جست‌وجوی استاد و پژوهشگر

Boaz Barakمشاهده پروفایل

استاد

• Theoretical Computer Science
• Machine Learning Foundations
• Cryptography
• +۳ مورد دیگر

Boaz Barak is a Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. He is also a member of the technical staff at OpenAI , focusing on AI safety and alignment . Barak's career includes tenure at Princeton University and research roles at Microsoft Research New England . He is a co-director of undergraduate studies (DUS) in Harvard's Computer Science department, alongside Stephen Chong, and is involved in non-profits like AddisCoder and Jam Coders for coding education in Ethiopia and Jamaica. Education : Ph.D. from the Weizmann Institute of Science , postdoctoral fellowship at the Institute for Advanced Study in Princeton. Barak's research interests span theoretical computer science , machine learning foundations , cryptography , and quantum computing . His recent work emphasizes AI safety , deep learning mechanisms , and computational complexity . He co-organized the Harvard Machine Learning Foundations seminar and serves on advisory boards for Quanta Magazine and the Simons Institute . The articles highlight Barak's interdisciplinary work. Recent publications focus on AI safety (e.g., watermarking, alignment), deep learning theory (double descent, SGD dynamics), and computational complexity (planted clique, unique games). These reflect his dual emphasis on theoretical rigor and applied machine learning . Scientific Awards : ACM Dissertation Award Packard and Sloan Fellowships Foreign Policy's 100 Leading Global Thinkers (2014) Simons Investigator Fellow of the ACM FOCS Test of Time Award Barak advises current students like Gustaf Ahdritz and Natalie Abreu , with former advisees including Preetum Nakkiran and David Xiao . He is supported by grants from the Simons Investigator Fellowship , Kempner Institute , NSF , DARPA , and DOE , among others.

• Finite Geometry
• Projective Spaces
• Combinatorial Designs
• +۳ مورد دیگر

Francesco Pavese is an Associate Professor of Geometry at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research primarily focuses on finite geometry, combinatorial structures, and their applications to coding theory. His research interests include: Finite projective and polar spaces Strongly regular graphs and geometric constructions Blocking sets and special configurations in finite geometries Subspace codes and network coding applications Combinatorial designs and q-analogs Arcs, ovoids, and m-ovoids in projective spaces Professor Pavese's recent work demonstrates a consistent focus on the interplay between geometric structures and combinatorial properties in finite settings. His research often explores connections between classical geometric objects and their applications in coding theory, with particular attention to structures in projective spaces over finite fields. His publications reveal expertise in Cameron-Liebler line classes, intriguing sets of strongly regular graphs, and the geometry of Hermitian varieties. His scholarly output shows significant contributions to geometric combinatorics, with recent work exploring q-analogs of design structures, properties of special sets in projective spaces, and connections between geometric objects and strongly regular graphs. The trend in his research indicates growing interest in modular properties of geometric structures and their applications to coding theory. Professor Pavese maintains an active research program with numerous publications appearing in leading mathematics journals specializing in finite geometry and combinatorial mathematics.

• Complex Analysis
• Mathematics Education
• Matrix Theory
• +۴ مورد دیگر

Dr. Erin R. Militzer is a Professor in the Mathematics Department at Ferris State University’s College of Arts, Sciences and Education. She earned her PhD (2004) and MA (2004) from the University of Kentucky, where her thesis focused on Complex Analysis under the guidance of Dr. James Brennan. Education : PhD, MA in Mathematics (University of Kentucky); BS in Mathematics (Central Michigan University) Her research spans Complex Analysis , Matrix Theory , and Mathematics Education , with recent work on inquiry-based learning and quantitative reasoning for business. Earlier studies explored numerical ranges of matrices and rational approximation in L^p spaces. Her publications highlight interdisciplinary approaches, blending abstract mathematics with pedagogical innovation. Dr. Militzer has contributed to NSF-funded initiatives, including mentoring the 2012 Research Experience for Undergraduates (REU) project on Cops and Robbers on Graphs . Her academic honors include the National Science Foundation’s Algebra Cubed Fellowship and Project NExT Fellow recognition. She is also a certified instructor for the Intel® Math Program, emphasizing teacher training in STEM fields.

Robert H Morelos-Zaragozaمشاهده پروفایل

استاد

• Channel Coding
• Ultrawideband Communications
• RFID Systems
• +۵ مورد دیگر

Robert H Morelos-Zaragoza is a Professor in the Department of Electrical Engineering at San José State University , where he teaches undergraduate and graduate courses in digital communications and directs MSEE projects and theses. His research focuses on Channel Coding , Ultrawideband Communications , RFID Systems , and Digital Signal Processing . Doctor of Philosophy (PhD), Electrical Engineering, University of Hawaii at Manoa, 1992 Master of Science in Electrical Engineering (MSEE), Communications, National Autonomous University of Mexico (UNAM), 1987 Bachelor of Science in Electrical Engineering (BSEE), Electronics and Communications, UNAM, 1985 His research spans error correcting coding (ECC/FEC), software-defined radio (SDR), space-time signal processing, and ultra-wideband (UWB) communication systems. Recent publications highlight advancements in multi-valued sequence generation , reversible data hiding , and random number generator analysis . Professional affiliations include Senior Member of IEEE , IEICE (Japan) , and Eta Kappa Nu . His career includes roles as a Visiting Researcher at the University of California at Santa Cruz (2005), Visiting Professor at Intel Corporation (Summer 2004), and positions at LSI Logic, Sony Computer Science Laboratories, and Osaka University.

Ivashko Viktor Viktorovychمشاهده پروفایل

استادیار

• Computational Physics
• Solid State Physics
• Statistical Mechanics
• +۳ مورد دیگر

Ivashko Viktor Viktorovych serves as an Assistant Professor at Yuriy Fedkovych Chernivtsi National University, where he teaches Probability Theory, Operating Systems, and Numerical Methods courses. Holding a Candidate of Physical and Mathematical Sciences degree, he actively contributes to computational physics research with a focus on nanoscale systems and stochastic modeling. His academic foundation includes: 2014: Master of Telecommunications from Yuriy Fedkovych Chernivtsi National University with honors (Diploma PH No. 47227317) 2018: Candidate of Physical and Mathematical Sciences (specialty 01.04.07 – Solid State Physics) for dissertation 'Features of Transition Phenomena between Low- and High-Spin States of Spin-Crossover Compounds' (DK No. 046497) Dr. Ivashko's research centers on computational modeling of physical systems using Monte Carlo methods, particularly investigating spin-crossover compounds and nanomaterials. His work bridges solid-state physics and statistical mechanics, with significant applications in molecular electronics and nanodevice development. Recent publications reveal consistent focus on impurity effects in nanoparticles, magnetic properties of carbon-based systems, and pressure-induced electronic bistability in coordination polymers, demonstrating methodological expertise in simulating nanoscale phase transitions. His scientific recognition includes: Gratitude for significant scientific achievements (2016-2017) Through scientific consulting for MPP 'PROM.SOFT' and authorship of 12+ textbooks/monographs, he actively shapes academic curricula. Professional development includes Sigma Software's Teachers’ Smart Up programs (2023-2024), Softserve's Tech Summer Bootcamp, and Coursera specializations in Python and web development. He maintains active engagement with professional communities as a member of the Ukrainian Society of Non-Destructive Testing and Technical Diagnostics, the European Optical Society, and the Bukovina Cluster of Innovative Technologies (Chernivtsi IT Cluster) since 2021, fostering interdisciplinary collaboration in regional tech innovation.

Jeremy Mooreمشاهده پروفایل

دانشیار

• Algebra
• Abstract Algebra
• Module Theory
• +۱ مورد دیگر

Jeremy Moore serves as Associate Professor and Department Chair in the Department of Mathematics & Actuarial Science at Otterbein University. Born and raised in central Ohio, he holds a Ph.D. from Ohio University and actively contributes to both academic leadership and mathematical research. His educational background includes: Ph.D. in Mathematics, Ohio University (2011) M.S. in Mathematics, Ohio University (2009) B.S. in Mathematics, Ohio University (2004) Dr. Moore's research evolved from algebraic coding theory to specialized work on linear independence of invertible elements in modules, reflecting deep engagement with abstract algebraic structures. His teaching passion centers on modern algebra, though he embraces all departmental courses. No scientific awards were documented in the provided materials. He advises the Otterbein Christian Fellowship (OCF), drawing inspiration from student dedication, while no research grants or graduate student mentorship details appear in the source text.

Paul Downenمشاهده پروفایل

استادیار

• Programming Languages
• Logic
• Formal Methods
• +۵ مورد دیگر

Paul Downen is an Assistant Professor in the Miner School of Computer & Information Sciences at the University of Massachusetts Lowell, where he has been teaching since Fall 2021. His academic journey began with dual Bachelor's degrees in Computer Science and Computer Engineering from Lawrence Technological University in 2010, followed by a Ph.D. in Computer Science from the University of Oregon in 2017. He has also been a visiting researcher at INRIA and Microsoft Research. His educational background includes: Ph.D. in Computer Science (2017), University of Oregon - Eugene, OR Dissertation Title: Sequent Calculus: A Logic and a Language for Computation and Duality B.S. Computer Science (2010), Lawrence Technological University - Southfield, MI B.S. Computer Engineering (2010), Lawrence Technological University - Southfield, MI Dr. Downen's research centers on the intersection of logic and programming languages, with a focus on using logical foundations to improve the efficiency, correctness, and safety of programs and their compilation. His primary research lies in the Curry-Howard correspondence or proofs-as-programs paradigm, exploring how logical principles can inform both program design and compiler optimization. He is particularly interested in duality principles in computation, where he investigates how concepts like functional and object-oriented programming paradigms represent dual perspectives on the same underlying computational processes. His publication record shows a consistent focus on foundational aspects of programming languages, with recent work exploring copatterns, macro systems, evaluation strategies, and the relationship between type systems and machine representation. His research often bridges theoretical concepts with practical implementation, particularly through contributions to the Glasgow Haskell Compiler (GHC). Among his notable achievements are: Best Paper Award (2017) at Programming Languages Design and Implementation (PLDI) Oregon Doctoral Research Fellowship (2017) from the University of Oregon Best Paper Award nomination (2012) at European Joint Conferences on Theory and Practice of Software Best Paper Award nominee (2025) at Trends in Functional Programming Dr. Downen has been instrumental in organizing the Oregon Programming Languages Summer School, expanding its reach to diverse audiences through a "Foundations" lecture series. His teaching portfolio includes courses on Organization of Programming Languages, Assembly Language Programming, and Effective Functional Programming. His research has resulted in practical implementations within GHC, including Sequent Core (an alternative intermediate language based on sequent calculus) and Join Points (an optimization for control flow).

Muriel Médardمشاهده پروفایل

استاد

• Network Coding
• Error-Correcting Codes
• Algorithm Design
• +۴ مورد دیگر

Muriel Médard serves as the NEC Professor of Software Science and Engineering at the Massachusetts Institute of Technology (MIT), recognized for transformative contributions to network theory and hardware implementation. Elected Fellow of the Royal Academy of Engineering (FREng) in 2025, she bridges theoretical innovation with commercial applications. Her research pioneers algebraic and algorithmic principles in network coding, establishing universal error-correcting decoding frameworks applicable to any code. This work directly enhances network reliability, latency, and energy efficiency through novel circuit designs that set world records in performance metrics. Commercial protocols derived from her research demonstrate real-world impact on communication infrastructure. Scientific Awards: Fellow of the Royal Academy of Engineering (FREng), 2025

Narayana Prasad Santhanamمشاهده پروفایل

استاد

• Information Theory
• Statistical Learning
• Signal Processing
• +۱۱ مورد دیگر

Narayana Prasad Santhanam is a Professor in the Department of Electrical Engineering at the University of Hawaii, College of Engineering. His research spans theoretical and practical aspects of information theory, statistical learning, and signal processing, with particular focus on high-dimensional and complex problems that cannot be addressed by traditional statistical methods. Santhanam maintains an active research program funded by the National Science Foundation and teaches courses including Probability and Statistics, Linear Algebra and Machine Learning, and Information Theory. Santhanam's research interests center on the intersection of statistical learning and information theory, particularly in high-dimensional settings. His work addresses fundamental questions about when learning is possible, how to characterize non-uniform learning, and how to interpret data from complex sources like slow mixing Markov processes. He has made significant contributions to understanding the limitations of statistical methods in large alphabet scenarios, where traditional approaches fail. His research has important applications in diverse fields including genetic data analysis, risk management, smart grids, and text processing. His recent publications reveal a consistent focus on theoretical foundations with practical applications. Santhanam's work often explores the connections between seemingly disparate fields, bringing combinatorial and probabilistic approaches to bear on complex problems. A notable trend is his development of frameworks for pointwise convergence rather than uniform convergence in statistical estimation, which has significant implications for handling large alphabet problems where traditional methods break down. 2006 IEEE Information Theory Society Best Paper Award 2003 Capocelli Prize Santhanam has successfully secured significant research funding as Principal Investigator on an NSF award of approximately $1.1 million to examine the interplay of statistics and information theory, with applications to document classification and genetic analysis. He has also served as co-PI on multiple NSF awards totaling roughly $800,000 for research on channels with memory and smart grid organization. His research group includes students M. Asadi, A. Esraghi, A. Lee, M. Hosseini, R. Paravi, and G. Tobin, who contribute to projects spanning statistical learning, information theory, and their applications to biological and engineering problems. Santhanam has co-organized three major workshops on large alphabet information theory and statistics, bringing together over 80 researchers from diverse disciplines including biology, computer science, economics, information theory, mathematics, networking, and statistics.

• Automata theory
• Formal language theory
• Combinatorics on words
• +۲ مورد دیگر

Giuseppa Castiglione is a Researcher (INFO-01/A) in the Department of Mathematics and Computer Science at the University of Palermo's School of Basic and Applied Sciences. She maintains regular office hours on Tuesdays and Thursdays from 2:00 PM to 3:00 PM in Room 209, second floor, and teaches Theoretical Computer Science for both Computer Science and Mathematics degree programs. Her primary research interests span several interconnected areas within theoretical computer science and discrete mathematics. These include automata theory, formal language theory, combinatorics on words, enumerative combinatorics, and discrete two-dimensional structures. Her work demonstrates a consistent focus on the mathematical foundations of computer science, particularly in the analysis of string algorithms, word combinatorics, and discrete geometric structures. Analysis of her publication record from 2017-2025 reveals a strong concentration on combinatorial aspects of words and automata, with recent work expanding into isometric words, Fibonacci cubes, and Wheeler automata. Her research shows a progression from foundational work on polyominoes and automata minimization toward more complex combinatorial structures and their applications in string processing. Castiglione has participated in numerous significant research projects including the Prin 2010-11 project on mathematical aspects of automata and formal languages, the PO FESR 2007/2013 Neverlost project, PON Research and Competitiveness 2007/2013 SMART HEALTH initiative, and GNCS 2016 on combinatorial methods for handling large data sets. These projects demonstrate her ability to secure competitive research funding across multiple domains. She collaborates with researchers including Marinella Sciortino, Gabriele Fici, Chiara Epifanio, Sabrina Mantaci, and Antonio Restivio, forming a productive research network focused on theoretical computer science and combinatorics. Her extensive publication record in high-quality venues demonstrates her sustained contribution to the field over more than two decades.

Igor Ballaمشاهده پروفایل

پژوهشگر ارشد

• Extremal Combinatorics
• Combinatorics
• Linear Algebra
• +۱۰ مورد دیگر

Igor Balla is a Strauch Postdoctoral Fellow at the Simons Laufer Mathematical Sciences Institute (SLMath) in Berkeley, where he participates in a research program on extremal combinatorics. He holds a PhD in Mathematics from ETH Zürich, advised by Benny Sudakov, and earned prior degrees from New York University (Master’s) and Carnegie Mellon University (Bachelor’s). He has held postdoctoral positions at Tel Aviv University, the Hebrew University of Jerusalem, and Masaryk University. PhD : Mathematics, ETH Zürich Master’s : New York University Bachelor’s : Carnegie Mellon University His research lies at the intersection of combinatorics and linear algebra, focusing on extremal problems with connections to geometry, theoretical computer science, probability, and quantum physics. Key themes include equiangular lines, orthonormal representations, spectral graph theory, and extremal set systems such as union-closed families. His work often combines algebraic methods with combinatorial reasoning to solve long-standing open problems. The recent articles highlight a strong trend in equiangular lines, spectral extremal combinatorics, and the application of linear algebra to graph theory and coding. His work on extension complexity and the MaxCut problem bridges discrete mathematics and optimization. The recurring use of eigenvalues, matrix projections, and vector representations underscores a unified methodological framework across his publications. Strauch Postdoctoral Fellow Igor Balla has taught as a lecturer in Graph Theory and Advanced Combinatorics at Masaryk University and served as a teaching assistant at ETH Zürich and Carnegie Mellon University across courses in graph theory, algebra, and number theory. He has not been mentioned in connection with any specific grants, but his postdoctoral fellowship indicates competitive research support. His frequent invited talks at institutions like Princeton, MIT, Harvard, and ICERM reflect active engagement in the global combinatorics community. He is affiliated with the research program in extremal combinatorics at SLMath, a leading institute in mathematical sciences. His collaborations span institutions including ETH Zürich, Tel Aviv University, and the Hebrew University, indicating a strong, collaborative research network. His recent work with prominent mathematicians like Benny Sudakov, Po-Shen Loh, and Noga Alon further emphasizes his integration into elite research circles.

Matthew Fickusمشاهده پروفایل

استاد

• Applied Harmonic Analysis
• Frame Theory
• Compressed Sensing
• +۷ مورد دیگر

Matthew Fickus is a Professor of Mathematics at the Air Force Institute of Technology (AFIT), within the Graduate School of Engineering and Management. His research lies at the intersection of applied harmonic analysis, frame theory, and mathematical signal processing, with a strong focus on equiangular tight frames, fusion frames, and combinatorial constructions in coding and geometry. His educational background includes a PhD in Mathematics from the University of Maryland, College Park (2001), an MS in Applied Mathematics, and a BS in Mathematics, both from the University of Maryland, Baltimore County. Dr. Fickus's research interests span Applied Harmonic Analysis , Frame Theory , Compressed Sensing , Signal Processing , and Combinatorial Design . He investigates structured mathematical frameworks for optimal signal representation, particularly focusing on equiangular tight frames and their algebraic, geometric, and combinatorial properties. His work often bridges pure mathematics with engineering applications in imaging, coding, and data science. The trend in his recent publications reveals a deep and sustained exploration of equiangular tight frames, Grassmannian packings, and their connections to finite geometry, difference sets, and algebraic structures. His research integrates tools from linear algebra, functional analysis, and discrete mathematics to solve problems in optimal frame design and signal reconstruction. These works are published in high-impact journals such as Applied and Computational Harmonic Analysis , IEEE Transactions on Information Theory , and Linear Algebra and its Applications . While no scientific awards are explicitly listed in the provided text, his extensive publication record and editorial contributions (e.g., book chapters in Finite Frames: Theory and Applications ) reflect significant recognition in his field. Dr. Fickus has mentored numerous students and collaborators, though specific names are not listed. He has received research support likely through AFIT and Air Force funding, enabling sustained contributions to mathematical engineering. His work frequently involves interdisciplinary teams, particularly in biomedical image analysis and signal processing applications. He is affiliated with the Department of Mathematics at AFIT, where he conducts research in mathematical signal processing and frame theory, contributing to both theoretical advances and practical implementations in defense and engineering contexts.

• Error-correcting codes
• Quantum codes
• Discrete mathematics
• +۱۲ مورد دیگر

Olav Geil is a Professor and currently serves as Vice-dean at the Faculty of Engineering and Science, Aalborg University, Denmark. He is affiliated with the Department of Mathematical Sciences, where he conducts research in algebraic coding theory, discrete mathematics, and computational mathematics. His work bridges theoretical mathematics with applications in cryptography, data protection, and secure computation. His research interests center on error-correcting codes , quantum codes , affine variety codes , and secret sharing , with strong emphasis on algebraic structures such as polynomial codes, Weierstrass semigroups, and function fields. He applies these in privacy-preserving technologies and secure distributed systems. The recent publications reflect a sustained focus on coding theory and its applications in both classical and quantum domains, with increasing engagement in educational innovation, particularly in Project-Based Learning (PBL) in engineering education. His work spans theoretical mathematics, applied cryptography, and interdisciplinary educational development. Scientific Awards: No specific awards listed in the provided text. Geil has been involved in advising and research grants, including participation in multiple funded research projects such as SECURE , Quantum Codes , and Secret Sharing . He has contributed to educational projects like PBL and Mathematics and Microcredentials for PBL , indicating active mentorship and curriculum development. He has collaborated with PhD students and international researchers, suggesting an ongoing advisory role. He is involved in interdisciplinary teams focusing on digital transformation , secure estimation , and privacy-preserving computation , often working within Aalborg University’s PBL framework. His leadership as Vice-dean places him at the center of academic strategy and innovation in engineering and science education.

• Randomness and Computation
• Algorithms and Optimization
• Complexity Theory
• +۱۲ مورد دیگر

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study, Princeton, where he leads research in theoretical computer science and discrete mathematics. He organizes the CSDM (Computer Science and Discrete Mathematics) program at IAS, fostering interdisciplinary research and collaboration. Research Interests: His primary research areas include randomness and pseudorandomness, computational complexity, circuit and proof complexity, quantum computation, cryptography, and distributed computing. His work bridges computer science and pure mathematics, particularly through algebra, analysis, and combinatorics. He has pioneered foundational results in derandomization, lower bounds, and the interplay between computation and mathematical structures. The recent publications highlight his continued leadership in advancing algebraic and analytic techniques in complexity theory. Themes include operator scaling, invariant theory, expander graphs, arithmetic circuits, and the asymptotic analysis of tensors and matrix spaces. These works often unify computational problems with deep mathematical frameworks, demonstrating the power of interdisciplinary approaches. Scientific Awards: Edsger W. Dijkstra Prize in Distributed Computing (2023) for the paper 'Completeness Theorems for Non-cryptographic Fault-tolerant Distributed Computing' Advising and Grants: Wigderson has advised numerous PhD students, primarily at the Hebrew University and Princeton, and hosted over 50 postdoctoral researchers at IAS, including many who have become leading figures in theoretical computer science. While specific grants are not listed, his sustained productivity and influence suggest long-term funding from major sources such as the NSF, Simons Foundation, or IAS internal support. Labs and Teams: He leads the CSDM (Computer Science and Discrete Mathematics) group at the Institute for Advanced Study, organizing seminars, workshops, and collaborative research initiatives that bring together mathematicians and computer scientists from around the world.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۶ مورد دیگر

E. Alonso is a Professor in the Department of Computer Science at City, University of London, with extensive contributions to artificial intelligence, machine learning, and cognitive science. His work spans theoretical foundations of AI, practical applications in power systems, automotive diagnostics, and ethical considerations in generative AI. Faculty member at City, University of London since at least 2010 Active researcher with publications extending through 2025 Collaborates extensively with researchers in AI, power systems, and cognitive science Regular presenter at major AI conferences including IJCAI, AAAI, and IEEE conferences Professor Alonso's research focuses on the intersection of theoretical AI and practical applications. His work in neural networks spans reinforcement learning, time series forecasting, and multi-agent systems. He has made significant contributions to understanding the mathematical foundations of AI systems, particularly in agent representations and learning mechanisms. His recent work increasingly addresses the ethical and legal implications of AI, particularly concerning copyright issues in generative AI training and moral value alignment in democratic AI systems. Analysis of Professor Alonso's recent publications (2023-2025) reveals a strong trend toward practical AI applications with theoretical grounding. His work spans multiple domains including power systems optimization using graph neural networks, automotive diagnostics with specialized neural architectures, and creative AI approaches to generalization. A notable pattern is his ability to bridge theoretical AI concepts with real-world engineering problems, particularly in energy systems and automotive applications. His research increasingly incorporates ethical considerations, reflecting the growing importance of responsible AI development. Professor Alonso has supervised numerous students who have gone on to publish with him in top venues. His collaborative approach is evident in the diverse range of co-authors across different disciplines. While specific grant information isn't detailed in the provided materials, his sustained publication record across multiple domains suggests successful funding acquisition for his research programs. His work appears connected to the Centre for Computational and Animal Learning Research, suggesting involvement in interdisciplinary teams exploring the connections between biological learning systems and artificial intelligence. Recent collaborations with researchers in medical imaging and radiotherapy implementation further demonstrate the breadth of his research impact across different application domains.