particular emphasis on zero-shot learning and conversational AI systems that are safe, personalizable, and interpretable. Dr. Siddique earned his Ph.D. in Computer Science from the University of California, Riverside (2017-2021), his M.S. from Lahore University of Management Sciences, Pakistan (2008-2011), and his B.S. from International Islamic University, Pakistan (2003-2008). His research interests include: Natural Language Processing and Large Language Models Zero-shot and few-shot learning for conversational AI Safe, personalizable, and interpretable conversational systems Task-oriented dialog systems with domain generalization Mobile app recommendation systems Scalable machine learning methodologies Dr. Siddique's publications span top venues including WWW, SIGIR, KDD, and IEEE S&P, demonstrating his focus on developing practical AI solutions that can adapt to new domains without extensive retraining. His recent work shows increasing exploration of quantum software and the intersection of AI with mobile applications. His notable achievements include Best Paper Awards at the IEEE International Conference on Quantum Software (2025) and IEEE ICSC (2021). Dr. Siddique has secured significant funding from the National Science Foundation: CPS Medium: Calfhealth: Explainable AI for Pneumonia Detection in Dairy Calves ($941,359) SaTC CORE: Personalized and Trustworthy Mobile App Recommendations ($300,000) III Small: User-Centric Task-Oriented Dialog Systems ($599,898) DCL EPSCOR: Distributed Edge Intelligence ($100,000) He currently advises three PhD candidates (Adib Mosharrof, Moghis Fereidouni, and Muhammad Umair Haider) and has mentored several successful graduates. Dr. Siddique serves on program committees for major conferences including ACL, NeurIPS, ICML, and AAAI, and participates in outreach by hosting high school students through the University of Kentucky's Summer Youth Program.
Venkat Anantharam is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . His research spans Information Theory , Network Security , Coding Theory , and Stochastic Processes , with a focus on theoretical foundations and applications in communication systems, game theory, and data compression. He has supervised numerous PhD and Master’s students , including Soham Phade, Payam Delgosha, and Sudeep Kamath, and hosted postdoctoral fellows such as Lei Yu and Charles Bordenave. His recent publications address advanced topics like hypercontractivity in Boolean functions, universal compression of graphical data, and game-theoretic models for security. Articles from 2019-2021 highlight work on entropy power inequalities, error bounds for Markov chains, and distributed compression techniques. Venkat's research often bridges theoretical insights with practical applications, including LDPC decoders, network coding, and risk-sensitive control.
François Pirot is an Associate Professor (Maître de Conférences) at Université Paris-Saclay since September 1, 2021. He conducts research at the LISN laboratory within the GALaC team and teaches at the Faculty of Science of Orsay. PhD in Mathematics (Radboud University) and Computer Sciences (Université de Lorraine), 2019 Postdoctoral experience: ULB (2019), G-SCOP (2019-2020), Inria Sophia Antipolis (2020-2021) His research focuses on graph coloring problems in diverse contexts such as graph powers, locally sparse graphs, and distributed algorithms, utilizing probabilistic methods and connections to bio-informatics through circular codes. He has advanced bounds for h -conflict-free coloring, acyclic coloring, and dichromatic numbers in oriented graphs, with applications to minor-closed families and geometric group theory. Scientific contributions include: Asymptotically tight bounds for chromatic numbers in sparse graphs Efficient fractional coloring algorithms for K_t-minor-free graphs Structural analysis of comma-free and mixed circular codes in genetic alphabets Charles Delorme Prize for outstanding thesis in Graph Theory (2019) Collaborations span institutions like ULB, G-SCOP, Inria, and cross-disciplinary fields from computer science to mathematical biology.
Furkan Kıraç is an Assistant Professor in the Computer Science Department at Özyeğin University, specializing in Computer Vision and Machine Learning . He previously served as a Part-Time Instructor at the same university (2012-2013) and as a Research Assistant at Boğaziçi University (2009-2013). Education: PhD in Computer Engineering, Boğaziçi University (2013) MS in Systems and Control Engineering, Boğaziçi University (2002) BS in Mechanical Engineering, Boğaziçi University (2000) His research focuses on real-time hand pose estimation , deep learning , and computer vision applications in industrial automation. Recent publications highlight his work on pedestrian tracking, spatio-temporal mapping, and image processing pipelines for test oracle automation. Notable achievements include founding two computer vision companies ( Proksima and Fortibase ) and receiving awards at SIU conferences (2004, 2005, 2012). He has contributed to projects funded by TÜBİTAK and the Scientific and Technical Research Council of Turkey. Scientific Awards: 3rd place in best demo award (SIU 2012) Best application paper award (SIU 2012) 3rd degree in Turkish National Science Competition (1994, 1995) Gold/Silver/Bronze medals in National Computer Science Olympiads
