Find Academic Supervisors

• Control Theory
• Optimization
• Machine Learning
• +7 more

Michael M. Zavlanos is the Yoh Family Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University's Pratt School of Engineering. He also holds secondary appointments in the Department of Computer Science and the Department of Electrical and Computer Engineering. Currently serving as the Director of the Healthcare Systems Optimization program with Duke AI Health and as an Amazon Scholar with Amazon Robotics, his academic career spans control theory, optimization, and artificial intelligence with applications across multiple domains. Dr. Zavlanos received his educational foundation from prestigious institutions: Diploma in Mechanical Engineering from the National Technical University of Athens (NTUA), Greece (2002) M.S.E. in Electrical and Systems Engineering from the University of Pennsylvania (2005) Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2008) His research program spans multiple interconnected domains, with a strong foundation in control theory, optimization, and learning methodologies . This theoretical work directly enables applications in robotics and autonomous systems , where his team develops algorithms for multi-robot coordination, motion planning under complex constraints, and network connectivity maintenance. A significant portion of his work addresses networked and distributed control systems , focusing on how multiple agents can coordinate effectively with limited communication. More recently, he has expanded his research into cyber-physical systems with healthcare applications, leveraging his expertise to optimize healthcare delivery systems through the Duke AI Health initiative. Dr. Zavlanos' work demonstrates a consistent trajectory from theoretical foundations to real-world applications. His early work established fundamental principles for maintaining connectivity in mobile robot networks, which evolved into more sophisticated approaches for temporal task planning and risk-averse decision making in uncertain environments. The most recent phase of his research integrates machine learning with traditional control theory to address complex healthcare system optimization problems. His significant contributions to the field have been recognized through prestigious awards: Office of Naval Research Young Investigator Program (YIP) Award (2014) National Science Foundation Faculty Early Career Development (CAREER) Award (2012) National Science Foundation Faculty Early Career Development (CAREER) Award (2011) Duke University Distinguished Faculty Rank (2019) Duke University Distinguished Professor designation (2018) As an educator, Dr. Zavlanos has taught courses including ME 627: Linear System Theory, ME 592: Research Independent Study, ECE 391/291: Projects in Electrical and Computer Engineering, and CEE 627: Linear System Theory. His research program has been supported by multiple grants from the National Science Foundation and the Office of Naval Research, enabling him to mentor numerous graduate students and postdoctoral researchers in the development of cutting-edge control and optimization algorithms. Dr. Zavlanos leads research efforts at the intersection of control theory, optimization, and artificial intelligence, with particular focus on translating theoretical advances into practical applications. His recent work with Duke AI Health represents a strategic expansion of his research portfolio into healthcare systems optimization, where he applies his expertise in algorithmic decision making to improve patient scheduling, resource allocation, and operational efficiency in medical settings. Through his Amazon Scholar role, he also contributes to advancing robotics technologies for real-world applications.

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +6 more

Ayşenur Akyüz Birtürk serves as a Lecturer in the Department of Computer Engineering at Middle East Technical University (METU), Ankara, where she has taught since February 1994. Her academic career spans foundational programming courses to advanced graduate seminars in AI and Computational Linguistics, reflecting 30+ years of institutional commitment. She earned all her degrees from METU, culminating in a 1998 Ph.D. focused on Turkish language computational analysis. Her educational journey includes: B.S. in Computer Engineering (1985) M.S. in Computer Engineering (1988) with thesis on “A Model for Representing Concepts: Conceptual Dependency Theory” Ph.D. in Computer Engineering (1998) with thesis on “A Computational Analysis of Turkish using the Government-Binding Approach” Dr. Birtürk’s research centers on Artificial Intelligence and Natural Language Processing , with pioneering work in Turkish language parsing evolving into modern Recommender Systems . She integrates semantic relations and multi-domain data to build hybrid engines for movies, books, and music, emphasizing user modeling through knowledge representation and data mining techniques. Analysis of her 2010-2015 publications reveals two dominant threads: adaptive recommender systems (80% of output) using semantic similarity and dynamic clustering, and renewable energy analytics (20%) applying machine learning to wind/hydrological data. This pivot from NLP to energy forecasting demonstrates methodological versatility while maintaining core AI expertise. Her scientific recognition includes: TUBITAK scholarships throughout education (1977-1988) Multiple national contest awards in high school (1979-1980) Leadership in TUBITAK-funded energy and healthcare projects Dr. Birtürk has supervised 17 Master’s theses in NLP and recommender systems while securing competitive grants including METU-ISTEC #17435 (2006-2008; 504,000 YTL) and HASAT (2010-2013; 601,637 YTL). Her industry consultancy spans medical form design (FormAnalitik), question-answering systems, and retail intelligence platforms, translating academic research into real-world tools.

Eugene Y. VassermanView profile

Associate Professor

• Network and distributed system security
• Privacy and anonymity
• Censorship resistance
• +4 more

Eugene Y. Vasserman is an Associate Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as Director of the Center for Cybersecurity and Trustworthy Systems. His office is located in 2171 Engineering Hall, and he holds office hours on Tuesdays from 2:30 pm to 4:00 pm and Wednesdays from 11:30 am to 1:00 pm during the Fall 2025 semester. Dr. Vasserman's research spans multiple critical areas of cybersecurity including network and distributed system security, privacy and anonymity, censorship resistance, operating system security, medical and IoT security, usable security, and applied cryptography. His work bridges theoretical security concepts with practical implementations, addressing real-world challenges in diverse domains from medical systems to blockchain technologies. His recent publications reveal a research trajectory that has evolved from foundational network security work to increasingly interdisciplinary research intersecting with artificial intelligence, medical systems, and cybersecurity education. The 15 most recent publications demonstrate his ongoing commitment to both theoretical advances and practical security solutions across multiple domains. Outstanding short paper award for 'Hypersparse Traffic Matrix Construction using GraphBLAS on a DPU' at IEEE HPEC 2023 Best graduate student poster award for 'Empowering pre-service teachers to utilize programming in the classroom' at ASEE Midwest Conference 2013 Dr. Vasserman has mentored numerous graduate students through the Systems and Network Security (SyNeSec) Lab, with alumni now working at organizations including Paycom, Corelight, Sandia National Labs, Microsoft, and Cerner. He teaches multiple cybersecurity courses each semester including CIS 525: Introduction to Network Programming, CIS 755: Systems Security, and CIS 351: Cyber Defense Basics, demonstrating his long-term commitment to cybersecurity education since at least 2010. As Director of the Center for Cybersecurity and Trustworthy Systems, he leads initiatives that address critical security challenges across multiple domains, fostering collaboration between researchers, students, and industry partners to develop trustworthy systems for the future.

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Laura Brown is an Associate Professor of Computer Science and serves as the Associate Dean of Data Science Initiatives in the College of Computing at Michigan Technological University. She also directs the Data Science M.S. and B.S. programs. Her educational background includes: PhD in Biomedical Informatics from Vanderbilt University MS in Biomedical Informatics from Vanderbilt University MSE in Electrical Engineering and Computer Science from the University of Michigan BS in Engineering from Swarthmore College Laura's research centers on Artificial Intelligence , Machine Learning , and Data Science , with applications spanning energy systems (microgrids and power systems), health informatics , and computer systems . Her work bridges theoretical advancements with real-world problem solving, particularly in optimizing electrical grids and healthcare through data-driven approaches. Her recent publications (2017-2019) demonstrate a strong focus on applying machine learning to energy forecasting and computer architecture. Key trends include solar irradiance prediction, load forecasting for power systems, and memory management in data centers using neural networks and dynamic policies. Laura actively mentors students as co-advisor for Women in Computing Science (WiCS) and organizes Carpentries workshops, ICPC programming competitions, and Google research workshops. Her grant portfolio includes significant funding from NSF ($1.6M+), Google ($53K), and DoD (~$1M) for projects in smart grid technologies, microgrid management, and computer science education. She collaborates through the Institute of Computing and Cybersystems (ICC), Ecosystem Science Center (ESC), and Center for Agile Interconnected Microgrids (AIM) on interdisciplinary research initiatives.

Aline NormoyleView profile

Assistant Professor

• Digital Humans
• Character Animation
• Video Games
• +4 more

Aline Normoyle is an Assistant Professor of Computer Science at Bryn Mawr College, where she leads the MOVR (Motion Capture and Virtual Reality) lab. Her research focuses on character animation, video game design, and analyzing human motion from video. She holds a B.Sc. from McGill University, and a M.Sc. and Ph.D. from the University of Pennsylvania. Prior to academia, she worked as a software engineer for 10 years, specializing in networking, graphics, and behavioral modeling. Her work bridges computational methods with experimental approaches to study non-player characters, avatars, and game mechanics. Normoyle’s contributions include open-source tools for sign language analysis and VR experiments, and she actively collaborates across disciplines including linguistics and psychology. She has been recognized with the Best Student Presentation Award at the 2013 Symposium on Applied Perception. Her teaching spans foundational computer science courses and specialized topics in game programming and animation. Education: B.Sc. in Computer Science, McGill University (1999) M.Sc./Ph.D. in Computer Science, University of Pennsylvania (2015) Research Interests: Procedural character animation and behavior Motion capture and analysis techniques Immersive VR/AR applications Game-based experimental platforms Accessibility in digital human modeling Recent Work Trends: Normoyle’s recent publications explore gesture-based user identification, controller effects in AR games, and the role of hand motions in virtual communication. Her work emphasizes interdisciplinary approaches, combining AI with traditional animation techniques to create more expressive and interactive virtual characters. Awards: Best Student Presentation Award, Symposium on Applied Perception (2013) Advising & Grants: Normoyle advises students in animation, VR, and game design, requiring foundational coursework in computer graphics and systems programming. She has received support for her research through collaborations with institutions like Clemson University and New York University. Labs & Teams: Director of the MOVR lab at Bryn Mawr, focusing on motion capture, VR experiments, and open-source tools for sign language analysis.

• Distributed Systems
• Fault-Tolerant Computing
• Asynchronous Algorithms
• +3 more

Prof. Ulrich Schmid is a full professor at TU Wien, affiliated with the Forschungsbereich Embedded Computing Systems. His research focuses on distributed systems, fault-tolerant computing, and asynchronous algorithms. He leads projects like 'Asynchronous Distributed Algorithms in the Theta-Model' and 'Gracefully Degrading Agreement in Directed Dynamic Networks.' Prof. Schmid has received honors at TU Wien and actively supervises numerous students in PhD and Master's programs. His academic work spans theoretical foundations (e.g., epistemic logic in Byzantine systems) and practical applications (e.g., digital circuit delay modeling). Key contributions include hybrid delay models for integrated circuits and formal verification frameworks for timed circuits. Collaborations involve institutions like the Institut für Technische Informatik and the E191-02 research group. Notable research areas: Byzantine fault tolerance, consensus protocols, and embedded systems Leadership in 4 major projects addressing distributed computing challenges Recipient of TU Wien honors (2021) Prof. Schmid's lab emphasizes bridging theoretical computer science with hardware-software co-design, producing over 220 publications and 10+ supervised theses since 2000.

• Computer-Aided Design (CAD)
• Digital Circuit Design
• Low Power Design
• +4 more

Jing-Yang Jou is the current President and K.T. Li Chair Professor at National Central University in Taiwan. He holds MS (1983) and PhD (1985) degrees in Computer Science from the University of Illinois. His research focuses on logic synthesis, physical synthesis, design verification, CAD for low-power systems, and Network-on-Chips (NoC). With over 200 technical publications, he has made significant contributions to digital circuit design automation. Former Deputy Minister of National Science Council, Taiwan (2010-2012) Vice Chancellor for Academic Affairs, University System of Taiwan (2007-2010) Executive Director of National SoC Program (2007-2010) Director General of National Chip Implementation Center (2004-2007) Recipient of prestigious awards including IEEE Fellow distinction and multiple industry-academia collaboration recognitions. His career includes roles at AT&T Bell Laboratories (1986-1994) and GTE Laboratories (1985-1986). Research emphasizes bridging theoretical computer science with practical semiconductor design challenges.

• Statistical Relational Learning
• Machine Learning
• Probabilistic Graphical Models
• +5 more

Lise Getoor is a Professor in the Department of Computer Science at the University of California, Santa Cruz, within the Baskin School of Engineering. She previously held positions at the University of Maryland, College Park and has established herself as a leading researcher in statistical relational learning and neuro-symbolic artificial intelligence. Her research focuses on developing principled approaches to reasoning and learning with rich, relational data. She has made significant contributions to probabilistic soft logic, statistical relational learning, knowledge graph construction, and the integration of symbolic and neural approaches to AI. Her work bridges theoretical foundations with practical applications in areas including social network analysis, sustainable recommendations, cyberbullying detection, and fair machine learning systems. Analysis of her recent publications reveals a strong trend toward neuro-symbolic integration, with increasing focus on combining neural networks with logical reasoning frameworks. Her research demonstrates consistent innovation in developing scalable algorithms for structured prediction across diverse application domains while addressing critical issues of fairness and interpretability in AI systems. Professor Getoor has advised numerous PhD students who have become active contributors to the field, including Connor Pryor, Charles Dickens, Eriq Augustine, and Varun Embar. Her research has been supported by multiple grants from major funding agencies, though specific details aren't provided in the current data. She leads research in the Statistical Relational AI (StaRAI) Lab at UC Santa Cruz, where her team develops frameworks that combine logical reasoning with probabilistic modeling to address complex real-world problems requiring both structured knowledge and statistical learning.

Samuel R. BussView profile

Professor

• Mathematical Logic
• Proof Complexity
• Computer Graphics
• +3 more

Samuel R. Buss is a Professor of Mathematics and Computer Science at the University of California, San Diego (UCSD). He holds a Ph.D. in Mathematics from Princeton University (1985) and has expertise spanning mathematical logic, proof complexity, computational complexity, and computer graphics. His work bridges foundational mathematics with theoretical computer science, particularly in formal systems, automated reasoning, and algorithm design. Key research areas include proof complexity (e.g., resolution and extended resolution systems), computational logic, and applications in computer graphics (e.g., OpenGL implementations and ray tracing). He authored influential books such as 3D Computer Graphics: A Mathematical Introduction with OpenGL and Introduction to Mathematical Logic . His software contributions include OpenGL-based tools and algorithms for computer graphics and satisfiability solving. Buss has advised notable Ph.D. students, including David Robinson and Nathan Segerlind, and has received funding from NSF grants and other institutions. His research often intersects interdisciplinary topics like bounded arithmetic, computational geometry, and formal verification.

Georgios PortokalidisView profile

Research Professor

• Systems Security
• Binary Analysis
• Software Hardening
• +1 more

Georgios Portokalidis is an Associate Research Professor at IMDEA Software Institute and Visiting Research Professor at Stevens Institute of Technology. He leads research in software systems security with focus on binary analysis, software hardening, and operating system security. His research develops practical defenses against software vulnerabilities through techniques like binary debloating, system call filtering, and hardware-assisted security. Current projects explore Rust language security, processor-assisted protection mechanisms, and vulnerability mitigation through code reduction. Publication trends show evolution from foundational binary analysis to applied security hardening, with recent emphasis on Rust security, hardware-assisted defenses, and real-world system protection. Work demonstrates consistent focus on practical, deployable security solutions. He has supervised numerous PhD students and postdocs, currently mentoring researchers at IMDEA. His group investigates binary analysis, software debloating, and system hardening techniques. Major grants include DARPA YFA (2021-2023) and ONR funding (2017-2022) supporting adaptive binary debloating research. Leads the systems security research group at IMDEA Software Institute, collaborating with international partners. Maintains active research partnerships and regularly contributes to top security conferences as program committee member.

Andrei KrokhinView profile

Professor

• Computational Complexity
• Constraint Satisfaction Problems
• Universal Algebra
• +4 more

Andrei Krokhin is a Professor in the Department of Computer Science at Durham University, UK. His academic roles include being a member of the Algorithms and Complexity Research Group. He holds a PhD in Mathematics from Ural State University (Russia) and has held positions at Warwick University and Oxford University. His research focuses on computational complexity, constraint satisfaction problems (CSP), universal algebra, and combinatorics. Education: PhD in Mathematics, Ural State University, 1990s Research Interests: Professor Krokhin investigates the mathematical and algorithmic foundations of CSP, emphasizing complexity classification and approximation. His work bridges universal algebra, logic, combinatorics, and graph theory. Key themes include algebraic approaches to CSP, constraint optimization, and the interplay between computational complexity and structural mathematics. Awards: EPSRC Advanced Research Fellowship (2006) Principal organizer of the 2006 Oxford Workshop on Mathematics of Constraint Satisfaction Invited plenary speaker at ISMVL 2003 (Tokyo) Invited lectures at NATO ASI Summer School (2003) Advising & Grants: Supervises PhD students (e.g., Yiming Qiu) and leads EPSRC-funded projects like 'Promise Constraint Satisfaction Problems: Structure and Complexity.' He recruits students for research on CSP complexity and approximation. Labs/Teams: Member of the Algorithms and Complexity Research Group at Durham University, collaborating internationally on CSP theory and applications.

John McNamaraView profile

Research Fellow

• Artificial Intelligence
• Cybersecurity
• Transportation Systems
• +3 more

John McNamara is an IBM Master Inventor and Advanced Visiting Research Fellow at the University of Sheffield, holding Honorary Professorships at University College London (UCL) and Sheffield Hallam University. He specializes in interdisciplinary innovation across cybersecurity, transportation systems, and artificial intelligence, leveraging technologies like Cloud and Watson for Tech for Good initiatives. His roles include IBM Technical Specialist, IBM Thought Leader, and Open Group Distinguished Technical Specialist, with industry expertise spanning defense, finance, and healthcare sectors. Education: BSc (Hons) in Information Systems from the University of Hull. Research focuses on patentable inventions addressing complex event processing, automated systems optimization, and regulatory compliance. Key innovations include intelligent fuel management systems, automated style-checking frameworks, and goal-directed simulation tools for business processes. His work bridges academic research with real-world industry applications. Awards: IBM Master Inventor IBM Thought Leader Technical Specialist Open Group Distinguished Technical Specialist Collaborates with IBM UK University Programs to develop socially impactful technologies. Active in creating solutions for disease control, banking systems, and defense applications through cross-disciplinary partnerships.

Kristopher MicinskiView profile

Assistant Professor

• Programming Languages
• Static Analysis
• Formal Methods
• +4 more

Kristopher Micinski is an Assistant Professor in the Electrical Engineering and Computer Science Department at Syracuse University. His research focuses on scalable program analysis, static analysis, and formal methods applied to computer security and privacy. He holds a PhD in Computer Science from the University of Maryland and a BS in Computer Engineering from Michigan State University. Education: PhD, Computer Science, University of Maryland at College Park BS, Computer Engineering, Michigan State University Research Interests: His work bridges theory and application of program analyses, emphasizing scalable static analysis frameworks, Datalog optimization for distributed systems, and security applications. Recent efforts include GPU-accelerated Datalog engines and large-scale malware analysis pipelines like Assemblage. Grants & Projects: NSF PPoSS Large: $1M grant for declarative analytics DARPA V-SPELLS: $400K for legacy software optimization Assemblage: $350K DoD grant for malware classification Teaching: He teaches undergraduate and graduate courses on programming languages (CIS352) and formal methods (CIS700), with materials publicly available on YouTube.

Ruben MartinsView profile

Research Professor

• Constraint Programming
• Program Synthesis
• Software Analysis
• +3 more

Ruben Martins is an Assistant Research Professor and Master’s Program Director at the School of Computer Science, Carnegie Mellon University. He holds a Ph.D. from the Technical University of Lisbon, followed by postdoctoral research at the University of Oxford and UT Austin. His work focuses on constraint programming, program synthesis, and formal verification, with applications in improving programmer productivity and automating data science tasks. Education: Ph.D. in Computer Science, Technical University of Lisbon (2013) Postdoctoral Researcher, University of Oxford (2014-2015) Postdoctoral Researcher, UT Austin (2015-2017) Ruben's research bridges constraint programming with program synthesis, aiming to automate tasks such as vulnerability detection, code repair, and SQL synthesis. He developed Open-WBO , a MaxSAT solver that won multiple gold medals and is used in real-world optimization scenarios like seating arrangements for events. His work integrates large language models (LLMs) with traditional methods to enhance software analysis and debugging. Awards: Distinguished Paper Award at PLDI 2018 Gold Medal in MaxSAT Competitions for Open-WBO Advising & Grants: Ruben advises Master’s students and directs courses such as 15639, 15604, and others. His research has been supported by grants focusing on program synthesis and cybersecurity. Labs/Teams: Leads development of Open-WBO and contributes to projects like Crabtree (Rust API testing) and Pryde (evasion attack analysis).

Peter DybjerView profile

Professor

• Intuitionistic Type Theory
• Categorical Type Theory
• Proof Assistants
• +9 more

Peter Dybjer is a Professor of Computing Science at the Department of Computer Science and Engineering, Chalmers University of Technology in Sweden. He is a leading researcher in the field of type theory, with a focus on intuitionistic type theory, categorical semantics, and proof assistants. His work bridges foundational aspects of mathematics and computer science, emphasizing constructive methods and formal verification. Research Interests: Intuitionistic Type Theory and its categorical semantics Proof assistants and formal verification Inductive and inductive-recursive definitions Game semantics and normalization techniques Dependent type systems and their applications Homotopy Type Theory and univalent foundations Activities & Contributions: Editor of the Bulletin of Symbolic Logic Member of program committees for conferences like TYPES, FSCD, and MFPS Keynote speaker at events including the European Congress of Mathematics and the Hausdorff Research Institute's Summer School on Types, Sets, and Constructions Initiated and directed the Master's Program in Computer Science – Algorithms, Languages, and Logic at Chalmers Teaching & Outreach: Professor Dybjer has taught advanced courses at international summer schools, including: Inductive Definitions in Constructive Type Theory (TYPES Summer School, 2005) Dependent Types at Work (LerNet Summer School, Uruguay, 2008) Normalization by Evaluation (Midlands Graduate School, 2009 and 2015)