News

Algorithms, Theory & Logic

MPI-SWS researcher receives LICS 2021 Distinguished Paper Award

April 2021
Joël Ouaknine, along with his co-authors James Worrell and Florian Luca, has received a Distinguished Paper award at the 2021 ACM/IEEE 36th Annual Symposium on Logic in Computer Science (LICS 2021) for his paper Universal Skolem Sets.

Distinguished Paper awards are given to about 10% of papers at LICS. These are papers that, in the view of the LICS program committee, make exceptionally strong contribution to the field and should be read by a broad audience due their relevance, …
Joël Ouaknine, along with his co-authors James Worrell and Florian Luca, has received a Distinguished Paper award at the 2021 ACM/IEEE 36th Annual Symposium on Logic in Computer Science (LICS 2021) for his paper Universal Skolem Sets.

Distinguished Paper awards are given to about 10% of papers at LICS. These are papers that, in the view of the LICS program committee, make exceptionally strong contribution to the field and should be read by a broad audience due their relevance, originality, significance and clarity.

 
Read more

Joël Ouaknine is a co-recipient of the 2020 Salomaa prize

The third Salomaa prize has been awarded to MPI-SWS director Joël Ouaknine and James Worrell (Professor of Computer Science at Oxford University), for their outstanding contribution to Theoretical Computer Science, in particular to the theory of timed automata and to the analysis of dynamical systems.

The Salomaa prize in Automata Theory, Formal Languages and Related Topics is awarded each year by the Developments in Language Theory (DLT) Symposium. It was named to honour the scientific achievements and influence of Arto Salomaa, …
The third Salomaa prize has been awarded to MPI-SWS director Joël Ouaknine and James Worrell (Professor of Computer Science at Oxford University), for their outstanding contribution to Theoretical Computer Science, in particular to the theory of timed automata and to the analysis of dynamical systems.

The Salomaa prize in Automata Theory, Formal Languages and Related Topics is awarded each year by the Developments in Language Theory (DLT) Symposium. It was named to honour the scientific achievements and influence of Arto Salomaa, a founder of the DLT symposium. The prize consists of 2000 euros, funded by the University of Turku, Finland, the home university of Arto Salomaa.

 
Read more

Joël Ouaknine elected member of Academia Europaea

MPI-SWS faculty member Joël Ouaknine has been elected a member of the Academia Europaea in 2020.  This is the second election for MPI-SWS, following the election of Peter Druschel as a member in 2008.

The aim of the Academy is to promote European research, advise governments and international organisations in scientific matters, and further interdisciplinary and international research.

More information: Joel's Academia Europaea page and the list of all members elected in 2020

Anne-Kathrin Schmuck receives Emmy Noether Award

September 2020
Anne-Kathrin Schmuck, a postdoctoral fellow in the Rigorous Software Engineering group, was accepted to the Emmy Noether Programme of the German Science Foundation (DFG). This grant programme is the most prestigious programme for early career researchers from the DFG. It provides funding for an independent research group for a period of six years.

Anne-Kathrin's group will be hosted at MPI-SWS in Kaiserslautern and will develop automated modular synthesis techniques for reliable Cyber-Physical System (CPS) design. …
Anne-Kathrin Schmuck, a postdoctoral fellow in the Rigorous Software Engineering group, was accepted to the Emmy Noether Programme of the German Science Foundation (DFG). This grant programme is the most prestigious programme for early career researchers from the DFG. It provides funding for an independent research group for a period of six years.

Anne-Kathrin's group will be hosted at MPI-SWS in Kaiserslautern and will develop automated modular synthesis techniques for reliable Cyber-Physical System (CPS) design. Her work draws inspiration from both Control Theory and Computer Science and centers around Reactive Synthesis, Supervisory Control Theory and Abstraction-Based Controller Design.
Read more

Max Planck researchers publish 17 papers at LICS/ICALP 2020

Researchers from the Max Planck Institute for Software Systems (MPI-SWS), the Max Planck Institute for Informatics (MPI-INF), and the Max Planck Institute for Security and Privacy (MPI-SP) have coauthored 17 papers at the colocated LICS 2020 and ICALP 2020, two of the top conferences in theoretical computer science. LICS is the premier conference on logic in computer science and ICALP is the flagship conference of the European Association for Theoretical Computer Science. …
Researchers from the Max Planck Institute for Software Systems (MPI-SWS), the Max Planck Institute for Informatics (MPI-INF), and the Max Planck Institute for Security and Privacy (MPI-SP) have coauthored 17 papers at the colocated LICS 2020 and ICALP 2020, two of the top conferences in theoretical computer science. LICS is the premier conference on logic in computer science and ICALP is the flagship conference of the European Association for Theoretical Computer Science.

MPI-SWS papers:

  1. Invariants for Continuous Linear Dynamical Systems. Shaull Almagor, Edon Kelmendi, Joël Ouaknine and James Worrell. ICALP 2020, Track B. [ Video | Paper]

  2. The complexity of bounded context switching with dynamic thread creation. Pascal Baumann, Rupak Majumdar, Ramanathan Thinniyam Srinivasan and Georg Zetzsche. ICALP 2020, Track B. [ Video | Paper ]

  3. Extensions of ω-Regular Languages. Mikołaj Bojańczyk, Edon Kelmendi, Rafał Stefański and Georg Zetzsche. LICS 2020. [ Video | Paper ]

  4. Rational subsets of Baumslag-Solitar groups. Michaël Cadilhac, Dmitry Chistikov and Georg Zetzsche. ICALP 2020, Track B. [ Video | Paper ]

  5. On polynomial recursive sequences. Michaël Cadilhac, Filip Mazowiecki, Charles Paperman, Michał Pilipczuk and Géraud Sénizergues. ICALP 2020, Track B. [ Video | Paper ]

  6. An Approach to Regular Separability in Vector Addition Systems. Wojciech Czerwiński and Georg Zetzsche. LICS 2020. [ Video | Paper ]

  7. The complexity of knapsack problems in wreath products. Michael Figelius, Moses Ganardi, Markus Lohrey and Georg Zetzsche. ICALP 2020, Track B. [ Video | Paper ]

  8. The Complexity of Verifying Loop-free Programs as Differentially Private. Marco Gaboardi, Kobbi Nissim and David Purser. ICALP 2020, Track B. [ Video | Paper ]

  9. On Decidability of Time-bounded Reachability in CTMDPs. Rupak Majumdar, Mahmoud Salamati and Sadegh Soudjani. ICALP 2020, Track B. [ Video | Paper ]


MPI-INF papers:

  1. Scheduling Lower Bounds via AND Subset Sum. Amir Abboud, Karl Bringmann, Danny Hermelin and Dvir Shabtay. ICALP 2020, Track A.  [ Video | Paper ]

  2. Faster Minimization of Tardy Processing Time on a Single Machine. Karl Bringmann, Nick Fischer, Danny Hermelin, Dvir Shabtay and Philip Wellnitz. ICALP 2020, Track A. [ Video | Paper ]

  3. Hitting Long Directed Cycles is Fixed-Parameter Tractable. Alexander Göke, Dániel Marx and Matthias Mnich. ICALP 2020, Track A. [ Video | Paper ]

  4. A (2 + ε)-Factor Approximation Algorithm for Split Vertex Deletion. Daniel Lokshtanov, Pranabendu Misra, Fahad Panolan, Geevarghese Philip and Saket Saurabh. ICALP 2020, Track A. [ Video | Paper ]

  5. Hypergraph Isomorphism for Groups with Restricted Composition Factors. Daniel Neuen. ICALP 2020, Track A. [ Video | Paper ]

  6. Deterministic Sparse Fourier Transform with an l∞ Guarante. Yi Li and Vasileios Nakos. ICALP 2020, Track A. [ Video | Paper ]


MPI-SP papers:

  1. Deciding Differential Privacy for Programs with Finite Inputs and Outputs. Gilles Barthe, Rohit Chadha, Vishal Jagannath, A. Prasad Sistla and Mahesh Viswanathan. LICS 2020. [ Video | Paper ]

  2. Universal equivalence and majority on probabilistic programs over finite fields. Charlie Jacomme, Steve Kremer and Gilles Barthe. LICS 2020. [ Video | Paper ]

Read more

Research Spotlight: Logic and Learning

Software systems have become ubiquitous in our modern world and, consequently, so have bugs and glitches. While many software failures are harmless and often merely annoying, some can have catastrophic consequences. Just imagine the dire results of an autonomous car failing to stop at a red traffic light or a plane's control system becoming unresponsive during takeoff or landing.

In our research, we address these problems and develop intelligent tools that help engineers to build safe and reliable hardware, …
Software systems have become ubiquitous in our modern world and, consequently, so have bugs and glitches. While many software failures are harmless and often merely annoying, some can have catastrophic consequences. Just imagine the dire results of an autonomous car failing to stop at a red traffic light or a plane's control system becoming unresponsive during takeoff or landing.

In our research, we address these problems and develop intelligent tools that help engineers to build safe and reliable hardware, software, and cyber-physical systems. To this end, we employ a unique and promising strategy, which has recently also regained major attention in the artificial intelligence community: combining inductive techniques from the area of machine learning and deductive techniques from the area of mathematical logic (e.g., see the recent Dagstuhl seminar on "Logic and Learning", which was co-organized by one of our group members). Specifically, our research revolves around three topics, to which the remainder of this article is devoted: verification, synthesis, and formal specification languages.

Verification


Verification is an umbrella term referring to tools and techniques that formally prove that a given system satisfies its specification. In the context of software, a popular approach is deductive verification. The idea is easy to describe: first, the given program is augmented with annotations (typically loop invariants, pre-/post-conditions of method calls, and shape properties of data structures), which capture the developer's intent and facilitate the deductive reasoning in a later step; second, the program, together with its annotations, is translated into formulas in a suitable logic, called verification conditions; third, the verification conditions are checked for validity using automated theorem provers. Thanks to brilliant computer scientists, such as Edsger Dijkstra and Tony Hoare, as well as recent advances in constraint solving, the latter two steps can be (almost) entirely automated. However, the first step still remains a manual, error-prone task that requires significant training, experience, and ingenuity. In fact, this is one of the main obstacles preventing a widespread adaptation of formal verification in practice.

To also automate the challenging first step, we have developed a novel approach, called ICE learning [1], which intertwines inductive and deductive reasoning. The key idea is to pit a (deductive) program verifier against an (inductive) learning algorithm, whose goal is to infer suitable annotations from test-runs of the program and failed verification attempts. The actual learning proceeds in rounds. In each round, the learning algorithm proposes candidate annotations based on the data it has gathered so far. The program verifier then tries to prove the program correct using the proposed annotations. If the verification fails, the verifier reports data back to the learning algorithm explaining why the verification has failed. Based on this new information, the learning algorithm refines its conjecture and proceeds to the next round. The loop stops once the annotations are sufficient to prove the program correct.

ICE learning has proven to be a very powerful approach that allows fully automatic verification of a wide variety of programs, ranging from recursive and concurrent programs over numeric data types [1], to algorithms manipulating dynamically allocated data structures [2], to industry-size GPU kernels [3]. In addition, the principles underlying ICE learning can be lifted to other challenging verification tasks, such as the verification of parameterized systems [4] as well as—in ongoing research—to the verification of cyber-physical and AI-based systems. You might want to try a demo immediately in your browser.

Synthesis


Synthesis goes beyond verification and could be considered the holy grail of computer science. In contrast to checking whether a hand-crafted program meets its specification, the dream is to fully automatically generate software (or a circuit for that matter) from specifications in a correct-by-construction manner.

Although this dream is unrealistic in its whole generality, there exist various application domains in which automated synthesis techniques have been applied with great success. In our own research, for instance, we have developed techniques for synthesizing safety controllers for reactive, cyber-physical systems that have to interact with a complex–and perhaps only partially known–environment [5, 6]. Moreover, we have proposed a general framework for generating loop-free code from input-output examples and specifications written in first-order logic [7]. Similar to ICE learning, these methods combine inductive and deductive reasoning, thereby unveiling and exploiting synergies of modern machine learning algorithms and highly-optimized symbolic reasoning engines.

Formal Specification Languages


Both verification and synthesis rely on the ability to write correct formal specifications, which have to precisely capture the engineer’s intuitive understanding of the system in question. In practice, however, formalizing the requirements of a system is notoriously difficult, and it is well known that the use of standard formalisms such as temporal logics requires a level of sophistication that many users might never develop.

We have recently started a new research project to combat this serious obstacle. Its main objective is to design algorithms that learn formal specifications in interaction with human engineers. As a first step towards this goal, we have developed a learning algorithm for the specification language “Linear Temporal Logic (LTL)”, which is the de facto standard in many verification and synthesis applications. You might think of this algorithm as a recommender system for formal specifications: the human engineer provides examples of the desired and undesired behavior of the system in question, while the recommender generates a series of LTL specifications that are consistent with the given examples; the engineer can then either chose one of the generated specifications or provide additional examples and rerun the recommender.

In ongoing research, we are extending our learning algorithm to a wide range of other specification languages, including Computational Tree Logic, Signal Temporal Logic, and the Property Specification Language. Moreover, we are developing feedback mechanisms that allow for a tighter integration of the human engineer into the loop. Again, you can try our technology immediately in your browser.

References


[1] D’Souza, Deepak; Ezudheen, P.; Garg, Pranav; Madhusudan, P.; Neider, Daniel: Horn-ICE Learning for Synthesizing Invariants and Contracts. In: Proceedings of the ACM on Programming Languages (PACMPL), volume 2 issue OOPSLA, pages 131:1–131:25. ACM, 2018.

[2] Neider, Daniel; Madhusudan, P.; Garg, Pranav; Saha, Shambwaditya; Park, Daejun: Invariant Synthesis for Incomplete Verification Engines. In: 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018), volume 10805 of Lecture Notes in Computer Science, pages 232–250. Springer, 2018

[3] Neider, Daniel; Saha, Shambwaditya; Garg, Pranav; Madhusudan, P.: Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants. In: 26th International Static Analysis Symposium (SAS 2019), volume 11822 of Lecture Notes in Computer Science, pages 323–346. Springer, 2019

[4] Neider, Daniel; Jansen, Nils: Regular Model Checking Using Solver Technologies and Automata Learning. In: 5th International NASA Formal Method Symposium (NFM 2013), volume 7871 of Lecture Notes in Computer Science, pages 16–31. Springer, 2013

[5] Neider, Daniel; Topcu, Ufuk: An Automaton Learning Approach to Solving Safety Games over Infinite Graphs. In: 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2016), volume 9636 of Lecture Notes in Computer Science, pages 204–221. Springer, 2016

[6] Neider, Daniel; Markgraf, Oliver: Learning-based Synthesis of Safety Controllers. In: 2019 International Conference on Formal Methods in Computer Aided Design (FMCAD 2019), pages 120–128. IEEE, 2019

[7] Neider, Daniel; Saha, Shambwaditya; Madhusudan, P.: Synthesizing Piece-wise Functions by Learning Classifiers. In: 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2016), volume 9636 of Lecture Notes in Computer Science, pages 186–203. Springer, 2016

[8] Neider, Daniel; Gavran, Ivan: Learning Linear Temporal Properties. In: 2018 International Conference on Formal Methods in Computer Aided Design (FMCAD 2018), pages 148–157. IEEE, 2018
Read more

Filip Mazowiecki joins MPI-SWS

September 2019
Filip Mazowiecki is joining us from the University of Bordeaux, where he spent the last two years as a postdoc. His research area is formal verification, the study of verifying correct functioning of systems. He is mostly interested in the theoretical analysis of models like Petri nets and weighted automata, working on fundamental questions like reachability and equivalence.

Filip obtained his PhD at the University of Warsaw on the topic of database theory. After he obtained his PhD he switched to the area of formal verification, …
Filip Mazowiecki is joining us from the University of Bordeaux, where he spent the last two years as a postdoc. His research area is formal verification, the study of verifying correct functioning of systems. He is mostly interested in the theoretical analysis of models like Petri nets and weighted automata, working on fundamental questions like reachability and equivalence.

Filip obtained his PhD at the University of Warsaw on the topic of database theory. After he obtained his PhD he switched to the area of formal verification, spending almost four years as a postdoc at the Universities of Warwick, Oxford and Bordeaux.
Read more

Advanced Automata Theory Course at TU Kaiserslautern

May 2019
Damien Zufferey and Daniel Neider are co-teaching Advanced Automata Theory at the University of Kaiserslautern in the Summer 2019 semester.

Georg Zetzsche joins MPI-SWS

November 2018
Georg Zetzsche has joined the institute as a tenure-track faculty member, effective November 1, 2018. He is joining us from the Institut de Recherche en Informatique Fondamentale (IRIF) at Université Paris-Diderot, where he has been a postdoctoral researcher. Georg's goal is to understand which questions about program behaviors and other infinite structures can be answered efficiently.  His research focuses on issues of decidability, complexity, synthesis, and expressiveness arising in program verification and mathematics.

Georg was previously a postdoctoral researcher in the Laboratoire Spécification et Vérification (LSV) at ENS Paris-Saclay. …
Georg Zetzsche has joined the institute as a tenure-track faculty member, effective November 1, 2018. He is joining us from the Institut de Recherche en Informatique Fondamentale (IRIF) at Université Paris-Diderot, where he has been a postdoctoral researcher. Georg's goal is to understand which questions about program behaviors and other infinite structures can be answered efficiently.  His research focuses on issues of decidability, complexity, synthesis, and expressiveness arising in program verification and mathematics.

Georg was previously a postdoctoral researcher in the Laboratoire Spécification et Vérification (LSV) at ENS Paris-Saclay. He obtained his PhD from the University of Kaiserslautern and his Diplom degree from Universität Hamburg. For his PhD work, Georg received the EATCS Distinguished Dissertation Award.
Read more

Research Spotlight: From Newton to Turing to cyber-physical systems

In 1937, a young Englishman by the name of Alan M. Turing published a paper with the obscure title "On computable numbers, with an application to the Entscheidungsproblem'' in the Proceedings of the London Mathematical Society. In doing so, he arguably laid the mathematical foundations of modern computer science. Turing's seminal contribution was to show that the famous Entscheidungsproblem, formulated by the great German mathematician David Hilbert several years earlier, …
In 1937, a young Englishman by the name of Alan M. Turing published a paper with the obscure title "On computable numbers, with an application to the Entscheidungsproblem'' in the Proceedings of the London Mathematical Society. In doing so, he arguably laid the mathematical foundations of modern computer science. Turing's seminal contribution was to show that the famous Entscheidungsproblem, formulated by the great German mathematician David Hilbert several years earlier, could not be solved: more precisely, Turing proved (in modern parlance) that the problem of determining whether a given computer program halts could not be done algorithmically---in other words that the famous Halting Problem is undecidable.

Although seemingly at the time a rather esoteric concern, the Halting Problem (and related questions) have dramatically gained in importance and relevance in more contemporary times. Fast forward to the 21st Century: nowadays, it is widely acknowledged that enabling engineers, programmers, and researchers to automatically verify and certify the correctness of the computer systems that they design is one of the Grand Challenges of computer science. In increasingly many instances, it is absolutely critical that the software governing various aspects of our daily lives (such as that running on an aircraft controller, for example) behave exactly as intended, lest catastrophic consequences ensue.



What classes of infinite-state programs can be analyzed algorithmically?


Researchers at the Foundations of Algorithmic Verification group are investigating what classes of infinite-state programs can, at least in principle, be fully handled and analyzed algorithmically by viewing computer programs abstractly as dynamical systems, and they seek to design exact algorithms enabling one to fully analyse the behaviour of such systems. In particular, they are presently tackling a range of central algorithmic problems from verification, synthesis, performance, and control for linear dynamical systems, drawing among others on tools from number theory, Diophantine geometry, and algebraic geometry, with the overarching goal of offering a systematic exact computational treatment of various important classes of dynamical systems and other fundamental models used in mathematics, computer science, and the quantitative sciences. Some of their achievements include several decidability and hardness results for linear recurrence sequences, which can be used to model simple loops in computer programs, answering a number of longstanding open questions in the mathematics and computer science literature.

In a series of recent papers [1, 2],  they have attacked the so-called Zero Problem for linear differential equations, i.e., the question of determining algorithmically whether the unique solution to a given linear differential equation has a zero or not. Such equations, which go back as far as Newton, are ubiquitous in mathematics, physics, and engineering; they are also particularly useful to model cyber-physical systems, i.e., digital systems that evolve in and interact with a continuous environment. In their work, they obtained several important partial results: if one is interested in the existence of a zero over a bounded time interval, then it is possible to determine this algorithmically, provided that a certain hypothesis from the mathematical field of number theory, known as Schanuel's Conjecture, is true. They were also able to partially account for the fact that the Zero Problem has hitherto remained open in full generality: indeed, if one were able to solve it in dimension 9 (or higher), then in turn this would enable one to solve various longstanding hard open problems from a field of mathematics known as Diophantine approximation. In doing so, they therefore exhibited surprising and unexpected connections between the modelling and analysis of cyber-physical systems and seemingly completely unrelated deep mathematical theories dealing with questions about whole numbers.

References


[1] Ventsislav Chonev, Joel Ouaknine, and James Worrell. On recurrent reachability for continuous linear dynamical systems. In Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 2016.

[2] Ventsislav Chonev, Joel Ouaknine, and James Worrell. On the Skolem Problem for continuous linear dynamical systems. In Proceedings of the 43rd International Colloquium on Automata, Languages, and Programming (ICALP), 2016.
Read more

Amaury Pouly receives Ackermann Award

Amaury Pouly, a postdoc in Joël Ouaknine's Foundations of Automatic Verification Group, has received the 2017 Ackermann Award for his PhD thesis, “Continuous-time computation models: From computability to computational complexity.” The Ackermann Award is an international prize presented annually to the author of an exceptional doctoral dissertation in the field of Computer Science Logic.

Amaury Pouly's thesis shows that problems which can be solved with a computer in a reasonable amount of time (more specifically problems which belong to the class P of the famous open problem “P = NP?”) can be characterized as polynomial length solutions of polynomial differential equations. …
Amaury Pouly, a postdoc in Joël Ouaknine's Foundations of Automatic Verification Group, has received the 2017 Ackermann Award for his PhD thesis, “Continuous-time computation models: From computability to computational complexity.” The Ackermann Award is an international prize presented annually to the author of an exceptional doctoral dissertation in the field of Computer Science Logic.

Amaury Pouly's thesis shows that problems which can be solved with a computer in a reasonable amount of time (more specifically problems which belong to the class P of the famous open problem “P = NP?”) can be characterized as polynomial length solutions of polynomial differential equations. This result paves the way for reformulating certain questions and concepts of theoretical computer science in terms of ordinary polynomial differential equations. It also revisits analog computational models and demonstrates that analog and digital computers actually have the same computing power, both in terms of what they can calculate (computability) and what they can solve in reasonable (polynomial) time.
Read more

Advanced Automata Theory Course at TU Kaiserslautern

April 2017
Rupak Majumdar and Daniel Neider are co-teaching Advanced Automata Theory at the University of Kaiserslautern in the Summer 2017 semester.

The course meets Tuesdays 08:15-09:45 in room 48-210 and Wednesdays 13:45-15:15 in room 46-280 on the University of Kaiserslautern campus.

Complexity Theory Course at TU Kaiserslautern

November 2016
Rupak Majumdar is teaching Complexity Theory at the University of Kaiserslautern in the Winter 2016-17 semester.

The course meets Mondays 15:30-17:00 at 46-280 and Wednesdays 13:45-15:15 at 46-268.

More information about the course

Three MPI-SWS papers accepted to POPL'17

October 2016
Three papers from MPI-SWS were accepted to ACM POPL 2017:
  • A promising semantics for relaxed-memory concurrency
  • Relational cost analysis
  • Thread modularity at many levels: a pearl in compositional verification

Rupak Majumdar will chair CAV 2017

October 2016
Rupak Majumdar and Viktor Kuncak (EPFL) are co-chairs of the 29th International Conference on Computer-Aided Verification (CAV 2017), to be held between July 22 and 28, 2017 in Heidelberg, Germany.

CAV 2017 is the 29th in a series dedicated to the advancement of the theory and practice of computer-aided formal analysis and synthesis methods for hardware and software systems. The CAV home page has more information.

Joel Ouaknine will chair LICS 2017

October 2016
Joel Ouaknine is the Program Chair of the Thirty-Second Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), to be held between 20 and 23 June, 2017 in Reykjavik. The LICS Symposium is an annual international forum on theoretical and practical topics in computer science that relate to logic, broadly construed.

Rupak Majumdar joins the MPI-SWS faculty

Rupak Majumdar joins the institute's faculty as a scientific director. Rupak's research interests are in computer-aided verification and control of reactive, real-time, hybrid, and probabilistic systems; software verification and programming languages; and logic and automata theory.

Rupak's research spans the spectrum of formal verification techniques, ranging from theoretical foundations of logic and automata theory to practical software engineering tools that systematically analyze thousands of lines of code for programmer errors. In the field of software model checking, …
Rupak Majumdar joins the institute's faculty as a scientific director. Rupak's research interests are in computer-aided verification and control of reactive, real-time, hybrid, and probabilistic systems; software verification and programming languages; and logic and automata theory.

Rupak's research spans the spectrum of formal verification techniques, ranging from theoretical foundations of logic and automata theory to practical software engineering tools that systematically analyze thousands of lines of code for programmer errors. In the field of software model checking, Rupak has made major contributions. Rupak, along with Ranjit Jhala, wrote the the model checker Blast, which is able to analyze over 100,000 lines of code for complex temporal properties. This achievement was a major milestone and proof of feasibility in the field of software verification and led to a flurry of academic and industrial activity in the area.

Rupak joins MPI-SWS from the University of California, Los Angeles, where he was on the faculty of the computer science department. Prior to that, Rupak received his Ph.D. degree in Computer Science from the University of California at Berkeley, and his B.Tech. degree in Computer Science from the Indian Institute of Technology at Kanpur.
Read more